{"meta":{"query_hash":"ab92b74a1c85","filters":{"venue":"Statistical Science"},"cohort_total":32,"direct_labels_cover":1,"predictions_cover":32,"exported":32,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/ab92b74a1c85","api":"https://metacan.xera.ac/api/v1/cohort?venue=Statistical+Science"},"results":[{"id":"W1607848239","doi":"10.1214/ss/1009212815","title":"Bayesian backfitting (with comments and a rejoinder by the authors","year":2000,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Additive model; Generalization; Generalized additive model; Mathematics; Nonparametric statistics; Bayesian probability; Computer science; Mathematical optimization; Modular design; Econometrics; Artificial intelligence; Statistics","score_opus":0.04640613272490587,"score_gpt":0.375692202853479,"score_spread":0.32928607012857314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607848239","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090822025,0.010135095,0.14114769,0.7664766,0.07476832,0.00014920173,0.00032713238,0.0005018317,0.005585914],"genre_scores_gemma":[0.014471044,0.011311781,0.14541574,0.65661097,0.12613608,0.0008251763,0.0003387447,0.001219395,0.043671135],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96896255,0.014688404,0.003096293,0.003942132,0.00845658,0.00085405126],"domain_scores_gemma":[0.9247661,0.03223201,0.0032852888,0.007998421,0.029529355,0.0021887142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04383773,0.0018142919,0.0018160492,0.0031492333,0.0034397529,0.005535603,0.007024643,0.012796969,0.008410261],"category_scores_gemma":[0.10394001,0.0011342916,0.002433839,0.0032161523,0.009902518,0.01577238,0.0071412,0.033081174,0.013864575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073615454,0.000111183166,0.00060722965,0.00028769823,0.00018194164,0.00024514602,0.00096466654,0.0013738169,0.00036758723,0.11214948,0.8271933,0.056444347],"study_design_scores_gemma":[0.00007953882,0.000050573723,0.0010067369,0.00042223398,0.00008441451,0.0003716196,0.0003232816,0.0051781368,0.0008654117,0.39377996,0.59746,0.00037809127],"about_ca_topic_score_codex":0.008300979,"about_ca_topic_score_gemma":0.007316341,"teacher_disagreement_score":0.04383773,"about_ca_system_score_codex":0.0036506809,"about_ca_system_score_gemma":0.0034948126,"threshold_uncertainty_score":0.23183882},"labels":[],"label_agreement":null},{"id":"W1986014248","doi":"10.1214/09-sts283","title":"A Conversation with James Hannan","year":2010,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Military service; History; Feeling; China; World War II; Fall of man; Sociology; Management; Operations research; Gerontology; Psychology; Medicine; Law; Archaeology; Engineering; Political science; Politics","score_opus":0.11054459628420174,"score_gpt":0.4443533261635207,"score_spread":0.333808729879319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986014248","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017398066,0.012834777,0.00037018998,0.91977173,0.035505295,0.00001353444,0.000053396216,0.000057120145,0.029654084],"genre_scores_gemma":[0.043294363,0.009727553,0.0008195671,0.8045356,0.013943502,0.00006973353,0.000058198795,0.00017123073,0.12738033],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948086,0.0022709572,0.00012432165,0.00061141164,0.0012617211,0.00092309224],"domain_scores_gemma":[0.99182206,0.0023934639,0.00030741634,0.00017570196,0.0012277678,0.0040735705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051196204,0.0009791189,0.0010748509,0.0010988687,0.016621446,0.007823647,0.0016645343,0.0073216897,0.021566313],"category_scores_gemma":[0.018607184,0.00056431635,0.0005379036,0.001008466,0.0060183047,0.008395414,0.0049042394,0.019330898,0.0070201936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014796847,0.000040602696,0.00016386763,0.00002740039,0.000004068411,0.00024260023,0.004751479,0.000016205886,0.00009873849,0.009454862,0.97900075,0.0061845984],"study_design_scores_gemma":[0.0000042512806,0.000014006773,0.00031256408,0.00013072886,0.0000025807535,0.00041517668,0.010052931,0.000045179848,0.00005503526,0.0034441403,0.9854966,0.000026744801],"about_ca_topic_score_codex":0.016736062,"about_ca_topic_score_gemma":0.025910497,"teacher_disagreement_score":0.021566313,"about_ca_system_score_codex":0.0062866723,"about_ca_system_score_gemma":0.0071055293,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2015928670","doi":"10.1214/088342306000000033","title":"Elaboration on Two Points Raised in “Classifier Technology and the Illusion of Progress”","year":2006,"lang":"en","type":"article","venue":"Statistical Science","topic":"Mathematics Education and Teaching Techniques","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Elaboration; Illusion; Illusion of control; Elaboration likelihood model","score_opus":0.013158409313027176,"score_gpt":0.37363279327985655,"score_spread":0.36047438396682935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015928670","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045656005,0.0004205541,0.00064407394,0.983067,0.009249848,0.000025351763,0.000100636316,0.000047550653,0.005988445],"genre_scores_gemma":[0.009075238,0.00028286307,0.0005121769,0.9725118,0.010003489,0.00012477035,0.00003420612,0.00006460432,0.0073908484],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9855515,0.004415012,0.001124528,0.0023038425,0.0047288397,0.0018762046],"domain_scores_gemma":[0.95936227,0.027231963,0.0026780448,0.001471259,0.007812995,0.0014435097],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013500887,0.0017608561,0.0014624915,0.0019394497,0.010185933,0.0063289725,0.009539825,0.081612386,0.011038618],"category_scores_gemma":[0.07536413,0.0012670875,0.0029585273,0.0015327397,0.024350872,0.015585228,0.010612418,0.072600484,0.0054170103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008648265,0.000017295986,0.00030643283,0.000121990226,0.00001608835,0.000749336,0.006515261,0.00013162794,0.00043169843,0.091348305,0.89807683,0.0021986992],"study_design_scores_gemma":[0.000078916855,0.00003552356,0.0015567293,0.000717086,0.000029148185,0.0005154884,0.0076091485,0.00055622176,0.0009192778,0.068150975,0.91964144,0.00019001296],"about_ca_topic_score_codex":0.052870743,"about_ca_topic_score_gemma":0.031101422,"teacher_disagreement_score":0.98649913,"about_ca_system_score_codex":0.009572293,"about_ca_system_score_gemma":0.007970643,"threshold_uncertainty_score":0.10512602},"labels":[],"label_agreement":null},{"id":"W2018261002","doi":"10.1214/ss/1030550863","title":"A conversation with Samuel Kotz","year":2002,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"George (robot); Library science; Hebrew; Chapel; Compendium; Honor; Encyclopedia; Mathematics; Classics; Management; History; Computer science; Art history","score_opus":0.1103343508054152,"score_gpt":0.36598542371879855,"score_spread":0.25565107291338335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018261002","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016326287,0.052041765,0.00074616144,0.8903296,0.029556451,0.000011281463,0.00007558664,0.000057355082,0.025549196],"genre_scores_gemma":[0.06785975,0.07304806,0.0016247631,0.73301286,0.034110334,0.00009776872,0.00012969383,0.00032960778,0.089787155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972736,0.0013105331,0.000095806834,0.0003768717,0.0005894958,0.00035357222],"domain_scores_gemma":[0.995169,0.002925311,0.00026405853,0.00009012474,0.000590088,0.0009614506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004429463,0.0010256189,0.0010025201,0.0013289277,0.006832932,0.006061393,0.0012526147,0.0059274067,0.011532264],"category_scores_gemma":[0.013408345,0.00044568934,0.00049615226,0.0020165201,0.0051455856,0.011879746,0.0043011336,0.014460881,0.0040128846],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020500465,0.000021179923,0.0001831398,0.00008253215,0.000006558657,0.00044086546,0.0057538263,0.00003454273,0.00011230967,0.04354686,0.93677765,0.013019972],"study_design_scores_gemma":[0.0000056027457,0.0000119064025,0.00021209846,0.00023202847,0.0000025468835,0.00064581836,0.0053786593,0.000061685634,0.00006864906,0.007434164,0.98592836,0.000018518293],"about_ca_topic_score_codex":0.0049201753,"about_ca_topic_score_gemma":0.0065541663,"teacher_disagreement_score":0.011532264,"about_ca_system_score_codex":0.004483334,"about_ca_system_score_gemma":0.0036053993,"threshold_uncertainty_score":0.038579285},"labels":[],"label_agreement":null},{"id":"W2046736307","doi":"10.1214/07-sts241","title":"A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis","year":2007,"lang":"en","type":"article","venue":"Statistical