Concrete degradation

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Concrete degradation may have various causes. Concrete can be damaged by fire, aggregate expansion, sea water effects, bacterial corrosion, calcium leaching, physical damage and chemical damage (from carbonatation, chlorides, sulfates and non-distilled water). This process adversely affects concrete exposed to these damaging stimuli.

Video Concrete degradation

Aggregate expansion

Various types of aggregate undergo chemical reactions in concrete, leading to damaging expansive phenomena. The most common are those containing reactive silica, that can react (in the presence of water) with the alkalis in concrete (K₂O and Na₂O, coming principally from cement). Among the more reactive mineral components of some aggregates are opal, chalcedony, flint and strained quartz. Following the alkali-silica reaction (ASR), an expansive gel forms, that creates extensive cracks and damage on structural members. On the surface of concrete pavements the ASR can cause pop-outs, i.e. the expulsion of small cones (up to 3 cm (1 in) about in diameter) in correspondence of aggregate particles.

When some aggregates containing dolomite are used, a dedolomitization reaction occurs where the magnesium carbonate compound reacts with hydroxyl ions and yields magnesium hydroxide and a carbonate ion. The resulting expansion may cause destruction of the material. Far less common are pop-outs caused by the presence of pyrite, an iron sulfide that generates expansion by forming iron oxide and ettringite. Other reactions and recrystallizations, e.g. hydration of clay minerals in some aggregates, may lead to destructive expansion as well.

Maps Concrete degradation

Corrosion of reinforcement bars

The expansion of the corrosion products (iron oxides) of carbon steel reinforcement structures may induce mechanical stress that can cause the formation of cracks and disrupt the concrete structure. If the rebars have been poorly installed and are located too close to the concrete surface in contact with the air, spalling can easily occur: flat fragments of concrete are detached from the concrete mass by the rebars corrosion and may fall down.

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Chemical damage

Carbonatation

Carbon dioxide from air can react with the calcium hydroxide in concrete to form calcium carbonate. This process is called carbonatation, which is essentially the reversal of the chemical process of calcination of lime taking place in a cement kiln. Carbonatation of concrete is a slow and continuous process progressing from the outer surface inward, but slows down with increasing diffusion depth.

Carbonatation has two effects: it increases mechanical strength of concrete, but it also decreases alkalinity, which is essential for corrosion prevention of the reinforcement steel. Below a pH of 10, the steel's thin layer of surface passivation dissolves and corrosion is promoted. For the latter reason, carbonatation is an unwanted process in concrete chemistry. It can be tested by applying phenolphthalein solution, a pH indicator, over a fresh fracture surface, which indicates non-carbonatated and thus alkaline areas with a violet color.

Chlorides

Chlorides, particularly calcium chloride, have been used to shorten the setting time of concrete. However, calcium chloride and (to a lesser extent) sodium chloride have been shown to leach calcium hydroxide and cause chemical changes in Portland cement, leading to loss of strength, as well as attacking the steel reinforcement present in most concrete. The ten-storey Queen Elizabeth hospital in Kota Kinabalu contained a high percentage of chloride causing early failure.

Sulfates

Sulfates in solution in contact with concrete can cause chemical changes to the cement, which can cause significant microstructural effects leading to the weakening of the cement binder (chemical sulfate attack). Sulfate solutions can also cause damage to porous cementitious materials through crystallization and recrystallization (salt attack). Sulfates and sulfites are ubiquitous in the natural environment and are present from many sources, including gypsum (calcium sulfate) often present as an additive in 'blended' cements which include fly ash and other sources of sulfate. With the notable exception of barium sulfate, most sulfates are slightly to highly soluble in water. These include acid rain where sulfur dioxide in the airshed is dissolved in rainfall to produce sulfurous acid. In lightning storms, the dioxide is oxidised to trioxide making the residual sulfuric acid in rainfall even more highly acidic. Local government infrastructure is most commonly corroded by sulfate arising from the oxidation of sulfide which occurs when bacteria (for example in sewer mains) reduce the ever-present hydrogen sulfide gas to a film of sulfide (S-) or bi-sulfide (HS-) ions. This reaction is reversible, both readily oxidising on exposure to air or oxygenated stormwater, to produce sulfite or sulfate ions and acidic hydrogen ions in the reaction HS^- + H₂O+ O₂ -> 2H⁺ + SO₄-. The corrosion often present in the crown (top) of concrete sewers is directly attributible to this process - known as crown rot corrosion.

Leaching

When water flows through cracks present in concrete, water may dissolve various minerals present in the hardened cement paste or in the aggregates, if the solution is unsaturated with respect to them. Dissolved ions, such as calcium (Ca²⁺), are leached out and transported in solution some distance. If the physico-chemical conditions prevailing in the seeping water evolve with distance along the water path and water becomes supersaturated with respect to certain minerals, they can further precipitate, making calthemite deposits (predominately calcium carbonate) inside the cracks, or at the concrete outer surface. This process can cause the self-healing of fractures in particular conditions.

Decalcification

Within set concrete there remains some free "calcium hydroxide" (Ca(OH)₂), which can further dissociate to form Ca²⁺ and hydroxide (OH^-) ions". Any water which finds a seepage path through micro cracks and air voids present in concrete, will readily carry the (Ca(OH)₂) and Ca²⁺ (depending on solution pH and chemical reaction at the time) to the underside of the structure where leachate solution contacts the atmosphere. Carbon dioxide (CO₂) from the atmosphere readily diffuses into the leachate and causes a chemical reaction, which precipitates (deposits) calcium carbonate (CaCO₃) on the outside of the concrete structure. Consisting primarily of CaCO₃ this secondary deposit derived from concrete is known as "calthemite" and can mimic the shapes and forms of cave "speleothems", such as stalactites, stalagmites, flowstone etc. Other trace elements such as iron from rusting reinforcing may be transported and deposited by the leachate at the same time as the CaCO₃. This may colour the calthemites orange or red.

The chemistry involving leaching of calcium hydroxide from concrete can facilitate the growth of calthemites up to ?200 times faster than cave speleothems due to the different chemical reactions involved. The sight of calthemite is a visual sign that calcium is being leached from the concrete structure and the concrete is gradually degrading.

In very old concrete where the calcium hydroxide has been leached from the leachate seepage path, the chemistry may revert to that similar to "speleothem" chemistry in limestone cave. This is where carbon dioxide enriched rain or seepage water forms a weak carbonic acid, which leaches calcium carbonate (CaCO₃) from within the concrete structure and carries it to the underside of the structure. When it contacts the atmosphere, carbon dioxide is degasses and calcium carbonate is precipitated to create calthemite deposits, which mimic the shapes and forms of speleothems. This degassing chemistry is not common in concrete structures as the leachate can often find new paths through the concrete to access free calcium hydroxide and this reverts the chemistry to that previously mentioned where CO₂ is the reactant.

