CoChat
Sign Up
Cited by 105Race, Genetics, and SocietyGenetic Associations and EpidemiologyEvolution and Genetic Dynamics

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Open access
Rosenberg, Noah A, Edge, Michael D, Pritchard, Jonathan K, Feldman, Marcus W·2019·Evolution, Medicine, and Public Health
DOI 10.1093/emph/eoy036PMC PMC6393779
Open this paper in CoChat — free accountOpen PDF

Abstract

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that population differences in polygenic scores are often overinterpreted because environmental, interaction, and statistical factors can produce phenotypic differences without corresponding genetic-propensity differences. It emphasizes neutral null expectations and cautions against attributing human phenotypic differences, including health disparities, to polygenic adaptation without strong evidence.

Key findings

  • Polygenic score differences between populations do not directly reveal genetic propensity differences: environmental contributions can amplify, obscure, or reverse phenotype differences, and even high within-population heritability does not imply a between-population genetic difference (Fig. 1).
  • Under a selectively neutral null model, expected population differences in polygenic traits are comparable in magnitude to the apportionment of human genetic diversity (QST ≈ FST), so phenotypic differences need not reflect natural selection; only strong directional selection would make selection a major explanation.
  • In the health-disparities example, African Americans lost more years of life than European Americans in 28 of 36 physiologically diverse causes of death; under a neutral model with no genetic correlation, the binomial probability of at least 28 of 36 independent phenotypes being larger in one of a pair of populations is 0.0012, pointing toward systematic environmental effects such as racism rather than selection.
  • Statistical limitations include poor transferability of GWAS effect sizes across populations, especially because most GWAS are in European-ancestry samples; ascertainment, linkage disequilibrium differences, and subtle ancestry differences can bias polygenic scores and exaggerate signals of polygenic adaptation, as seen in height analyses.

Methods

  • Design: conceptual/review analysis rather than a primary data study; it synthesizes population-genetic theory, GWAS/polygenic score research, and polygenic adaptation literature.
  • Data or sample: no new dataset is analyzed; the paper cites prior studies and uses a health-disparities example involving 36 causes of death and prior population-genetic models.
  • Main analysis: verbal and graphical reasoning with null models, including neutral QST/FST expectations and a binomial computation for the 36-cause health-disparities example; figures illustrate genotype–environment–phenotype relationships.

Limitations

  • Stated by the paper: polygenic scores ignore environmental influences; gene–gene and gene–environment interactions can make variant effects differ among populations; overlapping polygenic score distributions limit individual prediction; effect estimates may not transfer across populations due to ascertainment, linkage disequilibrium, and European-ancestry calibration; polygenic adaptation tests are sensitive to the choice of GWAS effect sizes.
  • Evident from the text: the paper is a conceptual review/commentary without new empirical data, formal systematic review methods, or primary statistical analysis beyond illustrative models and cited computations.
  • Evident from the text: its arguments depend on cited studies and simulations/figures, so the strength of its conclusions rests on the quality and representativeness of those external sources.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1093/emph/eoy036

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

40 of 61 cited sources checked · 27 supported · 0 flagged · accuracy not yet measured

  • Interpretationcitation

    Many of the pre-genomic-era limitations on the use of heritability to make inferences about the genetic basis of phenotypic differences among populations continue to apply, in updated form, to polygenic scores.

    “Although estimates of the contributions of specific genetic variants advance modern analyses beyond classical heritability statistics, many of the pre-genomic-era limitations on the use of heritability to make inferences about the genetic basis of phenotypic differences among populations [26–28] continue to apply, in updated form (Fig. 1).”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites [26] Lewontin, 1974, The analysis of variance and the analysis of causes, Am J Hum Genet

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [27] Feldman, 1975, The heritability hang-up, Science source
    • cites [28] Feldman, 2018, Missing compared to what? Revisiting heritability, genes and culture, Philos Trans R Soc Lond B source
  • Interpretationcitation

    One key component of the inference of polygenic adaptation is the use of an appropriate null expectation for polygenic score distributions and phenotypic differences.

    “One key component of the inference of polygenic adaptation is the use of an appropriate null expectation for polygenic score distributions and phenotypic differences [12, 42].”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [12] Berg, 2014, A population genetic signal of polygenic adaptation, PLoS Genet source
    • cites [42] Edge, 2015, A general model of the relationship between the apportionment of human genetic diversity and the apportionment of human phenotypic diversity, Hum Biol source

      The Introduction (page not provided) gives an expected magnitude for between-group differences in a neutral polygenic trait. The Discussion’s “Interpreting Group Differences in Phenotype” section (page not provided) uses that neutral expectation to assess possible selection. Our copy does not address null distributions for polygenic scores, so it supports the phenotypic-differences part but not that part of the claim. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Premisecitation

    Large population differences in disease risk for well-known alleles such as APOE-ɛ4 in Alzheimer disease highlight the challenge of determining how population differences in effect size might be affected by interaction effects.

