{"meta":{"query_hash":"bea1bc066117","filters":{"venue":"Biometrics"},"cohort_total":266,"direct_labels_cover":0,"predictions_cover":266,"exported":266,"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/bea1bc066117","api":"https://metacan.xera.ac/api/v1/cohort?venue=Biometrics"},"results":[{"id":"W1482041358","doi":"10.1111/biom.12020","title":"Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Feature selection; Sample size determination; Regularization (linguistics); Parametric statistics; Computer science; Feature (linguistics); Population; Regression analysis; Regression; Variable (mathematics); Mathematics; Statistics; Artificial intelligence; Machine learning; Medicine","score_opus":0.021378384879803373,"score_gpt":0.2590494504146491,"score_spread":0.23767106553484574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482041358","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042870373,0.00013109756,0.95573616,0.00008662992,0.000070204165,0.00014753212,0.0000021745852,0.000016873577,0.00093893387],"genre_scores_gemma":[0.44189683,0.000027501914,0.5579474,0.000025402787,0.0000034494906,0.000003892977,0.0000016380768,0.0000045412444,0.00008932514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989662,0.000086620305,0.00025463986,0.00022821009,0.0003077855,0.00015655492],"domain_scores_gemma":[0.99899864,0.00017945906,0.00021587075,0.00038280728,0.00017357997,0.0000496385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030663598,0.000103283826,0.00021240412,0.0008316477,0.00001973963,0.000028876568,0.0003711476,0.00010077514,0.000004867075],"category_scores_gemma":[0.000097789656,0.0000730919,0.0000379706,0.005003843,0.000043487085,0.000484399,0.00011922901,0.00007861221,0.0000017551283],"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":[0.000039285682,0.00045156645,0.02015885,0.00030298645,0.00005372929,0.000014109231,0.0025866425,0.004794162,0.00987331,0.3015404,0.0004865988,0.65969837],"study_design_scores_gemma":[0.0008849195,0.0001661489,0.0056891562,0.00033473395,0.000014893857,0.000008711564,0.000025377174,0.7047323,0.027237345,0.26052523,0.000047667494,0.00033353572],"about_ca_topic_score_codex":0.0000678022,"about_ca_topic_score_gemma":6.2944497e-7,"teacher_disagreement_score":0.6999381,"about_ca_system_score_codex":0.00002029599,"about_ca_system_score_gemma":0.000030561263,"threshold_uncertainty_score":0.29806012},"labels":[],"label_agreement":null},{"id":"W1484865074","doi":"10.1111/j.1541-0420.2010.01491.x","title":"Estimating the Null Distribution to Adjust Observed Confidence Levels for Genome-Scale Screening","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":14,"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 Ottawa","funders":"","keywords":"Null distribution; Test statistic; Null hypothesis; Null (SQL); Estimator; Confidence interval; Statistical hypothesis testing; Statistical inference; Frequentist inference; p-value","score_opus":0.7212641075307934,"score_gpt":0.5508548686905355,"score_spread":0.17040923884025794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484865074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057373896,0.000020413869,0.9378065,0.00073327083,0.0014906115,0.001129624,0.0012576976,0.00010655033,0.00008144299],"genre_scores_gemma":[0.0900409,0.0000010793598,0.90870273,0.0002126855,0.00071380264,0.00012069997,0.000016745551,0.00003495751,0.00015638038],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975039,0.00021025074,0.00088476477,0.00043153967,0.0005010071,0.00046854277],"domain_scores_gemma":[0.93308115,0.065149315,0.00035420147,0.0006979232,0.00047179722,0.00024561177],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006960451,0.00020566654,0.000462702,0.00020023594,0.00030244034,0.00015073316,0.00067406,0.00022486718,0.00012260563],"category_scores_gemma":[0.35046446,0.00015031682,0.00016509659,0.0026545378,0.0001610715,0.00006861854,0.00020786628,0.0003686673,0.000036540598],"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":[0.00032896537,0.0006644398,0.002394373,0.0008618698,0.00023122955,0.000010549219,0.00067540293,0.00011018272,0.0758928,0.365658,0.011629113,0.5415431],"study_design_scores_gemma":[0.0018093982,0.00049889303,0.039658904,0.00013572017,0.00031699755,0.0000119792885,0.00017888047,0.026493298,0.010317898,0.906348,0.013385469,0.00084458763],"about_ca_topic_score_codex":0.000019673449,"about_ca_topic_score_gemma":0.000010808984,"teacher_disagreement_score":0.5406985,"about_ca_system_score_codex":0.00004702904,"about_ca_system_score_gemma":0.000050181843,"threshold_uncertainty_score":0.6550069},"labels":[],"label_agreement":null},{"id":"W1490191373","doi":"10.1111/biom.12344","title":"Assessing Incremental Value of Biomarkers with Multi-phase Nested Case-control Studies","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of General Medical Sciences; National Cancer Institute; National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institute on Aging; Harvard Medical School; Brigham and Women's Hospital","keywords":"Nested case-control study; Value (mathematics); Computer science; Statistics; Phase (matter); Mathematics; Case-control study; Chemistry","score_opus":0.8234003999282882,"score_gpt":0.648123226589282,"score_spread":0.17527717333900616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490191373","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4699535,0.000638716,0.5274117,0.00008476453,0.00067536766,0.0007472599,0.00020117231,0.00010228744,0.00018522948],"genre_scores_gemma":[0.3871131,0.0000055950723,0.6127342,0.000028628101,0.00006569369,0.000013317227,0.0000010062869,0.000025574034,0.000012895371],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99670345,0.0009479202,0.0010272743,0.0003403319,0.00067332474,0.0003076738],"domain_scores_gemma":[0.9538498,0.044251848,0.00061388157,0.00040727248,0.000626519,0.00025068776],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006863416,0.00023545686,0.00088349934,0.00087896595,0.00006936062,0.00005901146,0.00019723414,0.00015002218,0.000014985973],"category_scores_gemma":[0.20998058,0.0001694794,0.00009918313,0.003687204,0.00039243535,0.00015728417,0.00010685395,0.00014221405,0.0000057409384],"study_design_candidate":"design_other","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":[0.00826745,0.027904993,0.07419685,0.0059570177,0.024884917,0.021718638,0.0048667844,0.00005040107,0.025929417,0.111182176,0.013970051,0.6810713],"study_design_scores_gemma":[0.27486694,0.020001117,0.010614228,0.002559591,0.014953149,0.004876259,0.059850506,0.059227046,0.060144216,0.48613682,0.0008835268,0.0058866134],"about_ca_topic_score_codex":0.0000477326,"about_ca_topic_score_gemma":0.0000042819033,"teacher_disagreement_score":0.67518467,"about_ca_system_score_codex":0.00016789352,"about_ca_system_score_gemma":0.00012247186,"threshold_uncertainty_score":0.7966741},"labels":[],"label_agreement":null},{"id":"W1501519563","doi":"10.1111/biom.12105","title":"Modeling the impact of hepatitis C viral clearance on end‐stage liver disease in an HIV co‐infected cohort with targeted maximum likelihood estimation","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Victoria Hospital; McGill University","funders":"National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research","keywords":"Marginal structural model; Confounding; Hazard ratio; Medicine; Proportional hazards model; Missing data; Cohort; Population; Liver disease; Hepatitis C; Survival analysis; Statistics; Hepatitis C virus; Censoring (clinical trials); Internal medicine; Immunology; Confidence interval; Mathematics; Virus; Environmental health; Pathology","score_opus":0.015591051955605086,"score_gpt":0.27781635567787105,"score_spread":0.262225303722266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501519563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99727964,0.00043497139,0.00075845653,0.0000652392,0.000021356085,0.0010866303,0.0002568072,0.000037890753,0.00005902975],"genre_scores_gemma":[0.9985788,0.00026133665,0.0004434242,0.000062204694,0.000024634499,0.000111134694,0.00048514485,0.00002354495,0.000009810343],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988285,0.00005917895,0.00022649224,0.0002499804,0.00040177372,0.00023409116],"domain_scores_gemma":[0.99904275,0.00007789667,0.00008691505,0.00034857084,0.00019016388,0.00025370138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000867065,0.0001775904,0.00024642562,0.0006732443,0.00004906834,0.000038351693,0.00007948396,0.000051175623,0.00020701809],"category_scores_gemma":[0.000108073546,0.000104180086,0.00009922308,0.0014640959,0.000049204187,0.00020421854,0.000015251632,0.00009347307,0.000061977145],"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":[0.00032203083,0.0016255085,0.98317856,0.00004892354,0.00012228133,0.000058565245,0.00010017355,0.00714544,0.00011335802,0.000013529121,0.000078746,0.007192899],"study_design_scores_gemma":[0.0010834748,0.0007576924,0.67891175,0.00006701464,0.0000970783,7.159765e-7,0.000007872811,0.3188387,0.00013166737,0.00002111273,0.000001523824,0.00008144014],"about_ca_topic_score_codex":0.0049680704,"about_ca_topic_score_gemma":0.000024559453,"teacher_disagreement_score":0.31169325,"about_ca_system_score_codex":0.00023562466,"about_ca_system_score_gemma":0.00013692945,"threshold_uncertainty_score":0.7510269},"labels":[],"label_agreement":null},{"id":"W1503357936","doi":"10.1111/biom.12006","title":"A Generalized Kruskal–Wallis Test Incorporating Group Uncertainty with Application to Genetic Association Studies","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"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 Manitoba; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Mathematics; Kruskal–Wallis one-way analysis of variance; Statistics; Kruskal's algorithm; Test statistic; Null hypothesis; Generalization; Robustness (evolution); Pearson's chi-squared test; Statistic; Statistical hypothesis testing; Combinatorics; Mann–Whitney U test; Genetics; Spanning tree; Biology","score_opus":0.017145159624552306,"score_gpt":0.2755852198901973,"score_spread":0.258440060265645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503357936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97003967,0.0008675287,0.026923995,0.0012027092,0.000108366155,0.0006475214,0.000028669807,0.000029994086,0.00015153822],"genre_scores_gemma":[0.9467166,0.00027629972,0.050421275,0.0011193942,0.0002720589,0.0004204161,0.00019415424,0.000025897007,0.000553912],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985376,0.00010237774,0.00036497143,0.00043439644,0.00022130793,0.0003393211],"domain_scores_gemma":[0.99840856,0.00023827025,0.00035373194,0.00032202227,0.00055392564,0.00012347434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004904552,0.00018698195,0.00025874228,0.00029279996,0.0001488055,0.00004251762,0.00018471766,0.0002097244,0.000008561393],"category_scores_gemma":[0.0029334675,0.00015739135,0.000057932164,0.0015758376,0.00003617858,0.0000054806796,0.00012123076,0.000067496876,0.00008021605],"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":[0.000015879292,0.0001517164,0.7804564,0.000023883409,0.00024466138,6.7273953e-7,0.000087108936,0.0021401031,0.1708722,0.00008525775,0.024667777,0.02125431],"study_design_scores_gemma":[0.0013709057,0.0016332655,0.94794524,0.000017850964,0.00011310234,0.000007940042,0.00046286185,0.003444987,0.003166088,0.00080190104,0.04025146,0.0007844249],"about_ca_topic_score_codex":0.00033959374,"about_ca_topic_score_gemma":0.0001824427,"teacher_disagreement_score":0.1677061,"about_ca_system_score_codex":0.00019596532,"about_ca_system_score_gemma":0.000049000755,"threshold_uncertainty_score":0.64182323},"labels":[],"label_agreement":null},{"id":"W1536343953","doi":"10.1111/biom.12126","title":"A variational Bayes spatiotemporal model for electromagnetic brain mapping","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"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; Down Syndrome Research Foundation; Simon Fraser University; University of Victoria","funders":"","keywords":"Bayes' theorem; Computer science; Artificial intelligence; Bayesian probability; Machine learning; Statistical physics; Physics","score_opus":0.07611515801776193,"score_gpt":0.27147846905188155,"score_spread":0.19536331103411964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536343953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11377513,0.00011424487,0.84917915,0.033599447,0.0005388007,0.0012312109,0.00014892676,0.00022759811,0.001185474],"genre_scores_gemma":[0.96323377,0.000007132671,0.028001605,0.005547157,0.00016820266,0.00028546693,0.000014498906,0.00002383856,0.002718357],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986918,0.000040887124,0.00019489283,0.0004123676,0.00035854906,0.00030149164],"domain_scores_gemma":[0.9926858,0.0068503036,0.00009275138,0.00015616487,0.00015394218,0.00006104377],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00024446365,0.00012955333,0.00013754249,0.0009508641,0.0002735115,0.000095575524,0.00017095302,0.00006223463,0.00006376086],"category_scores_gemma":[0.024335643,0.00012758201,0.00007341737,0.0025723171,0.000055073215,0.00025237817,0.000069675625,0.00007189842,0.00009434558],"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":[0.000027471491,0.000262372,0.0013175672,0.00007511351,0.00002688921,0.0000013373951,0.00029462614,0.00069551385,0.72876143,0.044489693,0.2122019,0.011846059],"study_design_scores_gemma":[0.00064576615,0.00028900968,0.0126104895,0.0000057933876,0.000007181565,0.000006625541,0.00002029436,0.9238498,0.006781485,0.04446451,0.011017105,0.00030189275],"about_ca_topic_score_codex":0.000027510398,"about_ca_topic_score_gemma":0.000003158131,"teacher_disagreement_score":0.92315435,"about_ca_system_score_codex":0.00009538372,"about_ca_system_score_gemma":0.00008121885,"threshold_uncertainty_score":0.9838828},"labels":[],"label_agreement":null},{"id":"W1590238912","doi":"10.1111/biom.12351","title":"Mixtures of Multivariate Power Exponential Distributions","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Mathematics; Statistics; Exponential function; Natural exponential family; Applied mathematics; Exponential distribution; Mathematical analysis","score_opus":0.05033286292360348,"score_gpt":0.30972213223128797,"score_spread":0.25938926930768447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590238912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003697809,0.00053010136,0.99292547,0.00020899075,0.00083644316,0.00007507858,0.00002481388,0.000060075014,0.0016411938],"genre_scores_gemma":[0.5092462,0.000004885846,0.4905886,0.000029021183,0.000029615523,0.0000023475288,0.0000041555045,0.0000035568103,0.00009162215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99897873,0.00008760324,0.00020783425,0.00021970276,0.00031070103,0.00019545043],"domain_scores_gemma":[0.9989859,0.000094993076,0.00010104774,0.00042723128,0.00022246178,0.00016840601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000571636,0.000094414325,0.0001535651,0.0005534034,0.00003801538,0.000052430318,0.0005571583,0.000084317304,0.0000053947438],"category_scores_gemma":[0.00049104396,0.000078718294,0.00007349989,0.0032709006,0.000044243385,0.0001853791,0.00021857662,0.00007027007,0.000012244405],"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":[0.000016315602,0.00044723778,0.00030002993,0.000015711355,0.00005106335,0.000019047298,0.00080421334,0.0000050339227,0.029791547,0.8325943,0.012031071,0.12392444],"study_design_scores_gemma":[0.0062256064,0.0014813821,0.02273548,0.000076983815,0.00011074635,0.00010252313,0.000061435465,0.039385248,0.32867607,0.3469174,0.25218204,0.002045079],"about_ca_topic_score_codex":0.00003338437,"about_ca_topic_score_gemma":3.0264349e-7,"teacher_disagreement_score":0.50554836,"about_ca_system_score_codex":0.000031438558,"about_ca_system_score_gemma":0.00008003405,"threshold_uncertainty_score":0.3210039},"labels":[],"label_agreement":null},{"id":"W1683369961","doi":"10.1111/biom.12269","title":"On Bayesian Estimation of Marginal Structural Models","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":56,"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; University of Toronto","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Censoring (clinical trials); Marginal structural model; Inverse probability; Bayesian probability; Weighting; Bayesian inference; Posterior probability; Inverse probability weighting; Statistics; Inference; Covariate; Marginal likelihood; Population; Econometrics; Computer science; Marginal distribution; Mathematics; Confounding; Estimator; Artificial intelligence; Medicine; Random variable","score_opus":0.2902512635374343,"score_gpt":0.4371757447731041,"score_spread":0.1469244812356698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1683369961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10649766,0.000035433877,0.88941807,0.00003231068,0.00008210053,0.00015313715,0.000016950815,0.00015942755,0.0036049115],"genre_scores_gemma":[0.69145066,0.000002260132,0.308447,0.000012644984,0.0000121897265,0.0000042287206,0.0000058077603,0.000011158097,0.000054034837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908817,0.000024757053,0.00023405049,0.00012500986,0.00038147738,0.00014653984],"domain_scores_gemma":[0.9990459,0.0002849761,0.00015950609,0.0002562607,0.00017061338,0.00008275679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028312125,0.000105822335,0.00017361893,0.0008222827,0.00001805695,0.000012600464,0.00015092854,0.00008104888,0.000016394344],"category_scores_gemma":[0.0015111846,0.000090935275,0.000033079978,0.0015513999,0.00004551425,0.00018681888,0.000038799186,0.00007821248,0.0000057258653],"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":[0.000035178135,0.000058907117,0.00007441694,0.000060723192,0.000011123335,0.0000031713662,0.00016902748,0.0018042868,0.0002749835,0.9651593,0.0022757407,0.030073129],"study_design_scores_gemma":[0.00016449159,0.00021607403,0.0000485246,0.000019925894,0.000008891689,0.0000033812023,0.000023939316,0.089725144,0.0059837555,0.9036828,0.000025293395,0.00009776477],"about_ca_topic_score_codex":0.000009540743,"about_ca_topic_score_gemma":6.1124473e-7,"teacher_disagreement_score":0.584953,"about_ca_system_score_codex":0.000113244125,"about_ca_system_score_gemma":0.000038787468,"threshold_uncertainty_score":0.3708233},"labels":[],"label_agreement":null},{"id":"W1736428142","doi":"10.1002/9780470522356.ch2","title":"A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions for Biometric Signal Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"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":"University of Illinois at Urbana-Champaign","keywords":"Multilinear map; Linear discriminant analysis; Biometrics; Discriminant; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Pure mathematics","score_opus":0.1361821124535303,"score_gpt":0.28630619056693524,"score_spread":0.15012407811340495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1736428142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006418087,0.0015240507,0.98561734,0.00011749969,0.00033929534,0.0007844875,0.00069324835,0.00011398574,0.010745895],"genre_scores_gemma":[0.047707647,0.004406191,0.85277575,0.000364438,0.000854977,0.0004827077,0.0050419807,0.00016397164,0.08820233],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975035,0.000024905397,0.0007718525,0.00069698325,0.00055819674,0.00044453566],"domain_scores_gemma":[0.9974505,0.00039729287,0.00076245784,0.0005484042,0.0006884601,0.00015290828],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0005960972,0.00035125934,0.00066641415,0.018597396,0.0002151274,0.000103197155,0.00064184336,0.00039164434,0.000099507364],"category_scores_gemma":[0.00019907633,0.0003333578,0.00078786735,0.008341445,0.00006662304,0.000308162,0.0002122022,0.00017662207,0.00006240994],"study_design_candidate":"design_other","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":[0.000014561766,0.00014309143,0.0000063177795,0.00009532315,0.00039075658,0.0000038977614,0.00003640841,0.00003171293,0.00072566135,0.0017682876,0.0022842023,0.9944998],"study_design_scores_gemma":[0.002583526,0.0017955856,0.0005592009,0.000774656,0.005677704,0.000015637925,0.00008693724,0.23182224,0.008644549,0.023882153,0.7210643,0.0030934599],"about_ca_topic_score_codex":0.000025724372,"about_ca_topic_score_gemma":0.0000048047036,"teacher_disagreement_score":0.9914063,"about_ca_system_score_codex":0.000109635046,"about_ca_system_score_gemma":0.00010275034,"threshold_uncertainty_score":0.99991184},"labels":[],"label_agreement":null},{"id":"W1750424384","doi":"10.1111/biom.12243","title":"On the Selection of Ordinary Differential Equation Models with Application to Predator-Prey Dynamical Models","year":2014,"lang":"en","type":"article","venue":"Biometrics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Cancer Institute; Texas A and M University","keywords":"Ode; Ordinary differential equation; Applied mathematics; Context (archaeology); Ordinary least squares; Selection (genetic algorithm); Model selection; Mathematics; Estimator; Population; Estimation theory; Population model; Mathematical optimization; Differential equation; Computer science; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.014225737053445566,"score_gpt":0.2379149898299812,"score_spread":0.22368925277653562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1750424384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42642295,0.000011232313,0.5729899,0.000067531546,0.000025202062,0.00016956737,0.000010587354,0.000007735057,0.0002952803],"genre_scores_gemma":[0.99622434,0.000020263893,0.0032559223,0.00010793266,0.000057554225,0.000038785776,0.00015253057,0.000015341904,0.00012734639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917257,0.00005877791,0.00015718218,0.000251866,0.00022882677,0.00013079646],"domain_scores_gemma":[0.99937576,0.00003585671,0.000084306215,0.00028604682,0.00015995388,0.000058083897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016973878,0.00010539409,0.00009115383,0.00021813282,0.000059866543,0.000013652467,0.00015987143,0.00011713824,0.0000045250267],"category_scores_gemma":[0.000085462394,0.000075920514,0.000038480284,0.00084744784,0.000037402428,0.0000043076047,0.0000561515,0.000052738418,0.00000457119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061281264,0.0004631128,0.001052587,0.00004261876,0.000094816336,7.2261884e-8,0.000055331453,0.46363854,0.41532022,0.09756836,0.0013007245,0.01985081],"study_design_scores_gemma":[0.00026198395,0.00076901616,0.0018259704,0.000005522014,0.00001726155,0.000001526488,0.000007973107,0.9860609,0.007784504,0.0029132322,0.00023336124,0.00011871855],"about_ca_topic_score_codex":0.000012666458,"about_ca_topic_score_gemma":0.000020723784,"teacher_disagreement_score":0.5698014,"about_ca_system_score_codex":0.000027289292,"about_ca_system_score_gemma":0.00002885842,"threshold_uncertainty_score":0.30959487},"labels":[],"label_agreement":null},{"id":"W1767106723","doi":"10.1111/biom.12043","title":"A Natural Robustification of the Ordinary Instrumental Variables Estimator","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":20,"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":"","keywords":"Robustification; Instrumental variable; Estimator; Outlier; Asymptotic distribution; Robust statistics; Covariate; Robustness (evolution); Mathematics; Robust regression; Statistics; Applied mathematics; Econometrics; Computer science","score_opus":0.10151267496658431,"score_gpt":0.38026424544148496,"score_spread":0.2787515704749006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1767106723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12553374,0.000116434785,0.8726278,0.00015448105,0.00041359264,0.0003710688,0.000036771802,0.00003368643,0.00071244425],"genre_scores_gemma":[0.45964295,0.000003812408,0.54012215,0.000013850723,0.000013671805,0.000013418472,0.0000019438855,0.0000073288797,0.00018087697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99923456,0.000049857368,0.00024019717,0.00012870123,0.00021252765,0.00013416116],"domain_scores_gemma":[0.9986597,0.00077848515,0.00014533073,0.00025602028,0.0001186427,0.00004188041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018942807,0.00008389421,0.00014170159,0.00017821418,0.000069674024,0.000016477381,0.00018468864,0.000050431787,0.00006027543],"category_scores_gemma":[0.0027513434,0.00005251763,0.00004551928,0.0014081041,0.00008459347,0.00010345667,0.00008678884,0.00008377152,0.00000780342],"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":[0.000012401794,0.0003199155,0.00064671435,0.00023922112,0.000042970823,7.939891e-7,0.00008559742,0.000024973915,0.038880832,0.7284304,0.0043635564,0.22695264],"study_design_scores_gemma":[0.00045533216,0.00008362865,0.008744507,0.00006251615,0.000063270316,0.000013467069,0.00014584823,0.04067991,0.010500681,0.9379897,0.0010176905,0.00024344445],"about_ca_topic_score_codex":0.000020898044,"about_ca_topic_score_gemma":2.9797744e-7,"teacher_disagreement_score":0.33410922,"about_ca_system_score_codex":0.000040496372,"about_ca_system_score_gemma":0.000022388627,"threshold_uncertainty_score":0.32938138},"labels":[],"label_agreement":null},{"id":"W1829026320","doi":"10.1111/biom.12132","title":"Set‐valued dynamic treatment regimes for competing outcomes","year":2014,"lang":"en","type":"article","venue":"Biometrics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":87,"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 Waterloo","funders":"National Cancer Institute; National Institute of Mental Health; University of North Carolina at Chapel Hill; National Institutes of Health","keywords":"Outcome (game theory); Operationalization; Set (abstract data type); Sequence (biology); Construct (python library); Computer science; Enumeration; Integer (computer science); Function (biology); Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Mathematical economics","score_opus":0.40467981024790295,"score_gpt":0.45976890177571356,"score_spread":0.05508909152781061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1829026320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6302587,0.006197325,0.28667352,0.0473912,0.005128249,0.0043460447,0.002221347,0.00058901426,0.01719465],"genre_scores_gemma":[0.9497368,0.00015451162,0.03805695,0.00547044,0.0004106318,0.00032133205,0.00023445342,0.000096436364,0.0055184974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956677,0.00019384612,0.0028222478,0.00065568596,0.0001075844,0.0005529121],"domain_scores_gemma":[0.9940258,0.0032402086,0.0017756672,0.0006585955,0.000088217945,0.00021150007],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010590632,0.0002792731,0.0012163661,0.0017588963,0.00028674406,0.00012467038,0.00031863758,0.00018118475,0.000100991274],"category_scores_gemma":[0.009335319,0.00031011496,0.00028663335,0.0010630902,0.000060379414,0.00021948702,0.000045623914,0.00007159331,0.0012970803],"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":[0.00005695447,0.0006449373,0.29413375,0.0011782921,0.000731293,0.0000014467095,0.0045802114,0.00025422752,0.000026658132,0.6532338,0.03147508,0.013683325],"study_design_scores_gemma":[0.004388457,0.00076359586,0.064576,0.00005625626,0.00004318279,0.000005403016,0.001398025,0.075648956,0.00002903086,0.018043432,0.8340065,0.0010411929],"about_ca_topic_score_codex":0.0004320126,"about_ca_topic_score_gemma":0.00004465818,"teacher_disagreement_score":0.8025314,"about_ca_system_score_codex":0.0010427365,"about_ca_system_score_gemma":0.000076713724,"threshold_uncertainty_score":0.9999351},"labels":[],"label_agreement":null},{"id":"W1830111894","doi":"10.1111/biom.12302","title":"Penalized regression for interval‐censored times of disease progression: Selection of HLA markers in psoriatic arthritis","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":38,"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; Canadian Institutes of Health Research","keywords":"Psoriatic arthritis; Lasso (programming language); Medicine; Disease; Regression; Cohort; Confidence interval; Arthritis; Internal medicine; Statistics; Computer science; Mathematics","score_opus":0.04875086876952324,"score_gpt":0.3659708475179919,"score_spread":0.3172199787484687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1830111894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98846895,0.0077130455,0.0004897001,0.00026835897,0.00037142058,0.0020423892,0.00003929262,0.000039828552,0.00056702754],"genre_scores_gemma":[0.9923384,0.00031023458,0.006257558,0.000008038623,0.000075636985,0.00010438049,0.000041722153,0.000027716605,0.00083626824],"study_design_codex":"observational","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99796426,0.00014389722,0.000613067,0.00023679767,0.0007991863,0.00024276882],"domain_scores_gemma":[0.9981733,0.00031541503,0.00029693288,0.00026334228,0.00067986443,0.0002711619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012205432,0.00012835929,0.00051966705,0.0021794606,0.000016758748,0.000008577741,0.0001185295,0.000111200476,0.000064995336],"category_scores_gemma":[0.0050451467,0.00009600228,0.00012599234,0.0038882517,0.000084336694,0.00006777259,0.000058500606,0.000086845546,0.0000048805628],"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":[0.020093089,0.0018730264,0.71282506,0.009921929,0.00022946506,0.000056249402,0.0013840292,0.0000037389714,0.01089053,0.00023341102,0.036445748,0.20604372],"study_design_scores_gemma":[0.2522509,0.03111508,0.20864388,0.11852566,0.0014343222,0.0027886217,0.013172544,0.07878455,0.22886367,0.0045263735,0.056847796,0.0030465773],"about_ca_topic_score_codex":0.000028162916,"about_ca_topic_score_gemma":0.000002834644,"teacher_disagreement_score":0.50418115,"about_ca_system_score_codex":0.00020205791,"about_ca_system_score_gemma":0.0003676734,"threshold_uncertainty_score":0.60398763},"labels":[],"label_agreement":null},{"id":"W1857882766","doi":"10.1111/biom.12380","title":"Optimum Study Design for Detecting Imprinting and Maternal Effects Based on Partial Likelihood","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Syndromes and Imprinting","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Imprinting (psychology); Statistics; Econometrics; Computer science; Mathematics; Biology; Genetics","score_opus":0.033288574217492164,"score_gpt":0.2804025703448232,"score_spread":0.24711399612733104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1857882766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84054565,0.00015738994,0.15840459,0.00001279386,0.00029022474,0.00053937617,0.0000021810304,0.000014741749,0.0000330353],"genre_scores_gemma":[0.9723721,0.000004032905,0.027264906,0.000058305937,0.00019393286,0.000053992626,0.000003958514,0.000028278278,0.000020477983],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887395,0.000072533636,0.00019081219,0.00036696647,0.00017093343,0.0003248101],"domain_scores_gemma":[0.999325,0.000100394915,0.00009458748,0.00022348741,0.00010034647,0.00015621555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008824722,0.00015640938,0.00014750102,0.00026603654,0.00008960331,0.00008160075,0.00012781506,0.000097632896,8.6024585e-7],"category_scores_gemma":[0.0011039639,0.00014546438,0.000049891394,0.00038549688,0.00001815515,0.0000024750789,0.00011859279,0.00005196198,0.0000029464875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002201796,0.0005711753,0.21934123,0.00016262812,0.00014527862,0.000010495133,0.0001555685,0.001410497,0.5308626,0.0000041296503,0.00020557875,0.2469106],"study_design_scores_gemma":[0.006821854,0.012323016,0.09724372,0.000043045908,0.000104809114,0.000030420406,0.00036865904,0.04710907,0.83370554,0.00004220798,0.0015706165,0.00063704123],"about_ca_topic_score_codex":0.00001344648,"about_ca_topic_score_gemma":5.957182e-7,"teacher_disagreement_score":0.3028429,"about_ca_system_score_codex":0.00001623516,"about_ca_system_score_gemma":0.000050232935,"threshold_uncertainty_score":0.5931865},"labels":[],"label_agreement":null},{"id":"W1909655160","doi":"10.1111/biom.12327","title":"Rejoinder to “A note on the empirical likelihood confidence band for hazards ratio with covariate adjustment”","year":2015,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","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 Waterloo","funders":"National Science Foundation","keywords":"Covariate; Statistics; Confidence interval; Empirical likelihood; Econometrics; Mathematics; Computer science","score_opus":0.3826002592664287,"score_gpt":0.43617050272005414,"score_spread":0.05357024345362543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1909655160","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000055038872,0.00008027873,0.79184335,0.20297751,0.000757284,0.0015685295,0.0008032526,0.000062607585,0.0018521518],"genre_scores_gemma":[0.00028810083,0.00002275467,0.48579073,0.5079202,0.0026295164,0.00046781183,0.00009969768,0.00014370137,0.002637478],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99658585,0.00029489276,0.0005545012,0.0006655535,0.0012550888,0.00064411474],"domain_scores_gemma":[0.9846464,0.013029653,0.0003278602,0.0008971272,0.000885067,0.00021392279],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017474707,0.00048514822,0.00071983424,0.0007930679,0.0001424019,0.00020213675,0.0006036608,0.0006677591,0.0001637343],"category_scores_gemma":[0.019685766,0.00027407563,0.00012120811,0.002445867,0.00012671154,0.000038448252,0.00009137458,0.0009254574,0.000117153766],"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":[0.00008201031,0.0000625137,0.0000058292294,0.0001608623,0.000078010635,0.000024515763,0.00014316356,4.3300548e-7,0.000019556986,0.015347887,0.9783916,0.0056836545],"study_design_scores_gemma":[0.00076064095,0.0018512293,0.00012747609,0.0002276936,0.00029228715,0.000012863851,0.000027267333,0.0004111633,0.0003689394,0.1552761,0.8399902,0.0006541562],"about_ca_topic_score_codex":0.00002128743,"about_ca_topic_score_gemma":0.000004252406,"teacher_disagreement_score":0.30605263,"about_ca_system_score_codex":0.00023898497,"about_ca_system_score_gemma":0.0005473673,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W1920526183","doi":"10.1111/j.1541-0420.2012.01823.x","title":"Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models","year":2013,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency","funders":"National Cancer Institute; National Institutes of Health","keywords":"Prostate cancer; Markov chain Monte Carlo; Bayesian probability; Computer science; Medicine; Prostate-specific antigen; Statistics; Medical physics; Cancer; Artificial intelligence; Internal medicine; Mathematics","score_opus":0.28221421660582763,"score_gpt":0.40362805874221264,"score_spread":0.12141384213638501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1920526183","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6084378,0.00023496138,0.38611323,0.0000775252,0.00039052253,0.00076338666,0.0011348642,0.000102313184,0.0027454104],"genre_scores_gemma":[0.20009492,0.00030616904,0.7992691,0.000013118744,0.000052676285,0.00005627406,0.00000531185,0.0000210791,0.00018135297],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986622,0.000078202414,0.0004412543,0.00019570922,0.00038294913,0.00023973954],"domain_scores_gemma":[0.9982472,0.0007860735,0.00024061288,0.00023543688,0.00037956596,0.000111101974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000437909,0.00012130912,0.00026750556,0.0006072237,0.00006237857,0.00003999987,0.00015468801,0.00008446292,0.00035483195],"category_scores_gemma":[0.0023078031,0.000101198544,0.00004794107,0.0023820945,0.00010627044,0.00014662738,0.00009874309,0.00010533387,0.000015474889],"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":[0.00004547533,0.0019798186,0.012286911,0.0019628375,0.00058618846,0.000008472666,0.0051029357,0.0009715795,0.073204674,0.3512648,0.041551195,0.51103514],"study_design_scores_gemma":[0.00039077477,0.00023562326,0.011238946,0.0002527965,0.00015891928,0.0000056606527,0.00010593037,0.28925288,0.003881795,0.6940437,0.000072496114,0.00036045193],"about_ca_topic_score_codex":0.00076653174,"about_ca_topic_score_gemma":0.00000103048,"teacher_disagreement_score":0.51067466,"about_ca_system_score_codex":0.000063863954,"about_ca_system_score_gemma":0.00010417208,"threshold_uncertainty_score":0.41267568},"labels":[],"label_agreement":null},{"id":"W1922801329","doi":"10.1111/biom.12335","title":"Adjusting for Undercoverage of Access-Points in Creel Surveys with Fewer Overflights","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ministry of Forests; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; BC Hydro","keywords":"Recreation; Estimator; TRIPS architecture; Fishing; Aerial survey; Sample (material); Survey data collection; Component (thermodynamics); Computer science; Estimation; Operations research; Geography; Environmental science; Fishery; Statistics; Mathematics; Economics; Ecology; Cartography","score_opus":0.09521062195602989,"score_gpt":0.29458886605621,"score_spread":0.1993782441001801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1922801329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96254796,0.000038027127,0.012628621,0.0002038583,0.00016975014,0.00038437787,0.000013656977,0.000018824376,0.023994947],"genre_scores_gemma":[0.99724245,0.000015505979,0.0019208493,0.00014913549,0.000010727221,0.00001668618,0.0000050097947,0.0000068118015,0.0006328146],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99923813,0.000046276138,0.00014703548,0.00018605762,0.0001907543,0.0001917406],"domain_scores_gemma":[0.9995194,0.00021232651,0.00009026126,0.00011837965,0.000018700948,0.000040898627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010180651,0.000080263635,0.00014149271,0.00031872987,0.00004401681,0.000011964832,0.00019792083,0.00004741736,0.000065623804],"category_scores_gemma":[0.0004903171,0.00006418296,0.000017305545,0.0020567232,0.000095169176,0.00016906999,0.00027928376,0.000037641366,0.00002132635],"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":[0.000026526624,0.000118742944,0.98487335,0.000023838622,0.000015904227,0.0000036893073,0.00008886062,0.00031018842,0.00001774237,0.00013072802,0.012776509,0.0016138867],"study_design_scores_gemma":[0.0008677615,0.00022854333,0.9950518,0.000006708151,0.0000117078,3.5320517e-7,0.00007866407,0.0002399175,0.00019415533,0.000841415,0.0023779275,0.000101060534],"about_ca_topic_score_codex":0.00026708856,"about_ca_topic_score_gemma":0.0020617193,"teacher_disagreement_score":0.034694523,"about_ca_system_score_codex":0.00011517342,"about_ca_system_score_gemma":0.000008937992,"threshold_uncertainty_score":0.26173052},"labels":[],"label_agreement":null},{"id":"W1923634128","doi":"10.1111/biom.12312","title":"Estimation of covariate‐specific time‐dependent ROC curves in the presence of missing biomarkers","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"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 Waterloo","funders":"","keywords":"Covariate; Missing data; Estimator; Statistics; Receiver operating characteristic; Computer science; Robustness (evolution); Mathematics; Econometrics; Biology","score_opus":0.18521155920383958,"score_gpt":0.3968904491015443,"score_spread":0.21167888989770473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1923634128","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064395918,0.0008970047,0.9905834,0.00017245086,0.00009995669,0.00024750564,0.00004409289,0.000011826069,0.001504146],"genre_scores_gemma":[0.26926437,0.00006620103,0.7306051,0.000016731357,0.000012437433,0.000005602931,0.0000051082247,0.000008252865,0.00001616414],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844456,0.0002949733,0.00045140702,0.00013427841,0.00054027804,0.0001344938],"domain_scores_gemma":[0.9954287,0.0038315915,0.00025613813,0.0002796779,0.00015438705,0.000049547554],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0026470646,0.00008880288,0.00024748745,0.0005284021,0.000017428856,0.000020196667,0.0002800122,0.000060345825,0.0000345476],"category_scores_gemma":[0.012074712,0.00006165093,0.000033070668,0.002883454,0.00010258381,0.000066127584,0.000044245415,0.00006788791,0.000006301865],"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":[0.00012578222,0.001031186,0.0016137464,0.0017155431,0.00007255315,0.000028481498,0.0018653683,0.000023199615,0.0053223204,0.17713647,0.014481992,0.79658335],"study_design_scores_gemma":[0.0010567246,0.000381645,0.00796958,0.00092115666,0.00008653911,0.000022567347,0.00047455388,0.04163187,0.010988002,0.93576735,0.00037150164,0.00032848754],"about_ca_topic_score_codex":0.000038381877,"about_ca_topic_score_gemma":5.931624e-7,"teacher_disagreement_score":0.7962549,"about_ca_system_score_codex":0.000033361433,"about_ca_system_score_gemma":0.000063425185,"threshold_uncertainty_score":0.996247},"labels":[],"label_agreement":null},{"id":"W1964110958","doi":"10.1111/j.1541-0420.2007.00752.x","title":"A Mixed Mover–Stayer Model for Spatiotemporal Two‐State Processes","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Covariate; Statistics; Inference; Bayesian inference; Markov chain; Bayesian probability; Econometrics; Logistic regression; Monte Carlo method; Computer science; Mathematics; Artificial intelligence","score_opus":0.2113788817226411,"score_gpt":0.421296114128984,"score_spread":0.20991723240634289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964110958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008773996,0.000075302225,0.989012,0.00005159349,0.0001784165,0.00039017567,0.00022451761,0.00009101195,0.0012030021],"genre_scores_gemma":[0.1833757,0.000011888395,0.8159357,0.00007652504,0.000068874484,0.000021304677,0.000008608511,0.000028937417,0.00047250892],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998513,0.00002329339,0.00043274948,0.0002750366,0.00033813555,0.0004177736],"domain_scores_gemma":[0.9957473,0.0032609678,0.00018129175,0.00022993979,0.0004291244,0.00015132435],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0011964303,0.0001738555,0.00027300933,0.0006770684,0.00008529065,0.00006361883,0.00019038863,0.000085066196,0.00001845547],"category_scores_gemma":[0.010264216,0.00014749833,0.00006451296,0.002603335,0.000058564856,0.0000921752,0.000046237838,0.000086672335,0.000009476094],"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":[0.00017006096,0.0004513329,0.000964919,0.0012189568,0.000048827762,0.0000148969075,0.0004639272,0.000010620271,0.0007116007,0.4344514,0.0047295163,0.55676395],"study_design_scores_gemma":[0.00069529226,0.000114842085,0.0003386843,0.000028294437,0.000034765915,0.0000026158436,0.00002919708,0.052525774,0.0050594467,0.93955034,0.0013398302,0.00028094254],"about_ca_topic_score_codex":0.000014644878,"about_ca_topic_score_gemma":0.000029667659,"teacher_disagreement_score":0.556483,"about_ca_system_score_codex":0.00006240805,"about_ca_system_score_gemma":0.0001276811,"threshold_uncertainty_score":0.99807274},"labels":[],"label_agreement":null},{"id":"W1964212085","doi":"10.1111/j.1541-0420.2010.01472.x","title":"Dependence Calibration in Conditional Copulas: A Nonparametric Approach","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":101,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Covariate; Mathematics; Nonparametric statistics; Estimator; Statistics; Econometrics; Inference; Parametric statistics; Pointwise; Computer science; Artificial intelligence","score_opus":0.04129775495964639,"score_gpt":0.24135612359842903,"score_spread":0.20005836863878265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964212085","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.764405,0.00091999036,0.22761565,0.00007886262,0.0006370991,0.00024831388,0.00019550094,0.000048181937,0.005851412],"genre_scores_gemma":[0.9827224,0.00006558256,0.016728105,0.00008736775,0.00011605868,0.0000287416,0.00010861577,0.000016959999,0.0001261401],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985011,0.000011294014,0.0006318016,0.0004562064,0.00009669445,0.00030287763],"domain_scores_gemma":[0.9992386,0.00012599744,0.0002128882,0.00028535808,0.00004752101,0.00008965431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008883574,0.00014004552,0.0002951017,0.0030417235,0.00008458563,0.00009305372,0.0002588728,0.00024680805,0.000134087],"category_scores_gemma":[0.0013808834,0.00017065476,0.000080703365,0.006029247,0.000060169663,0.00037282845,0.000052875792,0.00037399292,0.0001697519],"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":[0.000019579515,0.00047897693,0.623493,0.000043802902,0.000010832728,0.000005176955,0.00016870363,0.00056522543,0.00030479126,0.37034884,0.0004787157,0.0040823254],"study_design_scores_gemma":[0.000976658,0.00007329525,0.3595316,0.0000060725815,0.000004077785,0.000009930838,0.000037600115,0.5498079,0.00018848115,0.07805938,0.0107701635,0.0005348262],"about_ca_topic_score_codex":0.000487024,"about_ca_topic_score_gemma":0.00006520383,"teacher_disagreement_score":0.5492427,"about_ca_system_score_codex":0.000081300605,"about_ca_system_score_gemma":0.00004376424,"threshold_uncertainty_score":0.6959099},"labels":[],"label_agreement":null},{"id":"W1964757745","doi":"10.1111/j.0006-341x.2001.00287.x","title":"Catch Estimation in the Presence of Declining Catch Rate Due to Gear Saturation","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Fishing; Statistics; Bycatch; Econometrics; Fishery; Mathematics; Computer science; Biology","score_opus":0.164411733545063,"score_gpt":0.3998633404193596,"score_spread":0.23545160687429662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964757745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6304937,0.000039154773,0.3683584,0.00040421393,0.00008376312,0.00028338833,0.0000051329084,0.00007100037,0.00026122824],"genre_scores_gemma":[0.8711765,0.000024363997,0.12860775,0.00006933177,0.000022095983,0.000028796423,0.0000142034605,0.000009849446,0.000047104444],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99882644,0.00017820453,0.00039998596,0.00015128987,0.00029356527,0.00015049533],"domain_scores_gemma":[0.99666786,0.0026475084,0.0001702917,0.00030909118,0.00017294456,0.000032296586],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003043872,0.00009164851,0.00016449542,0.0008960014,0.000056938945,0.000046704452,0.00023270115,0.00008334239,0.0000074426107],"category_scores_gemma":[0.010494836,0.00007098918,0.000031026277,0.0048845666,0.000022889255,0.00012124972,0.00003111572,0.00009683,0.000016968332],"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":[0.00042403804,0.0021726564,0.14917198,0.0010679862,0.00010679349,0.00009569335,0.044628132,0.006272997,0.016224412,0.040744144,0.039969623,0.69912153],"study_design_scores_gemma":[0.0016215907,0.00091367995,0.55164266,0.0007392374,0.00011828382,0.0001381361,0.0027098039,0.1697179,0.05493688,0.2103206,0.0057913507,0.0013498494],"about_ca_topic_score_codex":0.00033600643,"about_ca_topic_score_gemma":0.00004618899,"teacher_disagreement_score":0.6977717,"about_ca_system_score_codex":0.000046563862,"about_ca_system_score_gemma":0.00003231236,"threshold_uncertainty_score":0.99784017},"labels":[],"label_agreement":null},{"id":"W1964775321","doi":"10.1111/j.1541-0420.2007.00815.x","title":"A Bayesian Approach to the Multistate Jolly–Seber Capture–Recapture Model","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","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":"Simon Fraser University","funders":"Minnesota Department of Natural Resources","keywords":"Mark and recapture; Bayesian probability; Econometrics; Computer science; Statistics; Mathematics; Medicine","score_opus":0.07716775954596544,"score_gpt":0.33435171038219413,"score_spread":0.2571839508362287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964775321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01409077,0.00010531974,0.9734238,0.0004947493,0.00020951667,0.00047368268,0.000045023244,0.000089381196,0.01106778],"genre_scores_gemma":[0.75111,0.0000038309277,0.24607143,0.00040142177,0.00012765542,0.000010774133,0.000038046033,0.0000286748,0.002208146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986433,0.000027676204,0.00034481013,0.00022918968,0.0004433775,0.00031166387],"domain_scores_gemma":[0.9989148,0.0002590564,0.00012900036,0.0003968579,0.00015507117,0.00014521276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010416964,0.00016166535,0.00016422366,0.0007459133,0.00015051558,0.00005928239,0.00021986144,0.00013695624,0.000013906948],"category_scores_gemma":[0.0007395283,0.000110065615,0.000077909994,0.0032889855,0.000022256794,0.00006922582,0.00005120342,0.00014784533,0.000037904458],"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":[0.00022774645,0.0018932652,0.0080572115,0.00055215624,0.00020913263,0.000022363784,0.027163139,0.051205438,0.0010491048,0.40911585,0.31789953,0.18260504],"study_design_scores_gemma":[0.0007124395,0.00003627738,0.0128140785,0.000020548921,0.0000820961,0.000027876018,0.00033618987,0.90927726,0.00020874685,0.01966158,0.05626,0.0005629204],"about_ca_topic_score_codex":0.00006962524,"about_ca_topic_score_gemma":0.000060228256,"teacher_disagreement_score":0.8580718,"about_ca_system_score_codex":0.00009576263,"about_ca_system_score_gemma":0.000023629405,"threshold_uncertainty_score":0.44883457},"labels":[],"label_agreement":null},{"id":"W1964870095","doi":"10.1111/j.1541-0420.2009.01299.x","title":"Regression Analysis with a Misclassified Covariate from a Current Status Observation Scheme","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","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":"McMaster University; University of Waterloo; Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; Censoring (clinical trials); Statistics; Regression analysis; Nonparametric statistics; Seroconversion; Proportional hazards model; Estimator; Econometrics; Medicine; Mathematics","score_opus":0.7458845379643189,"score_gpt":0.5750133852493857,"score_spread":0.17087115271493314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964870095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1675898,0.0004219218,0.8299822,0.00028291176,0.00041393383,0.0003983405,0.00044606428,0.00017671853,0.00028810775],"genre_scores_gemma":[0.039231766,0.00017501596,0.96009827,0.00011486423,0.00018195984,0.000012480444,0.00006715136,0.000020122563,0.00009834678],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969221,0.00042980112,0.00085657794,0.000535383,0.00086120743,0.00039497126],"domain_scores_gemma":[0.983036,0.01512221,0.0005887505,0.0006783129,0.00031342194,0.0002613012],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001528679,0.0002493273,0.00079492584,0.001213969,0.00008461026,0.000099580684,0.00025318464,0.0001919262,0.00019650963],"category_scores_gemma":[0.061970152,0.0001708414,0.00018850308,0.012030124,0.00006740176,0.000089044224,0.000050377676,0.00027358215,0.00002423009],"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":[0.0017084517,0.0034058394,0.078243695,0.0001787255,0.0024052486,0.00005949524,0.00041925197,0.000012780482,0.0057485644,0.14225544,0.007747266,0.75781524],"study_design_scores_gemma":[0.0030901043,0.00058067223,0.29723495,0.00019805809,0.0023286298,5.121796e-7,0.000044088316,0.008503732,0.0012123857,0.6807596,0.0054176296,0.00062962883],"about_ca_topic_score_codex":0.00003632925,"about_ca_topic_score_gemma":0.0000051223706,"teacher_disagreement_score":0.75718564,"about_ca_system_score_codex":0.00013675707,"about_ca_system_score_gemma":0.000080384096,"threshold_uncertainty_score":0.94593126},"labels":[],"label_agreement":null},{"id":"W1965508964","doi":"10.1111/j.1541-0420.2007.00940.x","title":"Clustered Mixed Nonhomogeneous Poisson Process Spline Models for the Analysis of Recurrent Event Panel Data","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Carleton University","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging; Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson process; Spline (mechanical); Event (particle physics); Point process; Computer science; Poisson distribution; Process (computing); Event data; Statistics; Econometrics; Mathematics; Applied mathematics; Data