Sea water

Concrete exposed to seawater is susceptible to its corrosive effects. The effects are more pronounced above the tidal zone than where the concrete is permanently submerged. In the submerged zone, magnesium and hydrogen carbonate ions precipitate a layer of brucite, about 30 micrometers thick, on which a slower deposition of calcium carbonate as aragonite occurs. These layers somewhat protect the concrete from other processes, which include attack by magnesium, chloride and sulfate ions and carbonatation. Above the water surface, mechanical damage may occur by erosion by waves themselves or sand and gravel they carry, and by crystallization of salts from water soaking into the concrete pores and then drying up. Pozzolanic cements and cements using more than 60% of slag as aggregate are more resistant to sea water than pure Portland cement. Sea water corrosion contains elements of both chloride and sulfate corrosion.

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Bacterial corrosion

Bacteria themselves do not have noticeable effect on concrete. However, sulfate-reducing bacteria in untreated sewage tend to produce hydrogen sulfide, which is then oxidized by aerobic bacteria present in biofilm on the concrete surface above the water level to sulfuric acid. The sulfuric acid dissolves the carbonates in the cured cement and causes strength loss, as well as producing sulfates which are harmful to concrete. Concrete floors lying on ground that contains pyrite (iron(II) sulfide) are also at risk. Using limestone as the aggregate makes the concrete more resistant to acids, and the sewage may be pretreated by ways increasing pH or oxidizing or precipitating the sulfides in order to inhibit the activity of sulfide utilizing bacteria.

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Physical damage

Damage can occur during the casting and de-shuttering processes. For instance, the corners of beams can be damaged during the removal of shuttering because they are less effectively compacted by means of vibration (improved by using form-vibrators). Other physical damage can be caused by the use of steel shuttering without base plates. The steel shuttering pinches the top surface of a concrete slab due to the weight of the next slab being constructed.

Concrete slabs, block walls and pipelines are susceptible to cracking during ground settlement, seismic tremors or other sources of vibration, and also from expansion and contraction during adverse temperature changes.

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Thermal damage

Due to its low thermal conductivity, a layer of concrete is frequently used for fireproofing of steel structures. However, concrete itself may be damaged by fire. An example of this was the 1996 Channel fire, where the fire reduced the thickness of concrete in an undersea tunnel connecting France with England. For this reason, common fire testing standards, such as ASTM E119, do not permit fire testing of cementitious products unless the relative humidity inside the cementitious product is at or below 75%. Otherwise, concrete can be subject to significant spalling.

Up to about 300 °C, the concrete undergoes normal thermal expansion. Above that temperature, shrinkage occurs due to water loss; however, the aggregate continues expanding, which causes internal stresses. Up to about 500 °C, the major structural changes are carbonatation and coarsening of pores. At 573 °C, quartz undergoes rapid expansion due to phase transition, and at 900 °C calcite starts shrinking due to decomposition. At 450-550 °C the cement hydrate decomposes, yielding calcium oxide. Calcium carbonate decomposes at about 600 °C. Rehydration of the calcium oxide on cooling of the structure causes expansion, which can cause damage to material which withstood fire without falling apart. Concrete in buildings that experienced a fire and were left standing for several years shows extensive degree of carbonatation from carbon dioxide which is reabsorbed.

Concrete exposed to up to 100 °C is normally considered as healthy. The parts of a concrete structure that is exposed to temperatures above approximately 300 °C (dependent of water/cement ratio) will most likely get a pink color. Over approximately 600 °C the concrete will turn light grey, and over approximately 1000 °C it turns yellow-brown. One rule of thumb is to consider all pink colored concrete as damaged that should be removed.

Fire will expose the concrete to gases and liquids that can be harmful to the concrete, among other salts and acids that occur when gases produced by a fire come into contact with water.

If concrete is exposed to very high temperatures very rapidly, explosive spalling of the concrete can result. In a very hot, very quick fire the water inside the concrete will boil before it evaporates. The steam inside the concrete exerts expansive pressure and can initiate and forcibly expel a spall.

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Radiation damages

Exposure of concrete structures to neutrons and gamma radiations in nuclear power plants and high-flux material testing reactor can induce radiation damages in their concrete structures. Paramagnetic defects and optical centers are easily formed, but very high fluxes are necessary to displace a sufficiently high number of atoms in the crystal lattice of minerals present in concrete before significant mechanical damage is observed.

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Repairs and strengthening

It may be necessary to repair a concrete structure following damage (e.g. due to age, chemical attack, fire, impact, movement or reinforcement corrosion). Strengthening may be necessary if the structure is weakened (e.g. due to design or construction errors, excessive loading, or because of a change of use).

Repair techniques

The first step should always be an investigation to determine the cause of the deterioration. The general principles of repair include: arresting and preventing further degradation; treating exposed steel reinforcement; and filling fissures or holes caused by cracking or left after the loss of spalled or damaged concrete;

Various techniques are available for the repair, protection and rehabilitation of concrete structures, and specifications for repair principals have been defined systematically. The selection of the appropriate approach will depend on the cause of the initial damage (e.g. impact, excessive loading, movement, corrosion of the reinforcement, chemical attack, or fire) and whether the repair is to be fully load-bearing or simply cosmetic.

Repair principles which do not improve the strength or performance of concrete beyond its original (undamaged) condition include: replacement and restoration of concrete after spalling and delamination; strengthening to restore structural load-bearing capacity; and increasing resistance to physical or mechanical attack.

Repair principles for arresting and preventing further degradation include: control of anodic areas; cathodic protection, cathodic control; increasing resistivity; preserving or restoring passivity; increasing resistance to chemical attack; protection against ingress of adverse agents; and moisture control.

Techniques for filling holes left by the removal of spalled or damaged concrete include: mortar repairs; flowing concrete repairs and sprayed concrete repairs. The filling of cracks, fissures or voids in concrete for structural purposes (restoration of strength and load-bearing capability), or non-structural reasons (flexible repairs where further movement is expected, or alternately to resist water and gas permeation) typically involves the injection of low viscosity resins or grouts based on epoxy, PU or acrylic resins, or micronised cement slurries.

One novel proposal for the repair of cracks is to use bacteria. BacillaFilla is a genetically engineered bacterium designed to repair damaged concrete, filling in the cracks, and making them whole again.

Strengthening techniques

Various techniques are available for strengthening concrete structures, to increase the load-carrying capacity or else to improve the in-service performance. These include increasing the concrete cross-section, and adding material such as steel plate or fiber composites to enhance the tensile capacity or increase the confinement of the concrete for improved compression capacity.