Science","topic":"Census and Population Estimation","field":"Mathematics","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Variety (cybernetics); Multivariate statistics; Thematic map; Multivariate analysis; Spatial analysis; Literacy; Foundation (evidence)","score_opus":0.08649883077657106,"score_gpt":0.40226199249817723,"score_spread":0.3157631617216062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046736307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015794704,0.041950602,0.57207006,0.35022908,0.0025538893,0.00008304697,0.0010882397,0.00041941277,0.01581089],"genre_scores_gemma":[0.39956614,0.03635245,0.5175752,0.024109224,0.01115029,0.0006129764,0.00077218626,0.00052243203,0.009339179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.97979486,0.015697015,0.0007622811,0.001337512,0.0021215137,0.0002868298],"domain_scores_gemma":[0.84009844,0.1403157,0.003581303,0.0056874584,0.009266224,0.0010508397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039799504,0.00084250467,0.0020843223,0.0052715284,0.0024883482,0.006326934,0.0015712535,0.0020081524,0.0025717583],"category_scores_gemma":[0.16383031,0.0005411769,0.0016048454,0.0079641985,0.009193068,0.006792254,0.0031589454,0.007239245,0.0005380115],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029674973,0.000023087849,0.0060926937,0.00014270828,0.00010078109,0.00009483249,0.0015708071,0.004653691,0.000060622617,0.8425445,0.052284144,0.092402466],"study_design_scores_gemma":[0.000014099204,0.000023937258,0.004397736,0.00019285236,0.000022211261,0.00010272245,0.0007357366,0.016833337,0.00010733686,0.9006044,0.07690792,0.000057607616],"about_ca_topic_score_codex":0.035743993,"about_ca_topic_score_gemma":0.016963644,"teacher_disagreement_score":0.039799504,"about_ca_system_score_codex":0.003804682,"about_ca_system_score_gemma":0.0032487,"threshold_uncertainty_score":0.21048236},"labels":[],"label_agreement":null},{"id":"W2049645191","doi":"10.1214/08-sts272","title":"A Conversation with Martin Bradbury Wilk","year":2010,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Université Laval; Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Iowa State University; American Association for the Advancement of Science","keywords":"Statistician; Conversation; Library science; Management; Operations research; Sociology; Statistics; Mathematics; Computer science; Economics","score_opus":0.05654453792667748,"score_gpt":0.4258454905908548,"score_spread":0.36930095266417734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049645191","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066074455,0.06084251,0.0013597577,0.8509752,0.057469733,0.000016592772,0.00016251067,0.00015203393,0.028360885],"genre_scores_gemma":[0.028164038,0.0825212,0.002700105,0.6334592,0.050293278,0.00016257029,0.00031914125,0.0009024343,0.201478],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.995017,0.0019368675,0.00016780927,0.0006344049,0.0017017991,0.0005421591],"domain_scores_gemma":[0.98962885,0.004891478,0.00044879722,0.0002552148,0.0023701072,0.0024055515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076057473,0.0011849897,0.0011960241,0.0018315415,0.005332967,0.006591523,0.0015213201,0.0048557557,0.025718587],"category_scores_gemma":[0.02525237,0.000628992,0.0006218834,0.0017745078,0.004436972,0.0099198315,0.0036710016,0.014660251,0.012742159],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009918595,0.000008967579,0.00006113896,0.000032559405,0.000002508498,0.00006700146,0.00068278366,0.000016192196,0.000035668807,0.0063200817,0.9846748,0.00808832],"study_design_scores_gemma":[0.0000028742515,0.000006546487,0.0001477017,0.00014823725,0.0000014560608,0.00014534623,0.00081733137,0.000023726532,0.000036170193,0.0021851684,0.99647164,0.000013852162],"about_ca_topic_score_codex":0.009777323,"about_ca_topic_score_gemma":0.011445251,"teacher_disagreement_score":0.025718587,"about_ca_system_score_codex":0.0041584233,"about_ca_system_score_gemma":0.005274439,"threshold_uncertainty_score":0.08603728},"labels":[],"label_agreement":null},{"id":"W2052561043","doi":"10.1214/09-sts301","title":"The Impact of Levene’s Test of Equality of Variances on Statistical Theory and Practice","year":2009,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":650,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Levene's test; Mathematics; F-test of equality of variances; Statistics; Test (biology); Econometrics; Statistical hypothesis testing","score_opus":0.15129792034578257,"score_gpt":0.5467800411769967,"score_spread":0.39548212083121415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052561043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011254032,0.049085114,0.850602,0.04958291,0.0047401083,0.0004823006,0.00051637605,0.00042584597,0.033311352],"genre_scores_gemma":[0.4103006,0.025756178,0.5225715,0.021569418,0.011270716,0.0026363102,0.0005546539,0.0007181843,0.004622447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6831742,0.2113405,0.014182364,0.0299257,0.059097715,0.0022795752],"domain_scores_gemma":[0.18233556,0.7794645,0.0073258965,0.017576024,0.012213292,0.0010847256],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.26223272,0.0021054025,0.0067288093,0.008333516,0.003666768,0.010706438,0.005341335,0.008277722,0.004553822],"category_scores_gemma":[0.60200924,0.0017167438,0.0029376838,0.0090190815,0.034422137,0.016067872,0.010386609,0.020375244,0.00143755],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002959421,0.0001079933,0.010713097,0.0011142599,0.0010946826,0.0004476951,0.001955825,0.0105374,0.00047557897,0.784893,0.014976505,0.17338799],"study_design_scores_gemma":[0.000080268306,0.00034684656,0.0037605898,0.0012129588,0.00014527589,0.00046501678,0.0005737907,0.01893654,0.0010368068,0.9271874,0.046001773,0.0002528257],"about_ca_topic_score_codex":0.0053538135,"about_ca_topic_score_gemma":0.0020148493,"teacher_disagreement_score":0.26223272,"about_ca_system_score_codex":0.009022326,"about_ca_system_score_gemma":0.008791836,"threshold_uncertainty_score":0.9097984},"labels":[],"label_agreement":null},{"id":"W2056621649","doi":"10.1214/10-sts321","title":"Identification, Inference and Sensitivity Analysis for Causal Mediation Effects","year":2010,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1558,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; University of Wisconsin-Madison; University of Pennsylvania; Harvard University; National Science Foundation","keywords":"Causal inference; Estimator; Robustness (evolution); Identification (biology); Sensitivity (control systems); Randomized experiment; Inference; Causal model; Parametric statistics","score_opus":0.05207382361030993,"score_gpt":0.4371451219746848,"score_spread":0.38507129836437487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056621649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008993672,0.00089362945,0.9843215,0.0010868497,0.000108856075,0.00081599137,0.00021556464,0.00016110885,0.0034029013],"genre_scores_gemma":[0.49501795,0.0018066578,0.49221358,0.001310742,0.0003164785,0.007036163,0.00032903175,0.00013240986,0.0018369391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81093514,0.17198369,0.0030703836,0.0060669454,0.0067183916,0.001225491],"domain_scores_gemma":[0.3751533,0.5904462,0.009655176,0.020315966,0.0040000454,0.00042935018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18517108,0.0021291634,0.0041336953,0.0057519944,0.0021707069,0.0033838798,0.003480107,0.0040988857,0.007821467],"category_scores_gemma":[0.47418568,0.0013427153,0.0058378954,0.0038913556,0.006409148,0.0068610706,0.0074822805,0.007115659,0.000420116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045763233,0.00025576094,0.005671546,0.0016425176,0.0020606176,0.00059545087,0.00096143247,0.11072028,0.000849895,0.8128421,0.0018318933,0.062111],"study_design_scores_gemma":[0.00014928893,0.00020551166,0.0015733498,0.00026839532,0.0005821406,0.00024481385,0.00025536635,0.19189237,0.0012695969,0.80053854,0.002939982,0.00008069586],"about_ca_topic_score_codex":0.0020682684,"about_ca_topic_score_gemma":0.0010371889,"teacher_disagreement_score":0.18517108,"about_ca_system_score_codex":0.0037885462,"about_ca_system_score_gemma":0.0037098953,"threshold_uncertainty_score":0.97929},"labels":[],"label_agreement":null},{"id":"W2058153057","doi":"10.1214/088342306000000268","title":"Maty’s