mining; Covariate; Engineering","score_opus":0.42811642870102173,"score_gpt":0.47787328705360765,"score_spread":0.04975685835258592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965508964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021495549,0.0006051157,0.9756718,0.00007559087,0.00022873408,0.0004481174,0.0013844456,0.000019929348,0.00007066107],"genre_scores_gemma":[0.6232923,0.000101773214,0.37627456,0.000028226246,0.00006779836,0.000015385704,0.00014125988,0.0000186449,0.00006002268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820304,0.000049159215,0.0006658548,0.0003316278,0.00046184452,0.00028847397],"domain_scores_gemma":[0.99120754,0.007015588,0.00035054082,0.0009165558,0.00041737608,0.00009241732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029832448,0.00014823607,0.00043194578,0.0009687963,0.00007303453,0.000024210485,0.000744184,0.00009187627,0.000026272557],"category_scores_gemma":[0.008194443,0.00009647153,0.00012519027,0.006217217,0.00006011562,0.00004545403,0.00018970003,0.000081593294,9.770329e-7],"study_design_candidate":"design_other","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":[0.00028114917,0.0009735257,0.0002825695,0.00061623205,0.0014639274,0.000004386455,0.00041175538,0.00054848037,0.00032708346,0.030389069,0.0012415531,0.96346027],"study_design_scores_gemma":[0.00044435958,0.00019694891,0.002388862,0.000031017076,0.0023305782,0.0000020310583,0.00020101597,0.9271255,0.0013481935,0.06496381,0.00074655074,0.00022113151],"about_ca_topic_score_codex":0.000027464468,"about_ca_topic_score_gemma":0.000042331612,"teacher_disagreement_score":0.96323913,"about_ca_system_score_codex":0.000039596467,"about_ca_system_score_gemma":0.000043316493,"threshold_uncertainty_score":0.9810105},"labels":[],"label_agreement":null},{"id":"W1967190811","doi":"10.1111/j.1541-0420.2010.01445.x","title":"Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":23,"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":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"Statistics; Estimator; Proportional hazards model; Univariate; Regression analysis; Mathematics; Regression; Econometrics; Multivariate statistics","score_opus":0.34286132374714046,"score_gpt":0.5026135210837422,"score_spread":0.1597521973366018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967190811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097677976,0.0002781474,0.89636636,0.00016556133,0.0007734448,0.00037696265,0.004284822,0.000021286025,0.000055471253],"genre_scores_gemma":[0.19541284,0.00006648958,0.8040623,0.000013735091,0.0001515535,0.000026626318,0.00019277117,0.000012325015,0.00006137898],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99858665,0.00007960764,0.00045629052,0.00030185797,0.00042771982,0.00014786124],"domain_scores_gemma":[0.98433346,0.013910036,0.0003104732,0.00095425633,0.00044142155,0.0000503535],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0020557337,0.000119606135,0.0004531353,0.0005469678,0.0001355738,0.000030996278,0.00043288877,0.00009381366,0.00012974802],"category_scores_gemma":[0.023186434,0.00006543006,0.00010232545,0.003099309,0.00018398734,0.00005458769,0.00027359428,0.00012519136,0.0000012563772],"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":[0.00025158815,0.0009894557,0.12464035,0.0005124954,0.019702602,0.00013722126,0.00067297154,0.0000014943568,0.004211445,0.19790287,0.025049558,0.6259279],"study_design_scores_gemma":[0.0015452116,0.0002575084,0.28534353,0.000098771176,0.024146765,0.000045461173,0.0015834224,0.18435234,0.0020363587,0.49344468,0.0062649148,0.00088105194],"about_ca_topic_score_codex":0.00012423954,"about_ca_topic_score_gemma":0.00026591818,"teacher_disagreement_score":0.6250469,"about_ca_system_score_codex":0.000015035933,"about_ca_system_score_gemma":0.000055639295,"threshold_uncertainty_score":0.9850417},"labels":[],"label_agreement":null},{"id":"W1967991525","doi":"10.1111/j.1541-0420.2010.01404.x","title":"Inverse Probability of Censoring Weighted Estimates of Kendall's τ for Gap Time Analyses","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":13,"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; Université Laval","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Censoring (clinical trials); Statistics; Inverse probability; Mathematics; Econometrics; Inverse; Bayesian probability; Posterior probability","score_opus":0.26319550578882095,"score_gpt":0.4492367727857263,"score_spread":0.18604126699690537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967991525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28931072,0.00001205885,0.7076519,0.00015168241,0.00006707621,0.00053762394,0.0015434158,0.000074246054,0.00065125164],"genre_scores_gemma":[0.4818928,0.0000019938363,0.51790327,0.0000075285957,0.0000134925485,0.00003142856,0.000103423314,0.000008421903,0.000037633432],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991066,0.000013028722,0.00042543074,0.00014317439,0.00018947628,0.00012232928],"domain_scores_gemma":[0.9971252,0.0017723135,0.00025703423,0.00026868968,0.0005056873,0.000071052404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000293836,0.00008767158,0.00023776627,0.00029451915,0.000044941724,0.000009144445,0.00013553577,0.00007617161,0.00023248843],"category_scores_gemma":[0.007692674,0.000077245204,0.000086072505,0.0019051887,0.00015249228,0.000041099225,0.000028184351,0.000058021546,0.000011849346],"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":[0.000024795947,0.0008796568,0.0015906587,0.000714261,0.00007467374,1.5117885e-7,0.00005340725,0.00000533348,0.21688122,0.76840854,0.0056091133,0.0057581877],"study_design_scores_gemma":[0.00086764985,0.000117266514,0.011225652,0.000031728512,0.00024943321,0.0000019253687,0.000028962633,0.094297715,0.25902745,0.6317722,0.002112214,0.0002678086],"about_ca_topic_score_codex":0.0000078919475,"about_ca_topic_score_gemma":0.000001724543,"teacher_disagreement_score":0.19258207,"about_ca_system_score_codex":0.000019125313,"about_ca_system_score_gemma":0.000030936113,"threshold_uncertainty_score":0.9209405},"labels":[],"label_agreement":null},{"id":"W1968453480","doi":"10.1111/j.0006-341x.2002.00981.x","title":"A Statistical Model for Investigating Binding Probabilities of DNA Nucleotide Sequences Using Microarrays","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"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":"National Cancer Institute","keywords":"DNA microarray; Computational biology; DNA; DNA binding site; DNA sequencing; Statistical model; Biology; Genetics; Mathematics; Statistics; Gene; Promoter; Gene expression","score_opus":0.1182458388383464,"score_gpt":0.312329187877482,"score_spread":0.1940833490391356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968453480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86701626,0.0003848791,0.13206959,0.00004255761,0.00007131261,0.00017273321,0.0001174926,0.000007851197,0.000117355594],"genre_scores_gemma":[0.90229475,0.000051091018,0.09726752,0.00004145092,0.000048372804,0.000017063398,0.000039523187,0.0000126948335,0.00022752267],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992435,0.000022842298,0.00022782193,0.00022670405,0.00012150637,0.00015764913],"domain_scores_gemma":[0.9995048,0.000032338103,0.00013074848,0.00015989931,0.00011162131,0.0000605832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016097414,0.00008744907,0.00010571337,0.0002483959,0.000071617884,0.00001979332,0.00011685035,0.00008600857,0.0000102484855],"category_scores_gemma":[0.00052717526,0.00008307875,0.000045608034,0.0005464115,0.0001238592,0.0000045571087,0.00003643956,0.000029175637,9.269142e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000532801,0.000029515435,0.0011863108,0.000074030984,0.000009450073,6.0376024e-8,0.00009767675,0.0003984056,0.99519086,0.00034673017,0.0010444269,0.0016172036],"study_design_scores_gemma":[0.000389831,0.00016105975,0.00020836001,0.000036245234,0.000023971097,0.000002755986,0.00030787094,0.23283175,0.7632316,0.00065889955,0.0019298595,0.00021779377],"about_ca_topic_score_codex":0.0000056023637,"about_ca_topic_score_gemma":0.0000013026645,"teacher_disagreement_score":0.23243335,"about_ca_system_score_codex":0.000025476702,"about_ca_system_score_gemma":0.00005187002,"threshold_uncertainty_score":0.3387853},"labels":[],"label_agreement":null},{"id":"W1969326328","doi":"10.1111/j.1541-0420.2010.01437.x","title":"Simultaneous Inference and Bias Analysis for Longitudinal Data with Covariate Measurement Error and Missing Responses","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":39,"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; York University; University of Waterloo","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Inference; Missing data; Statistics; Computer science; Causal inference; Observational error; Longitudinal data; Econometrics; Mathematics; Data mining; Artificial intelligence","score_opus":0.46126242559040714,"score_gpt":0.4553943550827061,"score_spread":0.005868070507701051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969326328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058302585,0.00011257846,0.94076294,0.00014032677,0.000054785454,0.0002105201,0.00035648153,0.000029087461,0.000030691608],"genre_scores_gemma":[0.4789294,0.000011093295,0.5210003,0.000012347823,0.0000177373,0.0000041499366,0.00000707854,0.000007867413,0.000009997069],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99858737,0.0000846069,0.00026999533,0.0004444498,0.0003866753,0.00022687622],"domain_scores_gemma":[0.988192,0.010553468,0.00016247598,0.0005622369,0.00036900496,0.00016081237],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0020857055,0.00016241797,0.00035024897,0.000801613,0.00015399914,0.00019988355,0.00021171378,0.00008583384,0.000019845382],"category_scores_gemma":[0.051755868,0.00011743163,0.000022749748,0.002262596,0.00016830146,0.00008586038,0.00014008439,0.00012783949,5.4828064e-7],"study_design_candidate":"design_other","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":[0.0010982052,0.0005290017,0.17952445,0.0009378224,0.0016891072,0.0001087258,0.0003679373,0.0000030909275,0.009652396,0.16424897,0.00019817449,0.6416421],"study_design_scores_gemma":[0.0036219566,0.0018462623,0.26316497,0.0002509367,0.0073482366,0.00012449693,0.00020590628,0.26794204,0.001957496,0.44734028,0.0043193595,0.0018780812],"about_ca_topic_score_codex":0.000048538437,"about_ca_topic_score_gemma":0.00013840814,"teacher_disagreement_score":0.639764,"about_ca_system_score_codex":0.000016303431,"about_ca_system_score_gemma":0.000085762615,"threshold_uncertainty_score":0.9562316},"labels":[],"label_agreement":null},{"id":"W1972385242","doi":"10.1111/j.0006-341x.2005.021126.x","title":"Multi‐List Methods Using Incomplete Lists in Closed Populations","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd","keywords":"Human immunodeficiency virus (HIV); Computer science; Mark and recapture; Population; Statistics; Data mining; Econometrics; Medicine; Mathematics; Virology; Environmental health","score_opus":0.4779702054317932,"score_gpt":0.5273591692275992,"score_spread":0.049388963795805985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972385242","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43291494,0.0001599109,0.5656868,0.00020504343,0.00037323707,0.00026783708,0.000026448142,0.00009090586,0.0002748241],"genre_scores_gemma":[0.4164925,0.000002715185,0.5832619,0.000033349555,0.00007396864,0.0000027262067,0.000026470181,0.0000140282955,0.000092314775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987779,0.0001300633,0.0005030034,0.00018043719,0.00020826509,0.00020031123],"domain_scores_gemma":[0.99904895,0.0003462554,0.00019638368,0.00024463807,0.00009875752,0.00006502889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007967983,0.0001237078,0.00020184659,0.0016555085,0.000100020625,0.000042741573,0.000109540415,0.00010784538,0.0000791435],"category_scores_gemma":[0.0017083423,0.00012625274,0.000061277875,0.0038074886,0.000021883927,0.0001741467,0.00004482735,0.00009986834,0.000014842321],"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":[0.00003808981,0.0016498491,0.35344505,0.0003143342,0.000049298967,0.000011036471,0.0019988075,0.006306263,0.02534712,0.09396434,0.0024047208,0.5144711],"study_design_scores_gemma":[0.00086303573,0.000014916708,0.2393913,0.000032770553,0.000030553518,0.000008341334,0.000022203802,0.73281026,0.00031776621,0.008999017,0.017217102,0.00029274035],"about_ca_topic_score_codex":0.00024361751,"about_ca_topic_score_gemma":0.00030749658,"teacher_disagreement_score":0.726504,"about_ca_system_score_codex":0.00025612777,"about_ca_system_score_gemma":0.00002434363,"threshold_uncertainty_score":0.5148437},"labels":[],"label_agreement":null},{"id":"W1972693523","doi":"10.1111/j.1541-0420.2010.01534.x","title":"Exploring Spatial and Temporal Variations of Cadmium Concentrations in Pacific Oysters from British Columbia","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Oyster; Cadmium; Pacific oyster; Crassostrea; Bay; Fishery; Environmental science; Shellfish; Aquaculture; Oceanography; Biology; Fish <Actinopterygii>; Geology; Aquatic animal; Chemistry","score_opus":0.03899027015332763,"score_gpt":0.2214324929137678,"score_spread":0.18244222276044017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972693523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970883,0.000022449318,0.0015366374,0.000049477763,0.00045551767,0.00017933441,0.00011628017,0.00001200414,0.0005399849],"genre_scores_gemma":[0.9929993,0.00007113396,0.006746957,0.000015270365,0.000033705954,0.000025178031,0.000022935823,0.000009191305,0.00007629861],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99898976,0.000038290633,0.00028093904,0.00023913279,0.00028147904,0.00017040664],"domain_scores_gemma":[0.99949473,0.0001218089,0.00008554103,0.00019181237,0.0000039985844,0.00010209044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027353596,0.00006118454,0.0001288752,0.000104331164,0.00006655291,0.000075557364,0.00011391243,0.00006428034,0.0009653999],"category_scores_gemma":[0.00023749968,0.00009519266,0.000021836224,0.0010588561,0.00021206783,0.00024242028,0.00010396828,0.00012243999,0.000025181962],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.187795e-7,0.00008882937,0.9526866,0.000002364759,0.0000036418119,0.000004454364,0.00019287514,0.000007954515,0.03464072,0.000004303973,0.0001829113,0.0121843945],"study_design_scores_gemma":[0.000250054,0.00002532558,0.9922186,0.00000488228,0.0000074364225,0.0000021602993,0.00009293225,0.0003694209,0.0010746508,0.00006646075,0.0057835863,0.00010445719],"about_ca_topic_score_codex":0.10264704,"about_ca_topic_score_gemma":0.0656921,"teacher_disagreement_score":0.039532,"about_ca_system_score_codex":0.00008932186,"about_ca_system_score_gemma":0.000011393139,"threshold_uncertainty_score":0.99994785},"labels":[],"label_agreement":null},{"id":"W1973451885","doi":"10.1111/j.1541-0420.2009.01225.x","title":"An Exact Control‐Versus‐Treatment Comparison Test Based on Ranked Set Samples","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Security Agency; National Science Foundation","keywords":"Confidence interval; Statistics; Mathematics; Disjoint sets; Multiple comparisons problem; Null hypothesis; Test (biology); Null (SQL); Set (abstract data type); Combinatorics; Computer science; Data mining; Biology","score_opus":0.22723648231961352,"score_gpt":0.4461820229284438,"score_spread":0.21894554060883026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973451885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012350904,0.000018073311,0.98172504,0.000978172,0.00007333675,0.00045917003,0.0023108434,0.00025012382,0.0018343615],"genre_scores_gemma":[0.9727171,0.0000033105391,0.026287336,0.00027202995,0.00004157906,0.00002958528,0.00061531423,0.000009996851,0.000023746608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988875,0.00005211349,0.00031494736,0.00023288123,0.00030659957,0.00020595797],"domain_scores_gemma":[0.99567723,0.0035021405,0.00012680366,0.00038769006,0.00012299101,0.00018314167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016538304,0.00016486838,0.0002502158,0.00039215744,0.00014442485,0.00007079954,0.00013912661,0.000078025296,0.0002538075],"category_scores_gemma":[0.0022627672,0.00013929162,0.00006705441,0.0015829538,0.000042453255,0.00004380178,0.0000029325472,0.000059663726,0.00013594274],"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":[0.00046951353,0.011647775,0.0050504277,0.000038273705,0.00008590793,0.000007392502,0.0001234151,0.00051977613,0.0016410595,0.85642785,0.027729802,0.096258804],"study_design_scores_gemma":[0.017173521,0.007084539,0.26141027,0.000036810536,0.00039035297,0.0000020314576,0.00017856878,0.6493358,0.0030664396,0.03130327,0.029073108,0.0009453162],"about_ca_topic_score_codex":0.000009499154,"about_ca_topic_score_gemma":0.0000030251738,"teacher_disagreement_score":0.9603662,"about_ca_system_score_codex":0.00019943889,"about_ca_system_score_gemma":0.000040990828,"threshold_uncertainty_score":0.56801474},"labels":[],"label_agreement":null},{"id":"W1973761476","doi":"10.1111/j.1541-0420.2008.01058.x","title":"Joint Regression Analysis of Correlated Data Using Gaussian Copulas","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":204,"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","keywords":"Univariate; Copula (linguistics); Mathematics; Joint probability distribution; Regression analysis; Statistics; Generalized linear model; Logistic regression; Inference; Marginal model; Gaussian; Estimating equations; Multivariate statistics; Econometrics; Computer science; Estimator; Artificial intelligence","score_opus":0.48763925549063125,"score_gpt":0.460364406222413,"score_spread":0.02727484926821827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973761476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10190096,0.00014975607,0.8968605,0.000014727463,0.00015234695,0.00008272076,0.00029194096,0.000031598345,0.0005154512],"genre_scores_gemma":[0.2951652,0.00006509631,0.70463014,0.000010050905,0.0000179822,5.3186665e-7,0.000052236574,0.000011360677,0.00004740802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998472,0.00012167307,0.00052095135,0.00028519757,0.00041173067,0.00018848306],"domain_scores_gemma":[0.9975913,0.0009672343,0.0003221898,0.0008750946,0.0001416871,0.000102505204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057622377,0.00012684679,0.00052413467,0.0021182564,0.00009061584,0.0000126764535,0.00032983354,0.00012028592,0.00022065891],"category_scores_gemma":[0.006313239,0.00009508031,0.00008610574,0.012062449,0.00012467144,0.00007185654,0.00021473414,0.00010868329,0.0000052197543],"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":[0.00023265924,0.0028171183,0.32112396,0.0011162258,0.0063504437,0.0007462059,0.0015867855,0.000036733105,0.04123376,0.33442226,0.028026784,0.26230708],"study_design_scores_gemma":[0.0013015202,0.00030555663,0.28149962,0.000388419,0.006327394,0.00008819049,0.00017263705,0.6189803,0.004816142,0.08411794,0.0009521362,0.0010501306],"about_ca_topic_score_codex":0.000107817694,"about_ca_topic_score_gemma":0.0000028477511,"teacher_disagreement_score":0.6189436,"about_ca_system_score_codex":0.00004266105,"about_ca_system_score_gemma":0.00005508879,"threshold_uncertainty_score":0.7557993},"labels":[],"label_agreement":null},{"id":"W1974271203","doi":"10.1111/j.1541-0420.2008.01159.x","title":"Differential Expression and Network Inferences through Functional Data Modeling","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"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; Vancouver General Hospital","funders":"National Cancer Institute","keywords":"Microarray analysis techniques; Microarray databases; Expression (computer science); Gene regulatory network; Gene expression; Computational biology; Computer science; Gene expression profiling; DNA microarray; Transformation (genetics); Data set; Data mining; Functional data analysis; Gene; Biology; Artificial intelligence; Genetics; Machine learning","score_opus":0.14704620176490132,"score_gpt":0.3033494666888948,"score_spread":0.15630326492399346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974271203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6000508,0.0053244885,0.3934091,0.00006311046,0.00050231017,0.00008002864,0.00002715007,0.000018299777,0.0005247055],"genre_scores_gemma":[0.9906728,0.0054623727,0.0023750353,0.00008956742,0.0005656634,0.000007258212,0.00053347804,0.000008773652,0.00028504766],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991797,0.000027812624,0.00013563725,0.00034924215,0.00017132962,0.00013623649],"domain_scores_gemma":[0.9994387,0.00000943666,0.000053439795,0.00039050268,0.000055299108,0.00005264825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060622868,0.00009208163,0.00007979768,0.00007183458,0.00015963461,0.000022165623,0.00018473354,0.00011105389,0.000025080482],"category_scores_gemma":[0.00006120504,0.00007750481,0.000020038518,0.00031007934,0.00004772478,0.000010715199,0.00030118905,0.00005004215,0.0000023987818],"study_design_candidate":"bench_or_experimental","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":[0.00014323923,0.00013462764,0.0064273975,0.000022075932,0.00004196442,0.0000016789447,0.00006800088,0.0013984827,0.8832123,0.0002560771,0.09675228,0.011541863],"study_design_scores_gemma":[0.0030067058,0.00047032046,0.02058407,0.0000715565,0.00006986167,0.00008446569,0.00017203216,0.104253516,0.14786182,0.0009453622,0.72122157,0.0012587272],"about_ca_topic_score_codex":0.000011455638,"about_ca_topic_score_gemma":0.0000010911675,"teacher_disagreement_score":0.7353505,"about_ca_system_score_codex":0.000004967556,"about_ca_system_score_gemma":0.00004283377,"threshold_uncertainty_score":0.31605545},"labels":[],"label_agreement":null},{"id":"W1974553458","doi":"10.1111/j.0006-341x.2004.00232.x","title":"Confidence Interval Estimation of the Intraclass Correlation Coefficient for Binary Outcome Data","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intraclass correlation; Confidence interval; Estimator; Biometrics; Mathematics; Statistics; Interval estimation; Point estimation; Binary number; Binary data; Variance (accounting); Correlation; Correlation coefficient; Range (aeronautics); Interval (graph theory); Combinatorics; Computer science; Artificial intelligence","score_opus":0.2188643237565531,"score_gpt":0.4415134958533183,"score_spread":0.22264917209676519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974553458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052826866,0.000029613388,0.9930596,0.00032212157,0.00053753343,0.00038240015,0.00028449256,0.000021816082,0.00007976964],"genre_scores_gemma":[0.45247954,0.0000020364027,0.54743344,0.00002451504,0.000015421125,0.0000056226127,0.000017560496,0.0000062949493,0.000015578904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989285,0.000048449438,0.00043555422,0.00018564696,0.00027457517,0.00012730989],"domain_scores_gemma":[0.9968246,0.0021804005,0.00026510487,0.0005470463,0.00014702756,0.00003579877],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00085773505,0.00008690558,0.00018165045,0.00023448539,0.0000651317,0.000026075948,0.00047194806,0.00007051435,0.000012182102],"category_scores_gemma":[0.01592563,0.000059351507,0.000044076696,0.0014424729,0.00011308913,0.00008407153,0.00017948014,0.000081227605,0.0000046574364],"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":[0.00001659113,0.0001665419,0.0005451338,0.00019002067,0.000011281843,5.4410935e-7,0.00007740254,0.00034007727,0.0002338036,0.95750725,0.00036528098,0.040546086],"study_design_scores_gemma":[0.0005844645,0.00018999209,0.0075887637,0.00012712438,0.0000854054,0.0000052064415,0.000053675376,0.23594546,0.0011120484,0.75399375,0.00017468967,0.0001394408],"about_ca_topic_score_codex":0.000016196343,"about_ca_topic_score_gemma":0.0000017697379,"teacher_disagreement_score":0.44719684,"about_ca_system_score_codex":0.00006675604,"about_ca_system_score_gemma":0.000061245715,"threshold_uncertainty_score":0.99236363},"labels":[],"label_agreement":null},{"id":"W1983970019","doi":"10.1111/j.1541-0420.2009.01279.x","title":"Bayesian Estimation of the Probability of Asbestos Exposure from Lung Fiber Counts","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":6,"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":"Asbestos; Statistics; Bayesian probability; Sample (material); Asbestos fibers; Population; Econometrics; Computer science; Environmental health; Medicine; Mathematics","score_opus":0.050664596011150115,"score_gpt":0.3395205294343512,"score_spread":0.28885593342320104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983970019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056965385,0.000056031047,0.9354581,0.00028456413,0.000061435705,0.0003792923,0.0007616136,0.000036659716,0.0059968927],"genre_scores_gemma":[0.9600634,0.0000012586338,0.039748855,0.000019005724,0.000007764642,0.0000046413156,0.000038271835,0.0000026615548,0.00011415961],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994077,0.000020620257,0.00023972325,0.00007497314,0.00020508698,0.00005190822],"domain_scores_gemma":[0.9992128,0.0002745989,0.00014481372,0.00022726096,0.00011680329,0.00002374781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010858002,0.000046461184,0.0000935952,0.00007139555,0.000027378286,0.000005504484,0.00009887314,0.000046752062,0.00041812594],"category_scores_gemma":[0.0013794885,0.000034445293,0.00003741436,0.0011029885,0.000046780686,0.00002260755,0.000010455466,0.000035872403,0.000017119819],"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":[0.0000080224945,0.00069909147,0.0013241146,0.00013614057,0.000024582407,1.5411234e-7,0.00012421099,0.000083651226,0.00058483053,0.88288987,0.020694977,0.09343034],"study_design_scores_gemma":[0.00040594247,0.00005883095,0.2621363,0.00006355912,0.00010816024,8.983495e-7,0.000017164743,0.049513556,0.005021376,0.68076473,0.0017447372,0.00016474393],"about_ca_topic_score_codex":0.0000035563553,"about_ca_topic_score_gemma":2.0810106e-7,"teacher_disagreement_score":0.903098,"about_ca_system_score_codex":0.000029013381,"about_ca_system_score_gemma":0.00002723503,"threshold_uncertainty_score":0.45781857},"labels":[],"label_agreement":null},{"id":"W1984130041","doi":"10.1111/j.0006-341x.2000.00237.x","title":"A Nonparametric Mixture Model for Cure Rate Estimation","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":445,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland; Newcastle University","keywords":"Covariate; Nonparametric statistics; Proportional hazards model; Parametric statistics; Econometrics; Statistics; Parametric model; Nonparametric regression; Semiparametric model; Estimation; Semiparametric regression; Accelerated failure time model; Regression analysis; Mixture model; Computer science; Mathematics; Engineering","score_opus":0.03025689618870801,"score_gpt":0.29809567736429593,"score_spread":0.2678387811755879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984130041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065900985,0.00048845436,0.99640447,0.0008178469,0.00021255223,0.00034471578,0.00002101129,0.00016041686,0.0008915191],"genre_scores_gemma":[0.035738677,0.00012398201,0.95986927,0.00059431506,0.00006210097,0.000039912873,0.000010478049,0.000015432233,0.0035458242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868417,0.000058216854,0.00024250429,0.00043961068,0.00023756847,0.0003379571],"domain_scores_gemma":[0.99883527,0.00029128487,0.0000806304,0.0005287394,0.00012977465,0.00013432126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007785044,0.00017030448,0.00020418873,0.0013121495,0.00012755055,0.00020327944,0.0006824827,0.00017056402,0.000016039116],"category_scores_gemma":[0.00026266373,0.000130792,0.00011963564,0.008166847,0.000021887996,0.00040320947,0.000050325925,0.00010986745,0.000043277276],"study_design_candidate":"design_other","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":[0.000007689439,0.00006216502,0.000004247808,0.000022947928,0.000008784887,0.0000015956036,0.000113586655,0.0025946558,0.00016461834,0.022643149,0.005612776,0.96876377],"study_design_scores_gemma":[0.0003197915,0.00006195479,0.000059575406,0.0000060790003,0.00001119647,0.0000052357086,3.5623611e-7,0.92922807,0.0004135823,0.059880886,0.009824136,0.00018913392],"about_ca_topic_score_codex":0.0000033897115,"about_ca_topic_score_gemma":3.2351969e-7,"teacher_disagreement_score":0.96857464,"about_ca_system_score_codex":0.000048465085,"about_ca_system_score_gemma":0.00007588274,"threshold_uncertainty_score":0.53335434},"labels":[],"label_agreement":null},{"id":"W1984736952","doi":"10.1111/j.1541-0420.2006.00687.x","title":"Simultaneous Inference for Semiparametric Nonlinear Mixed‐Effects Models with Covariate Measurement Errors and Missing Responses","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":85,"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":"","keywords":"Covariate; Missing data; Inference; Econometrics; Semiparametric model; Mixed model; Computer science; Statistics; Semiparametric regression; Observational error; Mathematics; Artificial intelligence; Nonparametric statistics","score_opus":0.13161995996739115,"score_gpt":0.3702810088332143,"score_spread":0.23866104886582315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984736952","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020634688,0.00048193426,0.9777474,0.000060066235,0.00011201527,0.0006088064,0.000087421984,0.0001343205,0.00013333958],"genre_scores_gemma":[0.37111387,0.000015583339,0.62871605,0.000025183293,0.00003960407,0.000025321922,0.000003194164,0.000033223234,0.000027979951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797684,0.00016667736,0.0004031751,0.00041108357,0.0006283753,0.00041387483],"domain_scores_gemma":[0.98177516,0.01705214,0.00021061544,0.000278365,0.00053771364,0.00014600366],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012342342,0.00026769994,0.00043278458,0.0010925692,0.0001400323,0.00014442798,0.00015461755,0.00014159306,0.0000024454148],"category_scores_gemma":[0.024198081,0.00020523845,0.00004899708,0.0030667265,0.00012038058,0.00009039066,0.00005002491,0.00012517553,0.0000013364111],"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":[0.0009170375,0.0010484316,0.0014473161,0.0022856651,0.00017326012,0.00014507123,0.00016734301,0.00032146572,0.0050557363,0.5981638,0.0005458343,0.38972905],"study_design_scores_gemma":[0.0013939808,0.00085503986,0.0010692377,0.00024240732,0.0002207374,0.00002346651,0.000023426475,0.19474918,0.0046559274,0.79568774,0.0005343952,0.0005444822],"about_ca_topic_score_codex":0.00007100976,"about_ca_topic_score_gemma":0.000010083943,"teacher_disagreement_score":0.38918456,"about_ca_system_score_codex":0.00011316477,"about_ca_system_score_gemma":0.00011329727,"threshold_uncertainty_score":0.9840215},"labels":[],"label_agreement":null},{"id":"W1988615079","doi":"10.1111/j.0006-341x.2004.00188.x","title":"A Conditional Markov Model for Clustered Progressive Multistate Processes under Incomplete Observation","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Western Hospital; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Health Canada; Arthritis Society","keywords":"Markov chain; Generalization; Markov model; Multiplicative function; Random effects model; Computer science; Markov process; Mathematics; Econometrics; Statistics; Medicine; Internal medicine","score_opus":0.08145212404502242,"score_gpt":0.32566501476150006,"score_spread":0.24421289071647764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988615079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018522395,0.0003152803,0.99556524,0.0012221397,0.00019490825,0.0005699884,0.0001117265,0.00011877926,0.000049727805],"genre_scores_gemma":[0.14322305,0.000014971064,0.8555746,0.0006998588,0.000060668728,0.00012651716,0.00007777649,0.00001317287,0.00020934745],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870074,0.00002800237,0.00026067,0.0004075633,0.00031473328,0.00028829876],"domain_scores_gemma":[0.99872476,0.0002087527,0.00017998839,0.00026745,0.0005167778,0.00010224325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031489044,0.00015623729,0.00016961714,0.00058586785,0.00016215774,0.00019567188,0.0004783905,0.00009365942,0.0000014643613],"category_scores_gemma":[0.00030594337,0.00014061002,0.00006158319,0.0028439043,0.00005096174,0.0005673867,0.00011704824,0.0000687808,0.0000040576274],"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":[0.00011369843,0.0008380063,0.00017834196,0.0009069729,0.0001379175,0.000025894253,0.0015672931,0.015121037,0.0026345653,0.6918406,0.0029134979,0.2837222],"study_design_scores_gemma":[0.0012917252,0.000098087985,0.0008685162,0.000029463261,0.000010569682,0.000011597315,0.0000038805542,0.6415291,0.00083812437,0.3545856,0.0004970086,0.00023635536],"about_ca_topic_score_codex":0.000006327073,"about_ca_topic_score_gemma":0.000004347062,"teacher_disagreement_score":0.62640804,"about_ca_system_score_codex":0.00011656888,"about_ca_system_score_gemma":0.00033474126,"threshold_uncertainty_score":0.573391},"labels":[],"label_agreement":null},{"id":"W1989911828","doi":"10.1111/j.1541-0420.2010.01390.x","title":"Continuous Covariates in Mark‐Recapture‐Recovery Analysis: A Comparison of Methods","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":42,"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":"Simon Fraser University","keywords":"Covariate; Statistics; Bayesian probability; Imputation (statistics); Estimator; Econometrics; Mathematics; Computer science; Missing data","score_opus":0.08579637226899556,"score_gpt":0.4320264109402992,"score_spread":0.34623003867130364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989911828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66217023,0.00037372575,0.3351074,0.0000780449,0.0006091406,0.00024816138,0.00007372866,0.000052159226,0.0012873947],"genre_scores_gemma":[0.6145932,0.000006359134,0.38525245,0.000007939242,0.000024870971,0.000002294803,0.000057476485,0.0000074760574,0.000047926213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99871534,0.000118484946,0.00062437885,0.00019872627,0.00019946956,0.00014357096],"domain_scores_gemma":[0.997405,0.0014816034,0.00041039498,0.0005093079,0.0001494356,0.000044289525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018339895,0.00010646965,0.0004912106,0.0024843158,0.0000243609,0.000024788937,0.0002387506,0.00016641601,0.0001357874],"category_scores_gemma":[0.0038828773,0.00009729879,0.00009734908,0.0076829335,0.000029372855,0.000092493865,0.00007080798,0.00016922173,0.0000045687175],"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":[0.000052864754,0.0008732468,0.81817526,0.00015860588,0.00044146372,0.0000014004361,0.0006016527,0.00023142702,0.006774292,0.02777872,0.0022093598,0.1427017],"study_design_scores_gemma":[0.0009864732,0.00010770408,0.69379795,0.00002533852,0.0010561913,0.0000030256258,0.00018175259,0.26075932,0.0027548664,0.032127738,0.007761739,0.00043788875],"about_ca_topic_score_codex":0.00019312857,"about_ca_topic_score_gemma":0.00027949884,"teacher_disagreement_score":0.2605279,"about_ca_system_score_codex":0.000021019043,"about_ca_system_score_gemma":0.000025813892,"threshold_uncertainty_score":0.46484473},"labels":[],"label_agreement":null},{"id":"W1993487976","doi":"10.1111/j.0006-341x.2001.01080.x","title":"Most Powerful Permutation Invariant Tests for Relatedness Hypotheses Using Genotypic Data","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; St. Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Test statistic; Mathematics; Statistical hypothesis testing; Permutation (music); Statistic; Invariant (physics); Conditional independence; Inference; Alternative hypothesis; Resampling; Statistics; Independent and identically distributed random variables; Null hypothesis; Computer science; Random variable; Artificial intelligence","score_opus":0.1173845531130943,"score_gpt":0.3249684055352313,"score_spread":0.207583852422137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993487976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95613456,0.00092652615,0.04164076,0.00007412657,0.00037677036,0.00022576776,0.0003348579,0.000012902227,0.00027374807],"genre_scores_gemma":[0.9857932,0.00015692491,0.0111678215,0.00020601493,0.00018391467,0.0000011721621,0.0018549953,0.00001632954,0.0006196553],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992228,0.000024222487,0.0001450305,0.00031241463,0.00012846138,0.00016707732],"domain_scores_gemma":[0.99921453,0.000029869867,0.0000881854,0.00043396835,0.00017257142,0.000060882037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016314004,0.000103868966,0.00008926379,0.0002551326,0.00011713085,0.000045099914,0.0003008612,0.00018086302,0.000027913487],"category_scores_gemma":[0.000618695,0.000104551254,0.000027058632,0.0011017548,0.000033685425,0.000009057181,0.00016602577,0.000037349164,0.000009051282],"study_design_candidate":"bench_or_experimental","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":[0.00040498015,0.00023063707,0.1119334,0.00010435246,0.00027686733,0.000021582486,0.00024081625,0.0026319798,0.824485,0.000421241,0.017808227,0.041440915],"study_design_scores_gemma":[0.0026269082,0.00051294366,0.15898317,0.000027129103,0.00027963522,0.0001849114,0.0003825248,0.010999975,0.023246288,0.000744738,0.8010167,0.0009951086],"about_ca_topic_score_codex":0.000034482156,"about_ca_topic_score_gemma":0.000010991757,"teacher_disagreement_score":0.8012387,"about_ca_system_score_codex":0.000016840308,"about_ca_system_score_gemma":0.00007663221,"threshold_uncertainty_score":0.42634764},"labels":[],"label_agreement":null},{"id":"W1995494769","doi":"10.1111/j.1541-0420.2011.01568.x","title":"Buckley-James-Type Estimator with Right-Censored and Length-Biased Data","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":39,"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":"National Cancer Institute; Medical Research Council; National Institutes of Health; Health Canada; University of Ottawa","keywords":"Estimator; Censoring (clinical trials); Covariate; Statistics; Econometrics; Population; Computer science; Mathematics; Medicine","score_opus":0.3358128304814369,"score_gpt":0.3888563730054646,"score_spread":0.05304354252402771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995494769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17848386,0.0007809155,0.80202454,0.00012488465,0.0005608489,0.00063687214,0.00061635434,0.00029946753,0.016472276],"genre_scores_gemma":[0.13090724,0.00006772678,0.8687486,0.000052368996,0.000036075122,0.0000033742317,0.000018459556,0.000026698073,0.0001394513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99873906,0.0000707777,0.0002557703,0.00038463998,0.00028316295,0.00026660008],"domain_scores_gemma":[0.9972795,0.0014599037,0.000120416335,0.0008308313,0.00015828451,0.00015106739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005303376,0.00017580134,0.0002881475,0.00042178668,0.00008415707,0.000048111,0.00039100816,0.000098264725,0.00029953307],"category_scores_gemma":[0.005987049,0.00012203445,0.000014372661,0.0019444559,0.00016033583,0.00010779789,0.00018627655,0.00012519347,0.000037245052],"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":[0.00036140677,0.0010583787,0.011867899,0.0004896766,0.00022434664,0.00028694305,0.00067768403,9.580461e-8,0.00076428306,0.77186626,0.02959771,0.18280531],"study_design_scores_gemma":[0.00794785,0.0057154577,0.084030814,0.00056316576,0.0014731161,0.00044378208,0.000870559,0.037987698,0.015553461,0.7439364,0.09749331,0.003984387],"about_ca_topic_score_codex":0.000054404827,"about_ca_topic_score_gemma":0.0000023542232,"teacher_disagreement_score":0.17882094,"about_ca_system_score_codex":0.000016833657,"about_ca_system_score_gemma":0.000053169068,"threshold_uncertainty_score":0.71674895},"labels":[],"label_agreement":null},{"id":"W1996120033","doi":"10.1111/j.1541-0420.2010.01496.x","title":"A Likelihood Approach to Estimating Animal Density from Binary Acoustic Transects","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Statistics; Transect; Range (aeronautics); Abundance estimation; Cluster analysis; Poisson distribution; Mathematics; Binary number; Computer science; Abundance (ecology); Ecology; Biology","score_opus":0.021627843317631804,"score_gpt":0.24001802627063615,"score_spread":0.21839018295300433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996120033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98092043,0.000059114558,0.009830866,0.000080427824,0.00028187124,0.00027075337,0.000020939664,0.000087242006,0.008448374],"genre_scores_gemma":[0.86310184,0.000006970985,0.1363845,0.0003066054,0.0001241619,0.000015229342,0.0000076021734,0.00001826214,0.000034833687],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99841076,0.000024399342,0.00020882842,0.0004916778,0.00045865966,0.0004056869],"domain_scores_gemma":[0.9992359,0.00013639817,0.00006116642,0.00030476184,0.000014994779,0.00024675022],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030234398,0.00018914905,0.00021941333,0.00019463125,0.00018601942,0.000049432332,0.00038142665,0.00009902308,0.00064763025],"category_scores_gemma":[0.00050643727,0.00017572618,0.00006749916,0.0034441021,0.000078082994,0.00011274269,0.0005872172,0.00023158967,0.0008590995],"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":[0.00009145277,0.0011837841,0.3062903,0.000099175704,0.0000658426,0.00005410611,0.0010487465,0.00032670033,0.50564486,0.000050647428,0.013298138,0.17184623],"study_design_scores_gemma":[0.00025242317,0.00023524454,0.97793,0.0000062297827,0.00004493432,0.000008142332,0.000049461974,0.015372674,0.0008377755,0.00008791719,0.0048276307,0.00034756598],"about_ca_topic_score_codex":0.0035345173,"about_ca_topic_score_gemma":0.00068110804,"teacher_disagreement_score":0.6716397,"about_ca_system_score_codex":0.00009621932,"about_ca_system_score_gemma":0.000010012645,"threshold_uncertainty_score":0.9999188},"labels":[],"label_agreement":null},{"id":"W1997924917","doi":"10.1111/j.0006-341x.2001.00671.x","title":"Synthesis of Evidence from Epidemiological Studies with Interval-Censored Exposure Due to Grouping","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Medical Research Council Canada","keywords":"Covariate; Statistics; Censoring (clinical trials); Logistic regression; Multinomial logistic regression; Econometrics; Multinomial distribution; Confidence interval; Mathematics; Medicine","score_opus":0.42546113411426983,"score_gpt":0.45792702769210764,"score_spread":0.0324658935778378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997924917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22192827,0.000758835,0.77646047,0.00042252525,0.00009293544,0.00016830215,0.000040827512,0.000044737033,0.00008308435],"genre_scores_gemma":[0.39151046,0.00024824412,0.6080827,0.00006680516,0.000044372053,0.000022837992,4.2399128e-7,0.000009958816,0.000014184053],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981314,0.00035731573,0.00055580423,0.00035279413,0.00032881496,0.0002738976],"domain_scores_gemma":[0.9628702,0.036132608,0.00022059368,0.0003639243,0.00028239787,0.00013027558],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013391866,0.00019197527,0.0008090948,0.00047169405,0.000053885942,0.000019019404,0.0002871352,0.0001015792,0.000085660344],"category_scores_gemma":[0.114079095,0.00010766645,0.00007680925,0.0026517953,0.00015072113,0.0000693135,0.00014374519,0.00010921508,0.000015732356],"study_design_candidate":"design_other","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":[0.00055724155,0.0005114774,0.12037876,0.0005784341,0.0005645257,0.0003387826,0.0014221013,0.0000024151557,0.0040035206,0.06269499,0.0023472914,0.80660045],"study_design_scores_gemma":[0.00069264433,0.0029350573,0.3293464,0.005635124,0.00059752754,0.000079187324,0.0025815095,0.00063495873,0.018017394,0.63764775,0.0006332465,0.001199209],"about_ca_topic_score_codex":0.00006580447,"about_ca_topic_score_gemma":0.00000842102,"teacher_disagreement_score":0.80540127,"about_ca_system_score_codex":0.000060789913,"about_ca_system_score_gemma":0.000017863282,"threshold_uncertainty_score":0.8933834},"labels":[],"label_agreement":null},{"id":"W1997934667","doi":"10.1111/j.0006-341x.2005.030833.x","title":"Bias‐Corrected Maximum Likelihood Estimator of the Negative Binomial Dispersion Parameter","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":122,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Estimator; Negative binomial distribution; Restricted maximum likelihood; Quasi-likelihood; Bias of an estimator; Maximum likelihood; Minimum-variance unbiased estimator; Poisson distribution","score_opus":0.11036399242108111,"score_gpt":0.3339578366602562,"score_spread":0.22359384423917508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997934667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92467093,0.000091007874,0.07303534,0.0003806346,0.0004957547,0.00039762547,0.00006892129,0.0003014606,0.0005583411],"genre_scores_gemma":[0.78262544,0.000015377695,0.21706896,0.000056637542,0.000076183205,0.000013369067,0.000008232404,0.000024715291,0.00011112283],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986742,0.000121370824,0.00041069795,0.00019543705,0.00037661553,0.00022165499],"domain_scores_gemma":[0.99670094,0.0022780837,0.00030795403,0.00043478797,0.00021100366,0.00006720794],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006798297,0.00015876838,0.00024538054,0.0006928359,0.0001092206,0.000034945748,0.0003123102,0.0001417139,0.000059301896],"category_scores_gemma":[0.009915211,0.00010706461,0.00014725346,0.0032498112,0.00009935313,0.000099063334,0.00011345044,0.00014119822,0.000026506737],"study_design_candidate":"design_other","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":[0.00021652879,0.0019762125,0.052873794,0.00038715242,0.00023646397,0.0000028646134,0.0025128622,0.000049275422,0.0095803635,0.0037536698,0.08264417,0.84576666],"study_design_scores_gemma":[0.0032143898,0.000717809,0.07941839,0.0006005928,0.0003865924,0.000042471165,0.0005374598,0.045791257,0.6816632,0.17122734,0.01476064,0.0016398552],"about_ca_topic_score_codex":0.00006420394,"about_ca_topic_score_gemma":0.000008140232,"teacher_disagreement_score":0.84412676,"about_ca_system_score_codex":0.00009735299,"about_ca_system_score_gemma":0.00005420262,"threshold_uncertainty_score":0.9984247},"labels":[],"label_agreement":null},{"id":"W1999357871","doi":"10.1111/j.1541-0420.2006.00610.x","title":"Quantifying Genomic Imprinting in the Presence of Linkage","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Syndromes and Imprinting","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"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":"Imprinting (psychology); Genomic imprinting; Genetic linkage; Genetics; Biology; Computational biology; Gene","score_opus":0.029310173032650415,"score_gpt":0.2739906408264836,"score_spread":0.2446804677938332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999357871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956955,0.0014888372,0.0016955263,0.000032847536,0.000094991505,0.000107534666,0.0000029107712,0.0000032459545,0.0008786265],"genre_scores_gemma":[0.99724084,0.00009429187,0.0024572096,0.000026625523,0.00009618043,0.0000042264624,0.000010598224,0.00000913638,0.00006091004],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992,0.00004164425,0.00024903627,0.00018517791,0.00012873215,0.0001953978],"domain_scores_gemma":[0.99951977,0.000046006924,0.0001048454,0.00027656346,0.000038822793,0.000014003414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000520164,0.000079078316,0.000092259776,0.00024786824,0.00003451788,0.000022288617,0.0002946693,0.00007756976,0.000003587842],"category_scores_gemma":[0.00022173382,0.00006256593,0.00005782581,0.0009802782,0.00003945955,0.0000015928608,0.00013014922,0.000057664034,0.0000033843048],"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":[0.0000010330622,0.000030875854,0.19607462,0.000023575512,0.000004051862,8.8671834e-7,0.000026284195,0.00006748496,0.7976496,0.0000939203,0.000068336165,0.005959273],"study_design_scores_gemma":[0.00025146132,0.00007977389,0.88772196,0.00001212588,0.000005891626,0.000017129478,0.00016883001,0.00028333266,0.105855376,0.000038852017,0.005447117,0.00011817764],"about_ca_topic_score_codex":0.00019336988,"about_ca_topic_score_gemma":0.000019245723,"teacher_disagreement_score":0.6917943,"about_ca_system_score_codex":0.0000058919977,"about_ca_system_score_gemma":0.000022204325,"threshold_uncertainty_score":0.25513646},"labels":[],"label_agreement":null},{"id":"W2002252180","doi":"10.1111/j.1541-0420.2010.01525.x","title":"A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; National Institute of Mental Health","keywords":"Missing data; Estimator; Bivariate analysis; Independence (probability theory); Computer science; Parametric statistics; Binary number; Mathematics; Statistics","score_opus":0.1768870803951119,"score_gpt":0.4113721872145175,"score_spread":0.23448510681940563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002252180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018943341,0.00003759516,0.97835684,0.00022682628,0.00043869708,0.0005087116,0.0007905341,0.00009923848,0.0005981906],"genre_scores_gemma":[0.10704571,0.000006451316,0.8924673,0.00004197159,0.00020357291,0.000040852854,0.00007259412,0.00004821634,0.0000732978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980765,0.000055973393,0.0003982269,0.0006078056,0.00037515978,0.000486314],"domain_scores_gemma":[0.995077,0.00295134,0.0002234036,0.0012372992,0.00029023236,0.00022072691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012208879,0.0002585134,0.00044357684,0.0007137361,0.00024381984,0.00018943817,0.00084808544,0.00015716099,0.00011663367],"category_scores_gemma":[0.0045949672,0.00019676954,0.000046561006,0.0028278828,0.00016220286,0.0002086231,0.00029900437,0.0002705792,0.000019849758],"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":[0.00066868804,0.0021407164,0.011769915,0.0015507462,0.00033278117,0.00016036033,0.0001875832,6.805543e-7,0.03018628,0.68759465,0.012679006,0.25272858],"study_design_scores_gemma":[0.004688943,0.0018661824,0.024517536,0.00024493664,0.00065417535,0.00022222535,0.000091646434,0.043599155,0.003187077,0.8938187,0.025291681,0.0018177442],"about_ca_topic_score_codex":0.00007271767,"about_ca_topic_score_gemma":0.000022505852,"teacher_disagreement_score":0.25091082,"about_ca_system_score_codex":0.000024421268,"about_ca_system_score_gemma":0.00016562163,"threshold_uncertainty_score":0.8024029},"labels":[],"label_agreement":null},{"id":"W2003054692","doi":"10.1111/1541-0420.00037","title":"Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"British Columbia Centre of Excellence for Women's Health; University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Covariate; Bayesian inference; Bayesian probability; Gibbs sampling; Random effects model; Bayesian hierarchical modeling; Computer science; Statistics; Inference; Hierarchical database model; Monte Carlo method; Bayesian statistics; Econometrics; Machine learning; Artificial intelligence; Data mining; Mathematics; Medicine","score_opus":0.31827723231933863,"score_gpt":0.4326871592522046,"score_spread":0.11440992693286595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003054692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37342665,0.0006824575,0.6246227,0.0004709078,0.00022025083,0.000118999444,0.000012837869,0.000028885323,0.0004163296],"genre_scores_gemma":[0.9896201,0.00024237322,0.009734785,0.00031889553,0.000008097754,0.000001773578,0.00001709701,0.000010321368,0.00004658837],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973083,0.00025795586,0.0009629892,0.000344474,0.0009187101,0.00020757427],"domain_scores_gemma":[0.99805844,0.0006036644,0.00032849392,0.00027954008,0.0005785914,0.00015126399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035179823,0.00011471102,0.00029764266,0.0019681198,0.0001367689,0.000098606746,0.00019518175,0.00010617078,0.00003757674],"category_scores_gemma":[0.0034715715,0.000092097696,0.00005115505,0.009290309,0.000039686845,0.00015220992,0.00003465434,0.00010457871,0.000010420712],"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":[0.00013193108,0.0005642316,0.5072472,0.00021865734,0.00007236493,0.0000048990696,0.0035434826,0.12913072,0.00071056304,0.0383373,0.00031885607,0.3197198],"study_design_scores_gemma":[0.00033322367,0.00008170976,0.0046681743,0.00001371344,0.000008135876,0.0000036792367,0.00027662,0.9839176,0.000024490795,0.010261262,0.00031195488,0.00009943872],"about_ca_topic_score_codex":0.00014732115,"about_ca_topic_score_gemma":0.00005164491,"teacher_disagreement_score":0.8547869,"about_ca_system_score_codex":0.000019655243,"about_ca_system_score_gemma":0.000103401035,"threshold_uncertainty_score":0.44636855},"labels":[],"label_agreement":null},{"id":"W2004293089","doi":"10.1111/j.0006-341x.2000.00059.x","title":"Estimation of Age‐Specific Breeding Probabilities from Capture–Recapture Data","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":32,"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 Manitoba; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Hydro","keywords":"Mark and recapture; Covariate; Statistics; Estimation; Econometrics; Computer