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See also

• Calthemite
• Electrical resistivity measurement of concrete

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References

Source of article : Wikipedia

H-index

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The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications. The index can also be applied to the productivity and impact of a scholarly journal as well as a group of scientists, such as a department or university or country. The index was suggested in 2005 by Jorge E. Hirsch, a physicist at UCSD, as a tool for determining theoretical physicists' relative quality and is sometimes called the Hirsch index or Hirsch number.

Video H-index

Definition and purpose

The definition of the index is that a scholar with an index of h has published h papers each of which has been cited in other papers at least h times. Thus, the h-index reflects both the number of publications and the number of citations per publication. The index is designed to improve upon simpler measures such as the total number of citations or publications. The index works properly only for comparing scientists working in the same field; citation conventions differ widely among different fields.

Maps H-index

Calculation

Formally, if f is the function that corresponds to the number of citations for each publication, we compute the h index as follows. First we order the values of f from the largest to the lowest value. Then, we look for the last position in which f is greater than or equal to the position (we call h this position). For example, if we have a researcher with 5 publications A, B, C, D, and E with 10, 8, 5, 4, and 3 citations, respectively, the h index is equal to 4 because the 4th publication has 4 citations and the 5th has only 3. In contrast, if the same publications have 25, 8, 5, 3, and 3, then the index is 3 because the fourth paper has only 3 citations.

f(A)=10, f(B)=8, f(C)=5, f(D)=4, f(E)=3 -> h-index=4

f(A)=25, f(B)=8, f(C)=5, f(D)=3, f(E)=3 -> h-index=3

If we have the function f ordered in decreasing order from the largest value to the lowest one, we can compute the h index as follows:

h-index (f) = ${\displaystyle \max _{i}\min(f(i),i)}$

The Hirsch index is equivalent to the Eddington number, an earlier metric used for evaluating cyclists. The h-index serves as an alternative to more traditional journal impact factor metrics in the evaluation of the impact of the work of a particular researcher. Because only the most highly cited articles contribute to the h-index, its determination is a simpler process. Hirsch has demonstrated that h has high predictive value for whether a scientist has won honors like National Academy membership or the Nobel Prize. The h-index grows as citations accumulate and thus it depends on the "academic age" of a researcher.

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Input data

The h-index can be manually determined using citation databases or using automatic tools. Subscription-based databases such as Scopus and the Web of Science provide automated calculators. Harzing's Publish or Perish program calculates the h-index based on Google Scholar entries. From July 2011 Google have provided an automatically-calculated h-index and i10-index within their own Google Scholar profile. In addition, specific databases, such as the INSPIRE-HEP database can automatically calculate the h-index for researchers working in high energy physics.

Each database is likely to produce a different h for the same scholar, because of different coverage. A detailed study showed that the Web of Science has strong coverage of journal publications, but poor coverage of high impact conferences. Scopus has better coverage of conferences, but poor coverage of publications prior to 1996; Google Scholar has the best coverage of conferences and most journals (though not all), but like Scopus has limited coverage of pre-1990 publications. The exclusion of conference proceedings papers is a particular problem for scholars in computer science, where conference proceedings are considered an important part of the literature. Google Scholar has been criticized for producing "phantom citations," including gray literature in its citation counts, and failing to follow the rules of Boolean logic when combining search terms. For example, the Meho and Yang study found that Google Scholar identified 53% more citations than Web of Science and Scopus combined, but noted that because most of the additional citations reported by Google Scholar were from low-impact journals or conference proceedings, they did not significantly alter the relative ranking of the individuals. It has been suggested that in order to deal with the sometimes wide variation in h for a single academic measured across the possible citation databases, one should assume false negatives in the databases are more problematic than false positives and take the maximum h measured for an academic.

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Comparing results across fields and career levels

Little systematic investigation has been done on how the h-index behaves over different institutions, nations, times and academic fields/disciplines. Hirsch suggested that, for physicists, a value for h of about 12 might be typical for advancement to tenure (associate professor) at major [US] research universities. A value of about 18 could mean a full professorship, 15-20 could mean a fellowship in the American Physical Society, and 45 or higher could mean membership in the United States National Academy of Sciences.

For the most highly cited scientists in the period 1983-2002, Hirsch identified the top 10 in the life sciences (in order of decreasing h): Solomon H. Snyder, h = 191; David Baltimore, h = 160; Robert C. Gallo, h = 154; Pierre Chambon, h = 153; Bert Vogelstein, h = 151; Salvador Moncada, h = 143; Charles A. Dinarello, h = 138; Tadamitsu Kishimoto, h = 134; Ronald M. Evans, h = 127; and Axel Ullrich, h = 120. Among 36 new inductees in the National Academy of Sciences in biological and biomedical sciences in 2005, the median h-index was 57. However, he points out that values of h will vary between different fields.

Among the 22 scientific disciplines listed in the Thomson Reuters Essential Science Indicators Citation Thresholds [thus excluding non-science academics], physics has the second most citations after space science. During the period January 1, 2000 - February 28, 2010, a physicist had to receive 2073 citations to be among the most cited 1% of physicists in the world. The threshold for space science is the highest (2236 citations), and physics is followed by clinical medicine (1390) and molecular biology & genetics (1229). Most disciplines, such as environment/ecology (390), have fewer scientists, fewer papers, and fewer citations. Therefore, these disciplines have lower citation thresholds in the Essential Science Indicators, with the lowest citation thresholds observed in social sciences (154), computer science (149), and multidisciplinary sciences (147).

Numbers are very different in social science disciplines: The Impact of the Social Sciences team at London School of Economics found that social scientists in the United Kingdom had lower average h-indices. The h-indices for ("full") professors, based on Google Scholar data ranged from 2.8 (in law), through 3.4 (in political science), 3.7 (in sociology), 6.5 (in geography) and 7.6 (in economics). On average across the disciplines, a professor in the social sciences had an h-index about twice that of a lecturer or a senior lecturer, though the difference was the smallest in geography.

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Advantages

Hirsch intended the h-index to address the main disadvantages of other bibliometric indicators, such as total number of papers or total number of citations. Total number of papers does not account for the quality of scientific publications, while total number of citations can be disproportionately affected by participation in a single publication of major influence (for instance, methodological papers proposing successful new techniques, methods or approximations, which can generate a large number of citations), or having many publications with few citations each. The h-index is intended to measure simultaneously the quality and quantity of scientific output.

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Criticism

There are a number of situations in which h may provide misleading information about a scientist's output: Most of these however are not exclusive to the h-index.