    “Large population differences in disease risk for well-known alleles such as APOE-ɛ4 in Alzheimer disease [29, 30] highlight the challenge of determining how population differences in effect size might be affected by interaction effects.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [29] Tang, 1998, The APOE-ɛ4 allele and the risk of Alzheimer disease among African Americans, whites, and Hispanics, J Am Med Assoc source
    • cites [30] Rajabli, 2018, Ancestral origin of ApoE ɛ4 Alzheimer disease risk in Puerto Rican and African American populations, PLoS Genet source
  • Premisecitation

    Using polygenic scores, it is now possible to combine information from millions of genomic variants to identify people whose overall polygenic risk of coronary artery disease is as high as that of patients with monogenic lipid disorders such as familial hypercholesterolemia.

    “Using polygenic scores, it is now possible to combine information from millions of genomic variants to identify people whose overall polygenic risk of coronary artery disease is as high as that of patients with monogenic lipid disorders such as familial hypercholesterolemia [23].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [23] Khera, 2018, Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenetic mutations, Nat Genet source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
  • Premisecitation

    Effect estimates might rely on sites that were ascertained for variability in one set of populations and whose systematic differences in allele frequencies between populations contribute to systematic biases in polygenic score estimates in other populations.

    “Effect estimates might rely on sites that were ascertained for variability in one set of populations and whose systematic differences in allele frequencies between populations contribute to systematic biases in polygenic score estimates in other populations [31].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [31] Kim, 2018, Genetic disease risks can be misestimated across global populations, Genome Biol source
  • May combine two claims. 11
  • Classified as probably not a substantial claim. 8
  • The quote only partly states this claim. 1

  • May combine two claims.

    Premisereasoning

    Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small, with the expected difference among populations in the trait having comparable magnitude to the classical estimate of the 'apportionment of human genetic diversity'.

    “Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small.”
    • Also stated in: § The null expectation · plain
      “In deriving such an expectation, an important insight from selectively neutral population-genetic models is that irrespective of the number of genetic loci contributing to a polygenic trait, the expected difference among populations in the trait is predicted to have comparable magnitude to the classical estimate of the ‘apportionment of human genetic diversity’, the extent of human genetic differ”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [12] Berg, 2014, A population genetic signal of polygenic adaptation, PLoS Genet source
    • cites [43] Leinonen, 2013, QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity, Nat Rev Genet source
    • cites [44] Edge, 2015, Implications of the apportionment of human genetic diversity for the apportionment of human phenotypic diversity, Stud Hist Philos Biol Biomed Sci source
    • The authors state this as an assumption: In the null model, phenotypes are selectively neutral (no selection occurs), and genetic drift acts independently at each locus.

  • May combine two claims.

    Premisecitation

    Because most genome-wide association studies have been conducted in populations with European ancestry, the effect sizes used in calculating polygenic scores have been calibrated on Europeans, and their values might not transfer accurately to other populations.

    “So far, because most genome-wide association studies have been conducted in populations with European ancestry [33], the effect sizes used in calculating polygenic scores have been calibrated on Europeans, and their values might not transfer accurately to other populations.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [33] Popejoy, 2016, Genomics is failing on diversity, Nature source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations.

    “Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16].”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [14] Novembre, 2009, Spatial patterns of variation due to natural selection in humans, Nat Rev Genet source
    • cites [15] Lachance, 2013, Population genomics of human adaptation, Annu Rev Ecol Evol Syst source
    • cites [16] Jeong, 2014, Adaptations to local environments in modern human populations, Curr Opin Genet Dev source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Genomic investigations have provided insights into how natural selection has given rise to differences in phenotypes that vary geographically, such as skin pigmentation, lactase persistence and altitude-related physiology.

    “Genomic investigations have provided insights into how natural selection has given rise to differences in phenotypes that vary geographically, such as skin pigmentation, lactase persistence and altitude-related physiology [14–16].”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [14] Novembre, 2009, Spatial patterns of variation due to natural selection in humans, Nat Rev Genet source

      The section on geographically variable traits links skin pigmentation and lactose tolerance to environmental patterns and natural selection. The section on multiple mutations underlying adaptive phenotypes also discusses skin pigmentation and lactase persistence. Our copy does not provide a corresponding example for altitude-related physiology (searched: altitude and high-altitude hypoxia). (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [15] Lachance, 2013, Population genomics of human adaptation, Annu Rev Ecol Evol Syst source
    • cites [16] Jeong, 2014, Adaptations to local environments in modern human populations, Curr Opin Genet Dev source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Over the past 15 years, genomic analyses have identified thousands of genetic variants that contribute statistically to variation in complex phenotypes, traits that have complex patterns of inheritance and that are affected by large numbers of genes in combination with environmental factors.