science; Mathematics; Demography; Population","score_opus":0.15375844128083296,"score_gpt":0.3361193756879961,"score_spread":0.18236093440716314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004293089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9580474,0.0011034276,0.033692062,0.00024145267,0.0005769559,0.0005025768,0.00076416135,0.00018379431,0.004888162],"genre_scores_gemma":[0.88213104,0.000059601185,0.11591101,0.000017073735,0.00014615843,0.000003856075,0.0011239164,0.000020261443,0.000587079],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871373,0.000036637768,0.0004486063,0.00026088807,0.00039568453,0.0001444521],"domain_scores_gemma":[0.9985079,0.00049207575,0.00018221185,0.00068737846,0.00008113959,0.000049286493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003618747,0.00013091543,0.00022572106,0.0004346828,0.00006125734,0.000055657576,0.00031135764,0.00013142092,0.00074822083],"category_scores_gemma":[0.0006708044,0.00011979496,0.000042440268,0.0016482703,0.000053040152,0.00027705636,0.00005418104,0.00008837724,0.00003272458],"study_design_candidate":"design_other","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":[0.00007448695,0.00051308825,0.0051282593,0.00045535242,0.00009392584,0.00000837636,0.0037492297,0.0025193933,0.0009386836,0.041284613,0.06264724,0.8825874],"study_design_scores_gemma":[0.0022964762,0.00021654833,0.048141807,0.00043783785,0.00031336484,0.000021214006,0.00057244673,0.3671754,0.0015792976,0.40058517,0.17728806,0.0013723652],"about_ca_topic_score_codex":0.000196701,"about_ca_topic_score_gemma":0.000011790618,"teacher_disagreement_score":0.881215,"about_ca_system_score_codex":0.0000533467,"about_ca_system_score_gemma":0.000019908797,"threshold_uncertainty_score":0.8192494},"labels":[],"label_agreement":null},{"id":"W2005512764","doi":"10.1111/j.0006-341x.2002.00878.x","title":"Comparing the Effects of Continuous and Discrete Covariate Mismeasurement, with Emphasis on the Dichotomization of Mismeasured Predictors","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology","keywords":"Covariate; Contrast (vision); Observational error; Statistics; Logistic regression; Econometrics; Mathematics; Binary number; Information bias; Computer science; Selection bias; Artificial intelligence","score_opus":0.16079287318756325,"score_gpt":0.34078795487524355,"score_spread":0.1799950816876803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005512764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2009755,0.00054724544,0.7970508,0.000101216414,0.00007911791,0.00065659283,0.00002510288,0.000020346457,0.0005441067],"genre_scores_gemma":[0.9626555,0.00011835206,0.03710437,0.000025029487,0.000013571726,0.00001867024,0.00000102143,0.000016706332,0.00004682638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987657,0.0002092949,0.00028502606,0.00015183527,0.00044451657,0.0001436464],"domain_scores_gemma":[0.9951289,0.0041120513,0.00028801337,0.0002733399,0.00015068124,0.000046997473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006784713,0.00012754243,0.00030552968,0.00018109924,0.000084940446,0.000016946975,0.00013685905,0.000044235057,0.0000065478866],"category_scores_gemma":[0.0050096633,0.000062168176,0.000036790138,0.0010394966,0.00017405456,0.000032893633,0.000028669245,0.00008620436,2.9615916e-7],"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":[0.0014875937,0.0030775834,0.06590287,0.005905385,0.002793108,0.000028952247,0.012012258,0.0004872308,0.060308408,0.62596214,0.004018188,0.21801627],"study_design_scores_gemma":[0.031208374,0.013098813,0.16098234,0.0052622724,0.005481552,0.000050882867,0.0017551708,0.220086,0.23282233,0.3201215,0.0060248533,0.0031059312],"about_ca_topic_score_codex":0.000009831617,"about_ca_topic_score_gemma":0.0000018892945,"teacher_disagreement_score":0.76167995,"about_ca_system_score_codex":0.000018561375,"about_ca_system_score_gemma":0.0000063497637,"threshold_uncertainty_score":0.5997397},"labels":[],"label_agreement":null},{"id":"W2005957898","doi":"10.1111/j.1541-0420.2005.00357.x","title":"Robust Tests for Treatment Effects Based on Censored Recurrent Event Data Observed over Multiple Periods","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":24,"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 Waterloo","funders":"","keywords":"Statistics; Marginal model; Econometrics; Random effects model; Robustness (evolution); Poisson regression; Poisson distribution; Crossover; Mathematics; Computer science; Regression analysis; Medicine; Artificial intelligence; Meta-analysis","score_opus":0.4459618789367756,"score_gpt":0.4425715927507965,"score_spread":0.0033902861859790856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005957898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06709741,0.00041118922,0.92649025,0.00034413795,0.00081776315,0.0020373992,0.0024165786,0.00015685783,0.00022841459],"genre_scores_gemma":[0.21127626,0.000032433625,0.7879015,0.000116502444,0.00019334603,0.00011857205,0.0001993356,0.000036741407,0.00012526226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982427,0.00013266463,0.0003771273,0.0005107819,0.000359554,0.00037717447],"domain_scores_gemma":[0.9880245,0.010540367,0.0001516845,0.0009945933,0.000108645196,0.0001802642],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00056564587,0.000264522,0.00039565456,0.00043569054,0.00010433099,0.00006344792,0.00035889714,0.000115311734,0.000078408084],"category_scores_gemma":[0.020262612,0.00019960372,0.000106182786,0.0011449325,0.000038452883,0.0000579579,0.00008487787,0.00006219251,0.000021126114],"study_design_candidate":"design_other","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":[0.00031309843,0.0041681956,0.0041290317,0.000469522,0.00009491714,0.000009338582,0.00008259008,0.00015445272,0.0009822317,0.008650445,0.013419691,0.9675265],"study_design_scores_gemma":[0.0056675263,0.003193558,0.04280324,0.00019583103,0.00022103298,0.0000012295143,0.000012607399,0.87963116,0.0038699682,0.0026124371,0.061180826,0.00061056385],"about_ca_topic_score_codex":0.000017824825,"about_ca_topic_score_gemma":0.000019694926,"teacher_disagreement_score":0.9669159,"about_ca_system_score_codex":0.00030628167,"about_ca_system_score_gemma":0.00006859079,"threshold_uncertainty_score":0.98799014},"labels":[],"label_agreement":null},{"id":"W2006600757","doi":"10.1111/j.1541-0420.2009.01247_7.x","title":"Model Selection and Model Averaging by CLAESKENS, G. and HJORT, N. L.","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Morphological variations and asymmetry","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":"Simon Fraser University","funders":"","keywords":"Citation; Selection (genetic algorithm); Statistics; Computer science; Mathematics; Mathematical economics; Library science; Artificial intelligence","score_opus":0.05811155343046437,"score_gpt":0.3007435636379627,"score_spread":0.2426320102074983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006600757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3559922,0.00044961655,0.6418116,0.00030787557,0.00001902806,0.00010537281,0.000021595837,0.00007668891,0.0012160074],"genre_scores_gemma":[0.8609687,0.00023438704,0.13796756,0.00024283424,0.000018910729,0.0000021736373,0.000006051037,0.0000075494218,0.00055186055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929893,0.000013757017,0.00016754179,0.0002081492,0.00014846651,0.00016314378],"domain_scores_gemma":[0.9996153,0.00009960588,0.00006384478,0.00009492075,0.000043630134,0.000082681065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022722264,0.00010454625,0.00014131452,0.00043835514,0.00011309699,0.00006112367,0.00004882214,0.00010853007,0.0000062465356],"category_scores_gemma":[0.00022772967,0.000089739464,0.000021657805,0.0011758849,0.000015199057,0.00009155473,0.00002867847,0.00010017235,0.0000015082446],"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":[0.000050436676,0.0014325285,0.0064690635,0.00018933613,0.00009820565,0.000010906359,0.0004926272,0.0021457633,0.0642137,0.50150454,0.21153195,0.21186092],"study_design_scores_gemma":[0.00027030747,0.00006303705,0.00074180565,0.0000053595677,0.000023692104,0.000009938256,0.000007156988,0.92119455,0.00031853645,0.07665927,0.00055482576,0.000151513],"about_ca_topic_score_codex":0.0000048144843,"about_ca_topic_score_gemma":4.5495995e-7,"teacher_disagreement_score":0.9190488,"about_ca_system_score_codex":0.00002866483,"about_ca_system_score_gemma":0.000011234604,"threshold_uncertainty_score":0.36594692},"labels":[],"label_agreement":null},{"id":"W2009242172","doi":"10.1111/j.0006-341x.2003.00124.x","title":"A Bayesian<i>A</i>‐Optimal and Model Robust Design Criterion","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"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; Montreal General Hospital","funders":"","keywords":"Bayesian probability; TRACE (psycholinguistics); Optimal design; Set (abstract data type); Mathematical optimization; Mathematics; Limit (mathematics); Function (biology); Optimality criterion; Basis (linear algebra); Bayesian experimental design; Bayesian inference; Computer science; Applied mathematics; Statistics; Bayesian statistics","score_opus":0.3170732228480225,"score_gpt":0.4331611706651947,"score_spread":0.11608794781717224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009242172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011433378,0.0017097362,0.98196733,0.000061905455,0.00027222376,0.0002515335,0.0000103221955,0.000055884455,0.0042376975],"genre_scores_gemma":[0.28578272,0.000053259595,0.71279776,0.00018941089,0.000017592198,0.000011356332,6.838471e-7,0.000019906243,0.0011273284],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634445,0.00067196536,0.0005743596,0.00070534996,0.0012966159,0.00040724583],"domain_scores_gemma":[0.997286,0.0014655137,0.00016554372,0.00054937176,0.00023981988,0.0002937525],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0063292114,0.00022820011,0.00034143572,0.0021966486,0.0001852432,0.00052258064,0.00046860144,0.00016031499,0.00019564947],"category_scores_gemma":[0.0087798685,0.00018408873,0.000091315196,0.0073231626,0.00016366584,0.0004774207,0.00012894999,0.00012778403,0.00011438172],"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":[0.0005080558,0.0013397958,0.0055160983,0.000042738615,0.00010127834,0.00016469725,0.0027357566,0.14942262,0.43569583,0.029740635,0.05515502,0.3195775],"study_design_scores_gemma":[0.00081193063,0.0004401941,0.00044617598,0.000008917468,0.000020645308,0.00007978749,0.00040797054,0.9409352,0.036213942,0.012239127,0.007838743,0.00055734615],"about_ca_topic_score_codex":0.000004488971,"about_ca_topic_score_gemma":1.3670548e-7,"teacher_disagreement_score":0.7915126,"about_ca_system_score_codex":0.000075667354,"about_ca_system_score_gemma":0.00009344231,"threshold_uncertainty_score":0.9995696},"labels":[],"label_agreement":null},{"id":"W2009860000","doi":"10.1111/j.1541-0420.2007.00899.x","title":"A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"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; University of British Columbia Hospital","funders":"National Human Genome Research Institute","keywords":"Chip; Bayesian probability; Computer science; Bayesian hierarchical modeling; Hierarchical database model; Bayesian inference; Artificial intelligence; Data mining; Telecommunications","score_opus":0.03993924728223888,"score_gpt":0.333754593863387,"score_spread":0.29381534658114816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009860000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28964138,0.0015211503,0.7069318,0.00013763513,0.00015600458,0.00021628701,0.000020079453,0.000016810707,0.0013588053],"genre_scores_gemma":[0.985879,0.00011055117,0.011568094,0.00027572832,0.00013797905,0.000018176044,0.00007576227,0.000018077695,0.0019166397],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917555,0.000010671575,0.00015888784,0.00030476443,0.00012738795,0.00022271414],"domain_scores_gemma":[0.99951607,0.000014973636,0.000052782645,0.00021969725,0.000054168162,0.0001423164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024796434,0.00010436488,0.000085330976,0.0003463405,0.00006951762,0.000025253621,0.000115346695,0.0001483634,0.0000054913216],"category_scores_gemma":[0.00010165177,0.00009746052,0.00004931972,0.00049608713,0.000046939083,0.0000028856882,0.00006050416,0.000042827978,0.0000019599966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015751531,0.00008751857,0.00093989883,0.00001558601,0.000014240137,3.0000965e-7,0.000063448286,0.000009254394,0.9704773,0.000473218,0.0043834853,0.023378266],"study_design_scores_gemma":[0.0012008949,0.00031279333,0.0043159733,0.000007804849,0.00001175939,0.000005079893,0.000089931134,0.0060418313,0.89896005,0.0005195441,0.08825903,0.00027529782],"about_ca_topic_score_codex":0.0000015512096,"about_ca_topic_score_gemma":0.000001330465,"teacher_disagreement_score":0.6962376,"about_ca_system_score_codex":0.000015644342,"about_ca_system_score_gemma":0.000044969558,"threshold_uncertainty_score":0.39743248},"labels":[],"label_agreement":null},{"id":"W2011278320","doi":"10.1111/j.1541-0420.2008.01070.x","title":"Inference for Clustered Inhomogeneous Spatial Point Processes","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Arnold Arboretum; National Institute for Environmental Studies; John D. and Catherine T. MacArthur Foundation; National Science Foundation","keywords":"Resampling; Point process; Inference; Nonparametric statistics; Cluster analysis; Computer science; Confidence interval; Poisson distribution; Statistics; Parametric statistics; Point estimation; Econometrics; Artificial intelligence; Machine learning; Data mining; Mathematics","score_opus":0.15784923146499116,"score_gpt":0.35316399595230324,"score_spread":0.19531476448731208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011278320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3436363,0.0017031601,0.6502232,0.00025542077,0.00059684366,0.00087435707,0.000276079,0.00032399644,0.0021106608],"genre_scores_gemma":[0.9720923,0.00040504153,0.025151711,0.00014970092,0.0003043939,0.000094915464,0.00003251312,0.000039341918,0.0017301122],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982624,0.00002276717,0.00050558423,0.00031631583,0.00044598774,0.0004469658],"domain_scores_gemma":[0.99620134,0.0023039526,0.00024976395,0.00030648513,0.00081836263,0.00012012511],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003999627,0.0002270264,0.00036619368,0.0016364102,0.00023606938,0.000055213648,0.00032814077,0.00015190445,0.000067502486],"category_scores_gemma":[0.01866666,0.00019687066,0.00010274923,0.0055994224,0.000086384534,0.0001715352,0.00011313513,0.00009002351,0.00002709677],"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":[0.002518661,0.0116824545,0.04373989,0.061883055,0.0016418681,0.0007469605,0.03410162,0.00013602286,0.0039847316,0.1891189,0.21934737,0.43109846],"study_design_scores_gemma":[0.011346497,0.005637633,0.0033551827,0.0005059066,0.00048799787,0.0011016715,0.002178064,0.0032592108,0.09757808,0.5810952,0.28827372,0.0051808287],"about_ca_topic_score_codex":0.00008817506,"about_ca_topic_score_gemma":0.000038669714,"teacher_disagreement_score":0.628456,"about_ca_system_score_codex":0.000069467795,"about_ca_system_score_gemma":0.0003053379,"threshold_uncertainty_score":0.9895995},"labels":[],"label_agreement":null},{"id":"W2013010380","doi":"10.1111/j.1541-0420.2007.00958.x","title":"Stepwise Confidence Intervals for Monotone Dose–Response Studies","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Memorial University of Newfoundland; Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Acadia University","keywords":"Monotonic function; Confidence interval; Mathematics; Statistics; Monotone polygon; Sample size determination; Applied mathematics; Medicine; Mathematical analysis","score_opus":0.8882842078628477,"score_gpt":0.6527363765385226,"score_spread":0.2355478313243251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013010380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21175508,0.001744081,0.78104997,0.0007135888,0.0021514113,0.0017696416,0.0003536271,0.00025615768,0.00020644945],"genre_scores_gemma":[0.114715114,0.0006234127,0.8822211,0.00027414234,0.0002525771,0.00018594868,9.4606634e-7,0.000046618392,0.001680139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968066,0.00075921835,0.0011009258,0.00043997722,0.0004969809,0.00039629766],"domain_scores_gemma":[0.7850221,0.21337217,0.00034752415,0.00053365063,0.0005573265,0.00016722137],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007138255,0.0002347313,0.00089981296,0.0007671103,0.00015594163,0.000026998421,0.00040835794,0.00018185224,0.000067266716],"category_scores_gemma":[0.58946174,0.00019265579,0.00024132506,0.0023231565,0.00045695473,0.0000729462,0.00018889457,0.0001511106,0.00006764074],"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":[0.018909749,0.0042693075,0.0027359058,0.003245497,0.0029595776,0.0005680001,0.005986014,0.0000048854145,0.020300403,0.48021436,0.2825387,0.17826757],"study_design_scores_gemma":[0.0027335568,0.0011355933,0.0021945587,0.00012008836,0.0001645767,0.000025511275,0.0002720737,0.0001754817,0.009174191,0.9760141,0.007535533,0.00045473667],"about_ca_topic_score_codex":0.0000036315398,"about_ca_topic_score_gemma":5.199609e-7,"teacher_disagreement_score":0.5823235,"about_ca_system_score_codex":0.00011533807,"about_ca_system_score_gemma":0.00008290775,"threshold_uncertainty_score":0.7856275},"labels":[],"label_agreement":null},{"id":"W2015781727","doi":"10.1111/j.1541-0420.2007.00942.x","title":"Estimating a Predator‐Prey Dynamical Model with the Parameter Cascades Method","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":49,"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; Simon Fraser University","funders":"","keywords":"Ode; Ordinary differential equation; Generalization; Applied mathematics; A priori and a posteriori; Estimation theory; Dynamical systems theory; Smoothing; Computer science; Mathematics; Mathematical optimization; Differential equation; Algorithm; Statistics; Mathematical analysis","score_opus":0.051507435207158864,"score_gpt":0.36026021148967385,"score_spread":0.30875277628251496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015781727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17078787,0.00007687471,0.8226044,0.0020086055,0.000039722716,0.00020276502,0.0000032506168,0.000049923623,0.0042265463],"genre_scores_gemma":[0.6445968,0.0000025149338,0.35388443,0.00096781633,0.00004775677,0.000010970666,0.000004340696,0.000010646421,0.00047471334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987981,0.00007438142,0.00029757383,0.00024593715,0.00018486232,0.00039915496],"domain_scores_gemma":[0.99517316,0.004219634,0.00007656134,0.00026666746,0.00007282279,0.00019114159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026310303,0.00014207402,0.0003597788,0.00025046224,0.00012090756,0.00000949655,0.00012256563,0.00024162413,0.0001415728],"category_scores_gemma":[0.0032438303,0.00006762253,0.00008574643,0.00097305793,0.00034761624,0.00003383122,0.000059132333,0.00033692172,0.000027972581],"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":[0.00085386896,0.0006936653,0.0046997704,0.0003091776,0.00039193314,0.00007467716,0.00051679334,0.0026352936,0.00044743897,0.9529018,0.0012160578,0.035259575],"study_design_scores_gemma":[0.0003962413,0.00024367524,0.0013910015,0.000023366376,0.00014478309,0.00014843678,0.000033523083,0.9556627,0.00017074551,0.041583084,0.00010114663,0.00010125164],"about_ca_topic_score_codex":0.0000043479313,"about_ca_topic_score_gemma":0.0000032548767,"teacher_disagreement_score":0.9530274,"about_ca_system_score_codex":0.000032167558,"about_ca_system_score_gemma":0.000033295957,"threshold_uncertainty_score":0.3883402},"labels":[],"label_agreement":null},{"id":"W2016845295","doi":"10.1111/j.0006-341x.2004.172_6.x","title":"Modern Medical Statistics. A Practical Guide.","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":7,"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 Alberta","funders":"","keywords":"Citation; Library science; Computer science; Psychology; Information retrieval","score_opus":0.7097786706050814,"score_gpt":0.6479915868720674,"score_spread":0.061787083733014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016845295","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073447713,0.0000721037,0.99124795,0.002173906,0.0010205758,0.00030937517,0.00022693665,0.00019999396,0.0040147053],"genre_scores_gemma":[0.0050481185,0.00011529352,0.99311894,0.0007339966,0.00047897335,0.000023743345,0.0000048591196,0.000057051173,0.00041902222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995228,0.0004061344,0.0013144173,0.00050539634,0.0020088598,0.0005372057],"domain_scores_gemma":[0.93007326,0.067997955,0.00029915958,0.00063152844,0.00033126518,0.0006668208],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0057149683,0.00024518132,0.0006566536,0.00058971724,0.00008973276,0.00007436759,0.00042749417,0.00045994474,0.001257239],"category_scores_gemma":[0.6521127,0.00020440311,0.00012768454,0.0025266723,0.00032566622,0.00007982999,0.00024457884,0.00051852094,0.00040792857],"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":[0.000043814725,0.0008041715,0.000074745236,0.00009580656,0.00007718792,0.0003708167,0.000048205813,9.892773e-7,0.00005443405,0.8725552,0.03572282,0.0901518],"study_design_scores_gemma":[0.0015934522,0.00021387015,0.00016086981,0.00004453473,0.00008757323,0.000061341125,0.000014838925,0.00054814265,0.0002477483,0.98839086,0.008373393,0.00026334467],"about_ca_topic_score_codex":0.000030014691,"about_ca_topic_score_gemma":0.000006447446,"teacher_disagreement_score":0.64639777,"about_ca_system_score_codex":0.00020267678,"about_ca_system_score_gemma":0.00059617695,"threshold_uncertainty_score":0.9996557},"labels":[],"label_agreement":null},{"id":"W2016935685","doi":"10.1111/j.1541-0420.2010.01421.x","title":"Multistate Mark-Recapture Model Selection Using Score Tests","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":19,"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":"Engineering and Physical Sciences Research Council","keywords":"Model selection; Selection (genetic algorithm); Computer science; Set (abstract data type); Mark and recapture; Simple (philosophy); Data set; Statistics; Machine learning; Data mining; Artificial intelligence; Mathematics","score_opus":0.13002065484743458,"score_gpt":0.3704660671891699,"score_spread":0.24044541234173533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016935685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8586941,0.00001669922,0.14015353,0.000045531677,0.00040060832,0.0001704933,0.000025538631,0.00011145073,0.00038205722],"genre_scores_gemma":[0.75043535,0.000002987685,0.24904355,0.00002291335,0.00011784581,0.0000025111449,0.000021517224,0.000020875557,0.00033244415],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991766,0.000015008976,0.00022966751,0.00016477169,0.00024300344,0.0001709036],"domain_scores_gemma":[0.9992542,0.00015062526,0.00015473836,0.00016758099,0.00020269389,0.0000701052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027184404,0.000114714974,0.00011964256,0.0007293985,0.00012212651,0.000047824382,0.00007655489,0.00015143852,0.000048998274],"category_scores_gemma":[0.0010445709,0.00010786894,0.000045391138,0.0021659725,0.00002032191,0.00013628587,0.00002294887,0.0001784708,0.000015066762],"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":[0.000094658186,0.001094449,0.2232426,0.0007112171,0.00010699109,0.000012324886,0.001290229,0.018484278,0.47298473,0.08594691,0.020896992,0.17513461],"study_design_scores_gemma":[0.0002515925,0.000013686316,0.013468149,0.000012005927,0.000029758792,0.000018573151,0.0000042000556,0.9706312,0.0010534932,0.013342533,0.0009988867,0.0001759442],"about_ca_topic_score_codex":0.00005062991,"about_ca_topic_score_gemma":0.00006763184,"teacher_disagreement_score":0.9521469,"about_ca_system_score_codex":0.000062066756,"about_ca_system_score_gemma":0.0000402264,"threshold_uncertainty_score":0.43987677},"labels":[],"label_agreement":null},{"id":"W2017634977","doi":"10.1111/j.1541-0420.2011.01641.x","title":"A Bayesian Adjustment for Multiplicative Measurement Errors for a Calibration Problem with Application to a Stem Cell Study","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Blood Services; University of Saskatchewan; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Calibration; Multiplicative function; Computer science; Statistics; Mathematics","score_opus":0.2276577203057804,"score_gpt":0.3651108877187792,"score_spread":0.1374531674129988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017634977","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033867299,0.00002044969,0.9871881,0.00003816164,0.00003600351,0.00898396,0.00008880318,0.00008924125,0.00016856253],"genre_scores_gemma":[0.38579074,5.928893e-7,0.60928774,0.00002905911,0.000019982966,0.004813045,0.0000030685321,0.00002945875,0.000026319989],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985713,0.00006258252,0.00035835663,0.00039348737,0.00037554267,0.00023873795],"domain_scores_gemma":[0.9982125,0.00055678666,0.00020983518,0.00031727154,0.0005599522,0.00014364943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009307231,0.00017035128,0.0002443869,0.00039871302,0.00007238325,0.00002181656,0.00016688532,0.000055137836,0.0000035776225],"category_scores_gemma":[0.000413679,0.00012748809,0.000042420565,0.001399079,0.000017915658,0.000044959153,0.000032154458,0.00003991641,0.0000019757895],"study_design_candidate":"design_other","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":[0.0023494032,0.015761942,0.0046476773,0.0024086686,0.0004674682,0.0000015719207,0.029648907,0.000018042287,0.011999274,0.21618754,0.003535247,0.71297425],"study_design_scores_gemma":[0.028291197,0.08017688,0.039982233,0.0005230356,0.0030046024,0.000006724846,0.031052995,0.17946526,0.17708713,0.4426578,0.012766661,0.0049854666],"about_ca_topic_score_codex":0.000034982233,"about_ca_topic_score_gemma":0.00003095028,"teacher_disagreement_score":0.7079888,"about_ca_system_score_codex":0.00016073916,"about_ca_system_score_gemma":0.000058846752,"threshold_uncertainty_score":0.51988137},"labels":[],"label_agreement":null},{"id":"W2018205196","doi":"10.1111/j.0006-341x.2001.00584.x","title":"Inference Procedures for Assessing Interobserver Agreement among Multiple Raters","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Goodness of fit; Mathematics; Nominal level; Confidence interval; Binomial distribution; Range (aeronautics); Inference; Sample size determination; Binomial (polynomial); Multiple comparisons problem; Statistical inference; Computer science; Artificial intelligence","score_opus":0.3335008582243348,"score_gpt":0.43055005559190307,"score_spread":0.09704919736756829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018205196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8721611,0.00014999896,0.12491678,0.000505751,0.00082519476,0.000651903,0.000011173411,0.000039598854,0.00073851313],"genre_scores_gemma":[0.99216366,0.000046035082,0.0061534094,0.00025553256,0.00009918466,0.00007141311,0.000008675055,0.0000097453185,0.0011923565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99618274,0.00012371778,0.00082200987,0.00062715204,0.0018246293,0.00041972977],"domain_scores_gemma":[0.99592364,0.0021353406,0.00034410984,0.0005887104,0.00084828393,0.0001598927],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005012,0.00019633934,0.000280413,0.0012251597,0.00024613933,0.0008785136,0.00090784056,0.00009975907,0.0002331778],"category_scores_gemma":[0.023816785,0.00013870139,0.00017146148,0.0047872053,0.00012229061,0.00066699524,0.00018218445,0.00009668478,0.000117019656],"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":[0.0000326098,0.0002356668,0.93248504,0.00003115914,0.000021093714,0.0000025583538,0.00016626838,0.00023009913,0.0025908188,0.000077721576,0.007986862,0.05614012],"study_design_scores_gemma":[0.0012716079,0.0002642357,0.9088506,0.000101563266,0.000027130834,0.0000015962643,0.0012630161,0.008810538,0.00356037,0.0042660655,0.07113828,0.00044501576],"about_ca_topic_score_codex":0.00004830451,"about_ca_topic_score_gemma":0.000111690824,"teacher_disagreement_score":0.12000256,"about_ca_system_score_codex":0.00015346683,"about_ca_system_score_gemma":0.00009355439,"threshold_uncertainty_score":0.984406},"labels":[],"label_agreement":null},{"id":"W2019413263","doi":"10.1111/j.1541-0420.2011.01563.x","title":"Filtered Kriging for Spatial Data with Heterogeneous Measurement Error Variances","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":36,"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":"U.S. Department of Energy; Office of Research and Development; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Kriging; Statistics; Computer science; Observational error; Spatial analysis; Mathematics; Data mining","score_opus":0.2002467674076682,"score_gpt":0.28145565237016984,"score_spread":0.08120888496250164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019413263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021369172,0.0001813211,0.9708603,0.00004666915,0.00051143125,0.0005334672,0.00032482643,0.000059537448,0.0061132465],"genre_scores_gemma":[0.9104774,0.0000097325055,0.089257725,0.000076348326,0.000041758445,0.000018241904,0.0000407829,0.000012977004,0.00006503579],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989242,0.00001191612,0.00013399277,0.0003214712,0.00037423763,0.0002341739],"domain_scores_gemma":[0.9993883,0.000038185113,0.000082260914,0.00039347142,0.000021391974,0.00007640286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036717966,0.00009812592,0.00009575481,0.0001179264,0.00009884955,0.000030518655,0.0003870552,0.000027333435,0.00046469955],"category_scores_gemma":[0.00017537603,0.00007902001,0.000015508074,0.00069033925,0.00005671707,0.00009199177,0.00020195924,0.000028198747,0.00006214844],"study_design_candidate":"design_other","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":[0.00035609788,0.00075520005,0.06176353,0.00014543686,0.0002622052,0.00008534323,0.0011063366,0.0003695903,0.0057933773,0.00039585924,0.028784556,0.9001825],"study_design_scores_gemma":[0.0041660704,0.002237329,0.2320407,0.000108984874,0.00035405267,0.000069060385,0.0003030136,0.10852106,0.012297171,0.0018135741,0.63613653,0.0019524659],"about_ca_topic_score_codex":0.0011596358,"about_ca_topic_score_gemma":0.0003538964,"teacher_disagreement_score":0.89823,"about_ca_system_score_codex":0.000057841095,"about_ca_system_score_gemma":0.000014732949,"threshold_uncertainty_score":0.50881344},"labels":[],"label_agreement":null},{"id":"W2020076894","doi":"10.1111/j.0006-341x.2003.00098.x","title":"Flexible Maximum Likelihood Methods for Bivariate Proportional Hazards Models","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":46,"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; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Censoring (clinical trials); Mathematics; Piecewise; Statistics; Parametric statistics; Parametric model; Covariate; Econometrics; Maximum likelihood; Applied mathematics","score_opus":0.19252167580371551,"score_gpt":0.4697391338297554,"score_spread":0.2772174580260399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020076894","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008659435,0.000081368526,0.99139893,0.00030412502,0.0001619375,0.0005865414,0.00049040647,0.00017259666,0.0067174924],"genre_scores_gemma":[0.027459998,0.000010723369,0.97130936,0.000114323586,0.000033485725,0.0003072206,0.00015787767,0.000024626544,0.0005823766],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868536,0.000074425945,0.00042969975,0.00026474815,0.00025130794,0.0002944781],"domain_scores_gemma":[0.99792343,0.0010562085,0.00015903868,0.0002694421,0.00042905434,0.00016280795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010522541,0.00014092412,0.00020208866,0.0004505351,0.00018470647,0.000060036273,0.00013686648,0.00011004384,0.00029492192],"category_scores_gemma":[0.005126472,0.00013020606,0.00010396436,0.0025532828,0.00005453022,0.000095809984,0.000021610536,0.00007388553,0.000051202773],"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":[0.00000461979,0.00022856292,0.000005517223,0.000049206483,0.000020152242,1.5996753e-7,0.00001254082,0.000009348274,0.00029484957,0.95650417,0.0065245186,0.036346354],"study_design_scores_gemma":[0.0004256314,0.000037588034,0.0001012259,0.000005090381,0.00004588015,0.000004512971,0.00001805587,0.028411532,0.0030245977,0.9254481,0.042314503,0.00016327678],"about_ca_topic_score_codex":0.0000022746897,"about_ca_topic_score_gemma":1.6684979e-7,"teacher_disagreement_score":0.036183078,"about_ca_system_score_codex":0.00009359489,"about_ca_system_score_gemma":0.00016090658,"threshold_uncertainty_score":0.61372364},"labels":[],"label_agreement":null},{"id":"W2020183758","doi":"10.1111/j.1541-0420.2005.00501.x","title":"Nonparametric Inference for Local Extrema with Application to Oligonucleotide Microarray Data in Yeast Genome","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"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; York University; University of Waterloo","funders":"","keywords":"Maxima and minima; Smoothing; Nonparametric statistics; Inference; Kernel (algebra); Algorithm; Replication (statistics); Mathematics; Computer science; Genetics; Biology; Computational biology; Statistics; Artificial intelligence; Combinatorics","score_opus":0.027950467324131287,"score_gpt":0.3038155026413637,"score_spread":0.2758650353172324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020183758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22491631,0.00061236497,0.77328515,0.00031052763,0.00004201705,0.000484719,0.00013335768,0.000011858855,0.00020368827],"genre_scores_gemma":[0.9773637,0.00010373178,0.021108028,0.00030847907,0.00015204336,0.00010595666,0.0006581777,0.00001892438,0.00018094308],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.998942,0.000015775026,0.00019241592,0.0005070941,0.00013849552,0.00020419061],"domain_scores_gemma":[0.9989313,0.000017970373,0.000076819524,0.0007687119,0.00010138032,0.000103813465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021487514,0.00011452111,0.000105580795,0.0007431816,0.000038188657,0.000029671994,0.0004687314,0.00009968201,0.000006069695],"category_scores_gemma":[0.00016732677,0.0001040296,0.000019994743,0.0026236826,0.000035870627,0.00000829873,0.00013460459,0.000043407,0.00003553537],"study_design_candidate":"bench_or_experimental","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":[0.0001150468,0.00013668211,0.0023347286,0.00001877182,0.000009807237,2.0164946e-7,0.000020406107,0.0010100233,0.8121627,0.000033935565,0.0016899707,0.18246774],"study_design_scores_gemma":[0.0009255247,0.00027342682,0.037682895,0.000011478266,0.000013044873,0.0000037277257,0.00008035249,0.0038760535,0.114636995,0.0000077712675,0.84216714,0.00032161255],"about_ca_topic_score_codex":0.000017230816,"about_ca_topic_score_gemma":0.000075498785,"teacher_disagreement_score":0.84047717,"about_ca_system_score_codex":0.000053972242,"about_ca_system_score_gemma":0.00008298904,"threshold_uncertainty_score":0.42422038},"labels":[],"label_agreement":null},{"id":"W2020776773","doi":"10.1111/j.0006-341x.2003.00127.x","title":"Issues of Cost and Efficiency in the Design of Reliability Studies","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intraclass correlation; Reliability (semiconductor); Variance (accounting); Statistics; Reliability engineering; Mathematics; Computer science; Psychometrics; Engineering; Economics; Power (physics)","score_opus":0.24398368196916476,"score_gpt":0.4538264988293823,"score_spread":0.20984281686021755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020776773","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04685402,0.0020360935,0.95026535,0.0000553475,0.00004993377,0.00031823022,0.0000125391,0.00000465916,0.0004038056],"genre_scores_gemma":[0.4138798,0.000298155,0.5857992,0.0000068510703,0.000002525607,0.000005341414,6.16155e-8,0.0000023166579,0.0000057399716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990047,0.0003524993,0.00027480125,0.00009604012,0.00018024367,0.00009168604],"domain_scores_gemma":[0.9908141,0.008784233,0.00009384692,0.00017368725,0.000119501696,0.000014615044],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002791976,0.000057368758,0.00022217228,0.00024728716,0.000017780934,0.0000049797573,0.00009340186,0.000032609656,0.0000073620413],"category_scores_gemma":[0.038459715,0.000033497232,0.000015277585,0.0020595496,0.00020754038,0.000015184421,0.000018044335,0.00004456677,3.4211925e-7],"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":[0.000014723039,0.00052535534,0.015045601,0.00064285804,0.000016705111,0.0000027120002,0.0023057668,0.000001379006,0.0006251484,0.9505452,0.00046106652,0.029813468],"study_design_scores_gemma":[0.00025670847,0.00029450087,0.012711792,0.000054079035,0.000029140767,0.0000020920886,0.001232196,0.00020455327,0.0050160475,0.9798757,0.0002353852,0.000087807435],"about_ca_topic_score_codex":0.000009116605,"about_ca_topic_score_gemma":4.1685803e-7,"teacher_disagreement_score":0.3670258,"about_ca_system_score_codex":0.000010963051,"about_ca_system_score_gemma":0.000016579706,"threshold_uncertainty_score":0.9696397},"labels":[],"label_agreement":null},{"id":"W2023771054","doi":"10.1111/j.1541-0420.2005.00503.x","title":"Spatial Event Cluster Detection Using a Compound Poisson Distribution","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"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":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Poisson distribution; Geography; Cluster (spacecraft); Event (particle physics); Population; Poisson regression; Distribution (mathematics); Cartography; Disease surveillance; Computer science; Statistics; Disease; Medicine; Environmental health; Mathematics","score_opus":0.019553953257069916,"score_gpt":0.2865288886447513,"score_spread":0.26697493538768136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023771054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8203954,0.00027645327,0.17790577,0.000084784304,0.00041627532,0.00026951387,0.00040120276,0.00010499747,0.00014561931],"genre_scores_gemma":[0.99741054,0.000007717052,0.0005775288,0.000059871614,0.000424062,0.000004467009,0.001411986,0.000016874232,0.00008698199],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988377,0.00004548263,0.00026061598,0.00023533395,0.0003933428,0.00022750148],"domain_scores_gemma":[0.9993265,0.000052664484,0.00011759028,0.00025227823,0.00015038955,0.00010052599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022252112,0.00012087517,0.00018993675,0.0005049151,0.000081943144,0.000034257802,0.000056547917,0.00008666365,0.000031804146],"category_scores_gemma":[0.00023578056,0.000116329975,0.00008962626,0.002304667,0.000041573436,0.000076449665,0.000048543803,0.00008441751,0.00005610032],"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":[0.0016533323,0.0028468904,0.5710337,0.0006195179,0.00022565048,0.0002954176,0.000038110917,0.0004354746,0.18159606,0.00014387812,0.013229213,0.22788274],"study_design_scores_gemma":[0.0019655344,0.00018473106,0.90672225,0.000044276352,0.00011625448,0.000087786844,0.00000819946,0.027423173,0.0069910176,0.000055773435,0.056177687,0.00022332513],"about_ca_topic_score_codex":0.001204956,"about_ca_topic_score_gemma":0.000085585554,"teacher_disagreement_score":0.3356885,"about_ca_system_score_codex":0.00047082626,"about_ca_system_score_gemma":0.000049303,"threshold_uncertainty_score":0.47437987},"labels":[],"label_agreement":null},{"id":"W2024920291","doi":"10.1111/j.1541-0420.2007.00763.x","title":"Sampling for Conditional Inference on Case–Control Data","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and 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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Conditional probability distribution; Inference; Sampling (signal processing); Covariate; Mathematics; Poisson distribution; Statistics; Computer science; Algorithm; Artificial intelligence","score_opus":0.505160821243375,"score_gpt":0.5256345785298813,"score_spread":0.020473757286506244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024920291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049416176,0.000037976588,0.99045646,0.00005452976,0.00019983701,0.00025489368,0.0030942312,0.000043115797,0.0009173172],"genre_scores_gemma":[0.4253645,0.0000026484606,0.574184,0.00017594382,0.0001448401,0.000006679036,0.00007479011,0.000010534691,0.00003606135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988611,0.000029903176,0.0003151888,0.0002732967,0.00024649283,0.00027406806],"domain_scores_gemma":[0.97444314,0.024709864,0.000108040134,0.0004524887,0.0001746122,0.00011184715],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0019970464,0.00011201852,0.00018816699,0.0005041662,0.000111529305,0.00004327563,0.00026883915,0.00008691003,0.0001228233],"category_scores_gemma":[0.03734183,0.000095630014,0.00003276508,0.0010049943,0.000052598018,0.000055599918,0.00006359472,0.00009884467,0.00001946821],"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":[0.000039796243,0.00013471513,0.0003273507,0.0000640556,0.000033030883,0.0000556913,0.000014391426,0.0000010558191,0.00025081303,0.8484867,0.0026640901,0.14792828],"study_design_scores_gemma":[0.0015030584,0.00047654522,0.007320976,0.000035420086,0.000117351155,0.00012267701,0.00007016546,0.009654305,0.0004615181,0.9608844,0.018947162,0.00040642126],"about_ca_topic_score_codex":0.0000078046805,"about_ca_topic_score_gemma":0.0000033830772,"teacher_disagreement_score":0.42042288,"about_ca_system_score_codex":0.000039385573,"about_ca_system_score_gemma":0.00003684937,"threshold_uncertainty_score":0.970767},"labels":[],"label_agreement":null},{"id":"W2025742107","doi":"10.1111/j.1541-0420.2007.00978.x","title":"A Multistate Model for Bivariate Interval‐Censored Failure Time Data","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Waterloo","funders":"","keywords":"Bivariate analysis; Interval (graph theory); Statistics; Computer science; Econometrics; Accelerated failure time model; Bivariate data; Mathematics; Survival analysis; Combinatorics","score_opus":0.32796545071088834,"score_gpt":0.417841156774969,"score_spread":0.08987570606408068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025742107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011998961,0.000015318574,0.98743266,0.00061915343,0.00003446647,0.0004274443,0.009829665,0.0001650297,0.0002763566],"genre_scores_gemma":[0.17335619,0.00001600395,0.82020557,0.00012791638,0.000046098336,0.00008460142,0.0027332313,0.000031643067,0.0033987297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988737,0.000019237545,0.00035545736,0.00031008918,0.00022093518,0.00022054589],"domain_scores_gemma":[0.9980741,0.00077473576,0.00013362587,0.000658372,0.00023503881,0.00012408344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022728104,0.00012897704,0.00019881838,0.0003191786,0.0001813656,0.000033628432,0.00042776615,0.000077806086,0.00011416653],"category_scores_gemma":[0.0040650317,0.00012102664,0.000052703417,0.001459633,0.000083375475,0.00011623172,0.00013287908,0.00006529919,0.00025862918],"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":[0.00002794739,0.0004812532,0.000016417678,0.00010074362,0.000057279198,0.0000035078485,0.00015428572,0.00005912372,0.0012312557,0.5143994,0.47897142,0.004497345],"study_design_scores_gemma":[0.0005830886,0.000018270233,0.0001745224,0.0000073162087,0.000037371297,0.000008134498,0.000009845961,0.95280546,0.00009275108,0.03331529,0.012789783,0.00015817105],"about_ca_topic_score_codex":0.00000526236,"about_ca_topic_score_gemma":0.0000015400582,"teacher_disagreement_score":0.95274633,"about_ca_system_score_codex":0.00006160366,"about_ca_system_score_gemma":0.00005579984,"threshold_uncertainty_score":0.49353233},"labels":[],"label_agreement":null},{"id":"W2026680199","doi":"10.1111/j.0006-341x.2004.00236.x","title":"Letter to the Editor of <i>Biometrics</i>","year":2004,"lang":"en","type":"letter","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Population; Mathematics; Biometrics; Statistics; Library science; Demography; Computer science; Artificial intelligence; Sociology","score_opus":0.16610295402904326,"score_gpt":0.4268696127936606,"score_spread":0.26076665876461735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026680199","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060342987,0.0027842803,0.083253235,0.84100884,0.06540259,0.0013869406,0.00081648206,0.00009638535,0.0051909024],"genre_scores_gemma":[0.00019656247,0.00003416408,0.08553365,0.83246183,0.07326039,0.000057218946,0.000045495337,0.000107924505,0.0083027575],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.987307,0.00084451836,0.001768532,0.0013254038,0.0079037,0.00085082406],"domain_scores_gemma":[0.985563,0.009351943,0.001050967,0.0027169413,0.0011004525,0.00021673128],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","open_science","research_integrity","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.008369659,0.0006020074,0.0011143195,0.017236505,0.000171358,0.0006095623,0.0056265886,0.0013529754,0.0005091145],"category_scores_gemma":[0.022589521,0.00036437536,0.00065018,0.06899593,0.00035952128,0.00022405105,0.001044897,0.0015536874,0.00297151],"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":[0.000011429043,0.00005457927,0.000027124084,0.000019987063,0.0000351486,0.000048917085,0.000083450745,0.000014998793,0.0010314019,0.000020469302,0.98650664,0.012145865],"study_design_scores_gemma":[0.00022688974,0.00030867785,0.00009709276,0.000026730775,0.00003363907,0.000008424299,0.000024333673,0.000009717986,0.005759677,0.0006567909,0.99240375,0.00044430135],"about_ca_topic_score_codex":0.000090544425,"about_ca_topic_score_gemma":7.0236933e-7,"teacher_disagreement_score":0.05175943,"about_ca_system_score_codex":0.00042644344,"about_ca_system_score_gemma":0.00027604584,"threshold_uncertainty_score":0.9999435},"labels":[],"label_agreement":null},{"id":"W2028163014","doi":"10.1111/j.1541-0420.2005.00504.x","title":"Multiscale Processing of Mass Spectrometry Data","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"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":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Pattern recognition (psychology); Histogram; Computer science; Wavelet; Focus (optics); Scale (ratio); Feature (linguistics); Artificial intelligence; Image (mathematics); Physics","score_opus":0.024220757405454908,"score_gpt":0.2743949445770552,"score_spread":0.2501741871716003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028163014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8577014,0.033255875,0.09469386,0.00015572304,0.00045257993,0.00028263775,0.00050360424,0.000037715436,0.012916554],"genre_scores_gemma":[0.94589704,0.0006109373,0.05173906,0.000024103176,0.00033487316,0.0000031883458,0.00038355554,0.000021299547,0.0009859584],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878865,0.000020292966,0.0002911316,0.00041651944,0.00022493443,0.000258466],"domain_scores_gemma":[0.9989918,0.000017587752,0.00016964097,0.0006694971,0.00010964755,0.000041821735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030550116,0.00014434694,0.00022201757,0.00059162313,0.00006246817,0.00002383428,0.00048514557,0.00010683825,0.000016616785],"category_scores_gemma":[0.00024330516,0.00013255296,0.000057496352,0.0020827786,0.00009552947,0.0000062645527,0.00034115926,0.00005728308,0.0000049748014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016457789,0.00012215125,0.013807399,0.00004401613,0.000038345144,0.0000017042938,0.0000021780515,0.0000034859916,0.9756214,0.00024576005,0.0052904487,0.0048066555],"study_design_scores_gemma":[0.0011366267,0.00034894777,0.07385123,0.000014319549,0.00009078667,0.000013003466,0.00007157661,0.00049090915,0.7359581,0.00053054583,0.18695979,0.00053418084],"about_ca_topic_score_codex":0.00004579508,"about_ca_topic_score_gemma":0.00001052215,"teacher_disagreement_score":0.2396633,"about_ca_system_score_codex":0.000011916442,"about_ca_system_score_gemma":0.000042238244,"threshold_uncertainty_score":0.5405353},"labels":[],"label_agreement":null},{"id":"W2029728010","doi":"10.1111/1541-0420.00076","title":"Parametric Modeling of Reaction Time Experiment Data","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Australian National University","keywords":"Parametric statistics; Normality; Nonparametric statistics; Nonlinear system; Computation; Computer science; Parametric model; Applied mathematics; Algorithm; Intensity (physics); Mathematics; Statistics; Optics; Physics","score_opus":0.09530555923954115,"score_gpt":0.33124065971598504,"score_spread":0.2359351004764439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029728010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8594833,0.0143115595,0.015874095,0.000028076049,0.00019227318,0.0001017198,0.00013335259,0.00018222179,0.109693386],"genre_scores_gemma":[0.9935764,0.0003160294,0.0046300096,0.000010707656,0.000042524836,0.0000033098383,0.00010378527,0.000018679892,0.0012985569],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865806,0.000013362747,0.00034311766,0.00035983312,0.00039664583,0.00022895077],"domain_scores_gemma":[0.9985835,0.00016209493,0.00016240324,0.00091467716,0.00009376583,0.000083542844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002896072,0.0001395859,0.0002631038,0.0017849802,0.00005022851,0.00002306107,0.00042026406,0.00013465974,0.00061098195],"category_scores_gemma":[0.0014702718,0.00013553853,0.00006525073,0.009837436,0.00003094168,0.00013996716,0.00009207379,0.000107486245,0.000067239525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021551212,0.0007168612,0.0015817605,0.00010940474,0.0002413176,0.0000042700967,0.000036590558,0.00011191651,0.9922071,0.0004055686,0.001880939,0.0026827215],"study_design_scores_gemma":[0.00059425284,0.000051966803,0.000038337126,0.000012418531,0.00028237735,0.000012205921,0.00034604588,0.0601153,0.9264017,0.00017029158,0.011594393,0.00038071082],"about_ca_topic_score_codex":0.000057994057,"about_ca_topic_score_gemma":1.4152714e-7,"teacher_disagreement_score":0.13409308,"about_ca_system_score_codex":0.00011617029,"about_ca_system_score_gemma":0.000050791004,"threshold_uncertainty_score":0.6689824},"labels":[],"label_agreement":null},{"id":"W2033054774","doi":"10.1111/j.0006-341x.2001.00949.x","title":"Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Children's & Women's Health Centre of British Columbia; University of British Columbia","funders":"Ministry of Health, British Columbia","keywords":"Smoothing; Autoregressive model; Smoothing spline; Spline (mechanical); Autoregressive integrated moving average; Dimension (graph theory); Time series; Econometrics; Statistics; Mathematics; Computer science","score_opus":0.07884423877895083,"score_gpt":0.2654074435246301,"score_spread":0.18656320474567925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033054774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42365363,0.009292695,0.56087667,0.0014473059,0.001131732,0.00049854896,0.0009755486,0.00012873652,0.0019951689],"genre_scores_gemma":[0.9869853,0.0006290244,0.010526704,0.00028076998,0.00048096242,0.00002528282,0.00030142788,0.000032695247,0.00073779846],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984581,0.00001093461,0.0006144923,0.00049769745,0.000062909836,0.0003558536],"domain_scores_gemma":[0.99887896,0.00023084397,0.00043645146,0.00026179463,0.0000672751,0.00012469388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070512487,0.00019654579,0.0004609825,0.0018896831,0.00024239677,0.00022594776,0.00022495112,0.00014728,0.00014931054],"category_scores_gemma":[0.00078648655,0.00020837938,0.00013209556,0.0018934817,0.000058463094,0.00029333233,0.00010270142,0.00011265186,0.00006214714],"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":[0.000047734145,0.00010783062,0.92682195,0.00010904383,0.00019994029,0.000022446447,0.0006423723,0.000029718096,0.00017993806,0.008722939,0.001481893,0.061634168],"study_design_scores_gemma":[0.0023794987,0.00025803578,0.20421359,0.000075633514,0.00007266587,0.000023735383,0.00034588124,0.11943484,0.00026602868,0.016311666,0.6554223,0.0011961476],"about_ca_topic_score_codex":0.0029223182,"about_ca_topic_score_gemma":0.000091624366,"teacher_disagreement_score":0.7226084,"about_ca_system_score_codex":0.00006804914,"about_ca_system_score_gemma":0.000015667913,"threshold_uncertainty_score":0.84974647},"labels":[],"label_agreement":null},{"id":"W2034531877","doi":"10.1111/j.1541-0420.2012.01792.x","title":"Mapping Cancer Risk in Southwestern Ontario with Changing Census Boundaries","year":2012,"lang":"en","type":"article","venue":"Biometrics","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; University