• The h-index does not account for the typical number of citations in different fields. It has been stated that citation behavior in general is affected by field-dependent factors, which may invalidate comparisons not only across disciplines but even within different fields of research of one discipline.
• The h-index discards the information contained in author placement in the authors' list, which in some scientific fields is significant.
• The h-index has been found in one study to have slightly less predictive accuracy and precision than the simpler measure of mean citations per paper. However, this finding was contradicted by another study by Hirsch.
• The h-index is a natural number that reduces its discriminatory power. Ruane and Tol therefore propose a rational h-index that interpolates between h and h + 1.
• The h-index can be manipulated through self-citations, and if based on Google Scholar output, then even computer-generated documents can be used for that purpose, e.g. using SCIgen.
• The h-index does not provide a significantly more accurate measure of impact than the total number of citations for a given scholar. In particular, by modeling the distribution of citations among papers as a random integer partition and the h-index as the Durfee square of the partition, Yong arrived at the formula ${\displaystyle h\approx 0.54{\sqrt {N}}}$, where N is the total number of citations, which, for mathematics members of the National Academy of Sciences, turns out to provide an accurate (with errors typically within 10-20 percent) approximation of h-index in most cases.

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Alternatives and modifications

Various proposals to modify the h-index in order to emphasize different features have been made. As the variants have proliferated, comparative studies have become possible showing that most proposals are highly correlated with the original h-index, although alternative indexes may be important to decide between comparable CVs, as often the case in evaluation processes.

• An individual h-index normalized by the number of authors has been proposed: ${\displaystyle h_{I}=h^{2}/N_{a}^{(T)}}$, with ${\displaystyle N_{a}^{(T)}}$ being the number of authors considered in the ${\displaystyle h}$ papers. It was found that the distribution of the h-index, although it depends on the field, can be normalized by a simple rescaling factor. For example, assuming as standard the hs for biology, the distribution of h for mathematics collapse with it if this h is multiplied by three, that is, a mathematician with h = 3 is equivalent to a biologist with h = 9. This method has not been readily adopted, perhaps because of its complexity. It might be simpler to divide citation counts by the number of authors before ordering the papers and obtaining the h-index, as originally suggested by Hirsch.
• The m-index is defined as h/n, where n is the number of years since the first published paper of the scientist; also called m-quotient.
• There are a number of models proposed to incorporate the relative contribution of each author to a paper, for instance by accounting for the rank in the sequence of authors.
• A generalization of the h-index and some other indices that gives additional information about the shape of the author's citation function (heavy-tailed, flat/peaked, etc.) has been proposed.
• A successive Hirsch-type-index for institutions has also been devised. A scientific institution has a successive Hirsch-type-index of i when at least i researchers from that institution have an h-index of at least i.
• Three additional metrics have been proposed: h² lower, h² center, and h² upper, to give a more accurate representation of the distribution shape. The three h² metrics measure the relative area within a scientist's citation distribution in the low impact area, h² lower, the area captured by the h-index, h² center, and the area from publications with the highest visibility, h² upper. Scientists with high h² upper percentages are perfectionists, whereas scientists with high h² lower percentages are mass producers. As these metrics are percentages, they are intended to give a qualitative description to supplement the quantitative h-index.
• The g-index can be seen as the h-index for an averaged citations count.
• It has been argued that "For an individual researcher, a measure such as Erd?s number captures the structural properties of network whereas the h-index captures the citation impact of the publications. One can be easily convinced that ranking in coauthorship networks should take into account both measures to generate a realistic and acceptable ranking." Several author ranking systems such as eigenfactor (based on eigenvector centrality) have been proposed already, for instance the Phys Author Rank Algorithm.
• The c-index accounts not only for the citations but for the quality of the citations in terms of the collaboration distance between citing and cited authors. A scientist has c-index n if n of [his/her] N citations are from authors which are at collaboration distance at least n, and the other (N - n) citations are from authors which are at collaboration distance at most n.
• An s-index, accounting for the non-entropic distribution of citations, has been proposed and it has been shown to be in a very good correlation with h.
• The e-index, the square root of surplus citations for the h-set beyond h², complements the h-index for ignored citations, and therefore is especially useful for highly cited scientists and for comparing those with the same h-index (iso-h-index group).
• Because the h-index was never meant to measure future publication success, recently, a group of researchers has investigated the features that are most predictive of future h-index. It is possible to try the predictions using an online tool. However, later work has shown that since h-index is a cumulative measure, it contains intrinsic auto-correlation that led to significant overestimation of its predictability. Thus, the true predictability of future h-index is much lower compared to what has been claimed before.
• The h-index has been applied to Internet Media, such as YouTube channels. The h-index is defined as the number of videos with >= h × 10⁵ views. When compared with a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.
• The i10-index indicates the number of academic publications an author has written that have at least ten citations from others. It was introduced in July 2011 by Google as part of their work on Google Scholar.
• The h-index has been shown to have a strong discipline bias. However, a simple normalization ${\displaystyle h/\langle h\rangle _{d}}$ by the average h of scholars in a discipline d is an effective way to mitigate this bias, obtaining a universal impact metric that allows comparison of scholars across different disciplines. Of course this method does not deal with academic age bias.
• The h-index can be timed to analyze its evolution during one's career, employing different time windows.
• The o-index corresponds to the geometric mean of the h-index and the most cited paper of a researcher.

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See also

• Bibliometrics
• Comparison of research networking tools and research profiling systems

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References

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Further reading

• Alonso, S.; Cabrerizo, F. J.; Herrera-Viedma, E.; Herrera, F. (2009). "h-index: A Review Focused in its Variants, Computation and Standardization for Different Scientific Fields". Journal of Informetrics. 3 (4): 273-89. doi:10.1016/j.joi.2009.04.001.
• Ball, Philip (2005). "Index aims for fair ranking of scientists". Nature. 436 (7053): 900. Bibcode:2005Natur.436..900B. doi:10.1038/436900a. PMID 16107806.
• Iglesias, Juan E.; Pecharromán, Carlos. "Scaling the h-index for different scientific ISI fields" (PDF).
• Kelly, C. D.; Jennions, M. D. (2006). "The h index and career assessment by numbers". Trends Ecol. Evol. 21 (4): 167-70. doi:10.1016/j.tree.2006.01.005. PMID 16701079.
• Lehmann, S.; Jackson, A. D.; Lautrup, B. E. (2006). "Measures for measures". Nature. 444 (7122): 1003-04. Bibcode:2006Natur.444.1003L. doi:10.1038/4441003a. PMID 17183295.
• Panaretos, J.; Malesios, C. (2009). "Assessing Scientific Research Performance and Impact with Single Indices". Scientometrics. 81 (3): 635-70. arXiv:0812.4542. doi:10.1007/s11192-008-2174-9.
• Petersen, A. M.; Stanley, H. Eugene; Succi, Sauro (2011). "Statistical Regularities in the Rank-Citation Profile of Scientists". Scientific Reports. 181 (181): 1-7. arXiv:1103.2719. Bibcode:2011NatSR...1E.181P. doi:10.1038/srep00181. PMC 3240955. PMID 22355696.
• Sidiropoulos, Antonis; Katsaros, Dimitrios; Manolopoulos, Yannis (2007). "Generalized Hirsch h-index for disclosing latent facts in citation networks". Scientometrics. 72 (2): 253-80. CiteSeerX 10.1.1.76.3617. doi:10.1007/s11192-007-1722-z.
• Soler, José M. (2007). "A rational indicator of scientific creativity". Journal of Informetrics. 1 (2): 123-30. arXiv:physics/0608006. doi:10.1016/j.joi.2006.10.004.
• Symonds, M. R.; et al. (2006). Tregenza, Tom, ed. "Gender differences in publication output: towards an unbiased metric of research performance". PLoS ONE. 1 (1): e127. Bibcode:2006PLoSO...1..127S. doi:10.1371/journal.pone.0000127. PMC 1762413. PMID 17205131.
• Taber, Douglass F. (2005). "Quantifying Publication Impact". Science. 309 (5744): 2166a. doi:10.1126/science.309.5744.2166a. PMID 16195445.
• Woeginger, Gerhard j. (2008). "An axiomatic characterization of the Hirsch-index". Mathematical Social Sciences. 56 (2): 224-32. doi:10.1016/j.mathsocsci.2008.03.001.