Biography of Abraham De Moivre, Translated, Annotated and Augmented","year":2007,"lang":"en","type":"article","venue":"Statistical Science","topic":"Probability and Statistical Research","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Murdoch University; University of Chicago; University of Oxford; Université Laval; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Biography; Generalization; Binomial (polynomial); Mathematics; Philosophy; Classics; Art history; History; Statistics; Epistemology","score_opus":0.047024176506161364,"score_gpt":0.42348863862412095,"score_spread":0.3764644621179596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058153057","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007205729,0.2628863,0.017708082,0.26573446,0.10080218,0.00009744824,0.00375068,0.0010098434,0.34080532],"genre_scores_gemma":[0.16800031,0.089919165,0.013367568,0.054734834,0.067375064,0.0002307406,0.0019187462,0.0027186347,0.601735],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981445,0.0005823354,0.00009663207,0.0003817545,0.00068020483,0.00011456699],"domain_scores_gemma":[0.99713314,0.0015558028,0.00034904006,0.00014247515,0.0006692133,0.00015040429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013702471,0.0010441039,0.0009216035,0.0020841307,0.0020708465,0.003906766,0.0005156258,0.0012633824,0.023777135],"category_scores_gemma":[0.011675037,0.00034937164,0.00037828594,0.0017952013,0.002920377,0.0036073597,0.0014108053,0.0038278822,0.0107249785],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008088339,0.000011004426,0.00025429553,0.00024260422,0.00001794496,0.00012187895,0.0029261354,0.00013336509,0.00032515498,0.12388386,0.83319706,0.038805787],"study_design_scores_gemma":[0.000002951912,0.000007751226,0.00044424398,0.000100984376,0.00000253417,0.00016773964,0.00016928611,0.00006716817,0.000080858874,0.0043903696,0.99455667,0.000009460227],"about_ca_topic_score_codex":0.007869793,"about_ca_topic_score_gemma":0.006636405,"teacher_disagreement_score":0.023777135,"about_ca_system_score_codex":0.0030109002,"about_ca_system_score_gemma":0.0014490411,"threshold_uncertainty_score":0.0795424},"labels":[],"label_agreement":null},{"id":"W2105640027","doi":"10.1214/08-sts273","title":"Accurate Parametric Inference for Small Samples","year":2008,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Neuchâtel; University of Toronto; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Inference; Parametric statistics; Statistical inference; Logistic regression; Focus (optics); Computer science; Sampling (signal processing); Parametric model; Mathematics; Variety (cybernetics); Econometrics; Asymptotic analysis; Applied mathematics; Statistics; Machine learning; Artificial intelligence","score_opus":0.28638891724177035,"score_gpt":0.4476443073628183,"score_spread":0.16125539012104795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105640027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014237254,0.00024388937,0.9967385,0.0002784446,0.00003519133,0.000016559174,0.000033978613,0.00013216051,0.0010975174],"genre_scores_gemma":[0.21314351,0.0014141476,0.7806784,0.0007280765,0.00045482005,0.00053132995,0.00028964624,0.00026076342,0.002499313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9808553,0.011926457,0.000715684,0.0017772538,0.004237567,0.000487763],"domain_scores_gemma":[0.8898234,0.091359116,0.0033138816,0.011365111,0.0035641587,0.00057435996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02517937,0.0010278225,0.0022223261,0.0023656343,0.0011536996,0.0028792815,0.0025688855,0.001925095,0.00408793],"category_scores_gemma":[0.20021924,0.0009436457,0.0010531016,0.0021446785,0.0042666295,0.006110736,0.0042562257,0.004953767,0.0012921133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006371853,0.00003824952,0.0015693919,0.00021725698,0.00008952617,0.00026632313,0.0003796978,0.05723727,0.0009618601,0.8476161,0.0035544378,0.08800606],"study_design_scores_gemma":[0.000018111268,0.000019597373,0.00035384978,0.000051070736,0.00001721675,0.00011164112,0.00003824055,0.135696,0.00058665103,0.85866165,0.004429387,0.000016722239],"about_ca_topic_score_codex":0.0016750045,"about_ca_topic_score_gemma":0.0017172048,"teacher_disagreement_score":0.02517937,"about_ca_system_score_codex":0.0013783948,"about_ca_system_score_gemma":0.0017974826,"threshold_uncertainty_score":0.13316286},"labels":[],"label_agreement":null},{"id":"W2191641151","doi":"10.1214/15-sts524","title":"Functional Data Analysis of Amplitude and Phase Variation","year":2015,"lang":"en","type":"article","venue":"Statistical Science","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Functional data analysis; Amplitude; Computer science; Variation (astronomy); Curse of dimensionality; Phase (matter); Algorithm; Representation (politics); Variance (accounting); Function (biology); Mathematics; Mathematical optimization; Artificial intelligence; Machine learning; Physics; Optics","score_opus":0.2320737521945765,"score_gpt":0.4232358640783437,"score_spread":0.1911621118837672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191641151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017435404,0.000646125,0.9770783,0.00038672594,0.00007952245,0.000079660895,0.00080082664,0.00047580604,0.0030177678],"genre_scores_gemma":[0.46378788,0.001655781,0.52671224,0.00045148056,0.00026861113,0.00073021476,0.0025724624,0.0006001818,0.0032212238],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979438,0.0007312009,0.00015006246,0.0005801078,0.0004798533,0.000114934046],"domain_scores_gemma":[0.9930092,0.0041499874,0.0008536024,0.0011402959,0.00070653984,0.00014027799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00401682,0.001008222,0.0010728345,0.0032826304,0.00049552746,0.0017934522,0.0009607437,0.0012870015,0.004695035],"category_scores_gemma":[0.017779823,0.00036389503,0.0011782903,0.0031820266,0.002283256,0.0021657527,0.0014764224,0.0018049593,0.00095643103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065330905,0.00016976532,0.014600809,0.0013495192,0.000535205,0.00069365546,0.0008897179,0.101473555,0.058917996,0.29211915,0.007942192,0.52065516],"study_design_scores_gemma":[0.000044927852,0.00034323276,0.026357152,0.00024395643,0.00020973216,0.0015000673,0.00045620895,0.5331577,0.023572875,0.3798052,0.034114163,0.0001947717],"about_ca_topic_score_codex":0.0011308802,"about_ca_topic_score_gemma":0.0006955615,"teacher_disagreement_score":0.004695035,"about_ca_system_score_codex":0.00064329035,"about_ca_system_score_gemma":0.00081439264,"threshold_uncertainty_score":0.021243215},"labels":[],"label_agreement":null},{"id":"W2278053714","doi":"10.1214/15-sts531","title":"Analysis Methods for Computer Experiments: How to Assess and What Counts?","year":2016,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Smoothness; Computer science; Gaussian process; Bayesian probability; Bayes' theorem; Regression; Regression analysis; Code (set theory); Exponential function; Function (biology); Machine learning; Statistics; Algorithm; Econometrics; Artificial intelligence; Gaussian; Data mining; Mathematics","score_opus":0.050557874987754145,"score_gpt":0.425066255779265,"score_spread":0.37450838079151083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278053714","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001989025,0.07277792,0.81584483,0.083573945,0.008605572,0.0027719163,0.0012520096,0.0027632236,0.010421566],"genre_scores_gemma":[0.042077396,0.019997554,0.8899535,0.022387464,0.006686893,0.015438974,0.00050651876,0.0015564656,0.0013952592],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.24567322,0.672683,0.021521471,0.010655104,0.04833868,0.0011285066],"domain_scores_gemma":[0.08195647,0.8200286,0.02088174,0.04930747,0.025154117,0.0026716024],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.42719567,0.0056576706,0.013810332,0.0146967145,0.003945997,0.023361724,0.009501281,0.015655644,0.0071373805],"category_scores_gemma":[0.79039556,0.003881684,0.005368977,0.014627782,0.03213136,0.033779778,0.011579398,0.024507733,0.0036636544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000988444,0.00038754844,0.005190177,0.016556002,0.004605703,0.00018471216,0.0021576441,0.008305673,0.0008468534,0.40667152,0.099526666,0.4545792],"study_design_scores_gemma":[0.00062086846,0.00052397395,0.0020763653,0.011538086,0.0008850068,0.00020681064,0.0007645473,0.017808907,0.0008864639,0.8782441,0.08604413,0.00040080817],"about_ca_topic_score_codex":0.0022352415,"about_ca_topic_score_gemma":0.0019306659,"teacher_disagreement_score":0.57280433,"about_ca_system_score_codex":0.00792277,"about_ca_system_score_gemma":0.015038426,"threshold_uncertainty_score":0.70636976},"labels":[],"label_agreement":null},{"id":"W2574504272","doi":"10.1214/16-sts573","title":"Bayes, Reproducibility