of Toronto; Cancer Care Ontario","funders":"","keywords":"Census; Cancer; Geography; Cartography; Environmental health; Medicine; Population","score_opus":0.03840874474385368,"score_gpt":0.2744372537394891,"score_spread":0.23602850899563538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034531877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950619,0.0016973317,0.0015654534,0.00005522994,0.00053106854,0.00014694061,0.000012224945,0.00006044195,0.0008694405],"genre_scores_gemma":[0.99778557,0.000033678687,0.00092836696,0.00006132913,0.00025673432,0.000024071525,0.00000841797,0.0000147851915,0.0008870733],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991247,0.000014882462,0.00012500891,0.00014165536,0.00023028663,0.00036345463],"domain_scores_gemma":[0.99960655,0.000036059148,0.000076658456,0.00011924729,0.0000534726,0.00010803375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002736314,0.00010194491,0.0001684227,0.0017823017,0.00012391023,0.000046846315,0.000031639538,0.000067370864,0.00003896094],"category_scores_gemma":[0.00007216969,0.00008264502,0.00003234674,0.0041338783,0.00004887321,0.00008811425,0.000028605093,0.00019813237,0.000007733519],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044109512,0.000037329188,0.95682687,0.000027292946,0.000026374499,0.0000062442105,0.004393709,0.0000032490339,0.00007320614,0.0000036871506,0.000049114566,0.038111847],"study_design_scores_gemma":[0.00091481634,0.00038133617,0.9316505,0.00009860655,0.0000379844,0.000030117231,0.000706497,0.000065378634,0.000402373,0.000002741572,0.065578654,0.00013096134],"about_ca_topic_score_codex":0.06581747,"about_ca_topic_score_gemma":0.04743026,"teacher_disagreement_score":0.06552954,"about_ca_system_score_codex":0.00063127937,"about_ca_system_score_gemma":0.00015746772,"threshold_uncertainty_score":0.9699516},"labels":[],"label_agreement":null},{"id":"W2037191073","doi":"10.1111/j.0006-341x.2002.00232.x","title":"The Use of Frailty Hazard Models for Unrecognized Heterogeneity That Interacts with Treatment: Considerations of Efficiency and Power","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":17,"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":"National Cancer Institute; National Institutes of Health","keywords":"Hazard; Power (physics); Econometrics; Computer science; Hazard ratio; Statistics; Mathematics; Biology; Ecology; Confidence interval; Physics","score_opus":0.6900276415834307,"score_gpt":0.4218140471148722,"score_spread":0.26821359446855847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037191073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94507825,0.005520077,0.04219405,0.0036738107,0.00028941734,0.0013609413,0.0014202015,0.000021870397,0.00044136122],"genre_scores_gemma":[0.9899888,0.0006561405,0.008603433,0.0004591911,0.000019411273,0.00006741493,0.000011922481,0.000019054365,0.00017463995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975901,0.00010594521,0.0016621763,0.00031952065,0.000083457235,0.0002388046],"domain_scores_gemma":[0.9928942,0.0049344255,0.0015097988,0.00039729214,0.0001648566,0.00009945189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021321995,0.00015124163,0.0006438898,0.0007119194,0.00020664472,0.00009182364,0.00010273076,0.00009056043,0.000049075068],"category_scores_gemma":[0.0034215383,0.00012571298,0.000102692175,0.0005955871,0.00016699036,0.00038388686,0.000026830758,0.00004378838,0.000016159234],"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":[0.0008885404,0.0071014455,0.31894884,0.002521093,0.004210521,0.0000071120594,0.030303163,0.014103166,0.00026733015,0.5334689,0.066266336,0.02191353],"study_design_scores_gemma":[0.018283118,0.0060829497,0.047546517,0.00036791552,0.00024157471,0.00006092402,0.0020035408,0.73002416,0.00395886,0.031478655,0.15783015,0.002121622],"about_ca_topic_score_codex":0.00018154121,"about_ca_topic_score_gemma":0.0001254954,"teacher_disagreement_score":0.71592104,"about_ca_system_score_codex":0.00014621814,"about_ca_system_score_gemma":0.000047813213,"threshold_uncertainty_score":0.5126426},"labels":[],"label_agreement":null},{"id":"W2039341346","doi":"10.1111/j.1541-0420.2006.00706.x","title":"Our Future as History","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Race, Genetics, and Society","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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":"International Biometric Society","keywords":"Presidential address; Plan (archaeology); Political science; Engineering ethics; Public relations; Computer science; Data science; Management science; History; Public administration; Engineering; Archaeology","score_opus":0.017505007168880905,"score_gpt":0.27153774145407805,"score_spread":0.2540327342851971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039341346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9337189,0.035240233,0.005854167,0.000506322,0.0044903485,0.0001897168,0.000015969716,0.000046820223,0.019937482],"genre_scores_gemma":[0.9769601,0.0021182343,0.003174909,0.0012495565,0.0028540026,0.0000025597242,0.00007621187,0.00003101364,0.013533402],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.999047,0.000016265405,0.00016270782,0.00029004674,0.00019911204,0.00028487772],"domain_scores_gemma":[0.99931836,0.0000059530626,0.000066958586,0.0003574231,0.00011070414,0.00014059733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041754206,0.00012534847,0.00010011831,0.00021390343,0.00006067954,0.000010582473,0.00020157461,0.0003037523,0.000015759024],"category_scores_gemma":[0.0001411686,0.00012579966,0.00012507943,0.000529813,0.000034687677,0.0000015672362,0.00007327698,0.00008435038,0.000068324065],"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":[0.00005918251,0.00018451408,0.0067248424,0.000030512108,0.00008941606,0.000018717721,0.00031043353,0.0000022971433,0.50988054,0.00032987553,0.4403559,0.04201379],"study_design_scores_gemma":[0.0002616133,0.00017366298,0.007351735,0.0000011914761,0.000010212427,0.000009731585,0.00081617094,0.0000021155406,0.039357234,0.000021461396,0.95181364,0.00018123575],"about_ca_topic_score_codex":0.000012065792,"about_ca_topic_score_gemma":0.000008228788,"teacher_disagreement_score":0.51145774,"about_ca_system_score_codex":0.00008666004,"about_ca_system_score_gemma":0.000097944634,"threshold_uncertainty_score":0.51299614},"labels":[],"label_agreement":null},{"id":"W2039635914","doi":"10.1111/j.1541-0420.2009.01305.x","title":"Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent Observation","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":8,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Multivariate statistics; Computer science; Univariate; Bayesian probability; Bayesian inference; Econometrics; Kernel (algebra); Spatial analysis; Mathematics","score_opus":0.046237258666682005,"score_gpt":0.24803226168632125,"score_spread":0.20179500301963926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039635914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975286,0.000036815345,0.023064166,0.00019898907,0.00003895588,0.00011100348,9.1971015e-7,0.000016823424,0.0012463476],"genre_scores_gemma":[0.99892473,0.00017887441,0.0007121749,0.00007187943,0.00002001826,0.0000020226241,0.00000555547,0.0000031775458,0.000081560494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99949324,0.000006422934,0.00011497774,0.000116373936,0.00018472117,0.00008428473],"domain_scores_gemma":[0.999817,0.00000965975,0.000065272114,0.000059537833,0.000020441263,0.000028060655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009641767,0.000061169,0.000062399406,0.00025629497,0.0000309201,0.000020443624,0.000039670565,0.000025137224,0.000032321725],"category_scores_gemma":[0.00006146493,0.000047552647,0.000010471034,0.0015350181,0.000030392488,0.0001701741,0.000037430145,0.000032626904,0.000010227359],"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":[0.000101881706,0.00053643424,0.722511,0.00035073704,0.000028168215,0.0000017123776,0.0006641154,0.013326865,0.0013087247,0.0022121998,0.001190363,0.25776783],"study_design_scores_gemma":[0.00031470088,0.0006759728,0.9291662,0.000044029603,0.000023576347,0.0000015110128,0.000008881323,0.06725095,0.0005177403,0.0015204707,0.0003290034,0.00014696846],"about_ca_topic_score_codex":0.00097472785,"about_ca_topic_score_gemma":0.00010256079,"teacher_disagreement_score":0.25762087,"about_ca_system_score_codex":0.000047821948,"about_ca_system_score_gemma":0.0000051147126,"threshold_uncertainty_score":0.19391407},"labels":[],"label_agreement":null},{"id":"W2039857564","doi":"10.1111/j.0006-341x.2002.00997.x","title":"Flexible Weighted Log-Rank Tests Optimal for Detecting Early and/or Late Survival Differences","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":36,"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":"National Institute of Allergy and Infectious Diseases; Centers for Disease Control and Prevention; ACT Government; Clinical Trial Center, China Medical University Hospital","keywords":"Statistic; Log-rank test; Statistics; Rank (graph theory); Replication (statistics); Flexibility (engineering); Mathematics; Multiple comparisons problem; Survival analysis; Computer science; Combinatorics","score_opus":0.0776860117330204,"score_gpt":0.299239828659466,"score_spread":0.22155381692644563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039857564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919857,0.005164148,0.0013344206,0.00014325866,0.00032213432,0.0003429415,0.00018817822,0.00009464984,0.00042461272],"genre_scores_gemma":[0.99041027,0.00054380094,0.0011925726,0.000012603251,0.000032057742,0.00004484993,0.000045745262,0.000013773094,0.007704342],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99891204,0.00007448926,0.0001777267,0.0002825517,0.000056668167,0.00049651216],"domain_scores_gemma":[0.9987905,0.0008644175,0.00005806132,0.00014972533,0.00007946675,0.000057821395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019734101,0.00015262791,0.00023679395,0.0007218685,0.00027697821,0.000047777834,0.00014514981,0.00018007368,0.00040895093],"category_scores_gemma":[0.0004091916,0.00010585171,0.000058635364,0.0012661767,0.00013502385,0.00006539339,0.00006968087,0.00013230272,0.00045576788],"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":[0.0009659579,0.0011085179,0.15098892,0.0001388832,0.0010285603,0.00007015142,0.00073675235,9.383159e-7,0.11546117,0.00025575422,0.0024915703,0.7267528],"study_design_scores_gemma":[0.02084957,0.013903881,0.70967937,0.00010347662,0.00030659538,0.00015791687,0.0003522721,0.0007848018,0.1282238,0.00036486055,0.12375901,0.0015144419],"about_ca_topic_score_codex":0.00008461605,"about_ca_topic_score_gemma":0.000015106971,"teacher_disagreement_score":0.7252384,"about_ca_system_score_codex":0.000031813324,"about_ca_system_score_gemma":0.000018021168,"threshold_uncertainty_score":0.58581257},"labels":[],"label_agreement":null},{"id":"W2041200301","doi":"10.1111/j.0006-341x.2002.00727.x","title":"Marginally Specified Generalized Linear Mixed Models: A Robust Approach","year":2002,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized linear mixed model; Generalized linear model; Mixed model; Covariate; Interpretability; Random effects model; Weighting; Marginal model; Mathematics; Estimator; Econometrics; Inference; Linear model; Statistics; Flexibility (engineering); Population; Computer science; Regression analysis; Machine learning; Artificial intelligence","score_opus":0.32673138791350076,"score_gpt":0.3454491459047351,"score_spread":0.01871775799123432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041200301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010808605,0.00054553855,0.9622489,0.0151499435,0.0007874912,0.0005778623,0.00056656636,0.00022505417,0.01988782],"genre_scores_gemma":[0.0000075027315,0.00034966393,0.9588397,0.030555187,0.0035382386,0.00007764464,0.00030958,0.00018736858,0.0061350986],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99537206,0.00046274575,0.0009670184,0.0009930317,0.0013000277,0.0009051214],"domain_scores_gemma":[0.9952236,0.0025620053,0.0005224118,0.001138976,0.00036315038,0.00018986227],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00080908224,0.00074465823,0.0013147612,0.0023572198,0.00012777954,0.00020714292,0.0009548662,0.0016563784,0.00070054654],"category_scores_gemma":[0.002556932,0.0006391188,0.0003700485,0.004457113,0.00017263074,0.00009352253,0.00021166615,0.0018072414,0.0001445146],"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":[0.000015356,0.00018080787,0.0000026765856,0.0007688331,0.00012487549,0.00025439443,0.000034133216,0.0000053516846,0.0000039532983,0.054123916,0.9309279,0.013557838],"study_design_scores_gemma":[0.0009446931,0.00013076376,0.000021812375,0.0001150078,0.0003773593,0.00006845925,0.00001355805,0.081871,0.000019826835,0.4054411,0.50958127,0.0014151202],"about_ca_topic_score_codex":0.00002333228,"about_ca_topic_score_gemma":3.294763e-7,"teacher_disagreement_score":0.42134658,"about_ca_system_score_codex":0.00019921245,"about_ca_system_score_gemma":0.00008159213,"threshold_uncertainty_score":0.9996397},"labels":[],"label_agreement":null},{"id":"W2041304098","doi":"10.1111/j.0006-341x.2004.00157.x","title":"Loglinear Models for the Robust Design in Mark–Recapture Experiments","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Log-linear model; Mark and recapture; Statistics; Sampling (signal processing); Population; Econometrics; Sampling design; Poisson regression; Poisson distribution; Mathematics; Linear model; Computer science; Demography","score_opus":0.2734284867062761,"score_gpt":0.3700501551488151,"score_spread":0.096621668442539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041304098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008304445,0.00048219704,0.9898759,0.00032686695,0.00019110335,0.00061871624,0.000013994119,0.000033122356,0.00015365655],"genre_scores_gemma":[0.6296679,0.000039735263,0.36986226,0.00009039867,0.00008149581,0.00006254311,0.000016806132,0.000021438966,0.00015746315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999304,0.000021623679,0.0002219121,0.00012248193,0.00017855865,0.00015141857],"domain_scores_gemma":[0.99908644,0.00054367783,0.00008460181,0.00018335735,0.00007289074,0.000029056913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048214625,0.0000893081,0.00010802139,0.0004086386,0.00007044789,0.000029165045,0.00012085696,0.00008813713,0.0000129588125],"category_scores_gemma":[0.00052465237,0.00006343804,0.00004471021,0.0013966305,0.00001535761,0.00008849132,0.000019014875,0.000054981523,0.000005924621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014568269,0.0007457965,0.0007066928,0.00015461138,0.000058345868,0.0000046493674,0.0022974384,0.75509435,0.00049715984,0.20547757,0.008898516,0.025919184],"study_design_scores_gemma":[0.0023300392,0.00010336458,0.0015198725,0.0000430445,0.000040319872,0.000004751895,0.00016064954,0.7129363,0.001770049,0.27787128,0.0029319113,0.00028841346],"about_ca_topic_score_codex":0.00003239302,"about_ca_topic_score_gemma":0.0000060261573,"teacher_disagreement_score":0.6213634,"about_ca_system_score_codex":0.00011097216,"about_ca_system_score_gemma":0.000027269645,"threshold_uncertainty_score":0.25869283},"labels":[],"label_agreement":null},{"id":"W2044118951","doi":"10.1111/j.0006-341x.2000.00451.x","title":"Increased Power with Modified Forms of the Levene (Med) Test for Heterogeneity of Variance","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":54,"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 Waterloo; University of Guelph","funders":"","keywords":"Variance (accounting); Levene's test; Notice; Statistics; Econometrics; F-test of equality of variances; Analysis of variance; Mathematics; Test (biology); Power (physics); Linear model; Computer science; Statistical hypothesis testing; Economics","score_opus":0.11382243431910422,"score_gpt":0.38303431325770637,"score_spread":0.26921187893860216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044118951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.207852,0.000044545584,0.7903457,0.000029552675,0.000024166538,0.000366193,0.0004783518,0.000011376052,0.0008480592],"genre_scores_gemma":[0.5971286,0.0000066365023,0.40263525,0.000023073128,0.000006929059,0.000015600213,0.0000017983921,0.000013231389,0.00016883097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990772,0.000032661388,0.00031105266,0.00015640851,0.0002474438,0.00017523217],"domain_scores_gemma":[0.9971057,0.002115087,0.00019010973,0.00037177492,0.00016191647,0.000055386055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038216158,0.000110372785,0.00028538072,0.00013281897,0.000044416094,0.000004523056,0.00019995004,0.00006566742,0.000024672561],"category_scores_gemma":[0.0021077988,0.00006319918,0.00007871499,0.0012946313,0.0001078852,0.00004410868,0.000025762922,0.000050560404,4.3825176e-7],"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":[0.0013624431,0.00385438,0.0029336216,0.002029862,0.0003543028,0.0000053349318,0.00046949118,0.00042624082,0.0559411,0.63198245,0.00044723664,0.30019355],"study_design_scores_gemma":[0.003369779,0.0013512839,0.008299546,0.00015885233,0.00020437599,0.000010414967,0.00003536174,0.0071607446,0.13260853,0.8437394,0.0026103212,0.00045142474],"about_ca_topic_score_codex":0.000019224553,"about_ca_topic_score_gemma":0.0000052586865,"teacher_disagreement_score":0.38927665,"about_ca_system_score_codex":0.000021984484,"about_ca_system_score_gemma":0.00003935274,"threshold_uncertainty_score":0.25771877},"labels":[],"label_agreement":null},{"id":"W2047333075","doi":"10.1111/j.1541-0420.2011.01599.x","title":"Smoothing Population Size Estimates for Time-Stratified Mark-Recapture Experiments Using Bayesian P-Splines","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Hort Innovation; Pacific Institute for the Mathematical Sciences; National Science Foundation","keywords":"Salmo; Mark and recapture; Statistics; Bayesian probability; Sample (material); Population; Sample size determination; Smoothing; Population size; Sampling (signal processing); Fish <Actinopterygii>; Mathematics; Econometrics; Computer science; Fishery; Biology; Demography","score_opus":0.14916327247601005,"score_gpt":0.3677757090747258,"score_spread":0.21861243659871574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047333075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66592574,0.00026289627,0.33042732,0.00006186735,0.0007650878,0.0011631475,0.00008679776,0.00031065856,0.0009964987],"genre_scores_gemma":[0.5865582,0.0000013993092,0.41303456,0.00002275539,0.000096243646,0.000014299177,0.00008093184,0.00003301003,0.00015858684],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873716,0.000027387861,0.00048314486,0.00025182017,0.0002561873,0.00024430404],"domain_scores_gemma":[0.9986302,0.00053000066,0.0003280939,0.000246036,0.00018666276,0.00007895824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037172803,0.00020066445,0.00025161143,0.00050352304,0.00019304758,0.000062916224,0.00012220362,0.00018085745,0.00019665716],"category_scores_gemma":[0.001864958,0.0001899037,0.00009577718,0.0011198366,0.000019061346,0.00024879578,0.00002867014,0.000060631715,0.000009146379],"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":[0.0013809822,0.0044860956,0.46804684,0.0040282067,0.0010853951,0.000032602697,0.018024739,0.0011282132,0.17275687,0.14919168,0.017901316,0.16193707],"study_design_scores_gemma":[0.0024470424,0.00026667042,0.12931274,0.00027680898,0.00047094366,0.000026092055,0.00022338053,0.67635685,0.024346171,0.1639158,0.0008791094,0.0014783791],"about_ca_topic_score_codex":0.00012312195,"about_ca_topic_score_gemma":0.0000043289174,"teacher_disagreement_score":0.67522866,"about_ca_system_score_codex":0.00009305825,"about_ca_system_score_gemma":0.000019132724,"threshold_uncertainty_score":0.7744048},"labels":[],"label_agreement":null},{"id":"W2049010725","doi":"10.1111/j.0006-341x.2004.00159.x","title":"A Note on One‐Sided Tests with Multiple Endpoints","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":25,"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":"","keywords":"Mathematics; Statistics; Computer science; Econometrics","score_opus":0.23374589013598263,"score_gpt":0.45472142168100044,"score_spread":0.2209755315450178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049010725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7237896,0.00027398946,0.25267214,0.000724492,0.0008641135,0.00082664384,0.00005988386,0.0002493497,0.020539815],"genre_scores_gemma":[0.65703887,0.0000050897347,0.34222633,0.00027839522,0.000045460492,0.000008645699,0.0000021521269,0.000019719117,0.0003753453],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958728,0.0001652314,0.0004740883,0.0006810648,0.002415403,0.0003914387],"domain_scores_gemma":[0.99424696,0.0041845553,0.00022281094,0.0008174274,0.0002699327,0.00025832234],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020575942,0.0002187977,0.00032920716,0.0028462617,0.00013441972,0.00027242213,0.00077724335,0.00012090411,0.00015122023],"category_scores_gemma":[0.019313233,0.00015152081,0.00010209268,0.013328395,0.00014863258,0.00026379916,0.00014112768,0.00016249136,0.0018208815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013654345,0.004019281,0.028038098,0.000014162983,0.00010804129,0.00036360204,0.0014820964,0.0018902925,0.52561384,0.0056866137,0.002074164,0.4293444],"study_design_scores_gemma":[0.0064464207,0.0043542725,0.18337436,0.00011787729,0.00003369909,0.0000549571,0.00039046849,0.0010998615,0.78026414,0.012764696,0.010058198,0.001041032],"about_ca_topic_score_codex":0.000085702006,"about_ca_topic_score_gemma":0.000018893592,"teacher_disagreement_score":0.42830333,"about_ca_system_score_codex":0.00027822173,"about_ca_system_score_gemma":0.00011969104,"threshold_uncertainty_score":0.9989563},"labels":[],"label_agreement":null},{"id":"W2049197836","doi":"10.1111/j.1541-0420.2007.00856_10.x","title":"Data Analysis and Graphics using R, 2nd edition by J. MAINDONALD and J. BRAUN","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Data Analysis with R","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Citation; Graphics; Library science; Computer science; Mathematics; Computer graphics (images)","score_opus":0.05222379005985621,"score_gpt":0.3089617568347401,"score_spread":0.25673796677488386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049197836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055179052,0.0014580743,0.9426208,0.0001564159,0.00010159686,0.000050170154,0.00034659228,0.000040865758,0.000046429595],"genre_scores_gemma":[0.7940985,0.0008474201,0.20309319,0.0006930764,0.00015029703,7.837583e-7,0.0010488281,0.000015272994,0.00005264901],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982682,0.00003800134,0.0002720617,0.0006383343,0.0005083179,0.00027507267],"domain_scores_gemma":[0.99817425,0.00030205966,0.00015365763,0.0011035812,0.000085460066,0.0001809621],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0017526225,0.00012962734,0.00021952642,0.004538965,0.00014961658,0.00037231445,0.0008929712,0.000086711116,0.0000054404177],"category_scores_gemma":[0.0002646395,0.00012451048,0.000037824862,0.024551755,0.000099437304,0.0010282738,0.00091617025,0.00009405498,0.0000019658116],"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":[0.000024404015,0.0005412947,0.5287812,0.000114528484,0.0029750415,0.0001532138,0.000227408,0.000018070476,0.010398593,0.012635456,0.05693239,0.3871984],"study_design_scores_gemma":[0.0008816735,0.000113595524,0.33250836,0.000016383265,0.0017080517,0.000060620125,0.000055722594,0.57444245,0.0017377853,0.0015777051,0.085949466,0.00094817067],"about_ca_topic_score_codex":0.00023236667,"about_ca_topic_score_gemma":0.00009793032,"teacher_disagreement_score":0.7395276,"about_ca_system_score_codex":0.000028669412,"about_ca_system_score_gemma":0.000020327803,"threshold_uncertainty_score":0.99618185},"labels":[],"label_agreement":null},{"id":"W2052457798","doi":"10.1111/j.1541-0420.2011.01597.x","title":"Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"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":"Missing data; Multivariate statistics; Imputation (statistics); Statistics; Multivariate analysis; Computer science; Mathematics","score_opus":0.49540165008553494,"score_gpt":0.5023205246097163,"score_spread":0.006918874524181384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052457798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004925246,0.000052809988,0.9979946,0.000035281155,0.00014774577,0.00048079243,0.00020158684,0.00009766011,0.00049699843],"genre_scores_gemma":[0.01446322,0.000004337733,0.98528904,0.000037624053,0.00005603687,0.00002705177,0.00005459112,0.00003904486,0.000029053868],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985151,0.00019501719,0.00036885389,0.0004260999,0.00019006811,0.00030483204],"domain_scores_gemma":[0.98685676,0.011840045,0.00024282021,0.00066817977,0.00025681598,0.00013538894],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0017312081,0.00017254737,0.0003133518,0.00051998626,0.00011700206,0.00006509952,0.00040126574,0.000108350396,0.000042497857],"category_scores_gemma":[0.040330518,0.00013458575,0.000033655564,0.0017947697,0.00007036317,0.00017977064,0.00013670522,0.00009488278,0.0000053725157],"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":[0.00010071578,0.00030717254,0.00078310363,0.00017800232,0.00007157792,0.0000034152329,0.00031981224,6.5378195e-8,0.0047525303,0.04933858,0.00020990633,0.9439351],"study_design_scores_gemma":[0.0017012542,0.0004508865,0.016195105,0.00013601355,0.00026864777,0.000009735661,0.000082127044,0.06622376,0.011964872,0.90156955,0.0008995421,0.00049849553],"about_ca_topic_score_codex":0.000093453484,"about_ca_topic_score_gemma":0.0000062595177,"teacher_disagreement_score":0.9434366,"about_ca_system_score_codex":0.000037073587,"about_ca_system_score_gemma":0.00006834367,"threshold_uncertainty_score":0.9677532},"labels":[],"label_agreement":null},{"id":"W2052950835","doi":"10.1111/j.0006-341x.2001.00461.x","title":"Catch Estimation with Restricted Randomization in the Effort Survey","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Vancouver Island University","funders":"","keywords":"Estimator; Statistics; Sample (material); Fishing; Estimation; Econometrics; Computer science; Sample size determination; Mathematics; Fishery; Engineering; Biology","score_opus":0.01919131725050336,"score_gpt":0.2395490139043131,"score_spread":0.22035769665380975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052950835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9763647,0.000013519902,0.011938861,0.0005501648,0.000056674446,0.0003772668,0.00000149958,0.000020646296,0.010676708],"genre_scores_gemma":[0.9985641,0.00011574185,0.00070668187,0.000268295,0.0000047004655,0.00002473795,0.000031249852,0.0000029263854,0.00028157013],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994353,0.00007167507,0.0000963116,0.000112955255,0.00016792219,0.00011580215],"domain_scores_gemma":[0.99962157,0.00021339966,0.000043952743,0.000104275234,0.000006156906,0.000010621423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006404924,0.000052704527,0.00006720771,0.00021403198,0.00009592666,0.000014598172,0.00011850959,0.000030286677,0.00007100946],"category_scores_gemma":[0.00031832853,0.000032657772,0.0000073347383,0.0053463713,0.000060452065,0.00009319935,0.00004262285,0.00003968471,0.0000842551],"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":[0.00006800806,0.000054858705,0.98738736,0.000001303884,0.0000041691337,0.000004990946,0.00007050386,0.0010182247,0.000001590413,0.000012045887,0.009220925,0.002156039],"study_design_scores_gemma":[0.0009418668,0.000046784877,0.9942661,0.0000010365409,0.0000069222806,0.0000015186795,0.000027554972,0.0025562034,0.0000025185886,0.000041029107,0.0020606823,0.00004781938],"about_ca_topic_score_codex":0.00062117365,"about_ca_topic_score_gemma":0.003979898,"teacher_disagreement_score":0.022199437,"about_ca_system_score_codex":0.000054381748,"about_ca_system_score_gemma":0.000002449378,"threshold_uncertainty_score":0.25687543},"labels":[],"label_agreement":null},{"id":"W2052951278","doi":"10.1111/j.0006-341x.2001.00598.x","title":"Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Sinai Hospital; BC Cancer Agency; University of British Columbia","funders":"","keywords":"Bayes' theorem; Statistics; Odds; Computer science; Odds ratio; Control (management); Prior probability; Bayesian probability; Mathematics; Econometrics; Artificial intelligence; Logistic regression","score_opus":0.16163759940580535,"score_gpt":0.40607226386356937,"score_spread":0.24443466445776402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052951278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26201862,0.0005502031,0.7360422,0.00004586606,0.00011581537,0.00015058221,0.00006502319,0.00003018745,0.0009815018],"genre_scores_gemma":[0.9864686,0.00000900433,0.01282383,0.0000044238773,0.00007811993,0.00001908783,0.0000026199104,0.0000080321,0.0005862644],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99774593,0.00013569722,0.00064860354,0.00042630147,0.0008129932,0.00023049809],"domain_scores_gemma":[0.9947774,0.0032309168,0.00033179796,0.00051215105,0.0010224197,0.0001253596],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0013038593,0.00012575494,0.0003755425,0.0022561227,0.00011104192,0.00008813011,0.0003246342,0.00006929144,0.000100899924],"category_scores_gemma":[0.007823113,0.00008480739,0.0000933921,0.021290213,0.00020514907,0.00023597851,0.000035184257,0.00007865311,0.000028355571],"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":[0.00024253417,0.0004902093,0.6290369,0.000045759272,0.00037076435,0.00020532834,0.0010792237,0.0045433603,0.0006203052,0.0046121674,0.00014493168,0.35860854],"study_design_scores_gemma":[0.0073297652,0.0035678023,0.6965138,0.00007395953,0.004210709,0.0008499441,0.0146931885,0.14947098,0.0072092353,0.05040655,0.0631807,0.0024934197],"about_ca_topic_score_codex":0.000018364046,"about_ca_topic_score_gemma":0.00003417116,"teacher_disagreement_score":0.72445,"about_ca_system_score_codex":0.000062282874,"about_ca_system_score_gemma":0.000063706386,"threshold_uncertainty_score":0.99951273},"labels":[],"label_agreement":null},{"id":"W2054663330","doi":"10.1111/j.1541-0420.2008.01129.x","title":"A Multilevel Model for Continuous Time Population Estimation","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Estimation; Contingency table; Population; Population size; Estimator; Statistics; Computer science; Bayesian probability; Econometrics; Hierarchical database model; Statistical model; Data mining; Mathematics; Medicine","score_opus":0.09696894577694708,"score_gpt":0.3672199900365666,"score_spread":0.2702510442596195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054663330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11867387,0.000022667848,0.8799856,0.0002122429,0.00008162566,0.00055163656,0.00005915399,0.00016797893,0.00024524078],"genre_scores_gemma":[0.68840456,0.000001306814,0.3103797,0.00006515624,0.00004454356,0.000010976449,0.00024049613,0.000012469832,0.00084079825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905515,0.000014291714,0.00035634617,0.00017191237,0.00022992502,0.00017237653],"domain_scores_gemma":[0.9991208,0.00025251054,0.00021170081,0.00018045091,0.00017919533,0.00005536386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033018683,0.0001208024,0.00018865184,0.0006332669,0.00010593307,0.00004138634,0.00007497315,0.000113940565,0.000017116407],"category_scores_gemma":[0.0015313246,0.00011840859,0.000075683325,0.00070319546,0.0000069617354,0.00017411736,0.0000076939195,0.000039493847,0.000023797804],"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":[0.00009459796,0.0005716766,0.0018257083,0.00014000012,0.000026132782,8.036302e-7,0.0004923448,0.021597106,0.0019757778,0.13867046,0.01308161,0.8215238],"study_design_scores_gemma":[0.00043544587,0.000053248146,0.015379935,0.000011719237,0.000026694346,0.0000016635901,0.0000013434997,0.85526836,0.000061733575,0.1284926,0.00013857278,0.00012871835],"about_ca_topic_score_codex":0.0000074129307,"about_ca_topic_score_gemma":9.3509755e-7,"teacher_disagreement_score":0.8336712,"about_ca_system_score_codex":0.000088817236,"about_ca_system_score_gemma":0.000014214788,"threshold_uncertainty_score":0.4828562},"labels":[],"label_agreement":null},{"id":"W2055479965","doi":"10.1111/j.0006-341x.2004.00189.x","title":"Assessing the Goodness‐of‐Fit of Hidden Markov Models","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":22,"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":"","keywords":"Goodness of fit; Univariate; Hidden Markov model; Mathematics; Statistics; Markov chain; Marginal distribution; Empirical distribution function; Markov model; Computer science; Econometrics; Multivariate statistics; Artificial intelligence; Random variable","score_opus":0.08768316276170808,"score_gpt":0.3412104473876531,"score_spread":0.25352728462594504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055479965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013153511,0.0010789585,0.981426,0.00037610074,0.00031387203,0.00009938351,0.0000032757405,0.00003134472,0.0035175774],"genre_scores_gemma":[0.49932224,0.00003770251,0.5005269,0.00005415593,0.000022411743,0.0000016846085,2.8895136e-7,0.000004868343,0.000029777448],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987889,0.00008162913,0.00030392077,0.00022743561,0.00039920062,0.00019891637],"domain_scores_gemma":[0.99869645,0.00023404034,0.00021556405,0.00064175966,0.00015434058,0.00005782354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009916606,0.00010751933,0.00021079648,0.0006499541,0.00006414959,0.0000985951,0.0010536489,0.00008467762,0.0000022554052],"category_scores_gemma":[0.00013340471,0.00007225028,0.00009905994,0.0053819423,0.00008134425,0.0005259585,0.00025058608,0.0000965983,0.0000018275259],"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":[0.000001619274,0.000118565215,0.00009864931,0.000047567766,0.000022447603,0.0000043169393,0.00047157964,0.00011393834,0.0036572348,0.3154441,0.00009528163,0.6799247],"study_design_scores_gemma":[0.0013105728,0.0002509626,0.00730441,0.00016304517,0.000064398526,0.00004389003,0.00011697148,0.079957485,0.059422564,0.84981036,0.00094954896,0.00060576544],"about_ca_topic_score_codex":0.000052022573,"about_ca_topic_score_gemma":5.4967853e-7,"teacher_disagreement_score":0.67931896,"about_ca_system_score_codex":0.00003484062,"about_ca_system_score_gemma":0.00013519442,"threshold_uncertainty_score":0.29462808},"labels":[],"label_agreement":null},{"id":"W2055872736","doi":"10.1111/j.1541-0420.2007.00824.x","title":"Efficient Estimation for Patient‐Specific Rates of Disease Progression Using Nonnormal Linear Mixed Models","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":34,"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 Waterloo; University of Alberta","funders":"Cleveland Clinic Foundation","keywords":"Random effects model; Inference; Mixed model; Normality; Statistics; Generalized linear mixed model; Missing data; Linear model; Statistical inference; Computer science; Econometrics; Mathematics; Medicine; Artificial intelligence; Internal medicine","score_opus":0.21155438215368114,"score_gpt":0.44300532954099825,"score_spread":0.2314509473873171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055872736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3413448,0.00013889471,0.6579068,0.000004137603,0.00015847656,0.00031706537,0.00007415016,0.000017329043,0.000038361322],"genre_scores_gemma":[0.451124,0.000002531334,0.54882264,0.0000031665168,0.000022236369,0.0000052928053,0.00000811785,0.00000978469,0.0000022099796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874806,0.000038083628,0.0004616703,0.00017333488,0.00034799677,0.00023087577],"domain_scores_gemma":[0.9972975,0.0018064983,0.0002419433,0.00017979192,0.00034113906,0.0001331122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080592366,0.00011301576,0.00019121815,0.00060421636,0.000075570184,0.000017094033,0.00008655509,0.00006353388,0.000009038049],"category_scores_gemma":[0.003097559,0.0000908907,0.000063885265,0.0015871011,0.00006018569,0.000035473877,0.000045921817,0.000047129743,0.0000012717367],"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":[0.00071885594,0.0016767189,0.000681382,0.0011074783,0.000031291347,0.000006920462,0.00046195366,0.008933966,0.006621535,0.19739863,0.00022639887,0.78213483],"study_design_scores_gemma":[0.00033584127,0.00020001392,0.0005379029,0.00009426145,0.000031097916,4.633889e-7,0.000050274928,0.9480689,0.012146223,0.03834888,0.000056247452,0.00012987961],"about_ca_topic_score_codex":0.0000018934345,"about_ca_topic_score_gemma":6.854256e-8,"teacher_disagreement_score":0.93913496,"about_ca_system_score_codex":0.00004903599,"about_ca_system_score_gemma":0.000035220004,"threshold_uncertainty_score":0.37082914},"labels":[],"label_agreement":null},{"id":"W2056460814","doi":"10.1111/j.0006-341x.2002.00324.x","title":"A Semiparametric Model for the Analysis of Recurrent-Event Panel Data","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Semiparametric regression; Overdispersion; Semiparametric model; Quasi-likelihood; Nonparametric statistics; Parametric statistics; Consistency (knowledge bases); Statistics; Event (particle physics); Econometrics; Model selection; Parametric model; Estimating equations; Mathematics; Computer science; Count data; Poisson distribution; Artificial intelligence","score_opus":0.539409826650123,"score_gpt":0.46079025929451795,"score_spread":0.0786195673556051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056460814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006644832,0.0010337445,0.99602,0.00011620598,0.00010404404,0.00026777096,0.0016357979,0.00001929322,0.0001386302],"genre_scores_gemma":[0.15429826,0.00039464844,0.8449539,0.00003371707,0.000029830935,0.000024592904,0.00003607422,0.000013546908,0.00021542625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861544,0.000056826462,0.00044764264,0.00028740623,0.0003720989,0.00022058512],"domain_scores_gemma":[0.9884279,0.010038699,0.00024245704,0.0010541043,0.0001699583,0.000066926856],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0011287738,0.00011893395,0.0003845278,0.0017374286,0.000068838985,0.000033062093,0.0007616936,0.00007322839,0.0001242708],"category_scores_gemma":[0.016998308,0.00007705553,0.00014986919,0.014654171,0.00007023867,0.000048589583,0.00017960521,0.00008337716,0.0000040387313],"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":[0.000013770574,0.00047371243,0.00034170836,0.00014633655,0.00089295884,7.211359e-7,0.00015922578,0.0000626721,0.000032225347,0.0980935,0.015132793,0.88465035],"study_design_scores_gemma":[0.00014522664,0.000042230207,0.00057488267,0.0000072898247,0.0016733181,4.032943e-7,0.000019770625,0.9416124,0.000020916705,0.054852508,0.0009499264,0.00010111726],"about_ca_topic_score_codex":0.000010256304,"about_ca_topic_score_gemma":0.0000037717725,"teacher_disagreement_score":0.9415497,"about_ca_system_score_codex":0.000028404016,"about_ca_system_score_gemma":0.000015110836,"threshold_uncertainty_score":0.9912819},"labels":[],"label_agreement":null},{"id":"W2056519252","doi":"10.1111/j.1541-0420.2008.01105.x","title":"Median Regression Models for Longitudinal Data with Dropouts","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":60,"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; University of Waterloo","funders":"","keywords":"Statistics; Estimator; Regression; Dropout (neural networks); Regression analysis; Consistency (knowledge bases); Regression diagnostic; Mathematics; Regression toward the mean; Linear regression; Longitudinal data; Computer science; Polynomial regression; Data mining; Machine learning","score_opus":0.38589175227081896,"score_gpt":0.43936605213758806,"score_spread":0.053474299866769104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056519252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017248035,0.00010580608,0.9964874,0.00015028377,0.00013057578,0.00024069966,0.00035455002,0.000056972003,0.000748919],"genre_scores_gemma":[0.08153992,0.00006875968,0.9180302,0.000025197558,0.00010669115,0.000014405935,0.00005488003,0.000023090732,0.00013685669],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987286,0.00003623014,0.00022822098,0.00036536585,0.0003803198,0.00026131712],"domain_scores_gemma":[0.99681795,0.0020401333,0.00011507289,0.0007188052,0.00016539262,0.00014262325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044103063,0.00014184853,0.00025726543,0.00043025424,0.00014777179,0.000025478208,0.00043185434,0.0000865098,0.00003551975],"category_scores_gemma":[0.0032756925,0.00009143026,0.000027072554,0.0015854327,0.00012645418,0.00016173745,0.0001297149,0.000084511805,0.000006777278],"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":[0.00025912613,0.00065797503,0.0065180515,0.0005185531,0.000121250225,0.00020120043,0.00033715487,0.0000012967926,0.00018630577,0.6271976,0.066046536,0.29795492],"study_design_scores_gemma":[0.001143853,0.0004850059,0.0018583265,0.00013724236,0.00010457897,0.0001111779,0.000043612064,0.052246064,0.00050052215,0.9398539,0.0030668275,0.00044887568],"about_ca_topic_score_codex":0.000013392385,"about_ca_topic_score_gemma":0.000004592679,"teacher_disagreement_score":0.31265628,"about_ca_system_score_codex":0.000028384518,"about_ca_system_score_gemma":0.000079128884,"threshold_uncertainty_score":0.39215466},"labels":[],"label_agreement":null},{"id":"W2059885779","doi":"10.1111/j.1541-0420.2005.00517.x","title":"Local Influence Diagnostics for Quasi‐Likelihood and Lognormal Estimates of a Biological Reference Point from Some Fish Stock and Recruitment Models","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Marine and fisheries research","field":"Environmental Science","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":"Fisheries and Oceans Canada","funders":"","keywords":"Stock (firearms); Statistics; Econometrics; Maximum likelihood; Mathematics; Point estimation; Log-normal distribution; Fish stock; Fish <Actinopterygii>; Fishery; Biology; Geography","score_opus":0.08661601775271577,"score_gpt":0.2936057962063864,"score_spread":0.20698977845367061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059885779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96410066,0.0002675428,0.03351459,0.00014683501,0.000022617087,0.00055889686,0.0002975785,0.000020384183,0.0010708848],"genre_scores_gemma":[0.98783785,0.0010592645,0.010813496,0.00009330226,0.000017271628,0.00007912566,0.00006799849,0.0000080263935,0.000023684532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988291,0.000024633999,0.00025856757,0.0003269961,0.0002564394,0.00030425133],"domain_scores_gemma":[0.9989006,0.0007183863,0.00007639054,0.00016008908,0.000028907281,0.000115619296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022868752,0.00013309307,0.00020562,0.00014975763,0.00007354262,0.00003913745,0.0001677123,0.00012467713,0.00011398768],"category_scores_gemma":[0.00037024292,0.00010698352,0.000024107005,0.000616641,0.0004471798,0.00022393592,0.00046461364,0.000086470965,0.0000023194477],"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":[0.00013559198,0.00052167434,0.741403,0.000060604987,0.000020840245,0.0000111283225,0.00007592252,0.00025601863,0.0020870406,0.0013564333,0.0016883248,0.25238347],"study_design_scores_gemma":[0.0014974745,0.0023748514,0.8596972,0.00002334955,0.00003713571,0.000008352244,0.00011874713,0.064614005,0.0033056997,0.05590162,0.011882879,0.0005386456],"about_ca_topic_score_codex":0.003109279,"about_ca_topic_score_gemma":0.0000937436,"teacher_disagreement_score":0.25184482,"about_ca_system_score_codex":0.000057518766,"about_ca_system_score_gemma":0.000011524184,"threshold_uncertainty_score":0.47003207},"labels":[],"label_agreement":null},{"id":"W2064805293","doi":"10.1111/j.0006-341x.2004.00260.x","title":"Evaluation of Community‐Intervention Trials via Generalized Linear Mixed Models","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"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":"National Cancer Institute","keywords":"Generalized linear mixed model; Mixed model; Covariate; Random effects model; Linear model; Inference; Randomized controlled trial; Mathematics; Multilevel model; Sample size determination; Statistics; Medicine; Computer science; Artificial intelligence; Meta-analysis","score_opus":0.5595949354129924,"score_gpt":0.5150597011721977,"score_spread":0.04453523424079475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064805293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10562368,0.00017880382,0.8930754,0.000037718877,0.0002432224,0.00035577826,0.000061618106,0.000031404204,0.00039235628],"genre_scores_gemma":[0.45027587,0.00001549761,0.5496142,0.000010311116,0.000034419372,0.000016904794,0.000015254359,0.000010587771,0.000006978213],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995085,0.0028769223,0.00087841257,0.00010626608,0.0009033977,0.00014998567],"domain_scores_gemma":[0.9955522,0.0026424313,0.00047384042,0.00036910782,0.00089921686,0.00006323317],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02122113,0.00012060676,0.00049879425,0.000726273,0.00007449274,0.000020206988,0.00020041082,0.000119735945,0.00012251078],"category_scores_gemma":[0.029984163,0.00010057983,0.00018551318,0.0018431038,0.000058757494,0.000083909385,0.00005812174,0.0001373404,0.0000079950905],"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":[0.000043725613,0.0009873388,0.000009450365,0.00019801738,0.00013619786,6.0574723e-7,0.0003314481,0.00015434857,0.006039706,0.37945777,0.00017579184,0.6124656],"study_design_scores_gemma":[0.0022903534,0.00018457031,0.00022299077,0.00005430106,0.00036818537,0.0000016312123,0.000059626753,0.021095086,0.013312885,0.96228665,0.000016984377,0.000106753614],"about_ca_topic_score_codex":0.00022015943,"about_ca_topic_score_gemma":0.0000107746955,"teacher_disagreement_score":0.61235887,"about_ca_system_score_codex":0.00013533041,"about_ca_system_score_gemma":0.000069179514,"threshold_uncertainty_score":0.97818667},"labels":[],"label_agreement":null},{"id":"W2065291980","doi":"10.1111/j.1541-0420.2010.01509.x","title":"A Primer of Ecology with R by STEVENS, M. H. H.","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"","keywords":"Primer (cosmetics); Computational statistics; Statistical software; Inference; Bivariate analysis; R package; Humanities; Mathematics; Combinatorics; Statistics; Ecology; Philosophy; Computer science; Biology; Artificial intelligence; Physics","score_opus":0.006217168623040417,"score_gpt":0.2393911780126522,"score_spread":0.23317400938961177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065291980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965434,0.00033016104,0.0016291984,0.00010791882,0.00018336784,0.000082616185,0.000019702677,0.000005609069,0.0010980202],"genre_scores_gemma":[0.99691945,0.00007185804,0.0016815484,0.00008859287,0.000048990925,0.000010259182,0.000062207284,0.000009172793,0.0011078909],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953365,0.000013174728,0.000098986406,0.00016423654,0.000093747236,0.00009620508],"domain_scores_gemma":[0.9995256,0.0000072162256,0.00008333924,0.00024628203,0.00009047974,0.00004709433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008378755,0.00006074525,0.00006971443,0.00017004216,0.00001955891,0.000004690926,0.000117819895,0.00012191997,0.00003318854],"category_scores_gemma":[0.000062514795,0.000047987574,0.00002289042,0.000600664,0.00006368074,0.0000011434015,0.00003041823,0.000051659754,0.000004433912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026010257,0.00006200797,0.011872103,0.000005090295,0.00001141855,1.3250262e-7,0.000004326078,2.733601e-7,0.96546054,0.00005483565,0.017510843,0.004992416],"study_design_scores_gemma":[0.00025805942,0.00019990488,0.014194817,0.0000010049257,0.000005419671,0.0000028002248,0.000012747335,0.0000027620088,0.66698986,0.000005238506,0.31826636,0.00006101355],"about_ca_topic_score_codex":0.0000055222004,"about_ca_topic_score_gemma":0.000007406387,"teacher_disagreement_score":0.30075553,"about_ca_system_score_codex":0.0000032543105,"about_ca_system_score_gemma":0.0000576019,"threshold_uncertainty_score":0.19568764},"labels":[],"label_agreement":null},{"id":"W2065784951","doi":"10.1111/j.0006-341x.2000.00622.x","title":"On the Accuracy of Efficiency of Estimating Equation Approach","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Covariate; Estimator; Generalized estimating equation; Statistics; Regression analysis; Estimating equations; Regression; Econometrics; Linear regression","score_opus":0.2811482564638641,"score_gpt":0.4432947984390565,"score_spread":0.1621465419751924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065784951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051950417,0.00003343644,0.94168985,0.0000197634,0.00003186381,0.00017224842,0.000019429908,0.000012296954,0.0060707247],"genre_scores_gemma":[0.3797516,0.000005006447,0.6201573,0.00001075737,0.000009995246,0.000004388961,0.0000012848748,0.000005888006,0.00005379144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991019,0.000072903604,0.00031234336,0.00010982486,0.00029422602,0.000108851615],"domain_scores_gemma":[0.99140084,0.008076998,0.00019029425,0.00023020513,0.00007796877,0.000023701788],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00076880405,0.00006934543,0.000163936,0.00021220472,0.000041998454,0.000005473582,0.00014323829,0.000038252623,0.000086820364],"category_scores_gemma":[0.016170809,0.00004322436,0.00004166982,0.0016464408,0.00006478371,0.000031355088,0.00001628065,0.000063106345,0.0000033483275],"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":[0.000013148342,0.0002894985,0.0000050113663,0.00012841744,0.000007854798,1.7904365e-7,0.00021670281,0.0012085552,0.00066748925,0.6987081,0.00009008382,0.29866493],"study_design_scores_gemma":[0.0001752583,0.0001491684,0.00004520972,0.000036936042,0.000021201353,6.934262e-7,0.00004606917,0.35345668,0.0033311802,0.6426038,0.000055250286,0.000078542034],"about_ca_topic_score_codex":0.0000039689767,"about_ca_topic_score_gemma":2.3752031e-8,"teacher_disagreement_score":0.35224813,"about_ca_system_score_codex":0.000013676381,"about_ca_system_score_gemma":0.000014632593,"threshold_uncertainty_score":0.9921164},"labels":[],"label_agreement":null},{"id":"W2067567094","doi":"10.1111/j.0006-341x.2000.00496.x","title":"Marginal Models for Longitudinal Continuous Proportional Data","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Cancer Institute","keywords":"Applied mathematics; Mathematics; Zero (linguistics); Marginal model; Simplex; Longitudinal data; Function (biology); Statistics; Computer science; Combinatorics; Regression analysis","score_opus":0.33945697105169514,"score_gpt":0.2673015459231908,"score_spread":0.07215542512850437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067567094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7252647,0.0068320967,0.19771138,0.0011888886,0.0009336747,0.0012982487,0.0063175857,0.00011877025,0.060334634],"genre_scores_gemma":[0.9773736,0.0006707303,0.012186113,0.00014989614,0.00023003067,0.000046976136,0.0011987977,0.000028190008,0.008115693],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986461,0.0000047886547,0.0005033051,0.0005414568,0.000048593647,0.00025579982],"domain_scores_gemma":[0.99919057,0.000046875623,0.0001856664,0.0004882144,0.000013198341,0.00007545146],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007239133,0.00012330015,0.00024823294,0.00039179024,0.0001063037,0.000062775,0.00036123986,0.000087544184,0.0028716808],"category_scores_gemma":[0.000047912778,0.00014719502,0.00007010762,0.00046154408,0.00005818582,0.0005232287,0.00006718025,0.000056621648,0.0011117678],"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":[0.00014917253,0.00086455524,0.6201561,0.00009280002,0.00023506886,0.000005536923,0.00010513217,0.0033976352,0.000021552803,0.21408537,0.059412405,0.10147464],"study_design_scores_gemma":[0.0019825068,0.00024961994,0.3406826,0.000008658088,0.000029721183,0.000018224799,0.00002917724,0.1775697,0.000025088442,0.062072217,0.4166733,0.00065919],"about_ca_topic_score_codex":0.00006219838,"about_ca_topic_score_gemma":0.000002098304,"teacher_disagreement_score":0.3572609,"about_ca_system_score_codex":0.00012757374,"about_ca_system_score_gemma":0.000016222075,"threshold_uncertainty_score":0.999666},"labels":[],"label_agreement":null},{"id":"W2067883763","doi":"10.1111/j.1541-0420.2008.01013.x","title":"Bayesian Estimation of Inverse Dose Response","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"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":"National