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External links

• Google Scholar Metrics
• H-index for economists
• H-index for computer science researchers
• H-index for astronomers

Source of article : Wikipedia

Refund anticipation loan

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Refund anticipation loan (RAL) is a short-term consumer loan in the United States provided by a third party against an expected tax refund for the duration it takes the tax authority to pay the refund. The loan term was usually about two to three weeks, related to the time it took the U.S. Internal Revenue Service to deposit refunds in electronic accounts. The loans were designed to make the refund available in as little as 24 hours. They were secured by a taxpayer's expected tax refund, and designed to offer customers quicker access to funds.

The costs to the borrower could be significant compared to other lending and some consumer organizations warned consumers of the risk involved in this type of loan. They are a largely discontinued financial product and beginning with the 2013 tax filing season, they have been largely replaced with the similar refund anticipation checks (RAC), as well as a hodge podge of other financial products.

RACs are temporary accounts which wait for the client's IRS tax refund, and which also provides a way for the client to pay for tax preparation out of the refund. Both financial products have similar fees and similar risks of third-party bank "cross-collection".

A similar process in Canada to a RAL is termed "tax rebate discounting".

Video Refund anticipation loan

United States

In the United States prior to the 2013 tax filing season, taxpayers could apply for a refund anticipation loan through a paid professional tax preparation service, where a fee is typically charged for the preparation of the tax return. Internal Revenue Service rules prohibit basing this fee on the amount of the expected refund. An additional fee was usually charged for the services of originating a bank product and establishing a short-term bank account. By law this fee must be the same on both loan and non-loan bank products, and in 2004 the average fee was $32. The bank through which the loan was made charges finance charges.

According to the National Consumer Law Center, 12 million taxpayers used a RAL in 2004. With e-filing and IRS partnerships that help consumers e-file for free, U.S. taxpayers can generally receive their tax refunds within three weeks and sometimes as quickly as ten to fourteen days if they choose to receive their refund via direct deposit. As of 2017, 70% of US taxpayers have access to free e-file and tax preparation services. This rendered RALs less attractive to some.

Maps Refund anticipation loan

History

RALs began in 1985 when Ronald Smith, an Accountant in Virginia Beach, VA started the practice of Refund Advance Loans at his accounting firm, Action Accounting & Taxes located at 5441 Virginia Beach Blvd. This service of Loans On Tax Refunds was advertised widely through WYAH, TV and on Cox Cable in the Hampton Roads area by Action Accounting & Taxes in 1985, 1986 and 1987. The Advance Refund Loans became a huge business success from the start and a sensation in the area in 1986 and 1987 and was the first and only firm in the United States that was offering that service according to the IRS. In 1986 a salesman from Charlie Falk's Auto on Virginia Beach Blvd, where today Town Centre now exits, asked Mr. Smith if they could make an arrangement where Action Accounting & Taxes could prepare taxes for customers seeking to purchase cars in order to supplement their down payments on used or new car purchases. Mr. Smith agreed to this idea and that was the beginning of Loans on Tax Refunds being used in conjunction with car financing and that practice then quickly spread throughout the area and then throughout the entire United States. Mr. Smith was the first one to invent, organize and pioneer the process of Tax Refund Loans in this way and went on to make millions in this business. The financing was originally fully handled by Joel S. Coplon and Company, a small private financing company closely connected with Mr. Smith at the time. Action Accounting and Taxes, the firm that Mr. Smith owned, was one mile from where John Hewitt was just starting a new business venture. Mr. Hewitt had just recently purchased Mel Jackson's Tax Service which was a run down group of offices throughout the Hampton Road's area. Mr. Hewitt began to offer Refund Anticipation Loans in 1988 and built a national franchise out of the idea which funded and built Jackson Hewitt Tax Service. Then in 1989 H & R Block joined in the industry and it became a billion dollar industry across the United States being coopted into thousands of different accounting firms and tax practices across the United States and abroad. It was reported in 1989 that H & R Block had doubled it's business at over 4,000 locations due to the introduction of this new Refund Loan Service. It later spread to Canada as well through Liberty Tax Service and in time this company moved into the United States market as well offering the same service. The proliferation of this tax loan practice coincided with the introduction of electronic filing which IRS introduced electronic filing as a way to decrease its cost of operation. Previous to this time refunds would take on average two to three months to come back from the IRS which is why the Loan on Tax Refund also known as Refund Anticipations Loan business flourished. In 1988, Mr. Smith as well as Mr. Coplon were jointly sued by the Attorney General of Virginia in a Richmond State Court for charging usurious interest. Mr. Smith was eventually dismissed from the case and it was reported by the State's Attorney at the time that the reason for his dismissal from the case was that Mr. Smith was "not culpable".

A tax preparer would, within 24 hours of submission, receive from the IRS confirmation that the submission was free of mathematical errors, and that the filer had no liens or delinquent federal student loans. This meant that there was good chance that the IRS would pay the refund within weeks, barring fraudulent income reporting. At that point the preparer would issue the filer a check for the amount of the expected refund minus a commission. In 1995, the New York Times reported that Beneficial's $30 electronic filing fee and $59 loan fee amounted to a 250 percent APR on a refund of $1,000.

Exploitation of the system had begun by the early 1990s; filers misreported their income to inflate their refund. As a result of this, and also to discourage filers from this rather uneconomical offer, in 1994 the IRS stopped providing tax preparers a confirmation that a deposit would take place for a certain amount and that it would begin sending refunds directly to taxpayers instead of banks that made the loan, but not having the desired effects, the confirmations were re-instated the following year.