and the Quest for Truth","year":2016,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; York University; University of Toronto; University of British Columbia","funders":"","keywords":"Bayes' theorem; Prior probability; Mathematics; Bayes factor; Statistics; Computer science; Mathematical economics; Bayesian probability; Econometrics","score_opus":0.1288360619024471,"score_gpt":0.4732471539066497,"score_spread":0.3444110920042026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574504272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015368859,0.04208471,0.7589605,0.116775066,0.0035279437,0.00013863364,0.0005550737,0.0005542242,0.06203499],"genre_scores_gemma":[0.7142793,0.014144368,0.23059393,0.020055858,0.00971945,0.0005445879,0.00040957698,0.00061668415,0.009636256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9254267,0.04371591,0.0040639495,0.011791062,0.013161421,0.0018409879],"domain_scores_gemma":[0.68331915,0.254822,0.013872834,0.03293081,0.0133932065,0.0016619283],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.097199135,0.0014668208,0.003794169,0.006076367,0.005041739,0.013383194,0.005292518,0.00896158,0.0052971593],"category_scores_gemma":[0.23420733,0.0013429174,0.0028112426,0.003433588,0.05287349,0.025623735,0.007693401,0.010552625,0.0016375162],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025790401,0.000008841025,0.0004563254,0.00012553348,0.000047101188,0.000050755432,0.0007516222,0.0013288162,0.00004771884,0.9854553,0.0015692419,0.010133105],"study_design_scores_gemma":[0.0000061246037,0.0000059483928,0.000055518056,0.000051228373,0.0000064786495,0.000030167208,0.000033208904,0.0012437414,0.000049344653,0.99583,0.002675898,0.000012398663],"about_ca_topic_score_codex":0.004105827,"about_ca_topic_score_gemma":0.001806148,"teacher_disagreement_score":0.90280086,"about_ca_system_score_codex":0.0070773372,"about_ca_system_score_gemma":0.004195563,"threshold_uncertainty_score":0.5140443},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"theoretical_or_conceptual","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["metaresearch"],"domain":"reproducibility","study_design":"theoretical_or_conceptual","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2949308593","doi":"10.1214/088342306000000187","title":"Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges","year":2006,"lang":"en","type":"article","venue":"Statistical Science","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Estimation; Econometrics; Computer science; Data science; Economics","score_opus":0.27880005044210465,"score_gpt":0.4203782375386332,"score_spread":0.14157818709652853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949308593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5829362,0.06936613,0.22580218,0.06648226,0.0011830219,0.0012153906,0.011988172,0.00044138584,0.04058521],"genre_scores_gemma":[0.94272625,0.011654054,0.035591748,0.0035070458,0.0009800543,0.0002959063,0.004380978,0.00008078588,0.00078314816],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8341038,0.12749667,0.009662738,0.006088545,0.02143767,0.0012106538],"domain_scores_gemma":[0.1205106,0.81251895,0.025655517,0.023610247,0.016691165,0.0010135517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.124259755,0.0017228149,0.0031354453,0.008816033,0.0015048311,0.00856995,0.0083611775,0.0068076933,0.0047007566],"category_scores_gemma":[0.5433429,0.0014000509,0.0024068456,0.018338237,0.0069139283,0.011066298,0.003611453,0.0059442017,0.001700928],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011881214,0.001996686,0.58081377,0.0036588653,0.009541067,0.00066397805,0.0008846668,0.05052488,0.00020716168,0.09073326,0.020456865,0.23933065],"study_design_scores_gemma":[0.0019387024,0.0022324654,0.26692858,0.006667068,0.006105942,0.0010903216,0.005767345,0.32178712,0.0027723648,0.33710736,0.047003422,0.00059919123],"about_ca_topic_score_codex":0.013825628,"about_ca_topic_score_gemma":0.009163352,"teacher_disagreement_score":0.124259755,"about_ca_system_score_codex":0.0020930097,"about_ca_system_score_gemma":0.0028690118,"threshold_uncertainty_score":0.6571562},"labels":[],"label_agreement":null},{"id":"W2963175784","doi":"10.1214/19-sts706","title":"Comment: Minimalist $g$-Modeling","year":2019,"lang":"en","type":"article","venue":"Statistical Science","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Computer science; Simple (philosophy); Bayes' theorem; Nonparametric statistics; Maximum likelihood; Machine learning; Artificial intelligence; Mathematical optimization; Econometrics; Algorithm; Mathematics; Bayesian probability; Statistics","score_opus":0.01871884721556452,"score_gpt":0.3057036943803385,"score_spread":0.286984847164774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963175784","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005525615,0.0011672621,0.0030249085,0.9766411,0.009036554,0.000017244096,0.00040773104,0.0001759574,0.0089767035],"genre_scores_gemma":[0.013123438,0.0007575001,0.0025895685,0.96360296,0.01333267,0.00009752237,0.0001108085,0.000102665406,0.0062829223],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936992,0.0016591165,0.00061061943,0.0015148466,0.0020357429,0.00048046163],"domain_scores_gemma":[0.97686005,0.015832528,0.0009260404,0.0017246712,0.0040727407,0.000583935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009268876,0.0009307142,0.0010035426,0.0008776142,0.0027485318,0.0022877771,0.0046006106,0.020003838,0.012033422],"category_scores_gemma":[0.051372528,0.00062439055,0.0014333199,0.0011101787,0.009172664,0.008512096,0.0025301739,0.03506664,0.010445032],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042407857,0.000009331848,0.00018472751,0.00009947309,0.000017704244,0.00011832137,0.00021119774,0.00022926457,0.00012454785,0.11050284,0.8829199,0.0055403244],"study_design_scores_gemma":[0.0000849647,0.000029640029,0.0009864564,0.00028372862,0.000029845416,0.00068577525,0.00028582048,0.001990579,0.0008062837,0.31454083,0.6801664,0.000109697656],"about_ca_topic_score_codex":0.01173721,"about_ca_topic_score_gemma":0.008743817,"teacher_disagreement_score":0.020003838,"about_ca_system_score_codex":0.0029091309,"about_ca_system_score_gemma":0.0029744175,"threshold_uncertainty_score":0.0490191},"labels":[],"label_agreement":null},{"id":"W3033114400","doi":"10.1214/22-sts879","title":"The Role of Exchangeability in Causal Inference","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Causal inference; Inference; Property (philosophy); Contrast (vision); Bayesian probability; Predictive inference; Bayesian inference; Econometrics; Causal model; Frequentist inference; Computer science; Causal structure; Statistical inference; Mathematics; Artificial intelligence; Epistemology; Statistics; Philosophy","score_opus":0.12924522197799054,"score_gpt":0.4691296344740189,"score_spread":0.33988441249602835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033114400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010822509,0.000929812,0.9760573,0.0035499467,0.00013082953,0.00012491082,0.00017535633,0.00007781723,0.008131595],"genre_scores_gemma":[0.70885116,0.0018311363,0.28076908,0.0020480177,0.0011048269,0.00088621216,0.00035580827,0.00018871765,0.003964949],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.928901,0.051710088,0.0033949327,0.008322223,0.0062374044,0.0014342649],"domain_scores_gemma":[0.6418846,0.3125967,0.015576181,0.024005793,0.004461589,0.001475027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09960356,0.0012498975,0.0028726317,0.0032302712,0.0026924093,0.0057974095,0.00438309,0.003948933,0.00885619],"category_scores_gemma":[0.25620452,0.0013968977,0.0039504357,0.0035925116,0.020971078,0.025595041,0.008395898,0.010384178,0.0006872028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003206854,0.000015954896,0.00085035915,0.00006459915,0.00007338153,0.00010914439,0.0003022274,0.0021824227,0.00010003348,0.9900725,0.0001690634,0.0060282247],"study_design_scores_gemma":[0.000013591988,0.000022419148,0.00024205109,0.000026815296,0.000020496003,0.00008054949,0.00003290179,0.0058141695,0.000103565384,0.99298084,0.0006511806,0.000011423683],"about_ca_topic_score_codex":0.001792166,"about_ca_topic_score_gemma":0.00093322445,"teacher_disagreement_score":0.09960356,"about_ca_system_score_codex":0.0025255915,"about_ca_system_score_gemma":0.0022346121,"threshold_uncertainty_score":0.5267603},"labels":[],"label_agreement":null},{"id":"W3083830176","doi":"10.1214/20-sts786","title":"On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning","year":2020,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shanghai