Science Foundation","keywords":"Maximum a posteriori estimation; Posterior probability; Bayesian probability; Bayesian inference; Mathematics; Posterior predictive distribution; Statistics; Prior probability; A priori and a posteriori; Computer science; Bayes estimator; Inverse problem; Bayesian linear regression; Algorithm; Maximum likelihood","score_opus":0.26296731177884397,"score_gpt":0.46296430263117344,"score_spread":0.19999699085232947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067883763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7203886,0.00021026256,0.2762051,0.00010627097,0.00036202354,0.00016264047,0.0000151745535,0.000038709113,0.0025112354],"genre_scores_gemma":[0.6680736,0.00001099337,0.33092108,0.000056488,0.000012235143,0.000002776216,0.0000011573795,0.0000073336464,0.0009143303],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967359,0.0006487875,0.0006089654,0.0003131198,0.0015160367,0.00017723971],"domain_scores_gemma":[0.9953295,0.0034314243,0.00027902296,0.00057453115,0.00024561665,0.0001398824],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005490692,0.00010973231,0.00025052734,0.0034889465,0.00009705705,0.00003928715,0.0005290622,0.0000964971,0.00030133553],"category_scores_gemma":[0.025135763,0.00008852012,0.000106297055,0.013515117,0.00023641867,0.00031087722,0.00012123982,0.00006547537,0.00033064553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031303382,0.00092190754,0.019285845,0.000017065551,0.000042069165,0.00018355018,0.0035960558,0.0034869583,0.52172196,0.0012920131,0.04290957,0.4034127],"study_design_scores_gemma":[0.0027099035,0.0019163305,0.27190554,0.0000248324,0.000025725087,0.00017045025,0.0011438184,0.23784266,0.44868752,0.008078442,0.026675962,0.00081878685],"about_ca_topic_score_codex":0.000020945643,"about_ca_topic_score_gemma":2.7538528e-7,"teacher_disagreement_score":0.4025939,"about_ca_system_score_codex":0.00007565884,"about_ca_system_score_gemma":0.00011621827,"threshold_uncertainty_score":0.9830759},"labels":[],"label_agreement":null},{"id":"W2068225985","doi":"10.1111/j.0006-341x.2001.00158.x","title":"Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":531,"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","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Bayesian probability; Statistics; Econometrics; Inference; Conditional dependence; Bayesian inference; A priori and a posteriori; Statistical hypothesis testing; Mathematics; Computer science; Artificial intelligence","score_opus":0.38594037904516443,"score_gpt":0.3821515830412841,"score_spread":0.0037887960038803237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068225985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02111173,0.00006558136,0.9769057,0.00061959587,0.000090068854,0.0003526278,0.00013555812,0.00006818702,0.00065095053],"genre_scores_gemma":[0.6111028,0.000009193991,0.38855916,0.00008038792,0.00015900821,0.000040010807,0.000011259292,0.000014726114,0.000023446597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834204,0.00012373581,0.0003524833,0.00031835408,0.0005008559,0.00036253914],"domain_scores_gemma":[0.980568,0.018708464,0.0000775226,0.000350656,0.000094312476,0.00020103819],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007691923,0.00017547047,0.00024205756,0.0004528856,0.00020806836,0.00010042587,0.00040068117,0.00010062658,0.000072756986],"category_scores_gemma":[0.03158799,0.00012352859,0.000061511215,0.0027713117,0.00007081053,0.00007455652,0.000118700504,0.00018022367,0.00006897553],"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":[0.000025031493,0.0004152553,0.23916039,0.00013482367,0.000108626926,0.000071319904,0.00045769196,0.0003261666,0.00011205138,0.4845804,0.0018975434,0.27271068],"study_design_scores_gemma":[0.00029115865,0.000109736626,0.037245817,0.000048695907,0.00007696525,0.00002318753,0.00011331543,0.08938482,0.0001282143,0.8716113,0.0006044661,0.00036234982],"about_ca_topic_score_codex":0.000040396168,"about_ca_topic_score_gemma":0.000012488755,"teacher_disagreement_score":0.5899911,"about_ca_system_score_codex":0.000056830544,"about_ca_system_score_gemma":0.00004236433,"threshold_uncertainty_score":0.97656935},"labels":[],"label_agreement":null},{"id":"W2070761126","doi":"10.1111/j.0006-341x.2004.00162.x","title":"Estimation of Fish Abundance Indices Based on Scientific Research Trawl Surveys","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":30,"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":"Fisheries and Oceans Canada","keywords":"Statistics; Estimator; Abundance (ecology); Sampling (signal processing); Smoothing; Population; Econometrics; Mathematics; Fishery; Computer science; Biology","score_opus":0.08303459727798113,"score_gpt":0.3451496781082461,"score_spread":0.26211508083026497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070761126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9130673,0.0000071584495,0.0047425064,0.0005431048,0.0001626493,0.00029807683,0.000040223124,0.00002570659,0.08111331],"genre_scores_gemma":[0.99726343,0.000008201523,0.0015602408,0.000038322232,0.000012548204,0.000007834237,0.000032628137,0.000008440233,0.0010683328],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974035,0.00018927958,0.00018382558,0.00031204647,0.0015790182,0.00033232532],"domain_scores_gemma":[0.9991312,0.00030143428,0.00005570813,0.00036079405,0.00004650394,0.000104362785],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004624474,0.00007374595,0.00009994165,0.0012222228,0.00021680257,0.00012656953,0.00042752989,0.00006926643,0.0016780667],"category_scores_gemma":[0.0011035632,0.00006703196,0.000034317654,0.012291387,0.0007228276,0.0001796866,0.0001551501,0.00017399986,0.00024168493],"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":[0.000051626084,0.0010639108,0.20368697,0.000110289846,0.00000753619,0.000016030872,0.0002924634,0.011424583,0.0039487397,0.00024234032,0.0067305565,0.77242494],"study_design_scores_gemma":[0.0011987995,0.0010768974,0.87211704,0.00002945898,0.0000042509723,0.0000012868846,0.0000770464,0.027237717,0.028458446,0.0010652536,0.0684158,0.00031801296],"about_ca_topic_score_codex":0.0009426857,"about_ca_topic_score_gemma":0.00014724927,"teacher_disagreement_score":0.77210695,"about_ca_system_score_codex":0.00024734394,"about_ca_system_score_gemma":0.000065800195,"threshold_uncertainty_score":0.99923456},"labels":[],"label_agreement":null},{"id":"W2071731534","doi":"10.1111/j.1541-0420.2007.0786_1.x","title":"Correction to “A Note on One‐Sided Tests with Multiple Endpoints,” by M. D. Perlman and L. Wu; 60, 276–280, March 2004","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":1,"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":"","keywords":"Paragraph; Section (typography); Code (set theory); Statistics; Computer science; Mathematics; Arithmetic; Programming language; World Wide Web; Operating system","score_opus":0.015241263736670842,"score_gpt":0.2546162972626174,"score_spread":0.23937503352594658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071731534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21528183,0.00022120745,0.7812195,0.00032989858,0.0013961769,0.0003610425,0.00001822956,0.00021865925,0.00095349987],"genre_scores_gemma":[0.9483465,0.000020879752,0.05006115,0.00047035154,0.00009053857,0.000008347268,0.000008479642,0.000019740022,0.0009740161],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982846,0.00005546678,0.00020844284,0.00054058933,0.00047467076,0.00043624235],"domain_scores_gemma":[0.9980691,0.0010159393,0.000079806996,0.00043612483,0.000119350785,0.00027967745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092321006,0.00018980351,0.00017689838,0.0018804815,0.00020034655,0.00015285047,0.0004166352,0.000080686565,0.0000062477443],"category_scores_gemma":[0.0006452991,0.00016708093,0.000038341273,0.006966664,0.000065457825,0.00021862915,0.0001366096,0.0001904559,0.00006280821],"study_design_candidate":"design_other","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":[0.0002942233,0.0009543414,0.011648095,0.00003739379,0.00005126854,0.00012411995,0.0011627876,0.0000763067,0.0335561,0.0025818592,0.016149014,0.9333645],"study_design_scores_gemma":[0.0071601328,0.010611806,0.55590534,0.00053719175,0.00007337675,0.00032778174,0.00037832913,0.04363149,0.28873512,0.003447567,0.0858857,0.0033061795],"about_ca_topic_score_codex":0.00008092264,"about_ca_topic_score_gemma":0.00004182069,"teacher_disagreement_score":0.9300583,"about_ca_system_score_codex":0.00008599497,"about_ca_system_score_gemma":0.000030024967,"threshold_uncertainty_score":0.6813363},"labels":[],"label_agreement":null},{"id":"W2073143097","doi":"10.1111/j.1541-0420.2006.00533.x","title":"A Multivariate Two‐Sample Mean Test for Small Sample Size and Missing Data","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":40,"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":"Multivariate statistics; Statistics; Sample size determination; Missing data; Sample (material); Multivariate analysis; Mathematics; Test (biology); Biology; Chemistry; Chromatography","score_opus":0.7422006601323592,"score_gpt":0.5704161396961355,"score_spread":0.17178452043622372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073143097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002352821,0.00012051842,0.9886321,0.00028206228,0.00040209581,0.00068837265,0.007151003,0.00012343796,0.00024762203],"genre_scores_gemma":[0.009273853,0.0000141824885,0.9897879,0.000111615,0.0005751931,0.000020275402,0.000051829986,0.000056846446,0.00010833942],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975212,0.0002206229,0.0008899665,0.0006401982,0.0002970068,0.00043098908],"domain_scores_gemma":[0.4006431,0.5975673,0.00036626728,0.0010459167,0.00019344297,0.00018398186],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0047339313,0.0002252777,0.0005929332,0.00034462675,0.0001579654,0.00016418575,0.0005300662,0.00016520137,0.0000769232],"category_scores_gemma":[0.75924367,0.00019735812,0.000074456344,0.0017426519,0.00014430747,0.000073272204,0.00041960197,0.00013759447,0.00000468986],"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":[0.00032874517,0.0030511157,0.01216844,0.0016547494,0.00028003435,0.000022808872,0.00018122175,0.00000286416,0.013254037,0.2542286,0.015175734,0.69965166],"study_design_scores_gemma":[0.0021920588,0.00014008016,0.002061733,0.000039762777,0.00017047518,0.0000025577624,0.000014931018,0.0057799034,0.00059814216,0.9814374,0.0072731096,0.00028987837],"about_ca_topic_score_codex":0.00087290694,"about_ca_topic_score_gemma":0.0000564355,"teacher_disagreement_score":0.7545097,"about_ca_system_score_codex":0.000047120273,"about_ca_system_score_gemma":0.000052051848,"threshold_uncertainty_score":0.8048031},"labels":[],"label_agreement":null},{"id":"W2074244589","doi":"10.1111/j.0006-341x.2004.00183.x","title":"Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cargill (Canada); Montreal General Hospital; McGill University; Royal Victoria Hospital","funders":"","keywords":"Gold standard (test); Statistics; Test (biology); Bayesian probability; Sample size determination; Econometrics; Mathematics; Biology","score_opus":0.10669206443709843,"score_gpt":0.41185950575321645,"score_spread":0.305167441316118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074244589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02526932,0.0006764614,0.9723387,0.00020159215,0.000071761664,0.0006956189,0.00067816844,0.00001677136,0.00005160622],"genre_scores_gemma":[0.47688526,0.00040480247,0.5226061,0.000023708884,0.000016867341,0.000051600335,5.0091705e-7,0.0000061997353,0.000004989874],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989668,0.00004873121,0.0003444466,0.00019152099,0.0002676598,0.00018081468],"domain_scores_gemma":[0.80898917,0.19042483,0.00015585382,0.00019087303,0.00020342588,0.000035830206],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010578741,0.000116241994,0.0002497984,0.0002531659,0.00005015395,0.000030324449,0.00017472173,0.000052858188,0.00000452411],"category_scores_gemma":[0.41994914,0.00008015587,0.000029407169,0.0015609277,0.00021851518,0.000060441893,0.00004216915,0.00006616885,3.5279012e-7],"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":[0.00006817268,0.0016701344,0.2760469,0.011273218,0.00005002153,0.000049700204,0.0067782323,0.0000016293454,0.0017628138,0.2812442,0.0006307769,0.42042422],"study_design_scores_gemma":[0.00065363594,0.00086013554,0.061051756,0.00040123897,0.000046644465,0.00000834564,0.00033757256,0.00019798508,0.00084402395,0.9353852,0.000060226535,0.00015318875],"about_ca_topic_score_codex":0.000016940889,"about_ca_topic_score_gemma":0.000020895326,"teacher_disagreement_score":0.65414107,"about_ca_system_score_codex":0.000061280814,"about_ca_system_score_gemma":0.00004158808,"threshold_uncertainty_score":0.58493686},"labels":[],"label_agreement":null},{"id":"W2077082447","doi":"10.1111/j.1541-0420.2011.01592.x","title":"A New Semiparametric Estimation Method for Accelerated Hazard Model","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"Semiparametric regression; Computer science; Semiparametric model; Hazard; Estimation; Kernel density estimation; Applied mathematics; Limit (mathematics); Function (biology); Kernel (algebra); Kernel smoother; Mathematical optimization; Estimating equations; Mathematics; Maximum likelihood; Nonparametric statistics; Kernel method; Econometrics; Statistics; Estimator; Artificial intelligence; Support vector machine","score_opus":0.44359255370073464,"score_gpt":0.46341135802553,"score_spread":0.01981880432479538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077082447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011894072,0.000069107504,0.99521494,0.000028497658,0.00017424865,0.00043079097,0.000063580825,0.00012877338,0.0027006597],"genre_scores_gemma":[0.010505978,0.000011114643,0.98856384,0.00008049779,0.000040479383,0.00004307585,0.000008386252,0.00003259795,0.00071402284],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986466,0.000068120826,0.00041298536,0.0002968057,0.0002655331,0.00030993205],"domain_scores_gemma":[0.9960571,0.0029810613,0.00018094311,0.00030690336,0.00027526112,0.0001987625],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0009789645,0.00017650901,0.0003232069,0.0013169398,0.00007159211,0.000054159893,0.00024864718,0.00016584,0.00017197315],"category_scores_gemma":[0.017444663,0.0001522165,0.00009010741,0.005174552,0.000020055879,0.00009522009,0.00005298923,0.000103165185,0.00002560035],"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":[0.000045658024,0.00014093264,0.00006535532,0.00010831412,0.000038193084,0.0000011262186,0.0002377393,0.000035120454,0.00051217544,0.36506888,0.011896562,0.62184995],"study_design_scores_gemma":[0.00030566286,0.00011596697,0.00022950611,0.000008703048,0.00005671892,0.0000018374726,0.00000789284,0.49538648,0.0036923909,0.49987572,0.00017361357,0.00014548433],"about_ca_topic_score_codex":0.000047260146,"about_ca_topic_score_gemma":0.0000011646831,"teacher_disagreement_score":0.62170446,"about_ca_system_score_codex":0.00006295182,"about_ca_system_score_gemma":0.00012323135,"threshold_uncertainty_score":0.9908318},"labels":[],"label_agreement":null},{"id":"W2078030772","doi":"10.1111/j.1541-0420.2006.00523.x","title":"The Jolly–Seber Model with Tag Loss","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Mathematics; Econometrics; Computer science","score_opus":0.006148637511269787,"score_gpt":0.22739654970353654,"score_spread":0.22124791219226675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078030772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9461197,0.00092723826,0.03839495,0.0005514219,0.00012242056,0.00016936664,0.0000400775,0.000023086915,0.013651684],"genre_scores_gemma":[0.9738048,0.00037039074,0.002077186,0.00021529477,0.00011503075,0.000009075817,0.00009246858,0.000020833568,0.023294952],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992008,0.000017667966,0.00011084607,0.00019075023,0.00023292378,0.00024700435],"domain_scores_gemma":[0.99945915,0.000019186049,0.000040089915,0.0003113614,0.00010976987,0.000060471244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017474896,0.00009804151,0.00006447803,0.00013710847,0.0001486983,0.00007023881,0.00022461952,0.00008716767,0.0000038546937],"category_scores_gemma":[0.000063469,0.00005923927,0.00004614008,0.00082673755,0.00012268353,0.0000019670551,0.000094397416,0.000058614936,0.000013738545],"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":[0.00085030706,0.0013798694,0.044044882,0.000069777685,0.00025377618,0.00007652999,0.000057115896,0.008699068,0.6299535,0.043608822,0.18621716,0.08478922],"study_design_scores_gemma":[0.0018144296,0.0005624113,0.010418834,0.0000086625405,0.000029250088,0.00004116622,0.00006566885,0.049933095,0.022788238,0.0011761832,0.9125248,0.00063723366],"about_ca_topic_score_codex":0.000022558716,"about_ca_topic_score_gemma":0.0000673751,"teacher_disagreement_score":0.7263077,"about_ca_system_score_codex":0.000013484175,"about_ca_system_score_gemma":0.00007016723,"threshold_uncertainty_score":0.24157074},"labels":[],"label_agreement":null},{"id":"W2079234813","doi":"10.1111/j.0006-341x.2002.00209.x","title":"Interval Estimation for a Difference Between Intraclass Kappa Statistics","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Confidence interval; Cohen's kappa; Mathematics; Interval estimation; Kappa; Statistic; Sample size determination; Intraclass correlation; Reproducibility","score_opus":0.42560737613053634,"score_gpt":0.418085093842297,"score_spread":0.007522282288239368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079234813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090064935,0.000120899495,0.9074925,0.0007320164,0.00050189346,0.0003744795,0.0003080836,0.000030740506,0.00037443716],"genre_scores_gemma":[0.9084426,0.00002447549,0.08995767,0.00010142417,0.00012835297,0.00002741038,0.000022305478,0.000007736553,0.0012879974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968911,0.0001013549,0.00071540027,0.00039002602,0.0016428512,0.00025926644],"domain_scores_gemma":[0.99510074,0.0035713431,0.0002495965,0.00042977376,0.00052784104,0.00012070438],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0028637892,0.00013006983,0.00025797318,0.000938941,0.00013937576,0.00032182544,0.00064846186,0.00008446605,0.00035346596],"category_scores_gemma":[0.016152184,0.000095891,0.00008377169,0.0031776484,0.00008518841,0.0001359684,0.000088924186,0.00009148603,0.00035945405],"study_design_candidate":"design_other","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":[0.0000064301457,0.00013701993,0.024434526,0.000020711748,0.000014740395,6.265117e-7,0.00014866266,0.00003575697,0.00017701059,0.0013304463,0.03159675,0.9420973],"study_design_scores_gemma":[0.0016727042,0.0013568419,0.30197936,0.00005898427,0.00011068051,0.0000022318927,0.00028805446,0.35628706,0.0017312801,0.17399158,0.16175331,0.00076790707],"about_ca_topic_score_codex":0.000008432401,"about_ca_topic_score_gemma":0.0000034857608,"teacher_disagreement_score":0.9413294,"about_ca_system_score_codex":0.000100335616,"about_ca_system_score_gemma":0.000015338604,"threshold_uncertainty_score":0.99213517},"labels":[],"label_agreement":null},{"id":"W2079402423","doi":"10.1111/j.1541-0420.2007.00872.x","title":"Estimating Survival and Association in a Semicompeting Risks Model","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Estimator; Copula (linguistics); Mathematics; Censoring (clinical trials); Statistics; Econometrics; Applied mathematics","score_opus":0.2393230319998807,"score_gpt":0.44881596487114406,"score_spread":0.20949293287126336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079402423","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35460347,0.00002835839,0.64305085,0.000017279159,0.000092334805,0.00004958806,0.000006590388,0.0000225185,0.002128994],"genre_scores_gemma":[0.40269852,0.000003974087,0.5972219,0.000011700919,0.000029305218,0.0000010040085,6.3655006e-7,0.0000061618766,0.000026771073],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990318,0.000052440508,0.000298252,0.00013883982,0.00024765433,0.00023100797],"domain_scores_gemma":[0.99406344,0.0055862083,0.0001458138,0.00007744648,0.00007049027,0.000056579887],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003968628,0.00007489881,0.0001670606,0.0004952645,0.00004086817,0.000032186297,0.000057133326,0.00009466366,0.0000056213607],"category_scores_gemma":[0.02617821,0.00007122991,0.000015556165,0.0014374359,0.000013186202,0.00003586711,0.000048618236,0.00013789664,0.000002174922],"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":[0.000020673077,0.000222637,0.36541888,0.00026529454,0.000026245685,0.000014831443,0.0008521556,0.00014943228,0.0036819857,0.2771547,0.00019468708,0.35199848],"study_design_scores_gemma":[0.00038237183,0.000029113524,0.061125513,0.000049626964,0.000014851592,0.0000013751147,0.00012013243,0.6929215,0.000296779,0.24485765,0.000024893208,0.00017616485],"about_ca_topic_score_codex":0.000046062854,"about_ca_topic_score_gemma":0.0000117889895,"teacher_disagreement_score":0.6927721,"about_ca_system_score_codex":0.00013311309,"about_ca_system_score_gemma":0.000014520653,"threshold_uncertainty_score":0.9820247},"labels":[],"label_agreement":null},{"id":"W2079713743","doi":"10.1111/j.1541-0420.2007.00779.x","title":"Applications and Extensions of Chao's Moment Estimator for the Size of a Closed Population","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","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":"Université Laval","funders":"","keywords":"Estimator; Log-linear model; Statistics; Mathematics; Monte Carlo method; Sample size determination; Efficiency; Population; Econometrics; Grouped data; Null hypothesis; Linear model","score_opus":0.06873080995693362,"score_gpt":0.37327825541906573,"score_spread":0.3045474454621321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079713743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24238305,0.00028401118,0.75587356,0.00023126148,0.00007178846,0.0009953844,0.000067214925,0.000021646114,0.00007205878],"genre_scores_gemma":[0.9035807,0.000021934979,0.096261784,0.000010718736,0.000030054016,0.000028952703,0.000018541497,0.000008798795,0.00003847256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999254,0.000008323424,0.00037874578,0.000090207075,0.00017862699,0.00009009647],"domain_scores_gemma":[0.9970169,0.002252713,0.0002954108,0.00019582022,0.0002061328,0.000033011722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063378253,0.00006288122,0.00014183568,0.00031346903,0.00008914475,0.0000054367356,0.000057005032,0.000052483578,0.000006961299],"category_scores_gemma":[0.0009934942,0.000046415862,0.000045425753,0.0011849843,0.00003477708,0.000031444808,0.00001929895,0.000024948858,3.612617e-7],"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":[0.000081187325,0.00049415923,0.03734553,0.00070393935,0.00007776951,1.747911e-7,0.00042342377,0.000069819325,0.006850216,0.6895303,0.00083269755,0.26359078],"study_design_scores_gemma":[0.00092183246,0.00011989376,0.9067716,0.000039341525,0.00018605855,0.00000409927,0.00013911234,0.017487409,0.0022513405,0.06463754,0.0072788345,0.00016291799],"about_ca_topic_score_codex":0.000034699715,"about_ca_topic_score_gemma":0.000007587206,"teacher_disagreement_score":0.8694261,"about_ca_system_score_codex":0.000022368666,"about_ca_system_score_gemma":0.0000096767,"threshold_uncertainty_score":0.1892784},"labels":[],"label_agreement":null},{"id":"W2081138809","doi":"10.1111/j.0006-341x.2003.00101.x","title":"Semiparametric Estimation of Tag Loss and Reporting Rates for Tag‐Recovery Experiments Using Exact Time‐at‐Liberty Data","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Nonparametric statistics; Parametric statistics; Statistics; Econometrics; Computer science; Population; Estimation; Smoothing; Mathematics; Medicine","score_opus":0.1046416869081204,"score_gpt":0.35089532769282583,"score_spread":0.24625364078470544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081138809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98968494,0.00020054245,0.009378737,0.000020212628,0.00013052304,0.00024560143,0.0000280963,0.000013331812,0.0002980046],"genre_scores_gemma":[0.9579898,0.00002240622,0.04135572,0.00008120478,0.000010687342,0.000008449972,0.000095820695,0.000011207824,0.00042469322],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987804,0.000047157366,0.00050886534,0.00031581763,0.00017272645,0.0001749785],"domain_scores_gemma":[0.9984432,0.00041514164,0.0007662322,0.00031021418,0.000016502767,0.000048700535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011427904,0.00009713367,0.00017440351,0.0003310953,0.00014346791,0.000020927548,0.00013790457,0.00009938667,0.00018723427],"category_scores_gemma":[0.0049317568,0.00009401487,0.000024996882,0.0021432135,0.00009434229,0.000407351,0.0001735323,0.000035465993,0.0000188168],"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":[0.000026357518,0.00012375893,0.97255063,0.000029002853,0.000027114027,0.0000026367757,0.000049874914,0.0012413728,0.012769801,0.000012306939,0.0025598453,0.010607273],"study_design_scores_gemma":[0.0009885776,0.00023069857,0.7356034,0.000026161955,0.00010066149,0.000056203968,0.000051152823,0.20789507,0.050459705,0.0005978661,0.0035559977,0.0004345342],"about_ca_topic_score_codex":0.000061289196,"about_ca_topic_score_gemma":0.0000021341841,"teacher_disagreement_score":0.23694728,"about_ca_system_score_codex":0.00014476522,"about_ca_system_score_gemma":0.00002145398,"threshold_uncertainty_score":0.590413},"labels":[],"label_agreement":null},{"id":"W2081877752","doi":"10.1111/j.0006-341x.2004.00199.x","title":"Small‐Sample Inference for the Comparison of Means of Log‐Normal Distributions","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; Okanagan College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Statistics; Sample (material); Mathematics; Computer science; Artificial intelligence; Chromatography; Chemistry","score_opus":0.7734442251240047,"score_gpt":0.5978762975389873,"score_spread":0.17556792758501738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081877752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007298552,0.000115372415,0.9893421,0.0001800939,0.0003330702,0.000458232,0.0021460643,0.000021642338,0.00010486039],"genre_scores_gemma":[0.4086277,0.000018325023,0.5912649,0.000007923394,0.000042299245,0.000018599283,0.000006599993,0.0000072455437,0.000006393628],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982468,0.00011315053,0.0009798234,0.0001527186,0.0002980827,0.00020944297],"domain_scores_gemma":[0.8198406,0.1788306,0.00048020532,0.00040433952,0.00038046914,0.000063780666],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0021223114,0.00010754915,0.0005158485,0.00023063234,0.00007540958,0.000012240619,0.00040625516,0.00012789952,0.000043589032],"category_scores_gemma":[0.38180822,0.00007377218,0.00017225479,0.00227324,0.00031450024,0.000021416317,0.00010255958,0.00012271275,0.0000022740023],"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":[0.00005669799,0.00072062353,0.002911141,0.0002510847,0.00009447596,1.734002e-7,0.00012239645,0.00007323167,0.00047849255,0.96677524,0.00025841454,0.028258018],"study_design_scores_gemma":[0.0009675761,0.00040350546,0.002815583,0.00004068429,0.00019762837,2.465541e-7,0.00011009532,0.0006971993,0.022661552,0.9711733,0.0008228725,0.00010979685],"about_ca_topic_score_codex":0.00007854143,"about_ca_topic_score_gemma":0.000018540166,"teacher_disagreement_score":0.40132913,"about_ca_system_score_codex":0.000044306093,"about_ca_system_score_gemma":0.000088437846,"threshold_uncertainty_score":0.6233991},"labels":[],"label_agreement":null},{"id":"W2082993001","doi":"10.1111/j.0006-341x.2001.00197.x","title":"Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Ministry of Health, British Columbia","keywords":"Interaction; Simple (philosophy); Computer science; Econometrics; Test (biology); Random effects model; Statistics; Mathematics; Medicine","score_opus":0.053828481904653155,"score_gpt":0.32136784621170883,"score_spread":0.26753936430705566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082993001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99079883,0.00051715143,0.00528929,0.00018781204,0.00049897766,0.00054141175,0.000001280851,0.000083558865,0.0020816573],"genre_scores_gemma":[0.9986537,0.000699651,0.00023938305,0.000052031795,0.00022805792,0.000022082551,0.0000044764383,0.000009607025,0.00009101175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99856687,0.00028607133,0.00022532475,0.00026346926,0.000349905,0.0003083456],"domain_scores_gemma":[0.9989681,0.0005936367,0.00012431416,0.00014001306,0.00003833064,0.0001355899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011837286,0.00010634279,0.0001803641,0.0020810496,0.00028874312,0.00014148102,0.00013377242,0.00007089337,0.000003299295],"category_scores_gemma":[0.0013524191,0.0001122128,0.000064323605,0.005950924,0.00010296127,0.00025843742,0.00006011687,0.0001226981,0.00000626244],"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":[0.000032006683,0.000032002616,0.86916375,0.000056978464,0.000015237178,0.00011264132,0.001308595,0.0000027704716,0.00001883841,0.00014417601,0.0000420362,0.12907095],"study_design_scores_gemma":[0.0009471592,0.000020026184,0.97629035,0.00006972876,0.00002606206,3.9298106e-7,0.0008328075,0.00009032093,0.000008068778,0.0006458219,0.020914145,0.00015510335],"about_ca_topic_score_codex":0.001499568,"about_ca_topic_score_gemma":0.0004362328,"teacher_disagreement_score":0.12891585,"about_ca_system_score_codex":0.0001425857,"about_ca_system_score_gemma":0.000015696869,"threshold_uncertainty_score":0.45759052},"labels":[],"label_agreement":null},{"id":"W2085047213","doi":"10.1111/j.1541-0420.2005.00510.x","title":"The Performance of Random Coefficient Regression in Accounting for Residual Confounding","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":29,"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":"","keywords":"Frequentist inference; Estimator; Prior probability; Residual; Statistics; Confounding; Econometrics; Bayesian probability; Point estimation; Mathematics; Computer science; Bayesian inference; Algorithm","score_opus":0.08495373140715462,"score_gpt":0.3898854514665507,"score_spread":0.30493172005939606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085047213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8683967,0.0002674172,0.12959291,0.000040517134,0.00025715656,0.00031558416,0.00001717147,0.000014087006,0.0010984701],"genre_scores_gemma":[0.88913345,0.000035409837,0.11056538,0.000006039004,0.00006288694,0.000016583504,0.0000021275791,0.000008649,0.0001694424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990259,0.000049174043,0.0003886285,0.000106185646,0.00023543884,0.0001946671],"domain_scores_gemma":[0.9911354,0.008384756,0.00019236199,0.0001363211,0.0001356351,0.0000155224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020624124,0.000072053095,0.00017645028,0.00031042303,0.0001187756,0.000033958815,0.00013668527,0.000049574228,0.0000053944814],"category_scores_gemma":[0.007863834,0.000043227148,0.000030451258,0.0012229254,0.00007119281,0.000021408716,0.00003619652,0.00006146937,9.606377e-7],"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":[0.0006383287,0.0003839713,0.03865823,0.0009070345,0.000020324276,0.0000024361123,0.0002222264,0.000066204084,0.010348095,0.749603,0.0063847,0.19276547],"study_design_scores_gemma":[0.018967032,0.0016083949,0.1426447,0.0019962606,0.00018504723,0.000013720373,0.0011201256,0.3172297,0.14071976,0.33520257,0.03888317,0.0014295216],"about_ca_topic_score_codex":0.000024058392,"about_ca_topic_score_gemma":0.0000041659305,"teacher_disagreement_score":0.4144004,"about_ca_system_score_codex":0.000035303234,"about_ca_system_score_gemma":0.000025378788,"threshold_uncertainty_score":0.9414311},"labels":[],"label_agreement":null},{"id":"W2085872600","doi":"10.1111/j.1541-0420.2009.01343_5.x","title":"Statistical Learning from a Regression Perspective by BERK, R. A.","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"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":"Perspective (graphical); Citation; Computer science; Library science; Artificial intelligence","score_opus":0.11007464456739585,"score_gpt":0.41442127596150025,"score_spread":0.3043466313941044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085872600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016931167,0.0005763061,0.9728488,0.0003587605,0.00013410916,0.0001165465,0.00017807304,0.00011599473,0.008740282],"genre_scores_gemma":[0.36917418,0.00006405984,0.6299688,0.00012957491,0.00009026165,0.0000032384266,0.000026861853,0.000015396696,0.0005275895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998649,0.00016483816,0.0002437903,0.00030423672,0.00038267346,0.00025548507],"domain_scores_gemma":[0.9955321,0.003865652,0.00010565848,0.00018227583,0.00015821164,0.00015612748],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00032310284,0.00014974273,0.00026304697,0.0002847157,0.0000991872,0.00005895559,0.0001437261,0.00011634829,0.0004847868],"category_scores_gemma":[0.020031964,0.000116547344,0.000039288207,0.0013690691,0.000061222956,0.000042631364,0.00003618239,0.00026839718,0.00006379953],"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":[0.00004309445,0.00033836503,0.0007470254,0.000011228336,0.000026093294,0.000025641613,0.00039312307,1.8783291e-7,0.004711377,0.7182765,0.050219424,0.22520794],"study_design_scores_gemma":[0.0004191847,0.00050275965,0.008631466,0.000044534412,0.000046845358,0.0000023197197,0.0003982944,0.0013722986,0.0012807884,0.9803978,0.0066219936,0.00028166483],"about_ca_topic_score_codex":0.00007814215,"about_ca_topic_score_gemma":4.8435726e-7,"teacher_disagreement_score":0.35224304,"about_ca_system_score_codex":0.00010912465,"about_ca_system_score_gemma":0.000025577492,"threshold_uncertainty_score":0.9882227},"labels":[],"label_agreement":null},{"id":"W2086111854","doi":"10.1111/j.0006-341x.2000.00893.x","title":"A Simple Test of Association for Contingency Tables with Multiple Column Responses","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Contingency table; Categorical variable; Statistics; Test (biology); Null hypothesis; Test statistic; Chi-square test; Association (psychology); Simple (philosophy); Statistic; Pearson's chi-squared test; Statistical hypothesis testing; Mathematics; Column (typography); Biometrics; Computer science; Econometrics; Artificial intelligence; Psychology","score_opus":0.04901399032510706,"score_gpt":0.29440765740321456,"score_spread":0.2453936670781075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086111854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979408,0.000105300074,0.0003155839,0.00013146111,0.000016102791,0.00016573329,0.0008974656,0.000018597502,0.00040896426],"genre_scores_gemma":[0.9891256,0.000037999886,0.008393072,0.000040853934,0.000057940408,0.000011727498,0.00006596899,7.124858e-7,0.0022661414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992147,0.00007821502,0.00019776236,0.00015138445,0.00018931027,0.00016860658],"domain_scores_gemma":[0.991196,0.008410429,0.0001231747,0.000033285993,0.00018952799,0.000047605616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005697264,0.00006625375,0.00018641527,0.00004858895,0.000108745626,0.000030535513,0.00008699908,0.00006414625,0.00047852506],"category_scores_gemma":[0.0060758293,0.00002507225,0.000061136474,0.0022370198,0.000025791654,0.00003569245,0.0000065210843,0.000027531667,0.000004212331],"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":[0.00019207376,0.00025283507,0.5090864,0.0000119748665,0.000038174505,9.164486e-7,0.000016658412,0.0000040904906,0.15627404,0.00013324256,0.0009721772,0.33301747],"study_design_scores_gemma":[0.00034109305,0.0011126291,0.84040856,0.000007435755,0.00006523597,5.87837e-7,0.00009435066,0.0009964703,0.010337291,0.0004029146,0.14607345,0.00015997056],"about_ca_topic_score_codex":0.00031623818,"about_ca_topic_score_gemma":0.00029306178,"teacher_disagreement_score":0.3328575,"about_ca_system_score_codex":0.000021859418,"about_ca_system_score_gemma":0.0000052810087,"threshold_uncertainty_score":0.7273774},"labels":[],"label_agreement":null},{"id":"W2086622613","doi":"10.1111/j.0006-341x.2004.00234.x","title":"Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Waterloo","keywords":"Missing data; Imputation (statistics); Estimator; Inverse probability; Econometrics; Inverse probability weighting; Statistics; Longitudinal data; Binary data; Computer science; Drop out; Mathematics; Binary number; Data mining; Bayesian probability; Posterior probability; Economics","score_opus":0.30781272951886274,"score_gpt":0.4634387367916948,"score_spread":0.15562600727283205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086622613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2359715,0.00010854258,0.76239777,0.00019186897,0.00008367795,0.00023205124,0.00051923137,0.0002110456,0.00028429358],"genre_scores_gemma":[0.82330126,0.000045295317,0.1761205,0.00003347465,0.00003509846,0.000008178188,0.0004205006,0.00001759625,0.000018115712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99830526,0.000041598098,0.00046893262,0.0003924664,0.0005631538,0.00022857545],"domain_scores_gemma":[0.9978893,0.00063405535,0.00034538552,0.00083392684,0.00022420527,0.0000731195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005346804,0.00018419293,0.00040845742,0.004304598,0.00007041186,0.000019758629,0.00045211415,0.00011816051,0.000037546044],"category_scores_gemma":[0.0011066218,0.0001738642,0.00010997592,0.011919675,0.00011442798,0.00028633288,0.00025403808,0.00015123603,0.000015242573],"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":[0.0007516406,0.0059615993,0.036195725,0.0010455156,0.004294108,0.00054151495,0.00077964814,0.01861095,0.032701354,0.8297152,0.004405391,0.06499734],"study_design_scores_gemma":[0.0021411586,0.002692429,0.31010976,0.0002771126,0.0041940273,0.000047359903,0.000152152,0.02721343,0.01296174,0.63681245,0.001876941,0.0015214195],"about_ca_topic_score_codex":0.00007549175,"about_ca_topic_score_gemma":0.000019593563,"teacher_disagreement_score":0.58732975,"about_ca_system_score_codex":0.0003070493,"about_ca_system_score_gemma":0.000093752984,"threshold_uncertainty_score":0.7089976},"labels":[],"label_agreement":null},{"id":"W2090064355","doi":"10.1111/j.1541-0420.2008.01082_12.x","title":"Advanced Distance Sampling: Estimating Abundance of Biological Populations by BUCKLAND, S. T., ANDERSON, D. R., BURNHAM, K. P., LAAKE, J. L., BORCHERS, C. L., and THOMAS, L.","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Citation; Sampling (signal processing); Mathematics; Combinatorics; Statistics; Library science; Computer science","score_opus":0.30857341672865873,"score_gpt":0.3971899444011155,"score_spread":0.08861652767245676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090064355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3295765,0.0026475703,0.6666461,0.000083349696,0.00013476636,0.00023357809,0.00014207927,0.00029490492,0.00024110163],"genre_scores_gemma":[0.5167061,0.00033739718,0.48268762,0.000020295718,0.000027112606,0.00001574133,0.00006544107,0.000019957282,0.000120308556],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99839455,0.00008005848,0.00057774823,0.00034799287,0.00031051296,0.0002891188],"domain_scores_gemma":[0.9977729,0.0012013781,0.00039480816,0.00032083524,0.00019517314,0.000114922455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069973397,0.00022186956,0.00042143985,0.00043346937,0.00026979254,0.000029905033,0.00018732103,0.00018261088,0.0000124333765],"category_scores_gemma":[0.004428923,0.00019504581,0.00006862849,0.001927775,0.00023780318,0.00014775332,0.00006200892,0.00015271915,0.0000018580531],"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":[0.00038905733,0.0023546438,0.6650801,0.0017780546,0.00026934256,0.0000340853,0.0037184455,0.00033943012,0.024586849,0.059570964,0.08980732,0.15207174],"study_design_scores_gemma":[0.011554844,0.0032195724,0.28370228,0.0022224819,0.000389041,0.0007122683,0.0016242355,0.06829923,0.048761893,0.46369255,0.10824927,0.00757235],"about_ca_topic_score_codex":0.000048576603,"about_ca_topic_score_gemma":0.0000037300636,"teacher_disagreement_score":0.40412158,"about_ca_system_score_codex":0.000054020158,"about_ca_system_score_gemma":0.000028646626,"threshold_uncertainty_score":0.79537374},"labels":[],"label_agreement":null},{"id":"W2090656257","doi":"10.1111/1541-0420.00045","title":"A Test of Linkage for Complex Discrete and Continuous Traits in Nuclear Families","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"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 Toronto; Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Kurtosis; Nuclear family; Inheritance (genetic algorithm); Covariate; Exponential family; Linkage (software); Trait; Major gene; Mathematics; Locus (genetics); Polygene; Statistics; Genetics; Quantitative trait locus; Biology; Computer science; Gene","score_opus":0.02270699075249049,"score_gpt":0.2433947049073449,"score_spread":0.22068771415485441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090656257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99649346,0.0006167623,0.0005929899,0.000026930396,0.000029213481,0.00012054748,0.00022036102,0.0000034863954,0.0018962572],"genre_scores_gemma":[0.99538165,0.00036372922,0.0039726235,0.00005920077,0.0000143203815,0.00000136283,0.00002673398,0.000004944553,0.00017541242],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99957216,0.000011953645,0.00011167614,0.00013839293,0.000049781218,0.000116019895],"domain_scores_gemma":[0.99978364,0.000040882685,0.00004201733,0.00006138498,0.000036655034,0.000035426703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012216684,0.00006500444,0.000117052085,0.00017282712,0.000031287087,0.000011294867,0.000059574144,0.00007129098,0.000004628361],"category_scores_gemma":[0.00036752378,0.000060493938,0.0000326334,0.0002571621,0.00005126056,0.0000010039345,0.00002562861,0.000019014778,3.553319e-7],"study_design_candidate":"bench_or_experimental","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":[0.000033682732,0.00012540515,0.029490003,0.00010462054,0.000021499322,0.0000017033783,0.00012120924,0.0000030652513,0.96093714,0.0006263172,0.0022999356,0.0062354],"study_design_scores_gemma":[0.0043556374,0.0025750336,0.27918994,0.000056740377,0.000052827425,0.0000250014,0.0023296995,0.0003038012,0.05965381,0.0002168865,0.65062743,0.0006131888],"about_ca_topic_score_codex":0.000010571704,"about_ca_topic_score_gemma":0.0000035802286,"teacher_disagreement_score":0.9012833,"about_ca_system_score_codex":0.0000019133606,"about_ca_system_score_gemma":0.000009262662,"threshold_uncertainty_score":0.24668713},"labels":[],"label_agreement":null},{"id":"W2090853233","doi":"10.1111/j.1541-0420.2007.00767.x","title":"Multilist Population Estimation with Incomplete and Partial Stratification","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":11,"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 Laval; Simon Fraser University","funders":"","keywords":"Population stratification; Computer science; Stratification (seeds); Population; Statistics; Maximization; Population size; Mark and recapture; Estimation; Econometrics; Expectation–maximization algorithm; Data mining; Mathematics; Mathematical optimization; Maximum likelihood; Demography","score_opus":0.081416258894013,"score_gpt":0.35804716313293333,"score_spread":0.27663090423892034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090853233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5964854,0.000018950952,0.40284684,0.00006562262,0.00006413037,0.00021047366,0.0000068242543,0.00007380501,0.00022794474],"genre_scores_gemma":[0.8733544,0.0000024895353,0.12637313,0.000013820594,0.000053484488,0.0000041069975,0.00015318989,0.000012738688,0.000032674317],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990425,0.000022014481,0.00033008313,0.00017546849,0.0002856842,0.00014422857],"domain_scores_gemma":[0.9991506,0.00028416066,0.0002173023,0.0001639176,0.00011404649,0.000070020484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060744176,0.00010535435,0.00011618412,0.00056364434,0.00013447047,0.000053703458,0.00004147681,0.000075144526,0.000014020259],"category_scores_gemma":[0.00044188483,0.00009117277,0.000016350725,0.0012743297,0.000030510413,0.0002053156,0.000011621013,0.00005460378,0.0000053303484],"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":[0.00016098183,0.00026069544,0.2466895,0.00023658437,0.000032097058,0.00000453962,0.0006016923,0.0003203506,0.0015441189,0.214466,0.00024777616,0.5354357],"study_design_scores_gemma":[0.00048089272,0.00007755132,0.86716896,0.000019332565,0.000033642587,0.000011553931,0.00003298935,0.12201145,0.00044617045,0.009163497,0.00038036323,0.00017357779],"about_ca_topic_score_codex":0.00010490804,"about_ca_topic_score_gemma":0.00007465914,"teacher_disagreement_score":0.62047946,"about_ca_system_score_codex":0.00006411797,"about_ca_system_score_gemma":0.000009336487,"threshold_uncertainty_score":0.37179175},"labels":[],"label_agreement":null},{"id":"W2091808868","doi":"10.1111/j.1541-0420.2011.01577.x","title":"Robust Estimation for Ordinary Differential Equation Models","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Control Systems and Identification","field":"Engineering","cited_by":40,"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; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ordinary differential equation; Estimation; Applied mathematics; Mathematics; Computer science; Differential equation; Mathematical analysis","score_opus":0.17129048452326634,"score_gpt":0.2274063585025038,"score_spread":0.05611587397923745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091808868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044109095,0.0002003994,0.9537801,0.00000482533,0.0007326565,0.0002630448,0.0000149750485,0.00014707413,0.00074784114],"genre_scores_gemma":[0.98862827,0.000015356823,0.010998767,0.0000015315294,0.000086416556,0.00006257495,0.00006755756,0.000016307156,0.00012323131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995284,0.0000058421133,0.00017316577,0.00009263582,0.000096637516,0.00010330942],"domain_scores_gemma":[0.9997201,0.000028910406,0.000035102472,0.00011917207,0.00006690703,0.000029815705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010666309,0.000066262124,0.00008184719,0.00053152925,0.000037704933,0.000032849875,0.00006447166,0.00006091524,0.00001866344],"category_scores_gemma":[0.00004503705,0.00006730896,0.00004046249,0.000670733,0.0000048287384,0.00020822357,0.0000070939477,0.000022528728,0.000019400362],"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":[0.00007239936,0.0002562553,0.00031507574,0.0006649374,0.00016751746,0.0000011078804,0.0010408119,0.16224572,0.040103413,0.027121134,0.009479822,0.7585318],"study_design_scores_gemma":[0.00023682472,0.000025137833,0.0025976128,0.000005912893,0.000018205654,4.7120358e-7,0.000008466045,0.99450475,0.0007722442,0.0014328776,0.00031284522,0.000084642255],"about_ca_topic_score_codex":0.000030374214,"about_ca_topic_score_gemma":0.0000020936534,"teacher_disagreement_score":0.94451916,"about_ca_system_score_codex":0.000053721076,"about_ca_system_score_gemma":0.0000044533535,"threshold_uncertainty_score":0.27447796},"labels":[],"label_agreement":null},{"id":"W2095502480","doi":"10.1111/j.0006-341x.2000.00824.x","title":"Fractional Simplex Designs for Interaction Screening in Complex Mixtures","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Saskatchewan; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Centroid; Simplex; Simplex algorithm; Computer science; Component (thermodynamics); Factor (programming language); Design of experiments; Mathematical optimization; Mathematics; Statistics; Combinatorics; Linear programming; Artificial intelligence","score_opus":0.5679953532525431,"score_gpt":0.54489968650042,"score_spread":0.023095666752123156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095502480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09362973,0.00029386109,0.8972394,0.00035629736,0.00044507574,0.0005862556,0.00007322458,0.000068185196,0.0073079895],"genre_scores_gemma":[0.59455943,0.000011458469,0.40382656,0.00033966984,0.00012767295,0.000033507076,0.00002854159,0.000016076627,0.0010570629],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99723715,0.00029210217,0.00064990827,0.0004922677,0.0010124244,0.00031613105],"domain_scores_gemma":[0.9941377,0.0051063662,0.00016260313,0.00029464706,0.0001837201,0.000114936985],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030212656,0.00015650652,0.00027245763,0.002703595,0.00014809109,0.00028618163,0.00048401344,0.00011626334,0.0049267095],"category_scores_gemma":[0.003934832,0.00013487991,0.00014417434,0.0069255144,0.00006718122,0.0005242438,0.000047061872,0.00013349777,0.00023377308],"study_design_candidate":"design_other","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":[0.00038066876,0.00019545792,0.002880599,0.0000031587326,0.000013924229,0.0000049036144,0.00013236694,0.0018091759,0.033228636,0.00037345727,0.021697145,0.9392805],"study_design_scores_gemma":[0.0016490043,0.0004369767,0.077005535,0.000013595055,0.000011278171,0.000027475773,0.0006780348,0.11602998,0.018335609,0.009881669,0.77542603,0.0005048159],"about_ca_topic_score_codex":0.00007723742,"about_ca_topic_score_gemma":0.000004605977,"teacher_disagreement_score":0.9387757,"about_ca_system_score_codex":0.000100640005,"about_ca_system_score_gemma":0.000033210163,"threshold_uncertainty_score":0.99598294},"labels":[],"label_agreement":null},{"id":"W2098243005","doi":"10.1111/biom.12325","title":"Mixture regression models for closed population capture–recapture data","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Akaike information criterion; Statistics; Mathematics; Econometrics; Inference; Estimator; Population; Random effects model; Model selection; Statistical inference; Logit; Computer science; Meta-analysis","score_opus":0.2753151171504339,"score_gpt":0.40061360854648775,"score_spread":0.12529849139605387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098243005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071731284,0.0018607181,0.9169774,0.0016020543,0.002254201,0.0017653552,0.0012616516,0.00043864117,0.0021086808],"genre_scores_gemma":[0.82124513,0.000013642343,0.17318232,0.00009767364,0.00034590118,0.000014091438,0.004292129,0.000041293322,0.0007678037],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986096,0.000046732464,0.00035655618,0.00032442017,0.00046165928,0.00020100609],"domain_scores_gemma":[0.9982194,0.00028278978,0.00027515457,0.0007571279,0.0003258864,0.00013964773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077566446,0.00016767422,0.00022548914,0.00059551914,0.0000916908,0.00005523674,0.00031440638,0.00026196108,0.000009421138],"category_scores_gemma":[0.0023330164,0.00013245516,0.000053242875,0.0015275372,0.000012558968,0.0004242289,0.00009871892,0.00009819538,0.000006750708],"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":[0.00027280764,0.00049592374,0.007636472,0.0005227876,0.000079146834,0.0000049422047,0.0014262991,0.001663902,0.00029021633,0.156052,0.7395793,0.0919762],"study_design_scores_gemma":[0.001745362,0.00007763133,0.003157025,0.000077512705,0.00012964674,0.0000112004145,0.000103928935,0.5631068,0.00008550152,0.37784153,0.053209286,0.00045461295],"about_ca_topic_score_codex":0.00008274594,"about_ca_topic_score_gemma":0.000026386715,"teacher_disagreement_score":0.74951386,"about_ca_system_score_codex":0.000103996055,"about_ca_system_score_gemma":0.000041962838,"threshold_uncertainty_score":0.54013646},"labels":[],"label_agreement":null},{"id":"W2099785344","doi":"10.1111/j.0006-341x.2004.00151.x","title":"Testing for Common Structures in a Panel of Threshold Models","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":6,"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":"Autoregressive model; Panel data; Econometrics; Null (SQL); Mathematics; Wald test; Similarity (geometry); Null hypothesis; Statistics; Distribution (mathematics); Applied mathematics; Computer science; Statistical hypothesis testing; Data mining; Mathematical analysis; Artificial intelligence; Image (mathematics)","score_opus":0.13385551952815342,"score_gpt":0.243669617232636,"score_spread":0.10981409770448258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099785344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9738498,0.004174783,0.0024123765,0.00017797486,0.00027347883,0.00039849425,0.0003001841,0.000027021455,0.018385889],"genre_scores_gemma":[0.9886949,0.00007577558,0.010993925,0.00008852516,0.000058593054,0.00002013479,0.00002146532,0.000016779502,0.000029893812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987457,0.0000025935194,0.000663781,0.00029719318,0.000028561833,0.00026220974],"domain_scores_gemma":[0.99917775,0.00014908095,0.0003500438,0.00021992103,0.000047453326,0.000055774763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042644006,0.00013621863,0.00042624117,0.0012043884,0.00003860746,0.000034135133,0.0002695034,0.00014409705,0.0000060851635],"category_scores_gemma":[0.00023008266,0.00014221099,0.000102712234,0.0017456344,0.000036071644,0.0002097946,0.000057655296,0.000089736845,0.0000073512283],"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":[0.000043763044,0.00025690984,0.06469882,0.00014963592,0.00005490677,0.0000026349237,0.00028199333,0.00602094,0.00069733436,0.9245933,0.00028993157,0.0029097688],"study_design_scores_gemma":[0.0015473942,0.00026652214,0.10340444,0.000023814779,0.000006096812,0.0000035652831,0.000056741537,0.0019110801,0.0006251873,0.89094347,0.00090059475,0.00031108427],"about_ca_topic_score_codex":0.0002235241,"about_ca_topic_score_gemma":0.00004333681,"teacher_disagreement_score":0.038705617,"about_ca_system_score_codex":0.000110962086,"about_ca_system_score_gemma":0.000019635669,"threshold_uncertainty_score":0.5799196},"labels":[],"label_agreement":null},{"id":"W2107408580","doi":"10.1111/j.1541-0420.2007.00785.x","title":"Spatial Multistate Transitional Models for Longitudinal Event Data","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Multivariate statistics; Weibull distribution; Point process; Markov chain; Statistics; Random effects model; Parametric statistics; Proportional hazards model; Time point; Piecewise; Computer science; Mathematics; Econometrics; Medicine; Geography","score_opus":0.28688328751626624,"score_gpt":0.2879810887378306,"score_spread":0.0010978012215643873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107408580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02266923,0.0014861722,0.95308834,0.0003023693,0.00039895572,0.00020960438,0.02124592,0.000033718745,0.0005656611],"genre_scores_gemma":[0.98127335,0.00088518247,0.011584161,0.00010825302,0.00031196148,0.000024234807,0.0053264983,0.000021891028,0.0004644636],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984621,0.00000787703,0.00055629143,0.0006007076,0.00008905579,0.0002840061],"domain_scores_gemma":[0.99886125,0.00011722023,0.00020716681,0.0006357004,0.00006791876,0.00011076345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043889068,0.00014828489,0.00034833513,0.0010625046,0.00020170138,0.000040395444,0.00053098745,0.00008751183,0.00027139275],"category_scores_gemma":[0.00014175366,0.00016692998,0.0001472756,0.0015249585,0.00006394188,0.00040986645,0.00007286139,0.00006902952,0.00021347473],"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":[0.0011708485,0.005217679,0.48383424,0.00068097765,0.0033187896,0.0003034975,0.0024351063,0.021644369,0.0002518071,0.19115478,0.16961777,0.12037013],"study_design_scores_gemma":[0.0016482887,0.00013127968,0.06110464,0.00000689011,0.00005340166,0.000026013508,0.0000125574015,0.8070908,0.00004186104,0.012750932,0.11655583,0.00057745806],"about_ca_topic_score_codex":0.0018929631,"about_ca_topic_score_gemma":0.00011496021,"teacher_disagreement_score":0.9586041,"about_ca_system_score_codex":0.000057828624,"about_ca_system_score_gemma":0.000026959335,"threshold_uncertainty_score":0.6807207},"labels":[],"label_agreement":null},{"id":"W2108299000","doi":"10.1111/j.1541-0420.2012.01773.x","title":"Bayesian Meta‐Analysis of the Accuracy of a Test for Tuberculous Pleuritis in the Absence of a Gold Standard Reference","year":2012,"lang":"en","type":"article","venue":"Biometrics","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Gold standard (test); Sensitivity (control systems); Statistics; Bayesian probability; Computer science; Receiver operating characteristic; Meta-analysis; Mathematics; Medicine; Pathology","score_opus":0.733732116492757,"score_gpt":0.5181350156285705,"score_spread":0.2155971008641866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108299000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8040813,0.06276668,0.111465335,0.0020005812,0.00052116404,0.005898915,0.005602992,0.000007798939,0.0076551847],"genre_scores_gemma":[0.99599004,0.000115369854,0.0035142184,0.000060906972,0.000011185842,0.000042422424,0.0000038695853,0.00000490182,0.00025710955],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98674244,0.0027159683,0.0055438117,0.00036737684,0.004391789,0.0002386078],"domain_scores_gemma":[0.9511444,0.037374515,0.0061141825,0.0036316733,0.0016605725,0.00007467615],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07719249,0.00018029819,0.0036728699,0.0027736686,0.000041400923,0.000093658906,0.002661773,0.00007725269,0.00074277475],"category_scores_gemma":[0.12936524,0.00006422621,0.0033329153,0.03948031,0.00013219523,0.00016509973,0.00013966067,0.00008490589,0.000007635029],"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":[0.000048230777,0.001534561,0.84495646,0.0010305133,0.05587724,0.0000017606478,0.0072714374,0.00026506576,0.0026838628,0.016410341,0.021567807,0.048352703],"study_design_scores_gemma":[0.0008107804,0.00048583004,0.72570276,0.000085807274,0.1623334,0.000012046174,0.00524437,0.009654955,0.002959156,0.0046686293,0.0873899,0.0006523725],"about_ca_topic_score_codex":0.00013479844,"about_ca_topic_score_gemma":0.00013242694,"teacher_disagreement_score":0.19190867,"about_ca_system_score_codex":0.000019951867,"about_ca_system_score_gemma":0.000067878456,"threshold_uncertainty_score":0.980936},"labels":[],"label_agreement":null},{"id":"W2109363394","doi":"10.1111/biom.12274","title":"Rejoinder “On Bayesian Estimation of Marginal Structural Models”","year":2015,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimation; Bayesian probability; Econometrics; Computer science; Marginal structural model; Statistics; Artificial intelligence; Mathematics; Economics; Causal inference","score_opus":0.22213669909642714,"score_gpt":0.398850691423097,"score_spread":0.17671399232666984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109363394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002848626,0.00011069706,0.97052145,0.019288916,0.00079939933,0.0003422291,0.00058042724,0.000068571746,0.008003455],"genre_scores_gemma":[0.004939324,0.000010002383,0.9711745,0.02192856,0.00080472365,0.000013042085,0.00018885486,0.000078645964,0.00086234254],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99758893,0.0001619021,0.00055083464,0.00033730906,0.0010321592,0.0003288749],"domain_scores_gemma":[0.9966454,0.0019507508,0.0004399282,0.0005209217,0.00035787254,0.00008514193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005827013,0.0003033072,0.00058636826,0.0014512502,0.000036186917,0.00004252505,0.00032083268,0.00070338167,0.000156882],"category_scores_gemma":[0.0047175707,0.00024506,0.000103556486,0.0017333961,0.00010185612,0.00006061978,0.00006817194,0.0007495743,0.000016963762],"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":[0.000015722411,0.00002244518,0.0000064337555,0.00057101983,0.00004072021,0.00003162165,0.000044969755,0.000039169252,0.0000036159745,0.05376188,0.90920395,0.036258485],"study_design_scores_gemma":[0.00026487283,0.00026041514,0.000047646092,0.000120586934,0.00009621601,0.000010926311,0.000006257975,0.09304731,0.000051254417,0.89055514,0.015192336,0.00034701935],"about_ca_topic_score_codex":0.000019983525,"about_ca_topic_score_gemma":2.0191673e-7,"teacher_disagreement_score":0.89401156,"about_ca_system_score_codex":0.00014429672,"about_ca_system_score_gemma":0.000120641234,"threshold_uncertainty_score":0.99932563},"labels":[],"label_agreement":null},{"id":"W2117049515","doi":"10.1111/biom.12317","title":"A moving blocks empirical likelihood method for longitudinal data","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"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 Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India; Ministry of Earth Sciences; National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Empirical likelihood; Inference; Mathematics; Estimating equations; Generalized estimating equation; Statistics; Likelihood function; Maximum likelihood; Statistical inference; Longitudinal data; Computer science; R package; Econometrics; Asymptotic analysis; Applied mathematics; Estimator; Data mining; Artificial intelligence","score_opus":0.5635480323815723,"score_gpt":0.5286815330539608,"score_spread":0.03486649932761143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117049515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015786263,0.00023503968,0.9953213,0.0003179626,0.00039331612,0.0002691512,0.00039147737,0.000085473206,0.0014076597],"genre_scores_gemma":[0.006169221,0.000006752703,0.99314165,0.00013987081,0.00026847702,0.000021968941,0.000033259603,0.000027700316,0.00019112507],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981382,0.00012973059,0.00039666743,0.0004885438,0.00044317375,0.00040372208],"domain_scores_gemma":[0.99169683,0.006611691,0.00013382503,0.0008756411,0.00034954344,0.0003324508],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00352581,0.00016915084,0.0003584545,0.00053075724,0.00006988945,0.00008687871,0.00065794244,0.00013833087,0.000049039667],"category_scores_gemma":[0.057699487,0.00013841654,0.000057503097,0.0022972357,0.00004461638,0.00009142736,0.00045639026,0.000138417,0.000018507213],"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":[0.00010268768,0.000711074,0.0087425485,0.00025736124,0.00014404055,0.000024279128,0.00030174313,8.603224e-7,0.00023794595,0.07094612,0.28503916,0.6334922],"study_design_scores_gemma":[0.001503664,0.00064881524,0.0017329919,0.000043003107,0.00026132914,0.000037219448,0.00019572082,0.09761886,0.0005161201,0.8081234,0.08875089,0.00056795176],"about_ca_topic_score_codex":0.0000280438,"about_ca_topic_score_gemma":0.0000036915762,"teacher_disagreement_score":0.7371773,"about_ca_system_score_codex":0.00006861584,"about_ca_system_score_gemma":0.00016361472,"threshold_uncertainty_score":0.95023793},"labels":[],"label_agreement":null},{"id":"W2119513306","doi":"10.1111/j.0006-341x.2000.00879.x","title":"Sample Size Determination for Testing Whether an Identified Treatment Is Best","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Normality; Sample size determination; Statistics; Wilcoxon signed-rank test; Biometrics; Mathematics; Mann–Whitney U test; Sample (material); Normality test; Statistical hypothesis testing; Computer science; Artificial intelligence","score_opus":0.7958203069179602,"score_gpt":0.6066968572577125,"score_spread":0.18912344966024774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119513306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2820104,0.00008007046,0.7095717,0.00027290086,0.001011344,0.0023084225,0.0016509593,0.00031247645,0.0027817334],"genre_scores_gemma":[0.010410112,0.000026510575,0.98623276,0.00012124521,0.0003331095,0.00010372397,0.00000445438,0.000046775625,0.0027213304],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981226,0.00021653576,0.0006662095,0.0003922777,0.00030762568,0.00029476554],"domain_scores_gemma":[0.8275353,0.17141941,0.00020492075,0.00046318115,0.00022351311,0.00015368905],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016730101,0.00018166404,0.00040245627,0.00027816996,0.00012403405,0.000102737664,0.00019614892,0.00017613808,0.001291917],"category_scores_gemma":[0.23940408,0.00015333098,0.00012332748,0.001752432,0.000061943836,0.00008860831,0.000018825729,0.00005671545,0.000073662784],"study_design_candidate":"design_other","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":[0.000045426077,0.0006239164,0.00038614613,0.00005613467,0.000028127019,0.0000019989986,0.00013032317,3.0817944e-7,0.00073098077,0.0013087807,0.00036698766,0.99632084],"study_design_scores_gemma":[0.0019106714,0.0016352446,0.0009860698,0.00003881153,0.00025415936,0.0000031629634,0.000049782317,0.004239274,0.0059403726,0.9767527,0.007850332,0.0003394382],"about_ca_topic_score_codex":0.0000771583,"about_ca_topic_score_gemma":0.0000048103943,"teacher_disagreement_score":0.99598145,"about_ca_system_score_codex":0.00013007232,"about_ca_system_score_gemma":0.000034687713,"threshold_uncertainty_score":0.99962103},"labels":[],"label_agreement":null},{"id":"W2120027488","doi":"10.1111/j.1541-0420.2011.01648.x","title":"Constructing Normalcy and Discrepancy Indexes for Birth Weight and Gestational Age Using a Threshold Regression Mixture Model","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Birth, Development, and Health","field":"Medicine","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":"McGill University; Ottawa Hospital","funders":"National Institute for Occupational Safety and Health; National Institutes of Health; World Health Organization","keywords":"Birth weight; Gestational age; Medicine; Regression analysis; Statistics; Population; Gestation; Obstetrics; Pregnancy; Demography; Mathematics","score_opus":0.09953518730539902,"score_gpt":0.3264595443720438,"score_spread":0.22692435706664477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120027488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98194814,0.0017330783,0.0144762,0.00013651492,0.00012655785,0.0003649957,0.0000671166,0.0000353605,0.0011120259],"genre_scores_gemma":[0.76694024,0.0049330834,0.22773135,0.00019755762,0.00007777114,0.000007248107,0.000037025075,0.00001850646,0.00005722553],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990943,0.000008597155,0.00023108345,0.00024166044,0.00019260189,0.00023173148],"domain_scores_gemma":[0.9994357,0.00006009731,0.00012904614,0.00009871966,0.00010129762,0.00017511258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022831385,0.00013110464,0.00022035443,0.00080748636,0.00017779865,0.000021118563,0.00003513218,0.00013501364,0.000008632026],"category_scores_gemma":[0.00012111488,0.000098288976,0.000026635873,0.0007709372,0.000101705606,0.00011443395,0.000047282287,0.000118279684,3.376261e-7],"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":[0.00029286926,0.00013016324,0.85547733,0.0012048085,0.00006701106,0.00003551566,0.0029201044,4.720378e-7,0.0022313406,0.10554003,0.00029275738,0.03180758],"study_design_scores_gemma":[0.014535681,0.0010842737,0.73980784,0.0017344316,0.00046012437,0.0010824141,0.002368147,0.10409559,0.0077846805,0.123771176,0.001746652,0.0015289646],"about_ca_topic_score_codex":0.00002644161,"about_ca_topic_score_gemma":0.00001095635,"teacher_disagreement_score":0.2150079,"about_ca_system_score_codex":0.00005127007,"about_ca_system_score_gemma":0.0001731265,"threshold_uncertainty_score":0.4008108},"labels":[],"label_agreement":null},{"id":"W2124332133","doi":"10.1111/j.1541-0420.2011.01696.x","title":"Empirical Likelihood for Cumulative Hazard Ratio Estimation with Covariate Adjustment","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","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":"University of Waterloo; Queen's University","funders":"","keywords":"Covariate; Estimator; Hazard ratio; Statistics; Nonparametric statistics; Confidence interval; Proportional hazards model; Hazard; Econometrics; Parametric statistics; Statistic; Empirical likelihood; Mathematics","score_opus":0.32664140167097977,"score_gpt":0.4311705265947319,"score_spread":0.1045291249237521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124332133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00680642,0.000037272715,0.99063176,0.000055771023,0.00017163182,0.00054550444,0.00007558377,0.0000671942,0.0016088621],"genre_scores_gemma":[0.08908341,0.0000056066124,0.9105516,0.00010102752,0.000049161776,0.00008632959,0.0000122366555,0.000021052849,0.00008955101],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99885404,0.000069101356,0.00031117868,0.00024060371,0.00027235132,0.00025270754],"domain_scores_gemma":[0.99741566,0.0017974328,0.00016431126,0.00021894631,0.00028532435,0.00011832745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052004604,0.00015298623,0.00024901557,0.00037742793,0.000074884134,0.000027467335,0.00011665747,0.0000892298,0.00012129765],"category_scores_gemma":[0.0041247136,0.0001096021,0.000047076974,0.0013715626,0.000057616046,0.000075250195,0.000032546843,0.00007234485,0.000023081891],"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":[0.00044247057,0.0011775998,0.0028065855,0.000327552,0.00022493821,0.00000850465,0.0025700612,0.000007188626,0.00020372511,0.67898524,0.0064454875,0.30680066],"study_design_scores_gemma":[0.0016924484,0.0020844715,0.03497085,0.000058315578,0.00024405796,0.0000063821303,0.000114777584,0.08331733,0.0033710864,0.8727541,0.00093986973,0.00044632732],"about_ca_topic_score_codex":0.000012362348,"about_ca_topic_score_gemma":0.0000026645223,"teacher_disagreement_score":0.30635434,"about_ca_system_score_codex":0.00006844477,"about_ca_system_score_gemma":0.00007355906,"threshold_uncertainty_score":0.49379653},"labels":[],"label_agreement":null},{"id":"W2125190682","doi":"10.1111/j.1541-0420.2006.00665.x","title":"Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":58,"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; Montreal General Hospital","funders":"","keywords":"Medical diagnosis; Database; Diagnosis code; Bayesian probability; Robustness (evolution); Computer science; Reimbursement; Data mining; Medicine; Artificial intelligence; Health care; Environmental health; Population","score_opus":0.1719976113805118,"score_gpt":0.3665172051345096,"score_spread":0.19451959375399777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125190682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83798903,0.0057013202,0.026322141,0.00012756039,0.0003095417,0.00043869365,0.12841627,0.00015418013,0.000541231],"genre_scores_gemma":[0.8692943,0.00021685656,0.07419091,0.000043264128,0.00027160338,0.0000106750085,0.055814832,0.00003184353,0.0001257291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978525,0.000064600536,0.0005134819,0.0007179988,0.00059063226,0.00026079398],"domain_scores_gemma":[0.9961397,0.0011945996,0.0003231319,0.001953397,0.00021890788,0.00017028493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018756006,0.0002372848,0.00051575765,0.00045179352,0.00004597927,0.00003140664,0.00066840614,0.00007925057,0.0005571183],"category_scores_gemma":[0.0023616168,0.00021406435,0.000060047554,0.0020172163,0.00025895008,0.0002800098,0.00042082858,0.000109246044,0.000037955528],"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":[0.0009980603,0.0025283447,0.78135777,0.0008021631,0.0005474941,0.00028216152,0.00003326162,0.00009116763,0.135605,0.0000686522,0.06933739,0.008348507],"study_design_scores_gemma":[0.0015402761,0.0000942415,0.95257163,0.00027032912,0.00037645327,0.0000036894999,0.00006314839,0.011048741,0.029705686,0.000028315495,0.004016234,0.00028126052],"about_ca_topic_score_codex":0.0010430644,"about_ca_topic_score_gemma":0.00010606411,"teacher_disagreement_score":0.17121384,"about_ca_system_score_codex":0.00004943451,"about_ca_system_score_gemma":0.00025850022,"threshold_uncertainty_score":0.87292904},"labels":[],"label_agreement":null},{"id":"W2125391757","doi":"10.1111/j.1541-0420.2005.00399.x","title":"An Extension of the Cormack–Jolly–Seber Model for Continuous Covariates with Application to<i>Microtus pennsylvanicus</i>","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Covariate; Microtus; Statistics; Bayesian probability; Vole; Mark and recapture; Mathematics; Population; Econometrics; Variable (mathematics); Logistic regression; Demography; Biology; Ecology","score_opus":0.04355788341608539,"score_gpt":0.3283831130581857,"score_spread":0.2848252296421003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125391757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30954966,0.0000269295,0.68893516,0.00030088986,0.000047200203,0.00095506536,0.00007516234,0.000041188257,0.00006875927],"genre_scores_gemma":[0.807395,0.0000036120146,0.19211677,0.00016828286,0.000051161325,0.000042137493,0.00003580182,0.000021276133,0.00016595349],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990523,0.000020106187,0.00032882934,0.0001952584,0.00024872558,0.00015476743],"domain_scores_gemma":[0.9986079,0.00017904871,0.00028844067,0.00044272922,0.00041970823,0.00006220642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036060798,0.00011730331,0.00018530725,0.00028205116,0.00010206497,0.000023710292,0.0001789389,0.00007666719,0.0000058941746],"category_scores_gemma":[0.00025223417,0.00007858878,0.0000524162,0.0013481235,0.00002457113,0.00011258374,0.000027960812,0.00004573366,0.000005875955],"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":[0.0009674299,0.0027774123,0.021486986,0.0006373067,0.00013131391,5.046484e-7,0.0042316997,0.09434711,0.32134277,0.28315476,0.022004185,0.24891855],"study_design_scores_gemma":[0.0010206712,0.00017785258,0.008987626,0.00003216951,0.00010017714,0.0000068994937,0.000034755656,0.9635239,0.012817219,0.0069107944,0.0061496943,0.0002382272],"about_ca_topic_score_codex":0.00002349723,"about_ca_topic_score_gemma":0.000046168145,"teacher_disagreement_score":0.8691768,"about_ca_system_score_codex":0.000057993802,"about_ca_system_score_gemma":0.000036561407,"threshold_uncertainty_score":0.32047573},"labels":[],"label_agreement":null},{"id":"W2126729664","doi":"10.1111/j.0006-341x.2000.01109.x","title":"Estimation of Operating Characteristics for Dependent Diagnostic Tests Based on Latent Markov Models","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Waterloo","funders":"","keywords":"Markov model; Latent variable; Computer science; Latent variable model; Variable-order Markov model; Markov chain; Latent class model; Statistics; Markov process; Econometrics; Mathematics; Machine learning","score_opus":0.042106133745024726,"score_gpt":0.2774415699792313,"score_spread":0.23533543623420658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126729664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06864887,0.000040944764,0.93057185,0.0001252725,0.00013011236,0.0001981618,0.000033393182,0.000058571,0.00019284336],"genre_scores_gemma":[0.8422459,0.000020059824,0.15746188,0.00015380558,0.000021220892,0.000024294708,0.000017261567,0.000007718142,0.00004786923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890035,0.000029042276,0.00030526402,0.0002638093,0.0003057036,0.00019581467],"domain_scores_gemma":[0.99854773,0.0008226425,0.00009397784,0.0003281526,0.00013257303,0.0000749062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003572327,0.0001167687,0.00015739677,0.00043706837,0.00007580468,0.00011096762,0.00037772118,0.000069773145,0.000010709351],"category_scores_gemma":[0.0006377761,0.00010818158,0.000046865913,0.0012046366,0.000016131087,0.00017547164,0.000030797055,0.00006304922,0.000012669153],"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":[0.000007705412,0.00018537538,0.00033856364,0.00004941056,0.0000050398417,0.0000028019715,0.000049789647,0.15379919,0.0002074744,0.002663243,0.00006571757,0.8426257],"study_design_scores_gemma":[0.00023780107,0.00023017531,0.0030003528,0.00005948564,0.00000753725,0.0000012509606,6.385497e-7,0.9943371,0.0010446786,0.0009392853,0.0000151175145,0.00012661384],"about_ca_topic_score_codex":0.000011596201,"about_ca_topic_score_gemma":2.9388656e-7,"teacher_disagreement_score":0.8424991,"about_ca_system_score_codex":0.000044779696,"about_ca_system_score_gemma":0.00007005893,"threshold_uncertainty_score":0.44115168},"labels":[],"label_agreement":null},{"id":"W2128422439","doi":"10.1111/j.1541-0420.2008.00962_11.x","title":"The Nature of Statistical Evidence by B. Thompson","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistics Education and Methodologies","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":"Simon Fraser University","funders":"","keywords":"Citation; Statistics; Computer science; Library science; Mathematics","score_opus":0.33645677203280233,"score_gpt":0.4842069243601342,"score_spread":0.14775015232733185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128422439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079776786,0.022722093,0.88362634,0.00241974,0.004888704,0.00062391127,0.0008310772,0.0001886916,0.0049226545],"genre_scores_gemma":[0.3526727,0.0047583473,0.635698,0.000152652,0.00010056956,0.000016357124,0.000015879396,0.000022853803,0.0065625967],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990053,0.00014788666,0.00023796556,0.00011941937,0.00033811503,0.0001512768],"domain_scores_gemma":[0.9779594,0.021413771,0.0001358905,0.00025658833,0.00018425171,0.00005010336],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008474322,0.00007616837,0.0001440994,0.00018383597,0.00013360777,0.000014034977,0.00023449124,0.000103717546,0.00005868967],"category_scores_gemma":[0.052148636,0.00004866862,0.000029505452,0.0013567373,0.00022888792,0.000027083255,0.00004086483,0.00016081537,0.00001274544],"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":[0.00002127131,0.00008508681,0.0010442463,0.00005635238,0.000017103775,0.0000026026175,0.00019515601,6.953174e-8,0.0007813779,0.20468928,0.78076285,0.012344577],"study_design_scores_gemma":[0.0006681983,0.0005810664,0.03674195,0.0001131184,0.00013217541,0.00009154404,0.0013616326,0.00023168631,0.025800789,0.33131698,0.6023237,0.00063717813],"about_ca_topic_score_codex":0.000006129101,"about_ca_topic_score_gemma":6.2709876e-7,"teacher_disagreement_score":0.27289593,"about_ca_system_score_codex":0.0000293703,"about_ca_system_score_gemma":0.00008143973,"threshold_uncertainty_score":0.9558355},"labels":[],"label_agreement":null},{"id":"W2128898088","doi":"10.1111/biom.12296","title":"Multivariate longitudinal data analysis with mixed effects hidden Markov models","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":19,"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; Yale University","keywords":"Bivariate analysis; Univariate; Multivariate statistics; Bayesian probability; Random effects model; Statistics; Markov chain Monte Carlo; Multivariate analysis; Hidden Markov model; Computer science; Econometrics; Mixed model; Markov chain; Mathematics; Artificial intelligence; Medicine; Meta-analysis","score_opus":0.1268934855015197,"score_gpt":0.3224489525178476,"score_spread":0.19555546701632792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128898088","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081922853,0.0008981183,0.99643373,0.00015395186,0.00033661682,0.00013663946,0.000028016217,0.0001405175,0.0010531718],"genre_scores_gemma":[0.27798438,0.000016252032,0.72163725,0.0000789327,0.00005700303,0.000006395262,0.000035264835,0.000011786799,0.00017272691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748814,0.000249074,0.00024111079,0.0009113879,0.0007048694,0.00040544214],"domain_scores_gemma":[0.996558,0.0003438575,0.00015311595,0.0023269106,0.00023730822,0.0003807916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014999504,0.00023477411,0.0004201497,0.0022539438,0.00007714321,0.0002898787,0.0022975767,0.000116660885,0.0000018467936],"category_scores_gemma":[0.0002632161,0.00017412969,0.000080065154,0.016877156,0.000047616228,0.0009759368,0.0010796499,0.00012268838,0.000011249297],"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":[0.00005840099,0.000333514,0.00395997,0.000046621186,0.0012527129,0.00023719767,0.00029705223,0.00013008981,0.00010737128,0.06183411,0.0069604646,0.9247825],"study_design_scores_gemma":[0.0009655251,0.00019702052,0.0071288813,0.00001270398,0.0004588882,0.000018074566,0.000004449286,0.9740404,0.00026274388,0.015661282,0.00081846514,0.0004315703],"about_ca_topic_score_codex":0.00027698043,"about_ca_topic_score_gemma":0.000018703948,"teacher_disagreement_score":0.97391033,"about_ca_system_score_codex":0.000069039765,"about_ca_system_score_gemma":0.00012123773,"threshold_uncertainty_score":0.81089133},"labels":[],"label_agreement":null},{"id":"W2132467996","doi":"10.1111/j.1541-0420.2006.00576.x","title":"Adaptive Web Sampling","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Los Alamos National Laboratory; National Science Foundation","keywords":"Resampling; Computer science; Inference; Sampling (signal processing); Markov chain; Sampling design; Sample (material); Statistic; Population; Markov chain Monte Carlo; Adaptive sampling; Data mining; Statistics; Machine learning; Artificial intelligence; Mathematics; Monte Carlo method","score_opus":0.24144060586953597,"score_gpt":0.38212463865982765,"score_spread":0.14068403279029168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132467996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114717126,0.0002860928,0.8688769,0.0000704332,0.00025376384,0.00018148805,0.000043722976,0.0010802178,0.014490285],"genre_scores_gemma":[0.6407796,0.0000088861025,0.35862184,0.000019946798,0.00010721088,0.0000091391385,0.000012332539,0.000018110808,0.0004229357],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991734,0.00003015034,0.00023386853,0.00015294876,0.00023036993,0.00017926551],"domain_scores_gemma":[0.9986134,0.00092242856,0.0000993924,0.00020422487,0.00012556057,0.00003495357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062170136,0.00010083513,0.00013900214,0.0009010043,0.000079866186,0.000041850537,0.000121940415,0.000087434295,0.00004056284],"category_scores_gemma":[0.0010525212,0.00009386849,0.000055486893,0.0023942592,0.00002929503,0.00005964688,0.000032558477,0.000073191055,0.00005502031],"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":[0.00006669637,0.0012109304,0.025362918,0.0002458992,0.00010229287,0.000018216379,0.00020581548,0.00008625613,0.009529148,0.5895335,0.21951537,0.15412296],"study_design_scores_gemma":[0.0007426996,0.0002170045,0.01654248,0.00009244306,0.000060909424,0.000020692229,0.000106029824,0.007093769,0.014918758,0.9007625,0.05857314,0.00086954853],"about_ca_topic_score_codex":0.000080450816,"about_ca_topic_score_gemma":0.0000056682084,"teacher_disagreement_score":0.5260625,"about_ca_system_score_codex":0.00006145189,"about_ca_system_score_gemma":0.000025771804,"threshold_uncertainty_score":0.3827846},"labels":[],"label_agreement":null},{"id":"W2134387151","doi":"10.1111/j.1541-0420.2009.01229.x","title":"On the Role of Baseline Measurements for Crossover Designs under the Self and Mixed Carryover Effects Model","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Baseline (sea); Optimal design; Crossover study; Design of experiments; Lagrange multiplier; Statistics; Computer science; Mathematics; Econometrics; Mathematical optimization; Medicine; Machine learning","score_opus":0.2673972802127009,"score_gpt":0.4347266216222311,"score_spread":0.16732934140953015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134387151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45223674,0.0026144646,0.5404026,0.0007286993,0.00037475702,0.001616178,0.000058099453,0.000041309107,0.0019271787],"genre_scores_gemma":[0.9427237,0.000010130827,0.05623636,0.0008165256,0.000027037235,0.000020041545,8.6107076e-7,0.000011752941,0.0001535971],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970083,0.0004903231,0.00038627101,0.00035926988,0.0015074809,0.00024835288],"domain_scores_gemma":[0.9894493,0.009374247,0.00020430579,0.00056390977,0.00032615932,0.00008207754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007769553,0.00016729235,0.0002467212,0.0005361489,0.00021183724,0.00018302319,0.00062023854,0.00009009523,0.000015825815],"category_scores_gemma":[0.0069338735,0.000080695216,0.00012348652,0.0030628666,0.00010676465,0.00012396874,0.000071196526,0.000082897415,0.000011166099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066260004,0.0008822673,0.0006880287,0.0000148296485,0.00012472465,9.439335e-7,0.00085054524,0.0053122337,0.79282403,0.04072654,0.012365866,0.14554742],"study_design_scores_gemma":[0.0014559684,0.00082862994,0.007070724,0.00001608976,0.000064361004,0.000001863379,0.00037317857,0.19767252,0.6479823,0.14301768,0.0012421728,0.00027449525],"about_ca_topic_score_codex":0.000006426938,"about_ca_topic_score_gemma":5.566179e-7,"teacher_disagreement_score":0.49048695,"about_ca_system_score_codex":0.00006542032,"about_ca_system_score_gemma":0.00005932195,"threshold_uncertainty_score":0.8300995},"labels":[],"label_agreement":null},{"id":"W2137549109","doi":"10.1111/j.1541-0420.2009.01308.x","title":"Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement Errors","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; York University; University of British Columbia","funders":"","keywords":"Inference; Immune system; Human immunodeficiency virus (HIV); Joint (building); Dynamics (music); Computer science; Virology; Computational biology; Immunology; Biology; Artificial intelligence; Physics; Engineering","score_opus":0.042007567703102495,"score_gpt":0.28945768963249485,"score_spread":0.24745012192939236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137549109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944274,0.0037615597,0.00031770716,0.0003797706,0.00012097333,0.00026658422,0.00006922667,0.000025093712,0.0006316886],"genre_scores_gemma":[0.9993137,0.00032687068,0.000116659474,0.000012631252,0.0000027471488,0.000006079433,0.000058121885,0.000004890198,0.00015828],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99907464,0.000106603096,0.00024073763,0.00019424265,0.00012149811,0.00026226096],"domain_scores_gemma":[0.9995124,0.00010088799,0.00007210585,0.00020596034,0.00007978017,0.000028918468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036604566,0.00010946459,0.00020481712,0.00094476424,0.00003804562,0.000007596429,0.00012212197,0.000119115844,0.000026770213],"category_scores_gemma":[0.0006246758,0.00008436897,0.000033903638,0.00083958777,0.00010216752,0.00004697956,0.00006045592,0.00016107691,0.000045760302],"study_design_candidate":"bench_or_experimental","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":[0.00050069066,0.0017627159,0.040309336,0.000054461183,0.00008224698,0.000030189245,0.00034218776,0.000017770095,0.76427066,0.0026590466,0.0007799996,0.18919069],"study_design_scores_gemma":[0.002468394,0.0018378631,0.77317446,0.00015365468,0.000012160362,0.000006477321,0.00009760271,0.00014839043,0.22107561,0.0003020329,0.00055925746,0.0001641088],"about_ca_topic_score_codex":0.00010687704,"about_ca_topic_score_gemma":0.000022749782,"teacher_disagreement_score":0.7328651,"about_ca_system_score_codex":0.00013096226,"about_ca_system_score_gemma":0.000049431346,"threshold_uncertainty_score":0.34404668},"labels":[],"label_agreement":null},{"id":"W2138528407","doi":"10.1111/j.1541-0420.2011.01733.x","title":"Discussion of Adjustment Uncertainty and Propensity Scores","year":2012,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Citation; Library science; Computer science; Information retrieval","score_opus":0.13490393878624984,"score_gpt":0.36303175393148246,"score_spread":0.22812781514523262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138528407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001743328,0.0038318853,0.84350663,0.1409005,0.0030580657,0.0024161371,0.0018358673,0.00016975195,0.002537822],"genre_scores_gemma":[0.0018170439,0.00038422956,0.9675777,0.026678018,0.0020887796,0.000034843215,0.00011872635,0.00008092541,0.001219717],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983565,0.00017609265,0.00039708547,0.00027413343,0.0004911784,0.00030500794],"domain_scores_gemma":[0.99753565,0.0015387831,0.0003324174,0.00036889108,0.00014021638,0.00008403827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055513,0.0002405461,0.0006145849,0.0006546116,0.00004208622,0.000024002105,0.00018111354,0.00046279936,0.00011856857],"category_scores_gemma":[0.0027529865,0.00012839993,0.000071539886,0.0010128052,0.00018105998,0.00003911405,0.00019240225,0.0004756751,0.0000072497082],"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":[0.000013889193,0.00012355897,0.0010740983,0.0022746895,0.000064198684,0.000021933336,0.0000755609,6.704042e-9,0.00009991076,0.018095743,0.73164093,0.24651548],"study_design_scores_gemma":[0.0006704122,0.0005186085,0.018820485,0.0008981358,0.000724606,0.000031551594,0.000028860632,0.00008821001,0.0006205074,0.53630465,0.44019288,0.0011010726],"about_ca_topic_score_codex":0.000039844912,"about_ca_topic_score_gemma":9.2275616e-7,"teacher_disagreement_score":0.5182089,"about_ca_system_score_codex":0.000057080953,"about_ca_system_score_gemma":0.000043096217,"threshold_uncertainty_score":0.52359974},"labels":[],"label_agreement":null},{"id":"W2138565270","doi":"10.1111/j.0006-341x.2001.00518.x","title":"Bayesian Nonparametric Modeling Using Mixtures of Triangular Distributions","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":37,"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","funders":"Queensland University of Technology","keywords":"Markov chain Monte Carlo; Nonparametric statistics; Computer science; Bayesian probability; Context (archaeology); Mathematical optimization; Mathematics; Parametric statistics; Markov chain; Piecewise; Nonparametric regression; Algorithm; Focus (optics); Flexibility (engineering); Applied mathematics; Machine learning; Econometrics; Statistics; Artificial intelligence","score_opus":0.05482695784796059,"score_gpt":0.3136258099905117,"score_spread":0.2587988521425511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138565270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014402006,0.0022517738,0.98204535,0.00011044151,0.00038261252,0.00015831711,0.000018452738,0.000086936,0.000544126],"genre_scores_gemma":[0.47931015,0.00009237584,0.5204717,0.000031893087,0.000052887797,0.0000021014803,0.0000037612917,0.000008039462,0.000027080847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817485,0.0001242703,0.0004549065,0.00041163526,0.00043823617,0.00039609004],"domain_scores_gemma":[0.9985035,0.00019764357,0.00017277144,0.00071677146,0.00023625337,0.00017307478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083304784,0.00018366783,0.00032998025,0.0025116235,0.0001413979,0.00010704362,0.0008343147,0.00016391632,0.000010244987],"category_scores_gemma":[0.00060213427,0.00016842874,0.0001819486,0.017507127,0.000045312652,0.00031416543,0.00019592838,0.0001442006,0.00000380324],"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":[0.000035194556,0.00080154533,0.0016439859,0.0001023841,0.0001556065,0.00011576652,0.00032902567,0.0050642635,0.030470738,0.19298118,0.00047630505,0.767824],"study_design_scores_gemma":[0.000356907,0.000060017162,0.00009093779,0.000017233855,0.000028478991,0.000046161367,0.00000443787,0.9731759,0.0040098163,0.021189444,0.0007858966,0.00023481433],"about_ca_topic_score_codex":0.000083823536,"about_ca_topic_score_gemma":7.1119894e-7,"teacher_disagreement_score":0.9681116,"about_ca_system_score_codex":0.00008524678,"about_ca_system_score_gemma":0.00010323011,"threshold_uncertainty_score":0.84115934},"labels":[],"label_agreement":null},{"id":"W2139571183","doi":"10.1111/j.0006-341x.2001.00273.x","title":"Combining Band Recovery Data and Pollock's Robust Design to Model Temporary and Permanent Emigration","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ducks Unlimited Canada","funders":"","keywords":"Emigration; Pollock; Statistics; Mark and recapture; Sampling (signal processing); Population; Sampling design; Population model; Biological dispersal; Econometrics; Ecology; Geography; Mathematics; Biology; Demography; Computer science; Fishery","score_opus":0.09074280960736633,"score_gpt":0.25749267772419054,"score_spread":0.1667498681168242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139571183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9297093,0.00013534514,0.068661675,0.0008020747,0.000056800898,0.00015810868,0.000014022932,0.000018828208,0.00044384238],"genre_scores_gemma":[0.9883894,0.0002525352,0.0099231405,0.00088575354,0.000015768168,0.0000053300864,0.000045643366,0.0000061542896,0.00047632042],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993378,0.00003345274,0.0001190997,0.00026897408,0.00011989185,0.000120769095],"domain_scores_gemma":[0.99956983,0.000099358,0.00004182388,0.00020133873,0.0000051007905,0.00008255527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004213091,0.0000727711,0.00007417643,0.00020209566,0.00013319796,0.000039898343,0.00012698032,0.00006785823,0.000029287012],"category_scores_gemma":[0.00014205454,0.00007166286,0.00000565996,0.00096835155,0.00004908429,0.00037043076,0.00019800184,0.000046085734,0.000023480963],"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":[0.000029812041,0.00003658893,0.970981,0.0000020723653,0.0000054365078,0.000004106088,0.000096560696,0.003229872,0.00075299287,0.0000068297895,0.016604595,0.0082501285],"study_design_scores_gemma":[0.00028149495,0.00013717319,0.8314841,0.000005596262,0.000016399099,0.000019714142,0.00006674552,0.16613743,0.00009663023,0.00021732102,0.0013721661,0.0001652652],"about_ca_topic_score_codex":0.00011582847,"about_ca_topic_score_gemma":0.000050057122,"teacher_disagreement_score":0.16290754,"about_ca_system_score_codex":0.000044987733,"about_ca_system_score_gemma":0.000012361629,"threshold_uncertainty_score":0.29223266},"labels":[],"label_agreement":null},{"id":"W2140859919","doi":"10.1111/j.1541-0420.2008.01124.x","title":"Adjusted Exponentially Tilted Likelihood with Applications to Brain Morphology","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Eli Lilly and Company; National Institute on Aging; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Covariate; Likelihood-ratio test; Statistics; Estimator; Brain morphometry; Statistic; Nonparametric statistics; Exponential distribution; Mathematics; Maximum likelihood; Statistical hypothesis testing; Medicine","score_opus":0.14646485331289127,"score_gpt":0.36826434147195336,"score_spread":0.22179948815906209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140859919","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06975805,0.000039633473,0.9271066,0.00043492342,0.000063265936,0.0004422529,0.000061679246,0.0001176784,0.0019758684],"genre_scores_gemma":[0.15832633,0.0000140134935,0.8404172,0.00050249544,0.000075662974,0.00014361678,0.000012710725,0.00002827842,0.0004796595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99876875,0.00007493475,0.00027470276,0.00029019488,0.0002864401,0.00030498978],"domain_scores_gemma":[0.99751884,0.0015437439,0.00008935126,0.00039909894,0.00022931874,0.00021966216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026755317,0.00014062172,0.0002447626,0.0007932407,0.00011097315,0.000016182354,0.00023588783,0.00009738049,0.00020313484],"category_scores_gemma":[0.0026015232,0.00011378013,0.000033446984,0.004695472,0.00008950067,0.000027831038,0.0000710898,0.00010589205,0.0001839991],"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":[0.00023837894,0.0021773153,0.0073904493,0.00025666965,0.00022680608,0.00024790148,0.0009092662,0.000005362482,0.05810428,0.52547944,0.094383664,0.3105805],"study_design_scores_gemma":[0.008764615,0.007389308,0.25012568,0.0002121037,0.0005766582,0.0014377363,0.0005501025,0.0021852923,0.040352523,0.37078333,0.31311837,0.004504297],"about_ca_topic_score_codex":0.000024861723,"about_ca_topic_score_gemma":0.0000044112508,"teacher_disagreement_score":0.3060762,"about_ca_system_score_codex":0.00003838498,"about_ca_system_score_gemma":0.000071923496,"threshold_uncertainty_score":0.4639819},"labels":[],"label_agreement":null},{"id":"W2144914506","doi":"10.1111/j.1541-0420.2008.01018.x","title":"Estimating the Encounter Rate Variance in Distance Sampling","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":162,"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":"Raincoast Conservation Foundation; Leverhulme Trust","keywords":"Statistics; Distance sampling; Variance (accounting); Sampling (signal processing); Mathematics; Econometrics; Computer science; Biology; Economics","score_opus":0.20028066032329941,"score_gpt":0.3769286227303718,"score_spread":0.1766479624070724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144914506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1852697,0.00015306755,0.8133558,0.00011803809,0.0002395588,0.00014255906,0.000008921882,0.00018227467,0.0005300757],"genre_scores_gemma":[0.67739886,0.000025214507,0.32225734,0.000083014565,0.0000670521,0.000023909552,0.000003745774,0.00001651086,0.00012436011],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99892056,0.000089200206,0.0003496928,0.00019302641,0.00022062685,0.00022686712],"domain_scores_gemma":[0.9971696,0.0022182434,0.00015194922,0.00033639633,0.00009187405,0.000031916086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017850886,0.0001200856,0.00016928792,0.0004471248,0.00019202942,0.000043318323,0.00025750446,0.00006713731,0.000016871034],"category_scores_gemma":[0.0054334435,0.000088402645,0.0000413565,0.0030158458,0.00007506601,0.00011170198,0.00004576402,0.000158635,0.000019817146],"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":[0.00031936026,0.0027563714,0.5086824,0.002016729,0.0002800346,0.00032824793,0.023708027,0.008896714,0.011006562,0.1706892,0.062014922,0.2093014],"study_design_scores_gemma":[0.0020836694,0.00022035564,0.21466272,0.0009807297,0.000060041042,0.00021777117,0.00033748156,0.26654083,0.007853024,0.48018932,0.02474275,0.0021113066],"about_ca_topic_score_codex":0.00005463718,"about_ca_topic_score_gemma":0.000008179956,"teacher_disagreement_score":0.49212918,"about_ca_system_score_codex":0.00008394072,"about_ca_system_score_gemma":0.000033524095,"threshold_uncertainty_score":0.6504732},"labels":[],"label_agreement":null},{"id":"W2146392455","doi":"10.1111/biom.12222","title":"Case-Base Methods for Studying Vaccination Safety","year":2014,"lang":"en","type":"article","venue":"Biometrics","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Tekniikan Edistämissäätiö","keywords":"Pooling; Estimator; Statistics; Context (archaeology); Computer science; Econometrics; Nonparametric statistics; Population; Mathematics; Medicine; Artificial intelligence; Environmental health","score_opus":0.10340791373297933,"score_gpt":0.42031900760680474,"score_spread":0.31691109387382543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146392455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03838577,0.0006046171,0.9501115,0.0010102874,0.00081756426,0.00051007763,0.000008784841,0.000107374144,0.0084440205],"genre_scores_gemma":[0.90184206,0.00011116668,0.096388385,0.00021413359,0.0005492477,0.000022259836,0.000006544717,0.000013269176,0.0008529423],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989779,0.0002660032,0.00018314994,0.0001795776,0.00015750364,0.00023585728],"domain_scores_gemma":[0.9979211,0.0015399257,0.00008745605,0.00014109534,0.00022058614,0.000089834844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035797672,0.00007135555,0.00013124364,0.0006506341,0.0006673542,0.00008079283,0.00013712913,0.000084111736,0.000091588816],"category_scores_gemma":[0.0041727335,0.000070099966,0.00007161217,0.002817266,0.000004549646,0.00015900236,0.000026273061,0.000046130186,0.000014412478],"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":[0.000011443722,0.000055668606,0.0042063976,0.00002190545,0.000011914709,0.000007874444,0.0014532957,0.0000025334894,0.00013223878,0.02316001,0.0018147358,0.969122],"study_design_scores_gemma":[0.0015878483,0.00029154783,0.017318545,0.000009710024,0.00009117623,0.000018853923,0.0023470223,0.0017880716,0.0008251036,0.00737922,0.96791047,0.00043245143],"about_ca_topic_score_codex":0.00016785471,"about_ca_topic_score_gemma":0.00009495631,"teacher_disagreement_score":0.9686895,"about_ca_system_score_codex":0.00013263336,"about_ca_system_score_gemma":0.000056406458,"threshold_uncertainty_score":0.51328164},"labels":[],"label_agreement":null},{"id":"W2146876168","doi":"10.1111/j.0006-341x.2002.00964.x","title":"Ranked Set Sampling: Cost and Optimal Set Size","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"RSS; Ranking (information retrieval); Simple random sample; Sampling (signal processing); Set (abstract data type); Statistics; Computer science; Sample size determination; Population; Data mining; Mathematics; Information retrieval; Medicine; Telecommunications","score_opus":0.29950839546264835,"score_gpt":0.410846597518567,"score_spread":0.11133820205591866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146876168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028345665,0.00015034317,0.9655128,0.0010606425,0.000077290686,0.00041274092,0.0016586707,0.0001842798,0.00259754],"genre_scores_gemma":[0.8807451,0.000072524905,0.11803812,0.00020880121,0.0000459409,0.00005113665,0.00011369266,0.000017303571,0.00070734846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991545,0.000021636337,0.0002444869,0.00018319367,0.0002110645,0.00018508514],"domain_scores_gemma":[0.99765265,0.0018140251,0.000078350975,0.00019346687,0.000105648745,0.00015583409],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001711395,0.00010567315,0.00014662302,0.00019964536,0.0001326433,0.00006927977,0.00009582112,0.000075364165,0.0011296904],"category_scores_gemma":[0.0042714993,0.00010068911,0.00003168169,0.0017939218,0.000086998996,0.00004775273,0.000038755108,0.000078160374,0.00021666105],"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":[0.000009830225,0.00034231815,0.00040644928,0.00011146243,0.00005317269,0.0000041475137,0.0002774935,0.000006636662,0.00041670492,0.83776224,0.1331568,0.027452772],"study_design_scores_gemma":[0.006287767,0.00028951388,0.05167862,0.00007352467,0.00038688988,0.00012856256,0.00069970335,0.18065973,0.001170749,0.12821914,0.62857556,0.0018302457],"about_ca_topic_score_codex":0.00000252288,"about_ca_topic_score_gemma":2.9469206e-7,"teacher_disagreement_score":0.85239947,"about_ca_system_score_codex":0.000041265044,"about_ca_system_score_gemma":0.000006894836,"threshold_uncertainty_score":0.9997834},"labels":[],"label_agreement":null},{"id":"W2149281073","doi":"10.1111/j.0006-341x.2004.00241.x","title":"Estimation in Bayesian Disease Mapping","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayes' theorem; Inference; Bayesian inference; Markov chain Monte Carlo; Bayesian probability; Statistical inference; Computer science; Statistics; Bayes factor; Fiducial inference; Econometrics; Parametric statistics; Frequentist inference; Mathematics; Artificial intelligence","score_opus":0.08734313465568595,"score_gpt":0.3776616522745233,"score_spread":0.29031851761883737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149281073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0097165,0.00005708799,0.98886204,0.0002487177,0.00010596294,0.00013701584,0.00001616426,0.00005181886,0.00080471125],"genre_scores_gemma":[0.3975606,0.000006362132,0.6023508,0.000037964706,0.000015436864,0.000007749665,0.0000027735193,0.000007470324,0.000010897396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99912846,0.000038223603,0.00025269805,0.00016993178,0.00021503255,0.000195626],"domain_scores_gemma":[0.99900925,0.000583747,0.00006167957,0.00018217402,0.000031779207,0.0001313589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035812616,0.000092791895,0.00014371934,0.000926501,0.000036013153,0.000038657767,0.000108911096,0.000049029328,0.00004616954],"category_scores_gemma":[0.0062597166,0.00008513885,0.000031658186,0.0031466738,0.000034213044,0.00007148069,0.000030262234,0.000080780395,0.000028691107],"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":[0.0000070946685,0.00016670914,0.0024373163,0.0001247117,0.000004248077,0.000046620076,0.00011345727,0.000011169438,0.00007519971,0.7810608,0.00007153343,0.21588112],"study_design_scores_gemma":[0.00032484558,0.000019906756,0.029151578,0.00007164753,0.000007536297,0.0000014012908,0.000019664692,0.0045982404,0.0000918164,0.9654801,0.00011020422,0.0001230525],"about_ca_topic_score_codex":0.000020237552,"about_ca_topic_score_gemma":0.0000023547389,"teacher_disagreement_score":0.3878441,"about_ca_system_score_codex":0.00011627804,"about_ca_system_score_gemma":0.000057354162,"threshold_uncertainty_score":0.74939173},"labels":[],"label_agreement":null},{"id":"W2149783391","doi":"10.1111/j.0006-341x.2001.00757.x","title":"A Bootstrap Assessment of Variability in Pedigree Reconstruction Based on Genetic Markers","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","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":"Saint Mary's University; St. Mary's University","funders":"","keywords":"Statistics; Statistic; Confidence interval; Mathematics; Point estimation; Metric (unit); Sampling distribution; Population; Sampling (signal processing); Sample (material); Sample space; Point (geometry); Computer