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Controversy

According to the Consumer Federation of America and the National Consumer Law Center, RALs are controversial because, like payday loans and title loans, RALs are high-profit, low-risk loans marketed toward the working poor. A 2006 study by the NCLC and the Consumer Federation of America found that "Based upon the prices for RALs in 2006, a consumer can expect to pay about $100 in order to get a RAL for the average refund of about $2,150 from a commercial tax preparation chain this year".

Opponents of RALs, like the National Consumer Law Center, argue that the profit motive of the lender results in RALs being issued too often to low-income individuals who are made to believe the wait for their refund is longer than it really is, who do not realize they are taking a loan, do not understand the high interest rates charged by the loan (often exceeding 100% APR until the last two tax filing seasons), and who do not actually need the funds immediately.

src: www.capitaltaxusa.com

Third-party cross-collection of bank debt ("previous debt") for both RALs and RACs

As part of applying for both financial products, the client is directing his or her refund to the account-issuing bank. Cross-collection occurs in cases where the bank uses this occasion to collect debt owed another bank. As the IRS Taxpayer Advocate described the practice in 2006: "if a taxpayer owes money on a defaulted RAL to Bank A and subsequently attempts to buy another RAL from Bank B, Bank B is authorized to collect the outstanding debt from the RAL proceeds, transmit the funds to Bank A". It is somewhat unclear how broad is the type of debt for which banks cross-collect. This practice is often not adequately disclosed to the tax preparation client. As a lawsuit filing against H&R Block by the California Attorney General in February 2006 stated, "H&R Block does not adequately tell such customers about any alleged debts, or that when they sign the new RAL application, they agree to automatic debt collection--including collection on alleged RAL-related debts from other tax preparers or banks. These applications are denied, and the customer's anticipated refund is used to pay off the debt, plus a fee". Tax prep firms often vaguely refer to this practice merely as "previous debt".

This risk exists even if the client is only using the RAC account for purposes of taking the tax preparation fees out of the refund.

src: consumerist.com

Jan. 2011: IRS will not be providing "debt indicator"

On August 5, 2010, the IRS announced that for the upcoming 2011 tax filing season, the agency would no longer be providing preparers and associated financial institutions with the "debt indicator" (a one-letter code that discloses whether or not the taxpayer owes back taxes and whether or not the taxpayer owes federally collected obligations such as child support, student loans, etc.).

Taxpayers themselves will continue to have access to information about their refund through the "Where's My Refund?" feature at the irs.gov website.

In the same news release, the IRS stated it was exploring ways to allow filers to directly split off part of the refund to pay for professional tax preparation, possibly starting in January 2012. The IRS is asking for input from filers, consumer advocates, and those in the tax preparation community regarding whether this would be cost-effective.

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Jan. 2013: Major U.S. banks stop offering RALs

Beginning with the 2013 tax season, major U.S. banks will no longer be offering RALs. They will instead be offering the similar financial products of RACs, which are not loans but are rather temporary accounts which sit empty waiting for the client's IRS refund.

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See also

• Payday loan, another type of low-principal, high-interest short term loan
• Alternative financial services

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References

src: www.loanlendernetwork.com

Articles

• "Tax Refund Loans Are Revamped and Resurrected". The New York Times, Jan. 15, 2017
• "Why Refund Anticipation Loans Are (Still) Bad News", Credit.org.
• "Knowing The Dangers of Getting a Tax Refund Loan", Tax Refund Loans, April 7, 2016
• http://www.usatoday.com/money/perfi/general/2006-09-17-refund-loans-usat_x.htm
• "E-filing can make high-fee loans unnecessary", MSNBC, 2006-02-15

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External links

• Refund Anticipation Loans and Checks
• Educational Articles on Tax Refund Loan
• https://www.irs.gov/e-file-providers/tax-refund-related-products
• https://consumerfed.org/issues/banking-and-credit/tax-preparation/
• https://consumerfed.org/pdfs/RefundAnticipationLoanReport.pdf
• http://www.eitcoutreach.org/learn/tax-filing/rals/
• "Refund Anticipation Loans". Center for Responsible Lending. Archived from the original on 2008-04-29. Retrieved 2008-08-03.

Source of article : Wikipedia

Websites blocked in mainland China

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As of September 2018, about 10,000 domain names are blocked in mainland China under the country's Internet censorship policy, which prevents users from accessing proscribed websites from within the country.

This is a list of the most notable such blocked websites in the country. This page does not apply to the special administrative regions of Hong Kong and Macau, where most of Chinese law does not apply, nor does it apply to Taiwan.

Note that many of the sites listed may be occasionally or even regularly available, depending on the access location or current events.

Video Websites blocked in mainland China

Table of high-ranking websites blocked in mainland China

Maps Websites blocked in mainland China

See also

• Golden Shield Project
• GreatFire
• Censorship in China

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References

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External links

• Chinese Firewall Test - Instantly test if a URL is blocked by the Great Firewall of China in real time. Tests for both symptoms of DNS poisoning and HTTP blocking from a number of locations within mainland China.
• GreatFire.org - Test if any URL is blocked or otherwise restricted in China in real-time. The website uses test locations inside China and provides transparent test results and conclusions on censorship. Also provides a database of blocked URLs going back to February 2011.
• Blocked In China - Test if any domain is DNS poisoned in China in real-time. DNS poisoning is one way in which websites can be blocked. Others are IP blocking and keyword filtering.
• Inside the Firewall: Tracking the News That China Blocks, ProPublica, 17 December 2014.

Source of article : Wikipedia

H-index

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The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications. The index can also be applied to the productivity and impact of a scholarly journal as well as a group of scientists, such as a department or university or country. The index was suggested in 2005 by Jorge E. Hirsch, a physicist at UCSD, as a tool for determining theoretical physicists' relative quality and is sometimes called the Hirsch index or Hirsch number.

Video H-index

Definition and purpose

The definition of the index is that a scholar with an index of h has published h papers each of which has been cited in other papers at least h times. Thus, the h-index reflects both the number of publications and the number of citations per publication. The index is designed to improve upon simpler measures such as the total number of citations or publications. The index works properly only for comparing scientists working in the same field; citation conventions differ widely among different fields.

Maps H-index

Calculation

Formally, if f is the function that corresponds to the number of citations for each publication, we compute the h index as follows. First we order the values of f from the largest to the lowest value. Then, we look for the last position in which f is greater than or equal to the position (we call h this position). For example, if we have a researcher with 5 publications A, B, C, D, and E with 10, 8, 5, 4, and 3 citations, respectively, the h index is equal to 4 because the 4th publication has 4 citations and the 5th has only 3. In contrast, if the same publications have 25, 8, 5, 3, and 3, then the index is 3 because the fourth paper has only 3 citations.

f(A)=10, f(B)=8, f(C)=5, f(D)=4, f(E)=3 -> h-index=4

f(A)=25, f(B)=8, f(C)=5, f(D)=3, f(E)=3 -> h-index=3

If we have the function f ordered in decreasing order from the largest value to the lowest one, we can compute the h index as follows:

h-index (f) = ${\displaystyle \max _{i}\min(f(i),i)}$

The Hirsch index is equivalent to the Eddington number, an earlier metric used for evaluating cyclists. The h-index serves as an alternative to more traditional journal impact factor metrics in the evaluation of the impact of the work of a particular researcher. Because only the most highly cited articles contribute to the h-index, its determination is a simpler process. Hirsch has demonstrated that h has high predictive value for whether a scientist has won honors like National Academy membership or the Nobel Prize. The h-index grows as citations accumulate and thus it depends on the "academic age" of a researcher.