University of Finance and Economics; University of Toronto; Eidgenössische Technische Hochschule Zürich; University of Washington; Office of Naval Research; Harvard University; National Science Foundation","keywords":"Estimator; Mathematics; Smoothness; Statistics; Confidence interval; Null hypothesis; Null (SQL); Algorithm; Computer science; Mathematical analysis","score_opus":0.14827249785666008,"score_gpt":0.42738724172025827,"score_spread":0.2791147438635982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083830176","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07639241,0.004871472,0.90048414,0.0032070472,0.00032804225,0.00031793216,0.0019120023,0.00094391016,0.011542909],"genre_scores_gemma":[0.8549904,0.002035806,0.13384862,0.002423785,0.0011685619,0.0011989707,0.0030704215,0.00033426753,0.00092909404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9070449,0.06290876,0.004211684,0.010373526,0.013398319,0.0020628516],"domain_scores_gemma":[0.18686557,0.77420753,0.01626034,0.015953356,0.005371536,0.0013417248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09213686,0.002029962,0.0039606616,0.005596853,0.0016891315,0.0049096225,0.005704682,0.0040669264,0.005661245],"category_scores_gemma":[0.55774504,0.000922098,0.0034443554,0.0056192614,0.013540949,0.009473423,0.0072099925,0.0065207104,0.0009943149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022471652,0.00023568793,0.057264734,0.0017139019,0.002453558,0.0017614014,0.0016209095,0.120092615,0.001808531,0.6576873,0.0055826926,0.1475316],"study_design_scores_gemma":[0.0003493077,0.0006996385,0.016349567,0.00090292393,0.00042056027,0.001106255,0.00054260157,0.2737927,0.0029480837,0.69569415,0.0069645415,0.00022966762],"about_ca_topic_score_codex":0.0021236816,"about_ca_topic_score_gemma":0.0008198161,"teacher_disagreement_score":0.09213686,"about_ca_system_score_codex":0.0017711569,"about_ca_system_score_gemma":0.0022715435,"threshold_uncertainty_score":0.48727208},"labels":[],"label_agreement":null},{"id":"W3088631473","doi":"10.1214/23-sts885","title":"Parameter Restrictions for the Sake of Identification: Is There Utility in Asserting That Perhaps a Restriction Holds?","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identification (biology); Statistical model; Key (lock); Context (archaeology); Computer science; Bayesian probability; Value (mathematics); Mathematical economics; Econometrics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.2103441734683346,"score_gpt":0.45051168967869337,"score_spread":0.24016751621035878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088631473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011834566,0.0036294884,0.91083014,0.049791846,0.0012951166,0.00012055908,0.000384239,0.00042125146,0.02169284],"genre_scores_gemma":[0.63124835,0.0047405427,0.32802048,0.02359824,0.0056294315,0.00095347327,0.0006964406,0.00064277253,0.004470327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9475793,0.029624859,0.0040607993,0.008026798,0.009234868,0.0014734039],"domain_scores_gemma":[0.6328414,0.26583594,0.028358402,0.05878408,0.011457303,0.0027227926],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08633738,0.0015542087,0.0045938287,0.0033553697,0.0039787595,0.008874421,0.0071757874,0.008885055,0.007948688],"category_scores_gemma":[0.34262544,0.0019954483,0.003470199,0.0037394487,0.04112069,0.039427105,0.007271381,0.022867199,0.0035444836],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007545683,0.000041411175,0.0016161838,0.00029330925,0.00013397235,0.00025861556,0.0009326436,0.0032149532,0.00034835225,0.96921957,0.0033914018,0.020474263],"study_design_scores_gemma":[0.000013846824,0.000018347455,0.0002701879,0.00018637213,0.000022419012,0.00013004868,0.00014239177,0.0039280555,0.00030435572,0.99040246,0.0045545665,0.000027090193],"about_ca_topic_score_codex":0.002582122,"about_ca_topic_score_gemma":0.001436902,"teacher_disagreement_score":0.9136626,"about_ca_system_score_codex":0.0030255413,"about_ca_system_score_gemma":0.005205589,"threshold_uncertainty_score":0.45660114},"labels":[],"label_agreement":null},{"id":"W3099982168","doi":"10.1214/23-sts889","title":"Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review","year":2024,"lang":"en","type":"review","venue":"Statistical Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"National Institute on Aging; U.S. Food and Drug Administration","keywords":"Observational study; Causal inference; Inference; Randomized controlled trial; Econometrics; Computer science; Statistics; Medicine; Artificial intelligence; Mathematics; Internal medicine","score_opus":0.85000118132543,"score_gpt":0.7220045213661083,"score_spread":0.12799665995932175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099982168","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044532255,0.9901912,0.00769327,0.00083354977,0.0002871299,0.00014573616,0.00011227945,0.000037830312,0.0006544126],"genre_scores_gemma":[0.0012058703,0.9840373,0.013234085,0.00048642387,0.000340642,0.00039162845,0.00010243634,0.000021824582,0.00017969584],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9805644,0.0120476615,0.0027801124,0.0010900749,0.003306838,0.00021078628],"domain_scores_gemma":[0.8738598,0.11693716,0.00351258,0.0016841749,0.003670159,0.00033604278],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.030786518,0.0020693166,0.0061954292,0.009688971,0.00063349603,0.0027953296,0.003909955,0.0034299935,0.009882323],"category_scores_gemma":[0.08855056,0.0013090925,0.0063159503,0.010921369,0.0018442317,0.003340766,0.0017627741,0.004144238,0.0023497965],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010409484,0.00005846084,0.0002992343,0.16575164,0.0028456333,0.00008969566,0.00015248699,0.0017849251,0.00013533983,0.02287331,0.01593625,0.7899689],"study_design_scores_gemma":[0.00038172884,0.00027714123,0.0022846628,0.27252468,0.011193364,0.001163357,0.00018062752,0.0031596085,0.00060541346,0.10394499,0.60407645,0.00020794106],"about_ca_topic_score_codex":0.003427737,"about_ca_topic_score_gemma":0.0036261603,"teacher_disagreement_score":0.9692135,"about_ca_system_score_codex":0.002965529,"about_ca_system_score_gemma":0.009011729,"threshold_uncertainty_score":0.16281658},"labels":[],"label_agreement":null},{"id":"W3210093870","doi":"10.1214/23-sts888","title":"Living on the Edge: An Unified Approach to Antithetic Sampling","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agence Nationale de la Recherche; European Commission","keywords":"Mathematics; Estimator; Markov chain Monte Carlo; Sampling (signal processing); Monte Carlo method; Applied mathematics; Markov chain; Mathematical optimization; Computer science; Statistics","score_opus":0.17420648527956095,"score_gpt":0.4392437903483808,"score_spread":0.26503730506881984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210093870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006529419,0.00020123066,0.98910797,0.00030549202,0.000045878358,0.00003139727,0.000045794164,0.00004883395,0.0036839521],"genre_scores_gemma":[0.41880623,0.0009414339,0.56923234,0.0008289479,0.00037049747,0.00041023258,0.00025622867,0.00023532,0.008918751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9925311,0.0039911647,0.00030920946,0.0011804331,0.0016271407,0.0003609647],"domain_scores_gemma":[0.9788801,0.012423403,0.0016500934,0.0044314545,0.0020093347,0.000605643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012472045,0.0007010231,0.0014880536,0.0022431605,0.0018082217,0.0036169926,0.0038384174,0.0025627925,0.0059186895],"category_scores_gemma":[0.04242055,0.0007622027,0.001415289,0.002709607,0.0047614053,0.0058687152,0.004394708,0.0037737852,0.00091403304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001883448,0.0000129353075,0.0004611962,0.000026513344,0.000012156998,0.000051936062,0.00015787252,0.010947645,0.00022406389,0.9770628,0.00038702326,0.010636926],"study_design_scores_gemma":[0.000014288716,0.00005105964,0.00030951318,0.000038979466,0.000016534987,0.000101312755,0.000041501695,0.16609238,0.00034259478,0.8289104,0.004055518,0.000025936568],"about_ca_topic_score_codex":0.001866729,"about_ca_topic_score_gemma":0.001750991,"teacher_disagreement_score":0.012472045,"about_ca_system_score_codex":0.0016449686,"about_ca_system_score_gemma":0.001400733,"threshold_uncertainty_score":0.065959275},"labels":[],"label_agreement":null},{"id":"W4205708523","doi":"10.1214/21-sts829","title":"A