science; Demography","score_opus":0.02159433925443367,"score_gpt":0.2844809378360563,"score_spread":0.26288659858162267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149783391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9039513,0.000068015455,0.0888274,0.000033389144,0.00027432293,0.00019733458,0.00001975381,0.0000061768187,0.006622316],"genre_scores_gemma":[0.92436093,0.000040175408,0.07537362,0.000049375114,0.00007187358,0.000011718442,0.000020624066,0.000009892871,0.00006176965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988968,0.00014692315,0.00028135112,0.00030928155,0.00018442112,0.00018122644],"domain_scores_gemma":[0.9993366,0.000082126564,0.00009869748,0.00035658487,0.000064257394,0.00006170906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055774173,0.00012039816,0.00013569322,0.0003995857,0.000022444056,0.0000073445103,0.00014987527,0.0001574126,0.000062520034],"category_scores_gemma":[0.00027303904,0.00012056273,0.000064637876,0.0010136361,0.00008462197,0.0000015944873,0.000028242672,0.00007816251,0.0000013929734],"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":[0.00016290123,0.00048214712,0.8239332,0.000053088483,0.000020882506,0.0000015798166,0.000010231675,0.003391492,0.018127672,0.0003584106,0.00016659951,0.15329184],"study_design_scores_gemma":[0.0006315807,0.000677141,0.99359673,0.000012775935,0.000011376876,0.00000780716,0.000016978,0.0019151536,0.0015489089,0.00040972861,0.0010348216,0.00013701447],"about_ca_topic_score_codex":0.00002260589,"about_ca_topic_score_gemma":0.000009006599,"teacher_disagreement_score":0.16966355,"about_ca_system_score_codex":0.000039202838,"about_ca_system_score_gemma":0.00013566937,"threshold_uncertainty_score":0.49164054},"labels":[],"label_agreement":null},{"id":"W2150038257","doi":"10.1111/j.1541-0420.2010.01441.x","title":"PICS: Probabilistic Inference for ChIP-seq","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":71,"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; Montreal Clinical Research Institute; BC Cancer Agency; University of British Columbia","funders":"","keywords":"False discovery rate; Chromatin immunoprecipitation; Computer science; Inference; DNA binding site; Probabilistic logic; Computational biology; Statistical model; Bayesian probability; Bayesian inference; Synthetic data; Event (particle physics); Data mining; Algorithm; Biology; Artificial intelligence; Genetics; Promoter; Gene","score_opus":0.012732539639666945,"score_gpt":0.26451637733432637,"score_spread":0.2517838376946594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150038257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9838233,0.00011427826,0.014398667,0.00006412974,0.00058840273,0.00027697222,0.000099615165,0.000014904931,0.00061970094],"genre_scores_gemma":[0.98143476,0.000048343773,0.017453382,0.0000952444,0.00027147762,0.00004047071,0.00020985707,0.000021863942,0.00042461255],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992601,0.0000069185653,0.00016591817,0.00026287415,0.000086403845,0.00021775796],"domain_scores_gemma":[0.99930084,0.000040825547,0.00007414894,0.00035940655,0.00014231901,0.00008243791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018184664,0.000118115306,0.00010054724,0.00016980755,0.00006999308,0.000043877542,0.00025617093,0.00018251888,0.000012103899],"category_scores_gemma":[0.00097240374,0.00011292709,0.000073297095,0.00043393465,0.00006129098,0.0000016417612,0.000094414114,0.00007912691,0.000010890377],"study_design_candidate":"bench_or_experimental","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":[0.000016864953,0.000107691165,0.0037057241,0.000059030343,0.000025373238,4.703368e-7,0.000015745269,0.000027607122,0.97787094,0.0037788912,0.0010045881,0.0133870775],"study_design_scores_gemma":[0.0035812352,0.0024617852,0.063726,0.000022220032,0.000140114,0.00004253391,0.00009949515,0.019570617,0.1990069,0.018759023,0.69045275,0.0021373192],"about_ca_topic_score_codex":0.0000045685865,"about_ca_topic_score_gemma":0.000029770972,"teacher_disagreement_score":0.778864,"about_ca_system_score_codex":0.000009236624,"about_ca_system_score_gemma":0.000086189364,"threshold_uncertainty_score":0.4605033},"labels":[],"label_agreement":null},{"id":"W2152824434","doi":"10.1111/j.1541-0420.2009.01336.x","title":"Utilizing Gaussian Markov Random Field Properties of Bayesian Animal Models","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SNC-Lavalin (Canada)","funders":"Norges Forskningsråd","keywords":"Bayesian probability; Statistical physics; Gaussian; Computer science; Random field; Markov chain; Variable-order Bayesian network; Mathematics; Bayesian inference; Econometrics; Statistics; Artificial intelligence; Physics","score_opus":0.0255347440096777,"score_gpt":0.24516126183656717,"score_spread":0.21962651782688947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152824434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8868796,0.00663955,0.092106715,0.0003226695,0.00020006664,0.00029578913,0.000015494388,0.000022711798,0.013517393],"genre_scores_gemma":[0.9791172,0.0001038499,0.02013787,0.00021448985,0.0001282804,0.000002440832,0.000010627296,0.000010670657,0.0002745723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991183,0.000033479453,0.00023612134,0.00023856205,0.00015948094,0.00021404329],"domain_scores_gemma":[0.9994903,0.000013548585,0.00007801639,0.00027439266,0.00006744205,0.000076259734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013906801,0.00013455088,0.00016701885,0.0002161681,0.000048024784,0.000015327616,0.00019792069,0.00016332547,0.000014428392],"category_scores_gemma":[0.00012749775,0.00011446298,0.00008929715,0.0005274056,0.00005054281,0.0000042600072,0.00004629945,0.00006147977,0.0000015740601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009444539,0.00027850436,0.00076308724,0.000074246054,0.00007158447,0.0000011401289,0.00020455847,0.00019046234,0.9220275,0.0056308582,0.002285895,0.0675277],"study_design_scores_gemma":[0.0025452431,0.0037914547,0.0241885,0.000068032874,0.00006661154,0.000019466388,0.0002772932,0.0008576489,0.95981854,0.0034207632,0.004359315,0.0005871074],"about_ca_topic_score_codex":0.000009962676,"about_ca_topic_score_gemma":0.000001224326,"teacher_disagreement_score":0.092237584,"about_ca_system_score_codex":0.0000054871207,"about_ca_system_score_gemma":0.000049567705,"threshold_uncertainty_score":0.46676648},"labels":[],"label_agreement":null},{"id":"W2154206493","doi":"10.1111/biom.12306","title":"Doubly‐robust dynamic treatment regimen estimation via weighted least squares","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Thrasher Research Fund","keywords":"Computer science; Robustness (evolution); Personalized medicine; Precision medicine; Data mining; Machine learning; Artificial intelligence; Medicine; Bioinformatics","score_opus":0.19478819623802732,"score_gpt":0.39683781714767796,"score_spread":0.20204962090965065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154206493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02459677,0.00022540633,0.97102827,0.0001941135,0.00034166613,0.00029183316,0.00005551606,0.00014614138,0.003120313],"genre_scores_gemma":[0.20949408,0.000020221709,0.78948826,0.000021771888,0.00004921699,0.000028303608,0.000032459484,0.000026015397,0.00083970185],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986651,0.00010140487,0.00032665764,0.00025865028,0.00038534764,0.00026283113],"domain_scores_gemma":[0.99813914,0.0009162784,0.00015190824,0.0003538297,0.00021478157,0.00022405088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042993025,0.00019044886,0.00028264202,0.0006816309,0.00006424082,0.000066019624,0.00015253412,0.00011564584,0.00008542821],"category_scores_gemma":[0.002254881,0.0001454239,0.00005507315,0.0021951029,0.0000653962,0.0000798617,0.000043647593,0.0000680484,0.00015517182],"study_design_candidate":"design_other","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":[0.000083342275,0.0008161452,0.00048565684,0.00010626126,0.000097453216,0.00002844972,0.00049408793,0.000043902895,0.0003230455,0.07071728,0.0061249402,0.92067945],"study_design_scores_gemma":[0.0027662457,0.0015348459,0.0028896455,0.000068544075,0.00022469423,0.00003632906,0.00019502436,0.40642166,0.0013754643,0.5762417,0.007546978,0.00069891574],"about_ca_topic_score_codex":0.00007733575,"about_ca_topic_score_gemma":0.0000055488026,"teacher_disagreement_score":0.9199805,"about_ca_system_score_codex":0.00037259367,"about_ca_system_score_gemma":0.00007445059,"threshold_uncertainty_score":0.59302145},"labels":[],"label_agreement":null},{"id":"W2156098549","doi":"10.1111/j.1541-0420.2006.00679.x","title":"On Robustness and Model Flexibility in Survival Analysis: Transformed Hazard Models and Average Effects","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"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":"Canadian Institutes of Health Research","keywords":"Interpretability; Covariate; Econometrics; Proportional hazards model; Hazard; Statistics; Mathematics; Inference; Robustness (evolution); Linear regression; Power transform; Computer science; Artificial intelligence","score_opus":0.10364934654496466,"score_gpt":0.36223680029277416,"score_spread":0.2585874537478095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156098549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44737175,0.000075955984,0.5516234,0.000016096481,0.000021688104,0.00013045462,0.000030205785,0.000017761286,0.00071271526],"genre_scores_gemma":[0.85203695,0.000036327572,0.14782844,0.000014668341,0.000010734137,0.000011767957,0.000004556827,0.000010165353,0.000046358236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986869,0.0001339163,0.0003171788,0.00034047934,0.00028189245,0.00023960836],"domain_scores_gemma":[0.9964458,0.003151131,0.00005253313,0.00020963106,0.00005424027,0.00008666874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094012916,0.00016550635,0.0004554921,0.0011281477,0.00004527672,0.0000497733,0.00007795237,0.00012268698,0.000005087055],"category_scores_gemma":[0.0012697504,0.00013693763,0.000053979198,0.0030180067,0.00007808795,0.00007350094,0.000028061399,0.00012541653,3.7365072e-7],"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":[0.00012684338,0.00065365725,0.004646756,0.00071868324,0.00011934096,0.000019699402,0.00017936557,0.041335844,0.00026868563,0.91569304,0.00004503867,0.036193024],"study_design_scores_gemma":[0.00039241207,0.000051506842,0.008319148,0.000012729497,0.000081432634,3.2506324e-7,0.0000045465667,0.59517425,0.0001382585,0.39571077,7.942123e-7,0.000113794646],"about_ca_topic_score_codex":0.000076215394,"about_ca_topic_score_gemma":0.00008036548,"teacher_disagreement_score":0.55383843,"about_ca_system_score_codex":0.000046747795,"about_ca_system_score_gemma":0.000020424142,"threshold_uncertainty_score":0.5584155},"labels":[],"label_agreement":null},{"id":"W2157443818","doi":"10.1111/j.1541-0420.2009.01377.x","title":"Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Simon Fraser University; University of British Columbia","funders":"Economic and Social Research Council; Canadian Institutes of Health Research","keywords":"Markov chain Monte Carlo; Posterior probability; Confounding; Bayesian probability; Computer science; Bayesian inference; Prior probability; Econometrics; Inference; Sensitivity (control systems); Hyperparameter; Statistics; Machine learning; Mathematics; Artificial intelligence","score_opus":0.1327259247998718,"score_gpt":0.3856908479612313,"score_spread":0.25296492316135955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157443818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042215817,0.000015061135,0.95657027,0.00019840723,0.00019651974,0.00026839564,0.00015192789,0.000061878534,0.00032173144],"genre_scores_gemma":[0.52391106,0.000002714868,0.47594234,0.000050965493,0.000036846894,0.000007922464,0.000008491408,0.000010651629,0.000029043618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987563,0.000102850565,0.00029217772,0.0003258679,0.00023851328,0.00028432204],"domain_scores_gemma":[0.99298877,0.0061017596,0.00013388826,0.00033285475,0.00024868787,0.00019406922],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0015188401,0.00016434606,0.00042360503,0.0008476205,0.00013020534,0.00011157313,0.00009157729,0.00017688815,0.00005016297],"category_scores_gemma":[0.012198651,0.00014498382,0.0001350894,0.0032179889,0.00013903299,0.000039617287,0.000036474532,0.00015255029,0.0000019231666],"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":[0.000068213645,0.00017079618,0.020093078,0.00024408092,0.00075038447,0.00001303669,0.00013999906,4.141186e-7,0.029989293,0.8344983,0.0006893092,0.1133431],"study_design_scores_gemma":[0.0011143251,0.00014297903,0.109471574,0.000009254809,0.0013064137,0.0000101353635,0.000112038775,0.02445893,0.002804694,0.8586912,0.0012950655,0.0005833443],"about_ca_topic_score_codex":0.000050505474,"about_ca_topic_score_gemma":0.00015753925,"teacher_disagreement_score":0.4816952,"about_ca_system_score_codex":0.000018609384,"about_ca_system_score_gemma":0.00003902962,"threshold_uncertainty_score":0.996122},"labels":[],"label_agreement":null},{"id":"W2158310202","doi":"10.1111/j.0006-341x.2001.01074.x","title":"Statistical Analysis of Uniparental Disomy Data Using Hidden Markov Models","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Syndromes and Imprinting","field":"Biochemistry, Genetics and Molecular Biology","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 British Columbia","funders":"National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute for Health and Care Research; March of Dimes Foundation","keywords":"Nondisjunction; Uniparental disomy; Computer science; Crossover; Markov chain; Hidden Markov model; International HapMap Project; Set (abstract data type); Data set; Genetic genealogy; Chromosome; Genetics; Machine learning; Artificial intelligence; Biology; Single-nucleotide polymorphism; Genotype; Aneuploidy","score_opus":0.08566248651909217,"score_gpt":0.33077814357963814,"score_spread":0.24511565706054597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158310202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8435324,0.0006351499,0.15497755,0.000008200712,0.00007277601,0.000056607165,0.0003505852,0.000003987232,0.00036278763],"genre_scores_gemma":[0.96615726,0.0002908495,0.03222671,0.00001799059,0.000045636272,8.4853093e-7,0.0011679459,0.000012793803,0.00007993945],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892974,0.00003257104,0.00026670514,0.00035256366,0.00020403686,0.00021438405],"domain_scores_gemma":[0.9990313,0.000026150414,0.00010285868,0.00068700494,0.000069424816,0.00008326059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020688427,0.0001080858,0.00020737368,0.00064319593,0.00003773622,0.000021207297,0.00039832733,0.00008970881,0.000052823485],"category_scores_gemma":[0.0001591041,0.000105224855,0.00006960697,0.0028240806,0.000061536215,0.0000052227497,0.0005086332,0.000030428102,0.0000011864626],"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":[0.000037308382,0.0004336126,0.5981001,0.00005277786,0.0022463596,0.00001270811,0.000039728417,0.0013613496,0.24939474,0.00020839769,0.0011076115,0.14700526],"study_design_scores_gemma":[0.0011620963,0.00039032524,0.48610967,0.00001856742,0.0023729154,0.00007494916,0.00037742502,0.48805714,0.007339659,0.00014231067,0.013139625,0.0008153035],"about_ca_topic_score_codex":0.0001361694,"about_ca_topic_score_gemma":0.000014404733,"teacher_disagreement_score":0.4866958,"about_ca_system_score_codex":0.000012476773,"about_ca_system_score_gemma":0.000042531883,"threshold_uncertainty_score":0.42909452},"labels":[],"label_agreement":null},{"id":"W2159845576","doi":"10.1111/j.1541-0420.2009.01380.x","title":"Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":52,"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":"National Institute of Allergy and Infectious Diseases; National Institutes of Health; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Identifiability; Surrogate endpoint; Computer science; Mathematics; Statistics; Econometrics; Computational biology; Medicine; Biology; Internal medicine","score_opus":0.10773568475868464,"score_gpt":0.4320993986802391,"score_spread":0.3243637139215545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159845576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10770292,0.000016927184,0.8892772,0.00070952345,0.000047473666,0.0017430115,0.000067247805,0.00017964878,0.00025601554],"genre_scores_gemma":[0.6613379,0.000011276455,0.3382289,0.000050805338,0.00003990272,0.00025946504,0.000009110797,0.000016122429,0.000046527402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99856544,0.00016433696,0.00049981405,0.00028695,0.00029516642,0.00018831628],"domain_scores_gemma":[0.99388623,0.005019386,0.00023146639,0.00052577135,0.00022491276,0.00011222633],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005415164,0.00014001077,0.0004008441,0.00034076528,0.000078364654,0.00008112501,0.00018986147,0.00007960399,0.000043648422],"category_scores_gemma":[0.012541904,0.00007681686,0.000029047564,0.0016241556,0.00013303572,0.00008764884,0.00010593776,0.0002069598,0.000012212564],"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":[0.00037219134,0.00016553965,0.0014887839,0.00012262624,0.000033710832,0.0000016979764,0.00020319388,6.3730994e-7,0.015865639,0.9263962,0.0011903088,0.054159503],"study_design_scores_gemma":[0.0013690109,0.00018932502,0.006204351,0.000012607831,0.000096984804,0.000010545697,0.00004466647,0.0001831933,0.015569319,0.9700447,0.0060395463,0.00023578336],"about_ca_topic_score_codex":0.00004205647,"about_ca_topic_score_gemma":0.00008455331,"teacher_disagreement_score":0.553635,"about_ca_system_score_codex":0.00003145905,"about_ca_system_score_gemma":0.00002649947,"threshold_uncertainty_score":0.9957759},"labels":[],"label_agreement":null},{"id":"W2163206453","doi":"10.1111/biom.12271","title":"Discussion of “On Bayesian Estimation of Marginal Structural Models”","year":2015,"lang":"en","type":"letter","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"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":"Covariate; Bayesian probability; Bayesian inference; Posterior probability; Outcome (game theory); Computer science; Inference; Context (archaeology); Model selection; Econometrics; Statistics; Mathematics; Artificial intelligence; Mathematical economics","score_opus":0.21176795249765404,"score_gpt":0.4128808229036175,"score_spread":0.20111287040596346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163206453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014393444,0.00015164317,0.96731645,0.025553858,0.00048654623,0.00093550683,0.0006568832,0.00032002747,0.0031397133],"genre_scores_gemma":[0.14694749,0.00005647225,0.8412169,0.0079739075,0.0007906436,0.00004762141,0.00094409863,0.00023421804,0.0017886041],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976814,0.000077076875,0.0006592191,0.00028587205,0.0010340428,0.00026239018],"domain_scores_gemma":[0.9975829,0.00042070376,0.00092560315,0.00064321543,0.00037517585,0.00005239598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035342242,0.00031850114,0.00065544504,0.0023646636,0.000022823158,0.0000136574545,0.00040439077,0.0007344379,0.000034074197],"category_scores_gemma":[0.0011007218,0.00020754397,0.00012900973,0.0020028153,0.00012465825,0.00017472522,0.00011037519,0.0006149102,0.0000026883263],"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":[0.00005127277,0.0000788923,0.00002113973,0.0020057752,0.00006514446,0.000027309368,0.00018557887,0.00033352108,0.000300127,0.019865219,0.9351155,0.041950528],"study_design_scores_gemma":[0.00024985723,0.0005104238,0.000013438791,0.0004613898,0.00009886632,0.000008293622,0.000017878232,0.022764402,0.0041083097,0.96394575,0.00740139,0.0004199831],"about_ca_topic_score_codex":0.000012887173,"about_ca_topic_score_gemma":4.018703e-7,"teacher_disagreement_score":0.94408053,"about_ca_system_score_codex":0.00021116264,"about_ca_system_score_gemma":0.00009415484,"threshold_uncertainty_score":0.84633976},"labels":[],"label_agreement":null},{"id":"W2166038367","doi":"10.1111/j.1541-0420.2005.00348.x","title":"Capture–Recapture Studies Using Radio Telemetry with Premature Radio‐Tag Failure","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chinook wind; Mark and recapture; Telemetry; Oncorhynchus; Fish <Actinopterygii>; Environmental science; Computer science; Fishery; Statistics; Telecommunications; Biology; Mathematics; Medicine","score_opus":0.018998796985091753,"score_gpt":0.24742743995444932,"score_spread":0.22842864296935758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166038367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9355726,0.013216747,0.0041583395,0.013616951,0.0012842927,0.0018871197,0.000057065394,0.0005627162,0.029644148],"genre_scores_gemma":[0.9564594,0.00048251037,0.037546486,0.0014693744,0.0002439576,0.00002425615,0.000009294158,0.000034753735,0.003729964],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998314,0.0000484163,0.00022947472,0.00048370092,0.000439333,0.00048503367],"domain_scores_gemma":[0.99930245,0.00010134548,0.00015260928,0.0003237875,0.000028471564,0.00009136484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031657543,0.00029069834,0.00034706795,0.0004941375,0.00037194556,0.000036208246,0.00032293494,0.0001897158,0.0004426248],"category_scores_gemma":[0.00020001302,0.00021297303,0.000064884676,0.0038997647,0.00038523058,0.00033931612,0.00036119673,0.0002550202,0.00017129823],"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":[0.000060310394,0.00030582532,0.28762484,0.00012454425,0.0007160657,0.00007925774,0.002061325,0.0021607282,0.0006620442,0.00018995826,0.6995632,0.0064518796],"study_design_scores_gemma":[0.0016386318,0.0002909799,0.28555858,0.00005451324,0.00041845732,0.0000964146,0.003876194,0.00058332446,0.0006764208,0.00014993991,0.70556957,0.0010869919],"about_ca_topic_score_codex":0.000025089941,"about_ca_topic_score_gemma":0.0006930484,"teacher_disagreement_score":0.033388145,"about_ca_system_score_codex":0.00039972985,"about_ca_system_score_gemma":0.000009444693,"threshold_uncertainty_score":0.8684788},"labels":[],"label_agreement":null},{"id":"W2166128449","doi":"10.1111/j.0006-341x.2003.00095.x","title":"Smoothing for Spatiotemporal Models and Its Application to Modeling Muskrat‐Mink Interaction","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Animal Ecology and Behavior Studies","field":"Environmental Science","cited_by":14,"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":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; University of Hong Kong; Universitetet i Oslo; Leverhulme Trust","keywords":"Smoothing; Mink; Computer science; Set (abstract data type); Sample (material); Ecology; Biology","score_opus":0.08018901073931801,"score_gpt":0.318185685995348,"score_spread":0.23799667525603002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166128449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6936521,0.00007530765,0.3050966,0.00008990647,0.00007549247,0.00027211136,0.0000034364605,0.000017859207,0.0007171585],"genre_scores_gemma":[0.9927136,0.000022501434,0.0069949776,0.00010124475,0.000011444843,0.000063110936,0.000003024096,0.0000062510067,0.00008387898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946547,0.000010611336,0.00011359588,0.00020244431,0.00008239671,0.00012550919],"domain_scores_gemma":[0.9998054,0.000039484974,0.000036958805,0.000056068555,0.000014958966,0.000047153313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020818334,0.00006683697,0.00007363943,0.00015129952,0.00016061871,0.000013117324,0.000047955615,0.000058553836,0.000012755624],"category_scores_gemma":[0.000105463856,0.00006486958,0.000017660677,0.0006452469,0.000014505162,0.00019120882,0.0000479374,0.00004101706,0.00002695874],"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":[0.0003193025,0.0011584458,0.18609871,0.00016615377,0.000085858635,0.0000052884147,0.006692805,0.20255677,0.15461032,0.024525113,0.0027985494,0.4209827],"study_design_scores_gemma":[0.0004253093,0.00032862753,0.01173641,0.000007836616,0.000045965047,0.000004957719,0.00044426043,0.97010696,0.0040419213,0.0023257541,0.010160367,0.00037160615],"about_ca_topic_score_codex":0.000046267043,"about_ca_topic_score_gemma":0.000046724632,"teacher_disagreement_score":0.76755023,"about_ca_system_score_codex":0.00008656734,"about_ca_system_score_gemma":0.000003043123,"threshold_uncertainty_score":0.26453048},"labels":[],"label_agreement":null},{"id":"W2168489635","doi":"10.1111/j.0006-341x.2001.00022.x","title":"Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":124,"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":"Missing data; Imputation (statistics); Multivariate statistics; Statistics; Particulates; Data mining; Computer science; Mathematics; Chemistry","score_opus":0.20693914482013628,"score_gpt":0.3957049269169166,"score_spread":0.18876578209678033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168489635","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015819274,0.000118469674,0.98280835,0.00029215682,0.000032565218,0.00049527764,0.00034166497,0.000011316135,0.00008090813],"genre_scores_gemma":[0.48713315,0.000011820578,0.5127903,0.000020100924,0.000011494864,0.0000074568034,0.000014424633,0.0000061917012,0.0000050588033],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999096,0.00007580124,0.0002663799,0.00017648155,0.00023173475,0.00015362425],"domain_scores_gemma":[0.99720263,0.002238804,0.00013503488,0.00025980306,0.00012978738,0.00003396247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087915844,0.000089012254,0.00016610247,0.00022064749,0.00009006481,0.00005378084,0.00017654631,0.00003998403,0.00000937886],"category_scores_gemma":[0.0068741967,0.000058237794,0.0000110743085,0.0013007437,0.00009166685,0.00016166494,0.000032526048,0.00004835186,4.2705486e-7],"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":[0.0009385356,0.0016448707,0.16970524,0.0011820201,0.00029202286,0.000027225367,0.00392265,0.000011094304,0.032466725,0.12138038,0.0004578787,0.6679714],"study_design_scores_gemma":[0.0073928805,0.0012088909,0.2545554,0.0006868713,0.00048133984,0.00007791393,0.0013738394,0.125469,0.0034954064,0.6037572,0.0007056818,0.0007955518],"about_ca_topic_score_codex":0.00003915487,"about_ca_topic_score_gemma":0.00003087671,"teacher_disagreement_score":0.66717577,"about_ca_system_score_codex":0.000022750146,"about_ca_system_score_gemma":0.00004169954,"threshold_uncertainty_score":0.8229552},"labels":[],"label_agreement":null},{"id":"W2171007688","doi":"10.1111/j.0006-341x.2004.00247.x","title":"Methods for the Statistical Analysis of Binary Data in Split‐Cluster Designs","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Cancer Care Ontario; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistic; Generalization; Test statistic; Statistics; Binary data; Statistical hypothesis testing; Binary number; Cluster (spacecraft); Mathematics; Chi-square test; Computer science; Data mining; Algorithm; Arithmetic","score_opus":0.8597660708145176,"score_gpt":0.6874922533691127,"score_spread":0.17227381744540493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171007688","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013387398,0.0002595285,0.99524504,0.00037029086,0.0003191888,0.00073043257,0.0016572508,0.000022021348,0.00005749757],"genre_scores_gemma":[0.008641589,0.00006496254,0.99096924,0.00012310426,0.00006704488,0.000041728217,0.00003780866,0.000026186268,0.000028346138],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964415,0.0010431467,0.0012909278,0.0004888657,0.00040191354,0.00033365504],"domain_scores_gemma":[0.7061752,0.29193038,0.00029289414,0.0013646565,0.00014539232,0.00009142991],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018224536,0.00016925679,0.00092570996,0.0016807512,0.000052063977,0.000032432643,0.0010507854,0.00018473246,0.00012712627],"category_scores_gemma":[0.3829158,0.00011273242,0.00017160164,0.011142483,0.00026998515,0.00005893201,0.0004322853,0.00018491247,0.000003910869],"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":[0.00049698097,0.0015858829,0.0018501396,0.00047956215,0.0027074274,0.000012378935,0.00022184713,0.0002990204,0.0010388235,0.63767016,0.002223301,0.35141447],"study_design_scores_gemma":[0.001791641,0.00028504813,0.017908327,0.000028980685,0.0036785188,9.300562e-7,0.00008493357,0.03994025,0.00039035495,0.93438965,0.0012575913,0.00024379468],"about_ca_topic_score_codex":0.00006737349,"about_ca_topic_score_gemma":0.0000149552625,"teacher_disagreement_score":0.36469126,"about_ca_system_score_codex":0.00008923046,"about_ca_system_score_gemma":0.000101836136,"threshold_uncertainty_score":0.63162965},"labels":[],"label_agreement":null},{"id":"W2194960242","doi":"10.1111/biom.12429","title":"False Discovery Rate Estimation for Large-Scale Homogeneous Discrete<i>p</i>-Values","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":26,"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","keywords":"Homogeneous; False discovery rate; Estimation; Scale (ratio); Statistics; Mathematics; Econometrics; Biology; Economics; Combinatorics; Geography; Cartography","score_opus":0.4759242506133713,"score_gpt":0.5376323145939031,"score_spread":0.06170806398053186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2194960242","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018038385,0.00016821972,0.9775482,0.0002859481,0.0015695689,0.000884588,0.0010284035,0.00016526494,0.00031146704],"genre_scores_gemma":[0.028014448,0.000034414767,0.96919006,0.00017110659,0.00045803102,0.000111836656,0.000032676184,0.000067984365,0.0019194284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971244,0.00047612778,0.0009044624,0.00046647625,0.00054134976,0.00048722423],"domain_scores_gemma":[0.96431583,0.03409061,0.00035897028,0.0005882582,0.00034265942,0.0003036846],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0069493097,0.00024946805,0.0006488034,0.0005362457,0.000109615474,0.0001837635,0.00035168094,0.00023595933,0.000019372761],"category_scores_gemma":[0.24330896,0.00018148597,0.00021852927,0.002125027,0.000100914905,0.00021027081,0.00016159947,0.00013503787,0.000057710015],"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":[0.0017766538,0.0047342,0.0033133554,0.002299961,0.0007487528,0.00009090204,0.0021595133,0.00046397417,0.002520185,0.49856097,0.2574347,0.22589684],"study_design_scores_gemma":[0.0019229543,0.00038236048,0.00014418506,0.0000367306,0.00017745496,0.000005104975,0.00013314911,0.013874765,0.002946907,0.9750614,0.004973951,0.00034099826],"about_ca_topic_score_codex":0.000005988837,"about_ca_topic_score_gemma":0.0000026858884,"teacher_disagreement_score":0.47650045,"about_ca_system_score_codex":0.00012662215,"about_ca_system_score_gemma":0.00010340438,"threshold_uncertainty_score":0.763065},"labels":[],"label_agreement":null},{"id":"W2203530869","doi":"10.1002/9780470522356.ch16","title":"Electrocardiogram (ECG) Biometric for Robust Identification and Secure Communication","year":2009,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":11,"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":"Biometrics; Identification (biology); Computer science; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Biology","score_opus":0.0401950483041478,"score_gpt":0.28745441889689977,"score_spread":0.24725937059275196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203530869","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022827638,0.4552796,0.24247122,0.005938906,0.002026495,0.0078043034,0.00095823786,0.0017518776,0.2814866],"genre_scores_gemma":[0.08363834,0.07291345,0.057515454,0.00023923357,0.0015204527,0.00008525455,0.0045510815,0.00024564014,0.7792911],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983936,0.000015014696,0.00047763053,0.000445702,0.0004341959,0.00023388253],"domain_scores_gemma":[0.9979861,0.00023636859,0.00038081087,0.0007416405,0.0004985593,0.00015654626],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056000304,0.000289172,0.0005963464,0.008188001,0.00016461902,0.00010358061,0.00017669806,0.00054250186,0.0000119132255],"category_scores_gemma":[0.00038617695,0.00028320515,0.00034612845,0.0032711043,0.000073067305,0.00005587928,0.00004228592,0.00030105983,0.00002108433],"study_design_candidate":"design_other","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":[0.000047602858,0.000100298595,0.0005192666,0.00034402605,0.0006695294,0.000004648819,0.000024322973,0.0000024624983,0.00065892417,0.0014650575,0.007441071,0.9887228],"study_design_scores_gemma":[0.0018868799,0.001240892,0.006006872,0.00053353177,0.0049743503,0.00007139625,0.000035716304,0.0030505566,0.0008312076,0.004097393,0.97625804,0.0010131863],"about_ca_topic_score_codex":0.000010318641,"about_ca_topic_score_gemma":9.999162e-7,"teacher_disagreement_score":0.9877096,"about_ca_system_score_codex":0.00018189938,"about_ca_system_score_gemma":0.000056628196,"threshold_uncertainty_score":0.99996203},"labels":[],"label_agreement":null},{"id":"W2209501224","doi":"10.1002/9780470522356.ch23","title":"Measuring Information Content in Biometric Features","year":2009,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Face recognition and analysis","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Biometrics; Content (measure theory); Computer science; Artificial intelligence; Information retrieval; Mathematics","score_opus":0.09694093503159364,"score_gpt":0.23191427742762197,"score_spread":0.13497334239602832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209501224","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034447592,0.007207433,0.106480174,0.0009831028,0.0008684991,0.00047152778,0.000058946058,0.00036589967,0.88352996],"genre_scores_gemma":[0.06013841,0.017210614,0.056551922,0.0063842703,0.00054354465,0.000039013375,0.0007771066,0.00011243664,0.8582427],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981735,0.0000151892145,0.0004961449,0.0002939869,0.00076717633,0.00025403622],"domain_scores_gemma":[0.9987807,0.000094562776,0.0003103459,0.0004019909,0.00029056886,0.00012179998],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00041365818,0.00026180962,0.0003688772,0.022299888,0.000053777647,0.0003569451,0.00068658876,0.00032873498,0.00004301519],"category_scores_gemma":[0.0002698751,0.00024773812,0.00021303988,0.00646745,0.00002378182,0.00053060515,0.00014383331,0.00028495167,0.0005458978],"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":[0.0000015435887,0.000020266873,0.000018017558,0.000025925217,0.000029216433,0.000011169271,0.00003131402,0.0000041963417,0.000013695801,0.019240294,0.001527797,0.97907656],"study_design_scores_gemma":[0.0009841045,0.00018426712,0.004687133,0.00029413702,0.000063104206,0.000031745123,0.000018293,0.0015654576,0.00044075603,0.0061094277,0.9844331,0.0011884819],"about_ca_topic_score_codex":0.000038134935,"about_ca_topic_score_gemma":0.0000073227357,"teacher_disagreement_score":0.98290527,"about_ca_system_score_codex":0.0002630335,"about_ca_system_score_gemma":0.0000662881,"threshold_uncertainty_score":0.9999975},"labels":[],"label_agreement":null},{"id":"W2277721996","doi":"10.1111/biom.12493","title":"Estimating Optimal Shared-Parameter Dynamic Regimens with Application to a Multistage Depression Clinical Trial","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Treatment of Major Depression","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Mental Health; Fonds de Recherche du Québec - Santé; University of Texas Southwestern Medical Center; Natural Sciences and Engineering Research Council of Canada; National University of Singapore; National Institutes of Health","keywords":"Depression (economics); Computer science; Econometrics; Mathematical optimization; Statistics; Medicine; Mathematics; Economics","score_opus":0.04977242541779203,"score_gpt":0.3884361073421701,"score_spread":0.33866368192437807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277721996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7137855,0.0000952701,0.28363928,0.00048943225,0.00023880866,0.0014519261,0.000033029126,0.00016492918,0.00010179995],"genre_scores_gemma":[0.7093594,0.000008970675,0.28949735,0.00008357245,0.00016011526,0.00014835522,0.000039050617,0.000037801503,0.00066540483],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99789244,0.00006311411,0.0005030776,0.00065910025,0.0005664608,0.0003158101],"domain_scores_gemma":[0.99778295,0.0007841647,0.00024269299,0.0007114447,0.00012995968,0.00034880693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043720592,0.00021680037,0.00037397866,0.0008420241,0.000076872195,0.000034430395,0.00015689041,0.00019888065,0.00004040554],"category_scores_gemma":[0.0020144521,0.00012008218,0.00010789323,0.0014363648,0.00006386005,0.00012665812,0.00012098937,0.00012380317,0.00020668423],"study_design_candidate":"design_other","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":[0.030595727,0.0015429655,0.07177593,0.000057110534,0.00012688701,0.000091467824,0.000052821997,0.000028139813,0.026733046,0.0000065182016,0.0017506243,0.86723876],"study_design_scores_gemma":[0.28774178,0.017358262,0.61818284,0.0018921205,0.0009194459,0.00020724813,0.00004275091,0.04313104,0.007847269,0.000036069974,0.021367049,0.0012741018],"about_ca_topic_score_codex":0.000013241307,"about_ca_topic_score_gemma":0.0000020560287,"teacher_disagreement_score":0.86596465,"about_ca_system_score_codex":0.00020949873,"about_ca_system_score_gemma":0.000064341635,"threshold_uncertainty_score":0.48968092},"labels":[],"label_agreement":null},{"id":"W2295493525","doi":"10.1111/biom.12503","title":"Marginal Regression Analysis of Recurrent Events with Coarsened Censoring Times","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"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 Alberta; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates; Government of Alberta","keywords":"Censoring (clinical trials); Statistics; Regression analysis; Regression; Econometrics; Marginal model; Computer science; Mathematics","score_opus":0.12046955122960885,"score_gpt":0.39536758971019526,"score_spread":0.2748980384805864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295493525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31918553,0.00011833458,0.6789842,0.00008781214,0.00014171845,0.00012008657,0.0001466223,0.000035294775,0.0011804068],"genre_scores_gemma":[0.6030938,0.00008282096,0.39626047,0.000005155302,0.000018952038,0.000004334161,0.0000029156338,0.000010746762,0.0005207808],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99890435,0.00008226228,0.00026960488,0.00018466897,0.00038993446,0.0001691812],"domain_scores_gemma":[0.997459,0.0018056639,0.00021596183,0.00026136395,0.00017847495,0.00007952499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003992795,0.00010715745,0.00033401506,0.0012797663,0.000028903924,0.0000064882897,0.00012627119,0.000050599912,0.00028799663],"category_scores_gemma":[0.0028115928,0.00005351566,0.00007168938,0.0047466415,0.000051856605,0.00003402025,0.000046449662,0.000038623883,0.000006895186],"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":[0.00027777272,0.00048507596,0.09068509,0.00018354937,0.0009444748,0.000011942315,0.00009231423,7.1022333e-7,0.0068557826,0.14610793,0.0010298494,0.7533255],"study_design_scores_gemma":[0.005062452,0.0032027827,0.7359159,0.002717369,0.006919343,0.000012586663,0.00021822448,0.006885251,0.06138397,0.16982459,0.0060563935,0.0018011412],"about_ca_topic_score_codex":0.0000057845323,"about_ca_topic_score_gemma":0.0000011989155,"teacher_disagreement_score":0.7515244,"about_ca_system_score_codex":0.000042174914,"about_ca_system_score_gemma":0.000020092672,"threshold_uncertainty_score":0.33659422},"labels":[],"label_agreement":null},{"id":"W2298009130","doi":"10.1111/biom.12457","title":"Interpretable Functional Principal Component Analysis","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Component (thermodynamics); Functional principal component analysis; Computer science; Statistics; Mathematics; Artificial intelligence","score_opus":0.06869988469016369,"score_gpt":0.29509746393790487,"score_spread":0.22639757924774118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298009130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8280144,0.0030834405,0.051537026,0.000143907,0.00034730107,0.000048216174,0.00008334416,0.0002926956,0.116449684],"genre_scores_gemma":[0.98999023,0.000024389428,0.0011903183,0.00007189362,0.00015163592,0.000007391074,0.00014513746,0.000012220068,0.008406779],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.998568,0.000009372105,0.00027114988,0.00031053548,0.00055661134,0.00028432687],"domain_scores_gemma":[0.9989669,0.000112559464,0.00011729296,0.00035109906,0.0001876601,0.0002645439],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022390454,0.00015645658,0.0003170654,0.0025742815,0.0000610672,0.00006994506,0.00024581407,0.000119777644,0.0035332667],"category_scores_gemma":[0.00041413345,0.00014298137,0.00022389325,0.015344295,0.00005510227,0.000090948226,0.00011904413,0.00014032354,0.00015852909],"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":[0.00030659494,0.0020422863,0.8635204,0.00013147682,0.010492692,0.0000606543,0.00036922542,0.0019619756,0.0679059,0.002045826,0.0459723,0.0051906593],"study_design_scores_gemma":[0.00498908,0.00031971824,0.07116234,0.000019781217,0.015301391,0.0000634139,0.0025317366,0.03848758,0.38547653,0.000984785,0.47798723,0.0026763978],"about_ca_topic_score_codex":0.00010165887,"about_ca_topic_score_gemma":0.0000042411207,"teacher_disagreement_score":0.79235804,"about_ca_system_score_codex":0.0002957187,"about_ca_system_score_gemma":0.00007048045,"threshold_uncertainty_score":0.99737763},"labels":[],"label_agreement":null},{"id":"W2308307784","doi":"10.1111/biom.12443","title":"Alessandro B. Antognini and Alessandra Giovagnoli, Adaptive Designs for Sequential Treatment Allocation. Boca Raton, FL: Chapman and Hall/CRC","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Complex Systems and Decision Making","field":"Decision Sciences","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":"Computer science","score_opus":0.5978225577623598,"score_gpt":0.4539909919060255,"score_spread":0.14383156585633433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308307784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21877286,0.02378185,0.74940056,0.0007359634,0.0017619177,0.0020291016,0.00029910475,0.0001421235,0.0030765221],"genre_scores_gemma":[0.98412764,0.000119145356,0.013803501,0.00009930151,0.00022779204,0.00004840454,0.000008822842,0.000025206855,0.0015401882],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964578,0.00018548012,0.0008195575,0.00085628795,0.001288414,0.000392431],"domain_scores_gemma":[0.99541694,0.002208764,0.00038617288,0.0005484563,0.0009878848,0.0004517845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026669796,0.00029767,0.000598118,0.0020616928,0.00033075854,0.00070824515,0.00036854306,0.0001560664,0.000025600795],"category_scores_gemma":[0.002417385,0.00021423824,0.00012269043,0.002702271,0.00020773095,0.00037429738,0.00021329604,0.000060268878,0.000037996306],"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":[0.0016268592,0.0010333749,0.03181897,0.000067555295,0.00053392013,0.00017187586,0.010934194,0.0002287738,0.003451664,0.083334744,0.15014233,0.71665573],"study_design_scores_gemma":[0.016133629,0.0063359314,0.021625726,0.00012873668,0.0002906754,0.00051786035,0.010278527,0.054268084,0.0030987011,0.09641406,0.7888289,0.0020791527],"about_ca_topic_score_codex":0.00017754483,"about_ca_topic_score_gemma":0.000121098834,"teacher_disagreement_score":0.76535475,"about_ca_system_score_codex":0.00013496168,"about_ca_system_score_gemma":0.00020495061,"threshold_uncertainty_score":0.87363815},"labels":[],"label_agreement":null},{"id":"W2407374792","doi":"10.1111/biom.12651","title":"A Generalized Levene's Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Hospital for Sick Children; University of Toronto","keywords":"Statistics; Levene's test; Variance (accounting); Correlation; Sample (material); Group (periodic table); Mathematics; Scale (ratio); Test (biology); Analysis of variance; One-way analysis of variance; Econometrics; Omnibus test; F-test of equality of variances; Variance components; Statistical hypothesis testing; Test statistic; Geography; Economics; Biology","score_opus":0.1784054581610316,"score_gpt":0.4080769432453158,"score_spread":0.2296714850842842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407374792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30949193,0.00006569941,0.6895597,0.00009664101,0.0000926969,0.00034027832,0.00027706724,0.000005699313,0.00007027359],"genre_scores_gemma":[0.6137748,0.00002983864,0.38610575,0.000020480993,0.0000222102,0.00003005045,0.0000039161164,0.0000045998468,0.000008379977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99920124,0.00008940726,0.00023776169,0.00016371463,0.0001686481,0.00013925321],"domain_scores_gemma":[0.9896234,0.0096432865,0.00022539753,0.0003849762,0.000091066206,0.0000318447],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012496893,0.000079253616,0.00018793932,0.00013228918,0.00014359687,0.00006389936,0.0002780453,0.00006421794,0.000005372108],"category_scores_gemma":[0.034492984,0.00005519459,0.000031904743,0.00039217697,0.00013568746,0.0000617421,0.00006380409,0.00005486784,3.1069e-7],"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":[0.0001063746,0.00060499134,0.37516397,0.000548757,0.000024509282,0.000001929127,0.0005843694,0.000018109675,0.003964883,0.49289396,0.0005946422,0.12549351],"study_design_scores_gemma":[0.0010398901,0.0002840488,0.4350353,0.000060049446,0.0000360574,0.000002713747,0.000042115018,0.05289754,0.0006588586,0.50908595,0.0006969753,0.00016049943],"about_ca_topic_score_codex":0.00035950253,"about_ca_topic_score_gemma":0.000121938276,"teacher_disagreement_score":0.30428284,"about_ca_system_score_codex":0.000016294174,"about_ca_system_score_gemma":0.000015145512,"threshold_uncertainty_score":0.9736399},"labels":[],"label_agreement":null},{"id":"W2425980697","doi":"10.1111/biom.12468","title":"Model Assessment in Dynamic Treatment Regimen Estimation via Double Robustness","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":23,"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":"National Center for Advancing Translational Sciences; University of California, Los Angeles; University of Pittsburgh; Northwestern University","keywords":"Robustness (evolution); Regimen; Mathematics; Computer science; Statistics; Medicine; Econometrics; Internal medicine; Biology","score_opus":0.20103595473419972,"score_gpt":0.4506263431265718,"score_spread":0.24959038839237208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2425980697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078086056,0.000032117234,0.92009443,0.0002344024,0.00004721116,0.00043344786,0.000011775182,0.00026063557,0.0007999374],"genre_scores_gemma":[0.6031792,0.0000820797,0.39589426,0.000005940617,0.0000061501632,0.0001166665,0.0000067180104,0.000022164555,0.0006868105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879676,0.000027461452,0.00034387407,0.00027784912,0.0002753877,0.00027865794],"domain_scores_gemma":[0.99891216,0.0003356781,0.00016842697,0.0004330775,0.000084987085,0.000065656386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031167627,0.00019021779,0.00025717952,0.0011963459,0.000036225534,0.000023169256,0.00016873349,0.00012728355,0.000021973186],"category_scores_gemma":[0.00013906618,0.00012828113,0.000047295416,0.0017028062,0.000041008665,0.00028108107,0.000057788202,0.000061743085,0.00001125763],"study_design_candidate":"design_other","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":[0.00012935705,0.0023041416,0.0020160354,0.00018264302,0.000092499315,0.000043596356,0.0002925809,0.010756189,0.047138028,0.16970168,0.0006516819,0.76669157],"study_design_scores_gemma":[0.0018545588,0.00029735305,0.00043710426,0.00010254926,0.00003166988,0.000006075489,0.000019161143,0.70065904,0.009435426,0.28669542,0.00009096956,0.00037071007],"about_ca_topic_score_codex":0.000022411547,"about_ca_topic_score_gemma":0.00003026992,"teacher_disagreement_score":0.7663208,"about_ca_system_score_codex":0.0015967258,"about_ca_system_score_gemma":0.00008777387,"threshold_uncertainty_score":0.5231153},"labels":[],"label_agreement":null},{"id":"W2471532197","doi":"10.1111/biom.12561","title":"Modeling of Successive Cancer Risks in Lynch Syndrome Families in the Presence of Competing Risks Using Copulas","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic factors in colorectal cancer","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Western University","funders":"National Cancer Institute; Canadian Institutes of Health Research; National Institutes of Health; National Center for Chronic Disease Prevention and Health Promotion; Mayo Clinic","keywords":"Penetrance; Inference; Covariate; Computer science; Statistics; Missing data; Colorectal cancer; Copula (linguistics); Selection (genetic algorithm); Causal inference; Econometrics; Medicine; Cancer; Mathematics; Internal medicine; Artificial intelligence; Biology; Genetics","score_opus":0.1691318257472267,"score_gpt":0.3950505188103593,"score_spread":0.2259186930631326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471532197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948215,0.0029338275,0.0014139138,0.00007932156,0.00021723792,0.00037396132,0.000038938855,0.0000067798205,0.000114539056],"genre_scores_gemma":[0.9978564,0.00072751177,0.0013304102,0.000020573663,0.000024998646,0.000018642415,0.0000012314705,0.000015086698,0.0000051685365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983285,0.00010923716,0.0005248225,0.00022960413,0.00054783607,0.00026000696],"domain_scores_gemma":[0.99840915,0.0008536095,0.00020799208,0.00028471032,0.00020522173,0.000039289815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067148847,0.00013108629,0.00039724328,0.0014508441,0.000022722897,0.00000594625,0.00025967928,0.00011499118,0.000028511737],"category_scores_gemma":[0.0017688499,0.00008006411,0.00005625029,0.0043258113,0.00015859016,0.000055765566,0.000097582306,0.00014534571,7.723675e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010189216,0.00012111871,0.9364828,0.00026832471,0.000025311218,0.000024023027,0.0010402418,0.011746594,0.042750124,0.000015618833,0.00000475703,0.0074192146],"study_design_scores_gemma":[0.0030898773,0.0005342695,0.7737503,0.0028424517,0.00011498916,0.000042511132,0.0029181042,0.1840895,0.032106865,0.00014137398,0.00002995982,0.00033976356],"about_ca_topic_score_codex":0.013689926,"about_ca_topic_score_gemma":0.00037538196,"teacher_disagreement_score":0.17234291,"about_ca_system_score_codex":0.00023902129,"about_ca_system_score_gemma":0.00018276916,"threshold_uncertainty_score":0.992878},"labels":[],"label_agreement":null},{"id":"W2514219976","doi":"10.1111/biom.12582","title":"Residual-Based Model Diagnosis Methods for Mixture Cure Models","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Epoxy Resin Curing Processes","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Residual; Computer science; Statistics; Mathematics; Algorithm","score_opus":0.07450822453407586,"score_gpt":0.34919159508302205,"score_spread":0.2746833705489462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514219976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002062083,0.005613644,0.99025285,0.0004281909,0.00028446002,0.00025496059,0.00015061225,0.0005446857,0.00040849677],"genre_scores_gemma":[0.2225871,0.0010912014,0.77521235,0.00008973987,0.00015267717,0.00033668647,0.000010909505,0.00012216729,0.00039714732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988886,0.000022982744,0.00024197511,0.00027449432,0.00019649415,0.00037545603],"domain_scores_gemma":[0.99802804,0.0012811882,0.00004468098,0.00036575992,0.00016281655,0.00011748921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003932039,0.00021532497,0.00023036439,0.0010692931,0.000057977126,0.000045540735,0.00030767254,0.00022762686,0.000011712999],"category_scores_gemma":[0.00076933485,0.0001604966,0.00009392759,0.0021445504,0.000033533364,0.00020111982,0.000032957298,0.00007728274,0.000009576541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004614555,0.00017157852,0.0012733068,0.0018753596,0.00016659118,0.0000040809873,0.00013697844,0.49313724,0.024356456,0.0027520892,0.05765925,0.41842094],"study_design_scores_gemma":[0.000745825,0.000056610465,0.00006475671,0.000113574235,0.000055609307,6.7190246e-7,0.0000031666104,0.74546075,0.2012283,0.012548703,0.039247103,0.0004749523],"about_ca_topic_score_codex":0.0000020461796,"about_ca_topic_score_gemma":0.0000022140525,"teacher_disagreement_score":0.41794598,"about_ca_system_score_codex":0.00010472753,"about_ca_system_score_gemma":0.000049035494,"threshold_uncertainty_score":0.6544861},"labels":[],"label_agreement":null},{"id":"W2519808874","doi":"10.1111/biom.12583","title":"Estimation