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Input data

The h-index can be manually determined using citation databases or using automatic tools. Subscription-based databases such as Scopus and the Web of Science provide automated calculators. Harzing's Publish or Perish program calculates the h-index based on Google Scholar entries. From July 2011 Google have provided an automatically-calculated h-index and i10-index within their own Google Scholar profile. In addition, specific databases, such as the INSPIRE-HEP database can automatically calculate the h-index for researchers working in high energy physics.

Each database is likely to produce a different h for the same scholar, because of different coverage. A detailed study showed that the Web of Science has strong coverage of journal publications, but poor coverage of high impact conferences. Scopus has better coverage of conferences, but poor coverage of publications prior to 1996; Google Scholar has the best coverage of conferences and most journals (though not all), but like Scopus has limited coverage of pre-1990 publications. The exclusion of conference proceedings papers is a particular problem for scholars in computer science, where conference proceedings are considered an important part of the literature. Google Scholar has been criticized for producing "phantom citations," including gray literature in its citation counts, and failing to follow the rules of Boolean logic when combining search terms. For example, the Meho and Yang study found that Google Scholar identified 53% more citations than Web of Science and Scopus combined, but noted that because most of the additional citations reported by Google Scholar were from low-impact journals or conference proceedings, they did not significantly alter the relative ranking of the individuals. It has been suggested that in order to deal with the sometimes wide variation in h for a single academic measured across the possible citation databases, one should assume false negatives in the databases are more problematic than false positives and take the maximum h measured for an academic.

src: blog.scopus.com

Comparing results across fields and career levels

Little systematic investigation has been done on how the h-index behaves over different institutions, nations, times and academic fields/disciplines. Hirsch suggested that, for physicists, a value for h of about 12 might be typical for advancement to tenure (associate professor) at major [US] research universities. A value of about 18 could mean a full professorship, 15-20 could mean a fellowship in the American Physical Society, and 45 or higher could mean membership in the United States National Academy of Sciences.

For the most highly cited scientists in the period 1983-2002, Hirsch identified the top 10 in the life sciences (in order of decreasing h): Solomon H. Snyder, h = 191; David Baltimore, h = 160; Robert C. Gallo, h = 154; Pierre Chambon, h = 153; Bert Vogelstein, h = 151; Salvador Moncada, h = 143; Charles A. Dinarello, h = 138; Tadamitsu Kishimoto, h = 134; Ronald M. Evans, h = 127; and Axel Ullrich, h = 120. Among 36 new inductees in the National Academy of Sciences in biological and biomedical sciences in 2005, the median h-index was 57. However, he points out that values of h will vary between different fields.

Among the 22 scientific disciplines listed in the Thomson Reuters Essential Science Indicators Citation Thresholds [thus excluding non-science academics], physics has the second most citations after space science. During the period January 1, 2000 - February 28, 2010, a physicist had to receive 2073 citations to be among the most cited 1% of physicists in the world. The threshold for space science is the highest (2236 citations), and physics is followed by clinical medicine (1390) and molecular biology & genetics (1229). Most disciplines, such as environment/ecology (390), have fewer scientists, fewer papers, and fewer citations. Therefore, these disciplines have lower citation thresholds in the Essential Science Indicators, with the lowest citation thresholds observed in social sciences (154), computer science (149), and multidisciplinary sciences (147).

Numbers are very different in social science disciplines: The Impact of the Social Sciences team at London School of Economics found that social scientists in the United Kingdom had lower average h-indices. The h-indices for ("full") professors, based on Google Scholar data ranged from 2.8 (in law), through 3.4 (in political science), 3.7 (in sociology), 6.5 (in geography) and 7.6 (in economics). On average across the disciplines, a professor in the social sciences had an h-index about twice that of a lecturer or a senior lecturer, though the difference was the smallest in geography.

src: www.bmj.com

Advantages

Hirsch intended the h-index to address the main disadvantages of other bibliometric indicators, such as total number of papers or total number of citations. Total number of papers does not account for the quality of scientific publications, while total number of citations can be disproportionately affected by participation in a single publication of major influence (for instance, methodological papers proposing successful new techniques, methods or approximations, which can generate a large number of citations), or having many publications with few citations each. The h-index is intended to measure simultaneously the quality and quantity of scientific output.

src: www.pnas.org

Criticism

There are a number of situations in which h may provide misleading information about a scientist's output: Most of these however are not exclusive to the h-index.

• The h-index does not account for the typical number of citations in different fields. It has been stated that citation behavior in general is affected by field-dependent factors, which may invalidate comparisons not only across disciplines but even within different fields of research of one discipline.
• The h-index discards the information contained in author placement in the authors' list, which in some scientific fields is significant.
• The h-index has been found in one study to have slightly less predictive accuracy and precision than the simpler measure of mean citations per paper. However, this finding was contradicted by another study by Hirsch.
• The h-index is a natural number that reduces its discriminatory power. Ruane and Tol therefore propose a rational h-index that interpolates between h and h + 1.
• The h-index can be manipulated through self-citations, and if based on Google Scholar output, then even computer-generated documents can be used for that purpose, e.g. using SCIgen.
• The h-index does not provide a significantly more accurate measure of impact than the total number of citations for a given scholar. In particular, by modeling the distribution of citations among papers as a random integer partition and the h-index as the Durfee square of the partition, Yong arrived at the formula ${\displaystyle h\approx 0.54{\sqrt {N}}}$, where N is the total number of citations, which, for mathematics members of the National Academy of Sciences, turns out to provide an accurate (with errors typically within 10-20 percent) approximation of h-index in most cases.

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Alternatives and modifications

Various proposals to modify the h-index in order to emphasize different features have been made. As the variants have proliferated, comparative studies have become possible showing that most proposals are highly correlated with the original h-index, although alternative indexes may be important to decide between comparable CVs, as often the case in evaluation processes.