Conversation with Ross Prentice","year":2022,"lang":"en","type":"article","venue":"Statistical Science","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Waterloo; Harvard University; American Association for Cancer Research","keywords":"Biostatistics; Statistician; Epidemiology; Public health; Conversation; Library science; Population; Gerontology; Medicine; Sociology; Demography; Pathology; Computer science","score_opus":0.010032419413305441,"score_gpt":0.2968069063300402,"score_spread":0.28677448691673474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205708523","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032214998,0.017235925,0.00034348524,0.94150794,0.033511788,0.000006191886,0.000035804376,0.000037455055,0.006999258],"genre_scores_gemma":[0.011925679,0.015486591,0.00093685935,0.90850997,0.022563074,0.00006463339,0.000061328275,0.00018710551,0.04026468],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9916121,0.0044526975,0.00029526046,0.0011623068,0.0015801464,0.0008974581],"domain_scores_gemma":[0.98334426,0.008084469,0.0007031059,0.00043959776,0.0027358718,0.004692666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010154127,0.0012145494,0.0015259942,0.0013929649,0.0092858765,0.009875886,0.0024492515,0.011033252,0.015072048],"category_scores_gemma":[0.03902088,0.0007436642,0.0008979834,0.0013485928,0.006744616,0.013886563,0.0055137435,0.031633664,0.0064210533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016027127,0.000013655583,0.00011221978,0.000039297538,0.0000049076107,0.000157337,0.0015734743,0.00001553558,0.00003147475,0.009876495,0.9818641,0.0062954216],"study_design_scores_gemma":[0.000007072792,0.0000151933955,0.00011434858,0.0002322119,0.0000030951385,0.0004114667,0.002958146,0.000033780136,0.000035421985,0.005809047,0.9903562,0.000024009281],"about_ca_topic_score_codex":0.00799382,"about_ca_topic_score_gemma":0.012254878,"teacher_disagreement_score":0.015072048,"about_ca_system_score_codex":0.0050963503,"about_ca_system_score_gemma":0.008894381,"threshold_uncertainty_score":0.053700805},"labels":[],"label_agreement":null},{"id":"W4313035772","doi":"10.1214/22-sts866","title":"In Praise (and Search) of J. V. Uspensky","year":2022,"lang":"en","type":"article","venue":"Statistical Science","topic":"History and Theory of Mathematics","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Praise; Mathematical proof; Subject (documents); Classics; Computer science; Mathematical economics; Mathematics; Sociology; History; Literature; Library science; Art","score_opus":0.0506324444195543,"score_gpt":0.3555073648379917,"score_spread":0.30487492041843745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313035772","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006754111,0.026533065,0.002476146,0.9042014,0.05726453,0.0000069693,0.00009652491,0.000118491254,0.008627434],"genre_scores_gemma":[0.026691364,0.028007839,0.006050122,0.7243079,0.1589568,0.0000673319,0.00019839627,0.0007650087,0.05495532],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945551,0.0016457841,0.0002885312,0.0013128532,0.0019214266,0.00027632114],"domain_scores_gemma":[0.980261,0.009535344,0.0013748958,0.0011410207,0.005587095,0.0021005345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007436506,0.0011869972,0.0027307319,0.0016726953,0.0051260553,0.007367918,0.002367456,0.005631547,0.007234462],"category_scores_gemma":[0.039163254,0.00046371834,0.00072277547,0.0016662374,0.009742308,0.018957695,0.0044218027,0.026982123,0.007268467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036662674,0.000028301365,0.00036863863,0.00007846068,0.000012001324,0.00012250904,0.00081865507,0.00005052486,0.00010072495,0.05854956,0.9254404,0.014393565],"study_design_scores_gemma":[0.000011829709,0.000020731437,0.00032690412,0.00016906594,0.000005580578,0.00038435895,0.00044695192,0.00021792713,0.00014506752,0.051614452,0.9466221,0.000035042485],"about_ca_topic_score_codex":0.0032315613,"about_ca_topic_score_gemma":0.0037970862,"teacher_disagreement_score":0.007436506,"about_ca_system_score_codex":0.0023724504,"about_ca_system_score_gemma":0.0032008367,"threshold_uncertainty_score":0.039328456},"labels":[],"label_agreement":null},{"id":"W4321505814","doi":"10.1214/22-sts877","title":"A Conversation with Mary E. Thompson","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"University of Waterloo; Royal Society; Royal Society of Canada","keywords":"Honour; Medal; Annals; Gold medal; Library science; Conversation; Statistician; Management; Sociology; Mathematics; History; Political science; Law; Classics; Statistics; Art history; Computer science","score_opus":0.01362446477603719,"score_gpt":0.2841100491595704,"score_spread":0.2704855843835332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321505814","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007487975,0.009206078,0.00029445303,0.9573296,0.025201868,0.0000073507995,0.000039772356,0.000027464066,0.0071445853],"genre_scores_gemma":[0.019022884,0.009697119,0.00080554094,0.9088723,0.018754777,0.000062054925,0.000056516852,0.00019061218,0.042538203],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99246573,0.0031378674,0.00028398773,0.0010414956,0.002177035,0.0008939664],"domain_scores_gemma":[0.97820455,0.011377803,0.00087137194,0.00047007573,0.003582806,0.005493497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009274711,0.0008228167,0.0013428201,0.001175882,0.009050353,0.0072673415,0.0018819924,0.009677402,0.010822461],"category_scores_gemma":[0.057663973,0.0005928563,0.0006881862,0.0016769095,0.005261641,0.009642718,0.004213113,0.021685801,0.004534997],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015395899,0.000015168125,0.0002545034,0.000033615583,0.0000058053474,0.00017979756,0.0026242512,0.000017501181,0.000051991945,0.006839302,0.9820557,0.007906982],"study_design_scores_gemma":[0.000007576958,0.000019851735,0.00043301785,0.00026555022,0.0000043538203,0.0004865689,0.0052074534,0.00006185923,0.00006120815,0.0048109083,0.9886126,0.000029162244],"about_ca_topic_score_codex":0.012554877,"about_ca_topic_score_gemma":0.01684549,"teacher_disagreement_score":0.012554877,"about_ca_system_score_codex":0.0047796112,"about_ca_system_score_gemma":0.0074300426,"threshold_uncertainty_score":0.049049914},"labels":[],"label_agreement":null},{"id":"W4379415007","doi":"10.1214/23-sts865a","title":"Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Econometrics; Randomization; Statistics; Computer science; Mathematics; History; Medicine; Randomized controlled trial; Archaeology","score_opus":0.18301209058970766,"score_gpt":0.45778046848013415,"score_spread":0.2747683778904265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379415007","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008018479,0.0007974048,0.00047205464,0.98842746,0.009525945,0.000010613326,0.00006907398,0.00002288169,0.00059436203],"genre_scores_gemma":[0.0010296138,0.00026818615,0.0004131971,0.9853467,0.012450364,0.000039780018,0.000012441658,0.000018537716,0.0004211639],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9487093,0.021529097,0.006743012,0.00810813,0.012171628,0.002738812],"domain_scores_gemma":[0.5730514,0.35393715,0.013079943,0.01310007,0.041724518,0.005106929],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06410407,0.0013284569,0.0025268148,0.0017692063,0.005046537,0.006776436,0.007984825,0.06971556,0.018419014],"category_scores_gemma":[0.39118233,0.0012066925,0.0034375617,0.002905442,0.019637039,0.013630577,0.005535863,0.07741229,0.010660519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015682267,0.000023136154,0.00047739258,0.00041008776,0.00009076451,0.00018603697,0.00056867703,0.00013882275,0.00013207732,0.037117288,0.9523668,0.008332071],"study_design_scores_gemma":[0.00053653686,0.00006148056,0.0012843844,0.0021665783,0.00016672192,0.0005282604,0.0011296677,0.0008319577,0.000787732,0.124867134,0.86743516,0.00020430509],"about_ca_topic_score_codex":0.0139237,"about_ca_topic_score_gemma":0.008662704,"teacher_disagreement_score":0.9358959,"about_ca_system_score_codex":0.006756218,"about_ca_system_score_gemma":0.011579005,"threshold_uncertainty_score":0.33901882},"labels":[],"label_agreement":null},{"id":"W4388460787","doi":"10.1214/23-sts909","title":"Editorial: Special Issue on Reproducibility and Replicability","year":2023,"lang":"en","type":"editorial","venue":"Statistical Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reproducibility; Computer science; Data science; Statistics; Mathematics","score_opus":0.07983773611749603,"score_gpt":0.44332107671701615,"score_spread":0.36348334059952014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388460787","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015094883,0.0018831169,0.00012131711,0.02238081,0.9747335,0.000021089356,0.00005685205,0.000038604554,0.0007497504],"genre_scores_gemma":[0.00023856922,0.001264756,0.00013265517,0.014461279,0.979572,0.00003492684,0.000045984827,0.00006149772,0.0041883485],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98081964,0.0044417214,0.00275388,0.00198843,0.00899226,0.0010039798],"domain_scores_gemma":[0.89091367,0.047858942,0.0068682223,0.0036779847,0.042446267,0.008234977],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023367584,0.0052413293,0.007233736,0.008261852,0.0054327683,0.01443476,0.005453021,0.02552852,0.030341906],"category_scores_gemma":[0.10779099,0.0020506917,0.004525694,0.0033020691,0.0037332666,0.004986375,0.0031585987,0.020916723,0.019626642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033582004,0.000008554462,0.00001975463,0.00014911486,0.000028373082,0.000071148315,0.000007407471,0.000015159855,0.000024677329,0.00014552566,0.99685526,0.0026415116],"study_design_scores_gemma":[0.00023534097,0.000033578886,0.00040957367,0.00092488783,0.00017442218,0.0002733343,0.000042419164,0.0003070804,0.0001338888,0.0028718424,0.9945503,0.00004337461],"about_ca_topic_score_codex":0.0024826166,"about_ca_topic_score_gemma":0.0077505517,"teacher_disagreement_score":0.9766324,"about_ca_system_score_codex":0.0050593237,"about_ca_system_score_gemma":0.0058903648,"threshold_uncertainty_score":0.12358105},"labels":[],"label_agreement":null},{"id":"W4391933919","doi":"10.1214/23-sts919","title":"Emerging Directions in Bayesian Computation","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Office of Naval Research; National Institutes of Health; European Commission","keywords":"Bayesian probability; Computer science; Computation; Approximate Bayesian computation; Artificial intelligence; Machine learning; Algorithm; Inference","score_opus":0.01065958484695598,"score_gpt":0.3056126727917594,"score_spread":0.2949530879448034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391933919","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035481167,0.0967181,0.8054925,0.06202986,0.0017653126,0.000055170047,0.0003155092,0.00030772728,0.029767584],"genre_scores_gemma":[0.21208632,0.1919894,0.5534542,0.013082184,0.016938152,0.0005367818,0.0007030718,0.0005221775,0.010687736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99108,0.0052802395,0.0002949124,0.0010496419,0.0020477576,0.00024758134],"domain_scores_gemma":[0.95900923,0.034785178,0.0006310812,0.0023460907,0.0025485475,0.0006798256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015433512,0.0011284901,0.0020818105,0.00295537,0.0016160206,0.0063728997,0.002968453,0.004745866,0.008570789],"category_scores_gemma":[0.043069728,0.0011311792,0.0017465766,0.004447668,0.008678225,0.014215235,0.0037092154,0.011495583,0.002273585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009836943,0.000017452427,0.00018424673,0.00016001207,0.000018722701,0.000016469363,0.00008199935,0.0032369944,0.000038182945,0.9634053,0.004280733,0.028550101],"study_design_scores_gemma":[0.0000047700146,0.0000037664463,0.00005080859,0.000060650073,0.0000029991008,0.000011967653,0.000020631293,0.008344358,0.000018129098,0.9826031,0.0088723805,0.000006427517],"about_ca_topic_score_codex":0.0039391345,"about_ca_topic_score_gemma":0.003514969,"teacher_disagreement_score":0.015433512,"about_ca_system_score_codex":0.0039606784,"about_ca_system_score_gemma":0.0031830494,"threshold_uncertainty_score":0.08162117},"labels":[],"label_agreement":null},{"id":"W4391944190","doi":"10.1214/23-sts907","title":"Past, Present and Future of Software for Bayesian Inference","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Office of Naval Research; Javna Agencija za Raziskovalno Dejavnost RS","keywords":"Computer science; Inference; Bayesian inference; Bayesian probability; Software; Artificial intelligence; Machine learning; Econometrics; Data science; Data mining; Mathematics; Programming language","score_opus":0.02079020362936505,"score_gpt":0.3162545814338298,"score_spread":0.2954643778044647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391944190","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009257721,0.1231962,0.83333415,0.011095756,0.0023933346,0.000104806684,0.0010762383,0.014330533,0.013543229],"genre_scores_gemma":[0.012565507,0.16952507,0.7910428,0.0061473185,0.0041931546,0.0006599586,0.003087673,0.008256693,0.004521892],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97884524,0.009455385,0.0028115949,0.0015113113,0.0068397545,0.00053670746],"domain_scores_gemma":[0.87702316,0.09818926,0.0029450802,0.008771706,0.010936171,0.0021346302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03005899,0.002805535,0.0030051214,0.009359104,0.0012040455,0.008417816,0.0056946916,0.0047291163,0.020877333],"category_scores_gemma":[0.10754549,0.002343479,0.0036220313,0.011768505,0.0056556566,0.014060057,0.005482204,0.011052801,0.017806333],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013039785,0.00011381912,0.0009424412,0.0056673503,0.0002984993,0.00022131248,0.00071095687,0.0069975196,0.0014277934,0.2228023,0.10848763,0.6521999],"study_design_scores_gemma":[0.000057066263,0.00005350526,0.0004904577,0.0034816808,0.000114865055,0.00051461376,0.00009735353,0.016365621,0.0013650961,0.36276922,0.6145178,0.00017272437],"about_ca_topic_score_codex":0.002321142,"about_ca_topic_score_gemma":0.0018825039,"teacher_disagreement_score":0.03005899,"about_ca_system_score_codex":0.0020952786,"about_ca_system_score_gemma":0.0066171824,"threshold_uncertainty_score":0.15896904},"labels":[],"label_agreement":null},{"id":"W4400119980","doi":"10.1214/23-sts916","title":"A General Construction of Multivariate Dependence Structures with Nonmonotone Mappings and Its Applications","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Multivariate statistics; Computer science; Mathematics; Applied mathematics; Statistics","score_opus":0.019458891396563316,"score_gpt":0.3138457855035256,"score_spread":0.2943868941069623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400119980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019549211,0.00025735257,0.9737256,0.00027072884,0.000065070104,0.000026981033,0.00011254165,0.00010354237,0.0058888686],"genre_scores_gemma":[0.46424586,0.0016054183,0.51212335,0.000505222,0.0006175277,0.00020975457,0.0005077153,0.00023613969,0.019949092],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993056,0.00021270952,0.00004719385,0.00019009892,0.00017994754,0.00006443036],"domain_scores_gemma":[0.9980769,0.0007459465,0.00027412674,0.00039822204,0.0002897615,0.00021504781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018886406,0.000976488,0.00087025296,0.0021512655,0.0011701381,0.0011955992,0.0012635684,0.0012604618,0.0031645074],"category_scores_gemma":[0.0051733484,0.0008191866,0.0018890612,0.0019387614,0.0018468975,0.003502588,0.0027001693,0.002604659,0.0005705025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014498493,0.000023982366,0.00037073143,0.000035013036,0.000013883066,0.00010686763,0.00010802295,0.00393011,0.0021126673,0.9821253,0.00069125125,0.010467617],"study_design_scores_gemma":[0.000009370735,0.00003989399,0.0006998659,0.00002217007,0.000019051417,0.00032149788,0.00003233161,0.08066935,0.0011445532,0.9114521,0.0055570914,0.000032764267],"about_ca_topic_score_codex":0.00076386344,"about_ca_topic_score_gemma":0.00091100903,"teacher_disagreement_score":0.0031645074,"about_ca_system_score_codex":0.00085215975,"about_ca_system_score_gemma":0.0010008452,"threshold_uncertainty_score":0.010586321},"labels":[],"label_agreement":null},{"id":"W4400120136","doi":"10.1214/24-sts926","title":"Antoine