    “Over the past 15 years, genomic analyses have identified thousands of genetic variants that contribute statistically to variation in complex phenotypes, traits that have complex patterns of inheritance and that are affected by large numbers of genes in combination with environmental factors [18–20].”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [18] Stranger, 2011, Progress and promise of genome-wide association studies for human complex trait genetics, Genetics source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [19] Welter, 2014, The NHGRI GWAS Catalog, a curated resource of SNP-trait associations, Nucleic Acids Res source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [20] Visscher, 2017, 10 years of GWAS discovery: biology, function, and translation, Am J Hum Genet source

  • May combine two claims.

    Premisecitation

    In the null model, systematic health disparities are unlikely: the binomial probability that the trait value is larger in one of a pair of populations for at least 28 of 36 independent phenotypes is 0.0012.

    “In this model, systematic health disparities are unlikely: assuming that no genetic correlation exists between phenotypic outcomes, the binomial probability that the trait value is larger in one of a pair of populations for at least 28 of 36 independent phenotypes is 0.0012 [42].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [42] Edge, 2015, A general model of the relationship between the apportionment of human genetic diversity and the apportionment of human phenotypic diversity, Hum Biol source
    • The authors state this as an assumption: In the simplest null model, many genes contribute to a trait chosen at random, with no directional selection, and each of a pair of groups has probability 0.5 of having the larger mean value for the trait.
    • The authors state this as an assumption: No genetic correlation exists between phenotypic outcomes.
    • The authors state this as an assumption: The 36 phenotypes are independent.

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing). May combine two claims.

    Premisecitation

    Some researchers have argued for a greater level of deleterious variation in non-Africans rather than in Africans, a pattern opposite to what might be expected given the direction of health disparities.

    “indeed, some researchers have argued for a greater level of deleterious variation in non-Africans rather than in Africans [50, 52], a pattern opposite to what might be expected given the direction of health disparities.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [50] Lohmueller, 2014, The distribution of deleterious genetic variation in human populations, Curr Opin Genet Dev source

      The 'Efficacy of Natural Selection' section reports a higher proportion of nonsynonymous variants in non-Africans, and Table 1 shows more homozygous derived genotypes in non-Africans. But Table 1 also shows more nonsynonymous variants in Africans and approximately equal numbers of derived alleles per individual, so the paper does not establish a generally greater level of deleterious variation in non-Africans. The passages do not address the claimed contrast with health disparities. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [52] Henn, 2015, Estimating the mutation load in human genomes, Nat Rev Genet source

  • The quote only partly states this claim.

    Premisecitation

    The erroneously exaggerated signatures of polygenic adaptation from using effect sizes from one study to assess a second dataset arise from subtle population differences between study samples.

    “This result, which arises from subtle population differences between study samples, calls into question claims about polygenic adaptation even of traits for which it has been most extensively investigated.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [34] Berg, 2018, Reduced signal for polygenic adaptation of height in UK Biobank, bioRxiv

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [35] Sohail, 2018, Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies, bioRxiv source

  • May combine two claims.

    Interpretationreasoning

    Population differences in phenotype distributions do not reveal which population has greater genetic propensities on average, whether the observed difference would persist if the environment were altered, or even whether a difference in genetic propensities exists at all.

    “Depending on environmental contributions, a difference in mean phenotype might or might not reflect a difference in the magnitude of genetic effects among populations; population differences in phenotype distributions do not reveal which population has greater genetic propensities on average, whether the observed difference would persist if the environment were altered, or even whether a difference”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings.

    “In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Beckwith, 2013 source

  • May combine two claims.

    Interpretationreasoning

    Polygenic scores encode knowledge about specific genetic correlates of trait variation, but they do not change the conceptual framework for genetic and environmental contribution to population differences.

    “Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    Although distributions of individual-level polygenic scores might differ among populations, differences in these distributions might have many potential causes, and might or might not reflect meaningful biological phenomena.

    “Although distributions of individual-level polygenic scores might differ among populations, differences in these distributions might have many potential causes, and might or might not reflect meaningful biological phenomena.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    Gene-gene and gene-environment interactions can cause the effects of a variant on a trait to differ among populations, and effects of multiple variants can combine in different ways.