• An individual h-index normalized by the number of authors has been proposed: ${\displaystyle h_{I}=h^{2}/N_{a}^{(T)}}$, with ${\displaystyle N_{a}^{(T)}}$ being the number of authors considered in the ${\displaystyle h}$ papers. It was found that the distribution of the h-index, although it depends on the field, can be normalized by a simple rescaling factor. For example, assuming as standard the hs for biology, the distribution of h for mathematics collapse with it if this h is multiplied by three, that is, a mathematician with h = 3 is equivalent to a biologist with h = 9. This method has not been readily adopted, perhaps because of its complexity. It might be simpler to divide citation counts by the number of authors before ordering the papers and obtaining the h-index, as originally suggested by Hirsch.
• The m-index is defined as h/n, where n is the number of years since the first published paper of the scientist; also called m-quotient.
• There are a number of models proposed to incorporate the relative contribution of each author to a paper, for instance by accounting for the rank in the sequence of authors.
• A generalization of the h-index and some other indices that gives additional information about the shape of the author's citation function (heavy-tailed, flat/peaked, etc.) has been proposed.
• A successive Hirsch-type-index for institutions has also been devised. A scientific institution has a successive Hirsch-type-index of i when at least i researchers from that institution have an h-index of at least i.
• Three additional metrics have been proposed: h² lower, h² center, and h² upper, to give a more accurate representation of the distribution shape. The three h² metrics measure the relative area within a scientist's citation distribution in the low impact area, h² lower, the area captured by the h-index, h² center, and the area from publications with the highest visibility, h² upper. Scientists with high h² upper percentages are perfectionists, whereas scientists with high h² lower percentages are mass producers. As these metrics are percentages, they are intended to give a qualitative description to supplement the quantitative h-index.
• The g-index can be seen as the h-index for an averaged citations count.
• It has been argued that "For an individual researcher, a measure such as Erd?s number captures the structural properties of network whereas the h-index captures the citation impact of the publications. One can be easily convinced that ranking in coauthorship networks should take into account both measures to generate a realistic and acceptable ranking." Several author ranking systems such as eigenfactor (based on eigenvector centrality) have been proposed already, for instance the Phys Author Rank Algorithm.
• The c-index accounts not only for the citations but for the quality of the citations in terms of the collaboration distance between citing and cited authors. A scientist has c-index n if n of [his/her] N citations are from authors which are at collaboration distance at least n, and the other (N - n) citations are from authors which are at collaboration distance at most n.
• An s-index, accounting for the non-entropic distribution of citations, has been proposed and it has been shown to be in a very good correlation with h.
• The e-index, the square root of surplus citations for the h-set beyond h², complements the h-index for ignored citations, and therefore is especially useful for highly cited scientists and for comparing those with the same h-index (iso-h-index group).
• Because the h-index was never meant to measure future publication success, recently, a group of researchers has investigated the features that are most predictive of future h-index. It is possible to try the predictions using an online tool. However, later work has shown that since h-index is a cumulative measure, it contains intrinsic auto-correlation that led to significant overestimation of its predictability. Thus, the true predictability of future h-index is much lower compared to what has been claimed before.
• The h-index has been applied to Internet Media, such as YouTube channels. The h-index is defined as the number of videos with >= h × 10⁵ views. When compared with a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.
• The i10-index indicates the number of academic publications an author has written that have at least ten citations from others. It was introduced in July 2011 by Google as part of their work on Google Scholar.
• The h-index has been shown to have a strong discipline bias. However, a simple normalization ${\displaystyle h/\langle h\rangle _{d}}$ by the average h of scholars in a discipline d is an effective way to mitigate this bias, obtaining a universal impact metric that allows comparison of scholars across different disciplines. Of course this method does not deal with academic age bias.
• The h-index can be timed to analyze its evolution during one's career, employing different time windows.
• The o-index corresponds to the geometric mean of the h-index and the most cited paper of a researcher.

src: blog.scopus.com

See also

• Bibliometrics
• Comparison of research networking tools and research profiling systems

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References

src: blog.scopus.com

Further reading

• Alonso, S.; Cabrerizo, F. J.; Herrera-Viedma, E.; Herrera, F. (2009). "h-index: A Review Focused in its Variants, Computation and Standardization for Different Scientific Fields". Journal of Informetrics. 3 (4): 273-89. doi:10.1016/j.joi.2009.04.001.
• Ball, Philip (2005). "Index aims for fair ranking of scientists". Nature. 436 (7053): 900. Bibcode:2005Natur.436..900B. doi:10.1038/436900a. PMID 16107806.
• Iglesias, Juan E.; Pecharromán, Carlos. "Scaling the h-index for different scientific ISI fields" (PDF).
• Kelly, C. D.; Jennions, M. D. (2006). "The h index and career assessment by numbers". Trends Ecol. Evol. 21 (4): 167-70. doi:10.1016/j.tree.2006.01.005. PMID 16701079.
• Lehmann, S.; Jackson, A. D.; Lautrup, B. E. (2006). "Measures for measures". Nature. 444 (7122): 1003-04. Bibcode:2006Natur.444.1003L. doi:10.1038/4441003a. PMID 17183295.
• Panaretos, J.; Malesios, C. (2009). "Assessing Scientific Research Performance and Impact with Single Indices". Scientometrics. 81 (3): 635-70. arXiv:0812.4542. doi:10.1007/s11192-008-2174-9.
• Petersen, A. M.; Stanley, H. Eugene; Succi, Sauro (2011). "Statistical Regularities in the Rank-Citation Profile of Scientists". Scientific Reports. 181 (181): 1-7. arXiv:1103.2719. Bibcode:2011NatSR...1E.181P. doi:10.1038/srep00181. PMC 3240955. PMID 22355696.
• Sidiropoulos, Antonis; Katsaros, Dimitrios; Manolopoulos, Yannis (2007). "Generalized Hirsch h-index for disclosing latent facts in citation networks". Scientometrics. 72 (2): 253-80. CiteSeerX 10.1.1.76.3617. doi:10.1007/s11192-007-1722-z.
• Soler, José M. (2007). "A rational indicator of scientific creativity". Journal of Informetrics. 1 (2): 123-30. arXiv:physics/0608006. doi:10.1016/j.joi.2006.10.004.
• Symonds, M. R.; et al. (2006). Tregenza, Tom, ed. "Gender differences in publication output: towards an unbiased metric of research performance". PLoS ONE. 1 (1): e127. Bibcode:2006PLoSO...1..127S. doi:10.1371/journal.pone.0000127. PMC 1762413. PMID 17205131.
• Taber, Douglass F. (2005). "Quantifying Publication Impact". Science. 309 (5744): 2166a. doi:10.1126/science.309.5744.2166a. PMID 16195445.
• Woeginger, Gerhard j. (2008). "An axiomatic characterization of the Hirsch-index". Mathematical Social Sciences. 56 (2): 224-32. doi:10.1016/j.mathsocsci.2008.03.001.

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External links

• Google Scholar Metrics
• H-index for economists
• H-index for computer science researchers
• H-index for astronomers

Source of article : Wikipedia