Gombaud, Chevalier de Méré","year":2024,"lang":"fr","type":"article","venue":"Statistical Science","topic":"Probability and Statistical Research","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Western University","funders":"","keywords":"Humanities; Mathematics; Art","score_opus":0.0783825922826476,"score_gpt":0.45791378123565657,"score_spread":0.37953118895300897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400120136","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009866586,0.3415307,0.061018314,0.40121213,0.0728913,0.00011247409,0.002349375,0.0023874037,0.10863173],"genre_scores_gemma":[0.13470034,0.14140068,0.04035866,0.039109968,0.06314237,0.0002652138,0.0013916208,0.0024119925,0.57721925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742836,0.0009314603,0.00009856103,0.000496523,0.0009399985,0.00010504824],"domain_scores_gemma":[0.9903369,0.005748172,0.0003724018,0.00046570113,0.0022794947,0.00079745473],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003564229,0.0013148241,0.0016434824,0.0021302376,0.0018436603,0.003646373,0.0009203175,0.0027721385,0.03953105],"category_scores_gemma":[0.026784062,0.0005942185,0.00062770565,0.0013763248,0.0017468752,0.0027883085,0.0012573759,0.005035889,0.022104662],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005044085,0.00009016247,0.0015780658,0.0007448509,0.00011828858,0.0006432202,0.0008060406,0.0012696045,0.0017303834,0.12707175,0.7164632,0.14897998],"study_design_scores_gemma":[0.0000457051,0.000028787656,0.0020180754,0.00012909522,0.000020503428,0.00085128297,0.00020098107,0.00078918063,0.00071002083,0.018414624,0.97674817,0.00004367062],"about_ca_topic_score_codex":0.011370679,"about_ca_topic_score_gemma":0.007375297,"teacher_disagreement_score":0.99815637,"about_ca_system_score_codex":0.0021242495,"about_ca_system_score_gemma":0.0024001503,"threshold_uncertainty_score":0.13224453},"labels":[],"label_agreement":null},{"id":"W4414298519","doi":"10.1214/25-sts1002","title":"An Adaptive Transfer Learning Perspective on Classification in Nonstationary Environments","year":2025,"lang":"en","type":"article","venue":"Statistical Science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Perspective (graphical); Regret; Class (philosophy); Covariate; Transfer of learning; Sequence (biology); Gradient descent","score_opus":0.021870121039291233,"score_gpt":0.3174130453699864,"score_spread":0.2955429243306951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414298519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013809484,0.00037276684,0.9820664,0.0013165056,0.000060764243,0.000050759874,0.000049519946,0.00014352429,0.0021302162],"genre_scores_gemma":[0.78058696,0.0009699054,0.20847371,0.00071264466,0.00059180503,0.00042213977,0.00021778379,0.00016006142,0.007865083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755496,0.0012166962,0.00006138962,0.0005638466,0.00038961432,0.00021357046],"domain_scores_gemma":[0.9901259,0.0075979964,0.00070187496,0.00077728863,0.00049194606,0.00030504155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004770692,0.0013897801,0.0015807027,0.00095131714,0.00072059047,0.0017179169,0.0037767498,0.0029645888,0.0027420018],"category_scores_gemma":[0.017135656,0.00059517013,0.0010143395,0.0012384104,0.0036113432,0.004533934,0.002787115,0.0037918848,0.0005026301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113590395,0.00013662342,0.0010304255,0.00009882203,0.00007897179,0.00021164565,0.00018694415,0.82536006,0.001713935,0.14316647,0.0010938399,0.026808677],"study_design_scores_gemma":[0.0000069296843,0.000033565753,0.0001332793,0.000005562288,0.0000045606103,0.000017004051,0.000012015975,0.92865074,0.0002638895,0.070572995,0.0002910832,0.000008355576],"about_ca_topic_score_codex":0.0027372804,"about_ca_topic_score_gemma":0.0014836872,"teacher_disagreement_score":0.004770692,"about_ca_system_score_codex":0.0020779595,"about_ca_system_score_gemma":0.0011026374,"threshold_uncertainty_score":0.02523017},"labels":[],"label_agreement":null},{"id":"W642399624","doi":"10.1214/088342304000000189","title":"The Reverend Thomas Bayes, FRS: A Biography to Celebrate the Tercentenary of His Birth","year":2004,"lang":"en","type":"article","venue":"Statistical Science","topic":"Philosophy and History of Science","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"University of Edinburgh; Presbyterian Historical Society","keywords":"Biography; Bayes' theorem; Character (mathematics); History; Epistemology; Sociology; Classics; Philosophy; Bayesian probability; Computer science; Artificial intelligence; Art history; Mathematics","score_opus":0.023380680097491884,"score_gpt":0.2380154953419277,"score_spread":0.2146348152444358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W642399624","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022996399,0.44497627,0.0048462325,0.3376769,0.09194931,0.000030092346,0.00055661984,0.00027782834,0.11738711],"genre_scores_gemma":[0.10934471,0.25851598,0.007290764,0.135889,0.1383779,0.000110087836,0.0006124171,0.0012686941,0.34859043],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961875,0.0015516719,0.00015706236,0.00061515684,0.0012124663,0.00027613214],"domain_scores_gemma":[0.99401385,0.0036231591,0.00039909748,0.00030202657,0.0010753991,0.0005865307],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0033144525,0.0013838111,0.0010747833,0.002276401,0.0038706078,0.006644584,0.0006486857,0.002721098,0.009248995],"category_scores_gemma":[0.017076872,0.0005180328,0.0005309899,0.0016620528,0.005501134,0.005663092,0.0017324629,0.008512366,0.008403337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027979153,0.000009964351,0.00021203757,0.00011721961,0.000011886298,0.00014399913,0.0035702658,0.000100018005,0.000096737596,0.050554264,0.9091897,0.035966005],"study_design_scores_gemma":[0.0000019203371,0.000007089,0.00017082812,0.00030258865,0.0000024779513,0.00021752967,0.000423499,0.000049967115,0.000049540788,0.0069285794,0.9918356,0.0000104489645],"about_ca_topic_score_codex":0.008264751,"about_ca_topic_score_gemma":0.006752044,"teacher_disagreement_score":0.9961294,"about_ca_system_score_codex":0.004752844,"about_ca_system_score_gemma":0.0037235124,"threshold_uncertainty_score":0.034484446},"labels":[],"label_agreement":null},{"id":"W7118628814","doi":"10.1214/25-sts1001","title":"Sample-Based Planning and Learning with Function Approximation","year":2025,"lang":"","type":"article","venue":"Statistical Science","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Function approximation; Core (optical fiber); Focus (optics); Dimension (graph theory); Function (biology); Approximation algorithm; Optimism","score_opus":0.015498831904994551,"score_gpt":0.28179290718513256,"score_spread":0.266294075280138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118628814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005196238,0.0051174397,0.9890637,0.0003635885,0.00014882731,0.00003528734,0.000098487115,0.00029478178,0.0043583275],"genre_scores_gemma":[0.070743054,0.022195054,0.8921829,0.0007754823,0.0013552798,0.00069336867,0.00079132913,0.0006291923,0.010634386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984748,0.0006130185,0.00010870833,0.00029601122,0.00041910718,0.00008837882],"domain_scores_gemma":[0.9982249,0.0014070064,0.00007895041,0.0001364755,0.000104868865,0.000047905578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024085634,0.0020518876,0.0012576412,0.0010586446,0.00038621452,0.0022541583,0.0017611169,0.0019272474,0.010957417],"category_scores_gemma":[0.00658453,0.0010462651,0.0015191025,0.0017356426,0.0022333134,0.0038222896,0.0019881546,0.003588252,0.0028716254],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007214645,0.00006205123,0.00027150594,0.00092720124,0.00008267877,0.000117884454,0.00016513887,0.08001696,0.00197843,0.72155136,0.011426811,0.18332785],"study_design_scores_gemma":[0.000028709468,0.00010425509,0.00025902942,0.00023452473,0.000032535594,0.0001539097,0.000027486907,0.18188122,0.0017139218,0.7678395,0.047678303,0.00004663612],"about_ca_topic_score_codex":0.0017270179,"about_ca_topic_score_gemma":0.0014846693,"teacher_disagreement_score":0.010957417,"about_ca_system_score_codex":0.0015111695,"about_ca_system_score_gemma":0.00096422515,"threshold_uncertainty_score":0.0366562},"labels":[],"label_agreement":null}]}