    “Second, gene–gene and gene–environment interactions influence traits, meaning that associations between specific genotypes and a phenotype—and the importance of the genetic contributions—might differ among populations with different allele frequencies or distributions of environmental variables.”
    • Also stated in: § Conceptual limitations · plain
      “In other words, because the contributions of genomic variants can differ among populations due to interactions with other variants and with environmental variables, the effects of a variant on a trait can have different magnitudes in different populations, and effects of multiple variants in one or more genes can combine in different ways.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Selection on a correlated trait might occur in different directions in a pair of populations or with different magnitudes in the same direction, and therefore need not increase genetic differences between populations in the way that divergent selection for the initial trait might suggest.

    “Selection on a correlated trait might occur in different directions in a pair of populations or with different magnitudes in the same direction, and therefore need not increase genetic differences between populations in the way that divergent selection for the initial trait might suggest (Fig. 2).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Scientists' publicity materials and news reports rely on racialized language and exaggerated views of race as biological.

    “Other problems include reliance in scientists’ publicity materials and in news reports on racialized language and exaggerated views of race as biological [57]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [57] Condit, 2007, How geneticists can help reporters get their story right, Nat Rev Genet source

  • May combine two claims.

    Interpretationreasoning

    Because of environmental effects, between-population differences in phenotype distributions need not reflect a parallel difference in genetic propensities; they may reflect no difference or a difference opposite in direction.

    “In other words, because of environmental effects, the difference in phenotype distributions in Fig. 1A–C need not reflect a parallel difference in genetic propensities as in Fig. 1A, but rather no difference as in Fig. 1B or a difference opposite in direction as in Fig. 1C”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Modern discourse in population genetics uses non-racial conceptual structures for characterizing and analyzing human variation.

    “when modern discourse in population genetics instead uses non-racial conceptual structures for characterizing and analyzing human variation [58].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [58] Rosenberg, 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    History suggests that multiple forms of misrepresentation of findings in human genetics can lend the authority of science to claims that the underlying research does not validate and might actively contradict.

    “Unfortunately, history suggests that multiple forms of misrepresentation of findings in human genetics can lend the authority of science to claims that the underlying research does not validate and might actively contradict.”
    Quote in the paperNot a substantial claim
18 of 18

  • The authors state this as an assumption.

    In the null model, phenotypes are selectively neutral (no selection occurs), and genetic drift acts independently at each locus.

    “Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small.”
    “In other words, analogous measures of population differences in quantitative phenotypic traits and genetic loci—termed QST and FST, respectively—are approximately equal in neutral evolutionary models that include genetic drift but not natural selection.”

  • The authors state this as an assumption.

    In the simplest null model, many genes contribute to a trait chosen at random, with no directional selection, and each of a pair of groups has probability 0.5 of having the larger mean value for the trait.

    “In the simplest null model in which many genes contribute to a trait chosen at random, with no directional selection, each of a pair of groups has probability 0.5 of having the larger mean value for the trait.”

  • The authors state this as an assumption.

    No genetic correlation exists between phenotypic outcomes.

    “assuming that no genetic correlation exists between phenotypic outcomes”

  • The authors state this as an assumption.

    The 36 phenotypes are independent.

    “the binomial probability that the trait value is larger in one of a pair of populations for at least 28 of 36 independent phenotypes is 0.0012 [42].”
4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Kahn, 2018, How not to talk about race and genetics, BuzzFeed.
  2. Harden, 2018, Genetic test scores predicting intelligence are not the new eugenics, Leapsmag.
  3. Zimmer, 2018, She Has Her Mother’s Laugh
  4. Rosenberg, 2019, The Race Debates from Metaphysics to Medicine

Cited by

Sorted by citations

Related

Authors
Rosenberg, N. A., Edge, M. D., Pritchard, J. K., & Feldman, M. W.
Affiliations
  • Stanford University (US)
  • University of California, Davis (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
Rosenberg, Noah A
ORCID
Venue
Evolution, Medicine, and Public Health
Publisher
Oxford University Press (OUP)
Published
2018-12-27
Volume
2019
Issue
1
Pages
26-34
ISSN
2050-6201 (ISSN-L)
DOI
10.1093/emph/eoy036
PMC
PMC6393779
OpenAlex
W2906825642
Cited by
105
References
54
Citations by year
2026: 8 · 2025: 6 · 2024: 12 · 2023: 12 · 2022: 16 · 2021: 24 · 2020: 17 · 2019: 9 · 2017: 1
FWCI
1.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Evolution Medicine and Public Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (GM108779, GM130050, HG005855)
  • National Science Foundation (DBI-1458059)
  • Stanford Center for Computational, Evolutionary and Human Genomics
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
polygenic risk score, polygenic adaptation, null models, selective neutrality, health disparities, African Americans, European Americans

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-29.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-29.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review