of Diagnostic Accuracy of a Combination of Continuous Biomarkers Allowing for Conditional Dependence Between the Biomarkers and the Imperfect Reference-Test","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":15,"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":"Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health","keywords":"Imperfect; Computer science; Statistics; Econometrics; Mathematics","score_opus":0.2859670164302872,"score_gpt":0.4834837546288036,"score_spread":0.1975167381985164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519808874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4274089,0.0002007027,0.5683406,0.0006701608,0.00016250504,0.0016640541,0.0014530302,0.00001854652,0.00008149409],"genre_scores_gemma":[0.88942105,0.000115806666,0.11034544,0.000009662331,0.000022987419,0.000056607263,0.000010895923,0.000012318249,0.000005222389],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974806,0.00055245217,0.0011196632,0.00020299945,0.00048372746,0.00016061524],"domain_scores_gemma":[0.4837425,0.5146769,0.000943234,0.0002573162,0.00034114663,0.00003884978],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0068810456,0.00013436584,0.00058147433,0.00035299986,0.000069583904,0.000015145233,0.0003176735,0.00013407439,0.000020274383],"category_scores_gemma":[0.6673185,0.00006642827,0.00013739042,0.0011632977,0.0008684734,0.0000882094,0.00009320863,0.00006193405,7.6650116e-7],"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":[0.00082438294,0.00056080887,0.052093536,0.0012776284,0.0012510988,0.0000012081675,0.00023258333,0.000001436427,0.012623892,0.28313014,0.0012804628,0.6467228],"study_design_scores_gemma":[0.0062230504,0.00061437435,0.08627595,0.00035454953,0.00063004764,0.0000024332646,0.0000895992,0.0010301367,0.026464641,0.8781227,0.000029607294,0.0001629031],"about_ca_topic_score_codex":0.000028303153,"about_ca_topic_score_gemma":9.223644e-7,"teacher_disagreement_score":0.6604375,"about_ca_system_score_codex":0.000030440942,"about_ca_system_score_gemma":0.0000625281,"threshold_uncertainty_score":0.33548376},"labels":[],"label_agreement":null},{"id":"W2577626336","doi":"10.1111/biom.12646","title":"Estimating Varying Coefficients for Partial Differential Equation Models","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute; National Natural Science Foundation of China","keywords":"Mathematics; Applied mathematics; First-order partial differential equation; Partial differential equation; Statistics; Mathematical analysis","score_opus":0.12096125993330843,"score_gpt":0.33309392237542645,"score_spread":0.21213266244211804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577626336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12699437,0.000007628669,0.8706583,0.000044225653,0.0012503209,0.00016764722,0.000023981132,0.000020262369,0.0008332703],"genre_scores_gemma":[0.9923129,6.33955e-7,0.006401775,0.000007888516,0.001001601,0.000026941536,0.00005618523,0.000012004043,0.00018004667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993182,0.000009156241,0.00015322753,0.00018034819,0.00014533823,0.00019372244],"domain_scores_gemma":[0.99942964,0.000049021663,0.00017551842,0.00021919474,0.00006361435,0.000063020365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008902422,0.00008853747,0.00010588227,0.0001583727,0.00058595743,0.00022285279,0.0001700501,0.00003324583,0.000045128054],"category_scores_gemma":[0.00003417222,0.000082928615,0.000078819496,0.0001751766,0.000026580477,0.00019259425,0.000057056895,0.000054284195,0.000007316452],"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":[0.00006516635,0.00034902047,0.0037142085,0.00003278664,0.00007522048,3.6097455e-7,0.00012909206,0.1265885,0.0027073869,0.043372124,0.0009373225,0.8220288],"study_design_scores_gemma":[0.00047508898,0.000020695954,0.00013100317,0.0000081069775,0.00001702591,9.391326e-8,0.000005794921,0.9951841,0.001342825,0.0024449893,0.00027171083,0.00009855062],"about_ca_topic_score_codex":0.000026749285,"about_ca_topic_score_gemma":5.342306e-8,"teacher_disagreement_score":0.8685956,"about_ca_system_score_codex":0.000009611293,"about_ca_system_score_gemma":0.000013291248,"threshold_uncertainty_score":0.450677},"labels":[],"label_agreement":null},{"id":"W2582743139","doi":"10.1111/biom.12657","title":"Improving Efficiency of Parameter Estimation in Case-Cohort Studies with Multivariate Failure Time Data","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"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 Calgary","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Multivariate statistics; Statistics; Estimation; Multivariate analysis; Cohort; Econometrics; Computer science; Mathematics; Engineering","score_opus":0.171967248849474,"score_gpt":0.43602665638802157,"score_spread":0.26405940753854756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582743139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09365992,0.00006289271,0.90568614,0.000041309304,0.000057134763,0.00027245044,0.000117808064,0.000018218752,0.000084111496],"genre_scores_gemma":[0.34614953,0.000004582545,0.65379864,0.0000026109726,0.000008460415,0.000005883945,0.000004538841,0.000008598679,0.000017172684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988628,0.00006837399,0.0003383163,0.00030055773,0.00024424153,0.00018573563],"domain_scores_gemma":[0.99531525,0.00304813,0.00039393545,0.0010516745,0.00014958151,0.000041401352],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012138172,0.00012749362,0.0003488619,0.00045589163,0.00012292324,0.00007521939,0.0004327646,0.00006886664,0.000010860372],"category_scores_gemma":[0.03710371,0.00008959212,0.000017015478,0.00073315465,0.00017580738,0.00021441023,0.00030470474,0.00009861838,0.000005788373],"study_design_candidate":"design_other","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":[0.00014040827,0.0012902176,0.07069537,0.0023991943,0.00062773074,0.0015526586,0.0013585468,0.00000987289,0.006918456,0.05331838,0.000747974,0.8609412],"study_design_scores_gemma":[0.00276489,0.0009147097,0.055532243,0.00075338193,0.0007074787,0.0003285339,0.00053765834,0.787253,0.003856504,0.14621459,0.00005607536,0.00108093],"about_ca_topic_score_codex":0.00026496645,"about_ca_topic_score_gemma":0.000027220485,"teacher_disagreement_score":0.85986024,"about_ca_system_score_codex":0.000041055293,"about_ca_system_score_gemma":0.000036387693,"threshold_uncertainty_score":0.97100717},"labels":[],"label_agreement":null},{"id":"W2593930685","doi":"10.1111/biom.12641","title":"Parametric Functional Principal Component Analysis","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Functional principal component analysis; Parametric statistics; Component (thermodynamics); Computer science; Mathematics; Econometrics; Statistics; Physics","score_opus":0.3031580114222798,"score_gpt":0.43155175498302634,"score_spread":0.1283937435607465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593930685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19639575,0.000063082305,0.7955767,0.00010982674,0.00045192867,0.00010937572,0.0000654714,0.000053434283,0.0071744095],"genre_scores_gemma":[0.7355667,0.000014468922,0.2638714,0.00002587183,0.000077882025,0.000007604532,0.000006751478,0.000008058399,0.00042128327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99868274,0.00005745395,0.00028812443,0.0002530228,0.00048735915,0.00023128992],"domain_scores_gemma":[0.996754,0.0019416829,0.0002658741,0.00072445045,0.00017043234,0.00014357436],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007640726,0.00012204469,0.0003151366,0.0017582374,0.0003315257,0.00018722822,0.000352154,0.0000844429,0.0005005581],"category_scores_gemma":[0.016588053,0.0001000092,0.0001595136,0.0035149648,0.00011588734,0.00005787527,0.00015947744,0.00011357757,0.000080702164],"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":[0.000031165957,0.0005565904,0.16620505,0.000072335686,0.0008814424,0.000022671162,0.000027361286,0.000009988136,0.00020581533,0.72590667,0.0028034924,0.103277415],"study_design_scores_gemma":[0.00028927354,0.000064023276,0.90578187,0.000004365232,0.00046374006,0.0000020391872,0.000008306311,0.007156408,0.00017837148,0.08145714,0.0043908623,0.00020362088],"about_ca_topic_score_codex":0.00004843423,"about_ca_topic_score_gemma":0.0000032148362,"teacher_disagreement_score":0.7395768,"about_ca_system_score_codex":0.000057175872,"about_ca_system_score_gemma":0.000026261368,"threshold_uncertainty_score":0.99169564},"labels":[],"label_agreement":null},{"id":"W2596072428","doi":"10.1111/biom.12691","title":"Joint Modeling of Zero-Inflated Panel Count and Severity Outcomes","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Joint (building); Zero (linguistics); Count data; Statistics; Computer science; Mathematics; Econometrics; Medicine; Engineering; Structural engineering; Poisson distribution","score_opus":0.284978990383951,"score_gpt":0.40615218498716454,"score_spread":0.12117319460321352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596072428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18318078,0.00005004631,0.81524104,0.00009474291,0.00013679855,0.000104677565,0.000069021204,0.000025009858,0.00109787],"genre_scores_gemma":[0.6109209,0.000039148603,0.38897482,0.0000136955305,0.000008922969,0.0000016762655,7.218155e-7,0.000007477156,0.000032661],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999061,0.000032763903,0.00033027018,0.00016605762,0.00024644216,0.00016350004],"domain_scores_gemma":[0.99849516,0.000585958,0.00023327801,0.00043796905,0.00016456704,0.00008308624],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006206788,0.00011391923,0.0003568549,0.000273685,0.0001444956,0.00008247226,0.0001806902,0.000094873045,0.000022303737],"category_scores_gemma":[0.009257613,0.0000884853,0.000045144592,0.0002730515,0.0001191826,0.00007408414,0.00014197215,0.00008806891,0.0000032696519],"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":[0.000039391754,0.0003825858,0.10959722,0.0010264491,0.000196953,0.00003122156,0.00044392253,0.0000027141814,0.0027706858,0.6273713,0.000524297,0.25761327],"study_design_scores_gemma":[0.0004869363,0.00006809537,0.11504318,0.00006297072,0.00006772539,0.000004830991,0.000027646493,0.06766927,0.0006620478,0.81559235,0.00008333106,0.0002316275],"about_ca_topic_score_codex":0.00012327233,"about_ca_topic_score_gemma":0.0000018053217,"teacher_disagreement_score":0.4277401,"about_ca_system_score_codex":0.000021203576,"about_ca_system_score_gemma":0.000023789833,"threshold_uncertainty_score":0.9990878},"labels":[],"label_agreement":null},{"id":"W2604023245","doi":"10.1111/biom.12685","title":"Estimating Time-Varying Directed Gene Regulation Networks","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computational biology; Gene regulatory network; Gene; Biology; Genetics; Gene expression","score_opus":0.015354681371267553,"score_gpt":0.2591898043408331,"score_spread":0.24383512296956555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604023245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9027989,0.0020513781,0.09267752,0.00006897285,0.00059795956,0.00018164658,0.000008352618,0.000087982175,0.0015272868],"genre_scores_gemma":[0.9690496,0.00005696639,0.028364273,0.000034836663,0.00096860307,0.000006727667,0.00027745176,0.0000327389,0.0012088349],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885374,0.000045364774,0.0002303571,0.00038729777,0.00019532563,0.0002879364],"domain_scores_gemma":[0.9984745,0.000017555849,0.00030844685,0.0009497474,0.00014698559,0.00010280834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034850786,0.00016316811,0.00017831498,0.0002837306,0.0005167943,0.00015365057,0.0003857944,0.0002072123,0.000030658222],"category_scores_gemma":[0.00049021986,0.00016911664,0.0001244827,0.0006586969,0.000076002434,0.000007987988,0.00023897657,0.000060599203,0.000028098922],"study_design_candidate":"bench_or_experimental","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":[0.000038961694,0.00006966347,0.020147333,0.000016869655,0.00028851745,0.0000066628622,0.000010183052,0.059225176,0.8238914,0.000011226605,0.0070705716,0.08922345],"study_design_scores_gemma":[0.00056283275,0.000090086665,0.1169766,0.000018516896,0.00012488672,0.000014821923,0.0000015912775,0.81347924,0.06133847,0.000052495,0.006853015,0.00048743014],"about_ca_topic_score_codex":0.000015727666,"about_ca_topic_score_gemma":0.000002324353,"teacher_disagreement_score":0.7625529,"about_ca_system_score_codex":0.000027651991,"about_ca_system_score_gemma":0.000026733233,"threshold_uncertainty_score":0.68963766},"labels":[],"label_agreement":null},{"id":"W2613828455","doi":"10.1111/biom.12701","title":"Hidden Markov Models for Extended Batch Data","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Computer science; Population; Markov chain; Expectation–maximization algorithm; Markov model; Maximization; Population size; Statistics; Set (abstract data type); Variance (accounting); Data mining; Maximum likelihood; Machine learning; Artificial intelligence; Mathematics; Mathematical optimization","score_opus":0.0981739367153838,"score_gpt":0.30894845077280986,"score_spread":0.21077451405742606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613828455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8980702,0.00014821213,0.053949926,0.0076995473,0.0015354144,0.0008693958,0.00035392263,0.00010012876,0.037273224],"genre_scores_gemma":[0.9694573,0.000041151518,0.026016908,0.00045755412,0.000084182844,0.000022057337,0.000092538976,0.000009634628,0.003818648],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999297,0.00001213113,0.000105460545,0.00027748002,0.00013759304,0.00017028944],"domain_scores_gemma":[0.99870825,0.000105410196,0.00010410327,0.0010227495,0.000009075867,0.00005041576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045357313,0.00006421738,0.00007669236,0.00008849861,0.0003863502,0.00006377372,0.0010388036,0.00008501178,0.0002733247],"category_scores_gemma":[0.00058472296,0.000060785234,0.000020703834,0.0002908875,0.00011150565,0.00061751896,0.00061698543,0.000038808805,0.00017620887],"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":[0.00004049691,0.00013825459,0.41451836,0.000010321915,0.000021284128,0.0000037612065,0.000035035093,0.000011506163,0.0003240755,0.000705451,0.21382134,0.37037012],"study_design_scores_gemma":[0.00029104904,0.000041537376,0.92452645,0.000001767595,0.000016437813,0.0000015249308,0.000009047011,0.038442366,0.000055461496,0.005846205,0.03064418,0.00012394274],"about_ca_topic_score_codex":0.00012451602,"about_ca_topic_score_gemma":0.000058936945,"teacher_disagreement_score":0.5100081,"about_ca_system_score_codex":0.00004696813,"about_ca_system_score_gemma":0.000011460557,"threshold_uncertainty_score":0.29927137},"labels":[],"label_agreement":null},{"id":"W2729708791","doi":"10.1111/biom.12741","title":"Cox Regression with Dependent Error in Covariates","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"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":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; ACT Government; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Banff International Research Station for Mathematical Innovation and Discovery","keywords":"Covariate; Heteroscedasticity; Statistics; Econometrics; Regression; Regression analysis; Inference; Standard error; Observational error; Errors-in-variables models; Variance (accounting); Proportional hazards model; Nonparametric statistics; Mathematics; Computer science; Artificial intelligence","score_opus":0.201242634077411,"score_gpt":0.43975600059113557,"score_spread":0.23851336651372457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729708791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37929976,0.00018078323,0.60754454,0.00045737502,0.00051885546,0.00040639195,0.00006561578,0.00007455854,0.0114521105],"genre_scores_gemma":[0.6163554,0.000016587916,0.38330293,0.000018716135,0.00002246748,0.0000070754813,9.476787e-7,0.000009794047,0.00026604885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991358,0.000049733102,0.00018201997,0.00017861977,0.00026956666,0.00018429714],"domain_scores_gemma":[0.9982401,0.000974282,0.00018008494,0.00046270288,0.00007267184,0.00007016312],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005818275,0.00010054933,0.00019768669,0.00037441545,0.00012531217,0.00010089337,0.00027919599,0.00007533611,0.00008506559],"category_scores_gemma":[0.009208877,0.000063840744,0.000017232493,0.0004944591,0.00007598378,0.00006121321,0.00009444981,0.00010567106,0.000013158286],"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":[0.00022707654,0.0007516038,0.17695664,0.00030811055,0.000049303868,0.00028235384,0.00034762133,0.0000011307542,0.0020277558,0.6189029,0.0023937128,0.19775175],"study_design_scores_gemma":[0.0030298606,0.00070863846,0.42024627,0.00055335567,0.00006985956,0.00002852802,0.00018715528,0.0048461584,0.00715754,0.56052524,0.0019182813,0.0007291157],"about_ca_topic_score_codex":0.000098644705,"about_ca_topic_score_gemma":0.000027995255,"teacher_disagreement_score":0.24328965,"about_ca_system_score_codex":0.000036874295,"about_ca_system_score_gemma":0.000031425956,"threshold_uncertainty_score":0.999137},"labels":[],"label_agreement":null},{"id":"W2752556006","doi":"10.1111/biom.12748","title":"FLCRM: Functional Linear Cox Regression Model","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":90,"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 Toronto","funders":"National Institute of General Medical Sciences; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Institute on Aging; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Linear regression; Proportional hazards model; Regression analysis; Regression; Proper linear model; Linear model; Statistics; Computer science; Mathematics; Bayesian multivariate linear regression","score_opus":0.4224366645261338,"score_gpt":0.46688941978742415,"score_spread":0.04445275526129033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752556006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019691236,0.00005364797,0.9700577,0.00019980689,0.00048939756,0.00008163243,0.000058231417,0.0000526943,0.009315629],"genre_scores_gemma":[0.19695468,0.00003771193,0.7998574,0.00007100572,0.0001832881,0.000006825624,0.000004686915,0.000016983939,0.0028674477],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990675,0.00002837366,0.00019426778,0.00019087804,0.00034253934,0.00017647745],"domain_scores_gemma":[0.9982601,0.00075140776,0.00017359664,0.0005429896,0.00016617813,0.00010576158],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000476972,0.00010612222,0.0001711133,0.00027656418,0.00032720974,0.000079464335,0.00025501603,0.000106154155,0.00017829266],"category_scores_gemma":[0.014238776,0.00007775365,0.000051400573,0.00033834722,0.00009525462,0.000073365314,0.0001337312,0.00011662419,0.000079347476],"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":[0.00006746574,0.00033590553,0.0023913344,0.00014482331,0.000038271824,0.000016789378,0.000090886104,0.000014604924,0.0032982996,0.7383991,0.05665333,0.19854923],"study_design_scores_gemma":[0.00057837216,0.00009888732,0.014503808,0.00006331759,0.000039308274,0.000005721309,0.000012094913,0.22834188,0.0018495374,0.74966055,0.004557118,0.0002894349],"about_ca_topic_score_codex":0.000009882089,"about_ca_topic_score_gemma":5.879524e-7,"teacher_disagreement_score":0.22832727,"about_ca_system_score_codex":0.000028907472,"about_ca_system_score_gemma":0.000043236472,"threshold_uncertainty_score":0.9940647},"labels":[],"label_agreement":null},{"id":"W2753115971","doi":"10.1111/biom.12772","title":"Analysis of Restricted Mean Survival Time for Length-biased Data","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":20,"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":"National Cancer Institute; Medical Research Council; National Institutes of Health","keywords":"Statistics; Mathematics; Computer science","score_opus":0.4459699259507882,"score_gpt":0.48039066212571907,"score_spread":0.034420736174930855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753115971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07324773,0.00006034328,0.91512465,0.00011552721,0.00032873522,0.00036958017,0.006477332,0.000055579254,0.004220497],"genre_scores_gemma":[0.2600156,0.000031647825,0.7390322,0.000012662802,0.00007849114,0.0000071814534,0.00028500118,0.00002283889,0.000514359],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987035,0.00007636519,0.00039253576,0.00029618174,0.00033474213,0.00019667485],"domain_scores_gemma":[0.9908894,0.006309692,0.00043493375,0.0019859883,0.0002933251,0.0000867137],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012816449,0.00011007048,0.00050717546,0.0011590621,0.00014212193,0.000076736214,0.0011097197,0.00009259658,0.00013010454],"category_scores_gemma":[0.061742406,0.00009322726,0.000102735445,0.0026640117,0.00010451397,0.00008254598,0.0002876217,0.000056500176,0.0000072741723],"study_design_candidate":"design_other","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":[0.0003140448,0.0016875357,0.03234741,0.0007085416,0.0064865313,0.000016535832,0.0002992916,0.0000030418041,0.011760819,0.371442,0.026982477,0.54795176],"study_design_scores_gemma":[0.0033905536,0.00082076114,0.39833912,0.00008529058,0.011128688,9.99272e-7,0.00013149383,0.41949856,0.004107785,0.14736573,0.013899349,0.0012316761],"about_ca_topic_score_codex":0.00009119813,"about_ca_topic_score_gemma":0.0000102256845,"teacher_disagreement_score":0.5467201,"about_ca_system_score_codex":0.000018830362,"about_ca_system_score_gemma":0.000042111467,"threshold_uncertainty_score":0.9461609},"labels":[],"label_agreement":null},{"id":"W2753354524","doi":"10.1111/biom.12767","title":"Eigenvalue Significance Testing for Genetic Association","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"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":"National Human Genome Research Institute; Ontario Genomics Institute; National Institute of Mental Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; North Carolina State University","keywords":"Association (psychology); Computational biology; Computer science; Mathematics; Genetics; Biology; Psychology","score_opus":0.05780171389339653,"score_gpt":0.319747633378039,"score_spread":0.2619459194846425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753354524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95983815,0.0006474253,0.03627663,0.0004499756,0.00066099945,0.00036765425,0.00009358178,0.000016863303,0.0016487165],"genre_scores_gemma":[0.9330941,0.00008673624,0.06376894,0.00015939405,0.0005557168,0.00006209006,0.000050996707,0.000018492769,0.0022035793],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990369,0.000040157698,0.00021698562,0.00029232743,0.00010962359,0.00030397894],"domain_scores_gemma":[0.9985947,0.00016578379,0.00044579766,0.00044062024,0.00029103926,0.00006204667],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00067536935,0.00010325399,0.00014299445,0.000112432834,0.00044523083,0.000066221364,0.00030754256,0.00022562771,0.0000040027962],"category_scores_gemma":[0.0139130065,0.00010690044,0.00008106105,0.00020327013,0.000034485827,0.0000026347013,0.000085644744,0.00004184548,0.000014385593],"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":[0.000007686122,0.000034777448,0.87407786,0.000011811594,0.00005450744,3.5057e-7,0.0000061104633,0.000067579436,0.08872478,0.00003293079,0.009658624,0.027322957],"study_design_scores_gemma":[0.00039207432,0.00024528857,0.9453398,0.0000029854848,0.000027660995,0.0000012907972,0.000009852627,0.0006459798,0.0046041105,0.00034454942,0.048213888,0.00017254348],"about_ca_topic_score_codex":0.000051104023,"about_ca_topic_score_gemma":0.0000125794795,"teacher_disagreement_score":0.08412067,"about_ca_system_score_codex":0.00006264134,"about_ca_system_score_gemma":0.00007799488,"threshold_uncertainty_score":0.9943932},"labels":[],"label_agreement":null},{"id":"W2758210255","doi":"10.1111/biom.12782","title":"Fully Bayesian Spectral Methods for Imaging Data","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; National Institute for Health and Care Research; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; National Science Foundation; Canadian Institutes of Health Research; University of Southern California; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Institutes of Health","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Bayesian inference; Inference; Markov chain; Sampling (signal processing); Pattern recognition (psychology); Spatial analysis; Artificial intelligence; Data mining; Algorithm; Statistics; Machine learning; Mathematics","score_opus":0.24286441162045255,"score_gpt":0.45215598660760653,"score_spread":0.20929157498715398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758210255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026333793,0.000497977,0.95941705,0.021628397,0.0041053817,0.00054060464,0.00042303614,0.00018457517,0.010569633],"genre_scores_gemma":[0.6412857,0.00008839678,0.35272962,0.0030730616,0.0009692791,0.000039833805,0.00001819231,0.000055519617,0.0017403985],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985908,0.000070452414,0.00014170345,0.00067215017,0.00021052874,0.00031432504],"domain_scores_gemma":[0.9910793,0.007138764,0.00015488318,0.0014967034,0.00006121929,0.00006916758],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0011396253,0.0001246741,0.00016323365,0.0005818523,0.0009781009,0.00033356296,0.0013732982,0.000031721116,0.000016572225],"category_scores_gemma":[0.1258494,0.00011758188,0.00005794038,0.0008251893,0.00019603717,0.0005502836,0.0008264997,0.00008342515,0.000022714583],"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":[0.00006422832,0.00015302452,0.009154699,0.000060052374,0.000029126397,0.000019800971,0.000055788994,0.0000015669126,0.21955243,0.01036361,0.07090271,0.68964297],"study_design_scores_gemma":[0.0008825156,0.00012554886,0.032032084,0.000011096073,0.00004350316,0.000035569854,0.000039415787,0.027325662,0.11740723,0.010005631,0.81162053,0.000471209],"about_ca_topic_score_codex":0.000024980855,"about_ca_topic_score_gemma":0.0000051093416,"teacher_disagreement_score":0.7407178,"about_ca_system_score_codex":0.00005524421,"about_ca_system_score_gemma":0.00004546713,"threshold_uncertainty_score":0.88151395},"labels":[],"label_agreement":null},{"id":"W2889050153","doi":"10.1111/biom.12965","title":"A Hidden Markov Model for Identifying Differentially Methylated Sites in Bisulfite Sequencing Data","year":2018,"lang":"en","type":"article","venue":"Biometrics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; HEC Montréal; Jewish General Hospital","funders":"Division of Materials Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ludmer Centre for Neuroinformatics and Mental Health","keywords":"Hidden Markov model; DNA methylation; Bisulfite sequencing; Identification (biology); Autocorrelation; Computer science; CpG site; Computational biology; Differentially methylated regions; Bisulfite; Methylation; Markov chain; Selection (genetic algorithm); Biology; Data mining; Genetics; Artificial intelligence; Statistics; Machine learning; Mathematics; DNA; Gene; Ecology","score_opus":0.1418959052330344,"score_gpt":0.3623946788056521,"score_spread":0.22049877357261774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889050153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73688304,0.0018151026,0.26059297,0.000027265196,0.0001629307,0.00022918955,0.00015001757,0.000012727386,0.00012676444],"genre_scores_gemma":[0.9406953,0.00037992635,0.056948535,0.000045446333,0.00025226607,0.00001341028,0.0012641537,0.0000323314,0.0003686182],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986068,0.000045704917,0.0003253422,0.0005383407,0.0001811699,0.00030265],"domain_scores_gemma":[0.9988692,0.000051885334,0.00012479149,0.0006761801,0.00020389674,0.00007406842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070466625,0.00015593329,0.00016386088,0.00069537555,0.00008666049,0.00007841368,0.00046739215,0.00020547224,0.000008234348],"category_scores_gemma":[0.00083648664,0.00015674418,0.000055236316,0.0012511689,0.000055958844,0.000011905984,0.0003833965,0.000057632576,0.0000048449338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042616128,0.00003259233,0.0029419172,0.00003002771,0.00003119628,7.243372e-7,0.00005424695,0.00002230203,0.9769073,0.000015416395,0.00021431451,0.019707324],"study_design_scores_gemma":[0.0011966478,0.00029871345,0.0063464385,0.000027186406,0.000058895344,0.0000012334359,0.000042959688,0.3556857,0.6327533,0.0010738581,0.0020432756,0.00047176884],"about_ca_topic_score_codex":0.000033162705,"about_ca_topic_score_gemma":0.00018185713,"teacher_disagreement_score":0.35566342,"about_ca_system_score_codex":0.00003988539,"about_ca_system_score_gemma":0.0000990266,"threshold_uncertainty_score":0.6391842},"labels":[],"label_agreement":null},{"id":"W2920925479","doi":"10.1111/biom.13053","title":"High Dimensional Mediation Analysis With Latent Variables","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":49,"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 Laval","funders":"National Institutes of Health","keywords":"Context (archaeology); Mediation; Breast cancer; Expectation–maximization algorithm; Statistics; Maximization; Outcome (game theory); Latent variable; Latent class model; Econometrics; Mathematics; Computer science; Medicine; Maximum likelihood; Cancer; Internal medicine; Biology; Mathematical optimization","score_opus":0.05411440635091664,"score_gpt":0.3228936179005686,"score_spread":0.26877921154965195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920925479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44663814,0.000034622433,0.5521921,0.00007304944,0.00020067886,0.00013126277,0.000055183657,0.000042978,0.0006320117],"genre_scores_gemma":[0.52095723,0.000004945418,0.4787136,0.000036564237,0.000023352666,0.0000034801726,0.000018580702,0.0000067265687,0.00023551298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99902576,0.000049254973,0.00018923255,0.00018195961,0.00040949433,0.00014426945],"domain_scores_gemma":[0.99774575,0.0017122716,0.00010563921,0.00021301539,0.00015553311,0.00006776875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042006702,0.00008897945,0.00024029023,0.0008448295,0.000027354892,0.000026735997,0.000084552776,0.00006043391,0.00083635235],"category_scores_gemma":[0.0012469228,0.000059502385,0.000041977968,0.0053974832,0.000021319887,0.000036128862,0.00003196853,0.000058546528,0.00007135231],"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":[0.0000451663,0.00028264354,0.12018354,0.00008967889,0.0007542915,0.0000070689016,0.000053318385,0.00020780944,0.0010637833,0.85904485,0.0009849574,0.017282886],"study_design_scores_gemma":[0.0018479914,0.00092328474,0.5123891,0.000044132044,0.0016885508,0.0000055656187,0.000037042024,0.06386401,0.0021125656,0.4149092,0.0012910528,0.0008874649],"about_ca_topic_score_codex":0.000034928216,"about_ca_topic_score_gemma":0.0000018998375,"teacher_disagreement_score":0.44413564,"about_ca_system_score_codex":0.00003527889,"about_ca_system_score_gemma":0.000025919724,"threshold_uncertainty_score":0.9157472},"labels":[],"label_agreement":null},{"id":"W2942481792","doi":"10.1111/biom.13384","title":"Poisson PCA: Poisson measurement error corrected PCA, with application to microbiome data","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Outlier; Principal component analysis; Poisson regression; Parametric statistics; Variance (accounting); Transformation (genetics); Latent variable","score_opus":0.33981897763157554,"score_gpt":0.3932750149486975,"score_spread":0.05345603731712195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942481792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006432941,0.00010571971,0.988953,0.0023744518,0.00014746911,0.0007321502,0.0005680495,0.00015964819,0.0005265952],"genre_scores_gemma":[0.28802112,0.000009592984,0.7106054,0.0009329284,0.00014437301,0.000040998628,0.000121363,0.000051232666,0.000073009396],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978127,0.00009769928,0.0003993302,0.0006339639,0.0007178605,0.00033846765],"domain_scores_gemma":[0.9976397,0.00050563505,0.00018186649,0.00085570244,0.00045774167,0.00035933044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008277507,0.00022834772,0.00035532244,0.00044929024,0.00008079733,0.000077240475,0.0007182813,0.00010215476,0.00008875313],"category_scores_gemma":[0.00803469,0.0001836882,0.00002854453,0.005993161,0.000043833334,0.00007861378,0.00029510586,0.00014958232,0.00018765999],"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":[0.00052762846,0.0011541647,0.0019156054,0.0009041135,0.0002801675,0.000028766544,0.0012258696,0.00000490079,0.3002149,0.026218437,0.2009098,0.46661568],"study_design_scores_gemma":[0.007915868,0.00787044,0.05039444,0.00082163006,0.0015772397,0.00008231499,0.0015218555,0.057030477,0.12198028,0.029245391,0.715848,0.005712067],"about_ca_topic_score_codex":0.00008479201,"about_ca_topic_score_gemma":0.000020778902,"teacher_disagreement_score":0.51493824,"about_ca_system_score_codex":0.00013655664,"about_ca_system_score_gemma":0.000083327,"threshold_uncertainty_score":0.9618856},"labels":[],"label_agreement":null},{"id":"W2950335439","doi":"10.1111/biom.13104","title":"Model Selection for G-Estimation of Dynamic Treatment Regimes","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Waterloo","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; National Institute of Mental Health; University of Waterloo","keywords":"Selection (genetic algorithm); Computer science; Model selection; Identification (biology); Variety (cybernetics); Function (biology); Information Criteria; Estimation; Quadratic equation; Maximum likelihood; Mathematical optimization; Machine learning; Data mining; Econometrics; Artificial intelligence; Mathematics; Statistics; Biology","score_opus":0.17582160060611435,"score_gpt":0.43713139605817636,"score_spread":0.261309795452062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950335439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19518492,0.000035379395,0.803598,0.000018917222,0.000035807836,0.00054408005,0.000021193424,0.00013837204,0.00042334216],"genre_scores_gemma":[0.58263624,0.000025871283,0.41621366,0.0000032394344,0.000003682877,0.000037746708,0.000011423772,0.000012210014,0.0010559505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994271,0.000008439156,0.00019716196,0.00013309337,0.000120784796,0.00011345482],"domain_scores_gemma":[0.9991851,0.00032493798,0.00016820074,0.00017404533,0.00012695446,0.000020803796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013173763,0.0000918463,0.00017593145,0.0006463495,0.000019041443,0.0000071725035,0.00006367852,0.00007857674,0.000007777582],"category_scores_gemma":[0.00036685434,0.000079376514,0.00005664662,0.0010497952,0.0000140875645,0.00010423052,0.0000114865015,0.000023220557,0.000005358916],"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":[0.00014967418,0.0010727673,0.0011157972,0.000965185,0.00015007809,2.5733786e-7,0.00048385843,0.0073735598,0.11301652,0.47453204,0.0016597667,0.3994805],"study_design_scores_gemma":[0.00026134716,0.0005027898,0.000044116325,0.000017893799,0.000028235065,0.0000010095132,0.000013471149,0.7041599,0.037873305,0.2568855,0.00011988783,0.00009252648],"about_ca_topic_score_codex":0.0000057492543,"about_ca_topic_score_gemma":0.0000020640846,"teacher_disagreement_score":0.69678634,"about_ca_system_score_codex":0.00024544235,"about_ca_system_score_gemma":0.00003689285,"threshold_uncertainty_score":0.32368803},"labels":[],"label_agreement":null},{"id":"W2964818795","doi":"10.1111/biom.13287","title":"Generalized reliability based on distances","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":17,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Intraclass correlation; Reliability (semiconductor); Set (abstract data type); Confidence interval; Data set; Correlation","score_opus":0.09668905902704032,"score_gpt":0.28822837219593744,"score_spread":0.1915393131688971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964818795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85345966,0.000235409,0.015063578,0.11110887,0.00195913,0.00064891175,0.00028439428,0.0007625853,0.016477447],"genre_scores_gemma":[0.9753265,0.000014735789,0.0008975082,0.023518521,0.00012531743,0.000013087118,0.0000017109497,0.0000100857815,0.000092553186],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99864984,0.00010196476,0.00012906994,0.0004905601,0.0004635923,0.00016498815],"domain_scores_gemma":[0.9947083,0.00489496,0.000052806252,0.00020677784,0.000043712895,0.00009346644],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00016719397,0.00010795253,0.00013823465,0.0002596252,0.00014145688,0.000036927227,0.00018206473,0.000035313915,0.00006210105],"category_scores_gemma":[0.042938743,0.00009137194,0.00006840194,0.0047741933,0.00010505529,0.00006642151,0.000053553238,0.00008718156,0.00014090288],"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":[0.0020967675,0.0018431898,0.047564525,0.000419713,0.00003156324,0.00013240805,0.00051406055,0.0069060703,0.53274125,0.033648737,0.33136737,0.042734317],"study_design_scores_gemma":[0.0016027539,0.001066247,0.015035277,0.000010163122,0.0000148963545,9.757955e-7,0.000030816343,0.053007163,0.25804633,0.0010081246,0.6696412,0.00053608493],"about_ca_topic_score_codex":0.0000051704887,"about_ca_topic_score_gemma":5.9363464e-7,"teacher_disagreement_score":0.33827382,"about_ca_system_score_codex":0.00006350123,"about_ca_system_score_gemma":0.000029852772,"threshold_uncertainty_score":0.965123},"labels":[],"label_agreement":null},{"id":"W2968714287","doi":"10.1111/biom.13131","title":"Improving estimation efficiency for regression with MNAR covariates","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","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":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Missing data; Statistics; Econometrics; Regression; Regression analysis; Biostatistics; Conditional probability distribution; Computer science; Mathematics; Medicine","score_opus":0.07232868597284486,"score_gpt":0.3749917727600537,"score_spread":0.3026630867872088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968714287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052444264,0.00004656645,0.94659764,0.00004131719,0.00012564517,0.00038579962,0.000014537022,0.000051736806,0.0002925121],"genre_scores_gemma":[0.18658374,0.0000024367641,0.81315017,0.00002184437,0.000020594201,0.000015245453,0.000005212105,0.000013747558,0.00018702247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992072,0.000025852753,0.00018280659,0.00019053464,0.00021639439,0.00017720445],"domain_scores_gemma":[0.99703807,0.0024186943,0.00014559434,0.0001989924,0.00014966363,0.00004896988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051604665,0.00009881024,0.0001678967,0.0003452946,0.000059355378,0.00004420856,0.00010794736,0.0000653278,0.000050567785],"category_scores_gemma":[0.0049302876,0.000063260915,0.000026953456,0.0013166085,0.000024061406,0.000054925942,0.000023213795,0.000051845494,0.000015172453],"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":[0.00013866524,0.00031129923,0.0015574733,0.001074712,0.0000258776,0.000003213317,0.00023202768,0.000031773718,0.01195101,0.61094856,0.0010322032,0.37269318],"study_design_scores_gemma":[0.0017347757,0.001785442,0.0013535332,0.00028360757,0.00008913693,0.000012129107,0.000121549936,0.77953464,0.012017331,0.20179015,0.0007899489,0.00048772365],"about_ca_topic_score_codex":0.000008780231,"about_ca_topic_score_gemma":1.5653345e-7,"teacher_disagreement_score":0.77950287,"about_ca_system_score_codex":0.00003333677,"about_ca_system_score_gemma":0.00003719296,"threshold_uncertainty_score":0.5902371},"labels":[],"label_agreement":null},{"id":"W2968938401","doi":"10.1111/biom.13135","title":"Data‐adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss‐based estimation","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Covariate; Causal inference; Confounding; Counterfactual thinking; Statistics; Marginal structural model; Observational study; Inverse probability weighting; Computer science; Population; Econometrics; Outcome (game theory); Mathematics; Medicine; Propensity score matching; Psychology; Environmental health","score_opus":0.18956229312117323,"score_gpt":0.41666249240000175,"score_spread":0.22710019927882852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968938401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19076088,0.00003156749,0.807662,0.000036233698,0.000035606226,0.0007241657,0.00022265573,0.00022530538,0.000301553],"genre_scores_gemma":[0.64304787,0.0000073022366,0.35669354,0.000017474851,0.000009866728,0.00003807553,0.00010852663,0.000024757912,0.00005261405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981061,0.00008094141,0.00039485915,0.00055262615,0.0005218532,0.00034359682],"domain_scores_gemma":[0.9976793,0.00086112175,0.00032777464,0.00049200567,0.0005611337,0.00007864424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004896165,0.00027465046,0.00037053888,0.001591904,0.000054160493,0.000058355825,0.00035836856,0.00018362592,0.00003397009],"category_scores_gemma":[0.0013997696,0.00024384397,0.000019185,0.00791445,0.000094593226,0.0008096364,0.000116364216,0.00027169078,0.000025652693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004312836,0.0055300137,0.29013392,0.0015917396,0.0006024441,0.00020200602,0.0025124478,0.3451632,0.056627277,0.2656639,0.0061301724,0.02153003],"study_design_scores_gemma":[0.00081008504,0.00069420907,0.0023659924,0.00011048361,0.00003667933,0.00000350027,0.00008139931,0.96281755,0.007941643,0.024705395,0.000031054835,0.0004019917],"about_ca_topic_score_codex":0.000039887906,"about_ca_topic_score_gemma":0.00017818205,"teacher_disagreement_score":0.6176544,"about_ca_system_score_codex":0.00038413765,"about_ca_system_score_gemma":0.0004782899,"threshold_uncertainty_score":0.9943669},"labels":[],"label_agreement":null},{"id":"W2969787869","doi":"10.1111/biom.13138","title":"Testing the heritability and parent‐of‐origin hypotheses for ages at onset of psoriatic arthritis under biased sampling","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":4,"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; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heritability; Inference; Statistics; Pairwise comparison; Sampling bias; Correlation; Selection bias; Selection (genetic algorithm); Medicine; Mathematics; Econometrics; Demography; Biology; Computer science; Sample size determination; Genetics; Artificial intelligence","score_opus":0.17455425766965998,"score_gpt":0.3379279191919413,"score_spread":0.16337366152228133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969787869","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921571,0.006644028,0.00004746059,0.00010719838,0.00009377437,0.00066216354,0.00024223521,0.000011595792,0.000034460267],"genre_scores_gemma":[0.99749285,0.00033286383,0.0020329156,0.00003903385,0.000023425699,0.000032808093,0.000024402589,0.000010004858,0.0000117037625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992938,0.000023804312,0.00021837541,0.0001641036,0.00017571545,0.0001241876],"domain_scores_gemma":[0.99637854,0.0030969118,0.00011951184,0.00022011324,0.00012459853,0.00006029385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021440485,0.00008798457,0.00025133605,0.0002038158,0.000042864063,0.000009926173,0.00004331155,0.000035286565,0.00003771412],"category_scores_gemma":[0.0011762803,0.000057066805,0.00007515348,0.000839333,0.000067156776,0.000024125202,0.000033939592,0.000023560322,0.0000024387696],"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":[0.000055156703,0.00034900758,0.957063,0.00035463367,0.000104669765,9.850795e-7,0.00006774885,0.0000033853091,0.0010929563,0.0000519181,0.000050745457,0.04080576],"study_design_scores_gemma":[0.0014566267,0.00042255508,0.99512833,0.00017812787,0.00016809358,0.0000034067782,0.00016483125,0.00008051098,0.0017752233,0.00016554068,0.0003962433,0.000060485272],"about_ca_topic_score_codex":0.00009941937,"about_ca_topic_score_gemma":0.0000044801723,"teacher_disagreement_score":0.040745277,"about_ca_system_score_codex":0.000054714776,"about_ca_system_score_gemma":0.00004552752,"threshold_uncertainty_score":0.23271169},"labels":[],"label_agreement":null},{"id":"W2974543344","doi":"10.1111/biom.13151","title":"A Bayesian approach to joint modeling of matrix‐valued imaging data and treatment outcome with applications to depression studies","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"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 Alberta","funders":"National Institute of Mental Health; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Outcome (game theory); Principal component analysis; Computer science; Bayesian probability; Artificial intelligence; Probabilistic logic; Machine learning; Data mining; Econometrics; Mathematics","score_opus":0.2880098725551863,"score_gpt":0.4545091987410998,"score_spread":0.1664993261859135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974543344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008233708,0.00030127037,0.9897798,0.000066694876,0.000024775914,0.0009637195,0.00014410718,0.00003133777,0.0004546394],"genre_scores_gemma":[0.28203267,0.000015239929,0.71780044,0.000021251994,0.000014879198,0.00006326496,0.000007264638,0.000014911502,0.000030085774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871415,0.000043703698,0.0003679546,0.0004307776,0.0002559716,0.00018742267],"domain_scores_gemma":[0.9984193,0.0004468753,0.000098809665,0.0007632638,0.00013430748,0.0001374494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045742202,0.00015657977,0.0004221361,0.00065484777,0.0000546428,0.000034721917,0.00021479906,0.00003050309,0.0000036409563],"category_scores_gemma":[0.00076768425,0.000103395214,0.000020935122,0.0016214757,0.00002598661,0.00005943229,0.00029995927,0.000044538574,0.0000058073833],"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":[0.00024629486,0.0020242583,0.10165419,0.0028338837,0.00059064553,0.000008007443,0.0036421702,0.0004059514,0.005118131,0.25429034,0.00046237,0.62872374],"study_design_scores_gemma":[0.0019818067,0.00068283203,0.00281586,0.00036524588,0.0005377837,0.00002800844,0.0033266468,0.9202351,0.0010411155,0.0674713,0.00058916386,0.00092512334],"about_ca_topic_score_codex":0.000030856925,"about_ca_topic_score_gemma":0.0000014555945,"teacher_disagreement_score":0.91982913,"about_ca_system_score_codex":0.00006433395,"about_ca_system_score_gemma":0.00002359047,"threshold_uncertainty_score":0.42163345},"labels":[],"label_agreement":null},{"id":"W2975521764","doi":"10.1111/biom.13464","title":"Testing for association in multiview network data","year":2021,"lang":"en","type":"preprint","venue":"Biometrics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"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 Waterloo","funders":"National Institute of General Medical Sciences; Simons Foundation; National Institutes of Health; National Science Foundation","keywords":"Stochastic block model; Computer science; Set (abstract data type); Association (psychology); Node (physics); Null (SQL); Block (permutation group theory); Latent variable; Null model; Data mining; Covariate; Null hypothesis; Data set; Theoretical computer science; Machine learning; Artificial intelligence; Mathematics; Econometrics; Cluster analysis; Psychology","score_opus":0.08131336680406438,"score_gpt":0.31359049843828507,"score_spread":0.2322771316342207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975521764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37314937,0.15362948,0.4375036,0.0011370508,0.01612439,0.0067691132,0.005153352,0.00014521465,0.006388415],"genre_scores_gemma":[0.61259675,0.0036574965,0.33635142,0.0009872519,0.0051146317,0.00019844987,0.03996386,0.00013190146,0.000998238],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984568,0.00004586062,0.00046029768,0.000508917,0.00015405199,0.00037408332],"domain_scores_gemma":[0.998223,0.00017549166,0.00042347872,0.00090755837,0.0002149228,0.000055512395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014212817,0.0001995778,0.0002996775,0.00018953408,0.00005076781,0.0001313909,0.00060256774,0.00070788874,0.0000026478665],"category_scores_gemma":[0.0022717942,0.00021894566,0.000086456974,0.0010157828,0.000012255179,0.0000037388188,0.0020096276,0.0002495395,0.0000019196634],"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":[0.00008465149,0.0005029574,0.13774525,0.0024882848,0.0009596422,0.0000131961815,0.00015395589,0.016243113,0.009858709,0.00008689708,0.19179001,0.6400733],"study_design_scores_gemma":[0.003551642,0.00050363474,0.061901566,0.0012069439,0.00040858725,0.000013224085,0.0001911858,0.26846424,0.001181729,0.001557121,0.6580638,0.0029562628],"about_ca_topic_score_codex":0.000043241995,"about_ca_topic_score_gemma":0.000104137514,"teacher_disagreement_score":0.6371171,"about_ca_system_score_codex":0.0000981035,"about_ca_system_score_gemma":0.00026538986,"threshold_uncertainty_score":0.8928345},"labels":[],"label_agreement":null},{"id":"W2996410419","doi":"10.1111/biom.13185","title":"On continuous‐time capture‐recapture in closed populations","year":2019,"lang":"en","type":"letter","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mark and recapture; Poisson distribution; Discretization; Bernoulli's principle; Computer science; Sampling (signal processing); Statistics; Discrete time and continuous time; Population size; Population; Mathematics; Econometrics; Applied mathematics","score_opus":0.07591859289225492,"score_gpt":0.3259205004281728,"score_spread":0.2500019075359179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996410419","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04381689,0.0013462917,0.010009653,0.86434716,0.016948847,0.008334276,0.003468215,0.0013654361,0.050363246],"genre_scores_gemma":[0.06023573,0.000049552495,0.04629566,0.7055911,0.007526799,0.00014774226,0.020295957,0.0008260595,0.15903142],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975263,0.00013900279,0.0007078484,0.00047039564,0.0007449182,0.00041154152],"domain_scores_gemma":[0.9972879,0.001288047,0.0005395288,0.00069004577,0.00015347777,0.000040994542],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037302836,0.00041119367,0.00066975906,0.00356985,0.000058836664,0.00008359272,0.0002909527,0.0015085482,0.00046256612],"category_scores_gemma":[0.0018636744,0.0003832783,0.00019859646,0.0029962978,0.000026002475,0.00008700223,0.000044538618,0.0012462265,0.0008322345],"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":[0.000007338085,0.00007562834,0.001025213,0.00016847225,0.000018189434,0.000036442187,0.0000851983,0.000029725032,0.0000097328775,0.0039798915,0.99357814,0.000986006],"study_design_scores_gemma":[0.00085950113,0.00008175065,0.0048077675,0.00022886804,0.00009319732,0.000010784909,0.0000064268793,0.0021077313,0.000005638961,0.0342916,0.956755,0.0007517675],"about_ca_topic_score_codex":0.00010636634,"about_ca_topic_score_gemma":0.000016803542,"teacher_disagreement_score":0.15875608,"about_ca_system_score_codex":0.0003160927,"about_ca_system_score_gemma":0.00006129159,"threshold_uncertainty_score":0.99994576},"labels":[],"label_agreement":null},{"id":"W2998704413","doi":"10.1111/biom.13210","title":"Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Observational study; Estimator; Medicine; Metric (unit); Tuberculosis; Identifiability; Context (archaeology); Statistics; Econometrics; Mathematics; Computer science; Biology","score_opus":0.36671154482235424,"score_gpt":0.4225620880670525,"score_spread":0.055850543244698236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998704413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99331886,0.004575819,0.0006213951,0.00039236463,0.000109514265,0.0006197677,0.00025759396,0.00005244029,0.00005225665],"genre_scores_gemma":[0.8995664,0.00014243412,0.09466363,0.00030240763,0.00012970189,0.000032661912,0.0050930763,0.00003075038,0.000038939255],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635357,0.00047911948,0.00083993323,0.00084805104,0.00074791897,0.00073143863],"domain_scores_gemma":[0.9972918,0.00091155834,0.00031126308,0.000988567,0.00017097057,0.00032583557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017796643,0.00029142344,0.0012619239,0.0019869073,0.00012484056,0.00004754158,0.00032078184,0.00018307712,0.00035476327],"category_scores_gemma":[0.0019636767,0.00022691744,0.00033845875,0.009636986,0.00005464291,0.00029687735,0.0002517695,0.0002984426,0.000020942036],"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":[0.00011516195,0.0004620282,0.9058617,0.00003630229,0.02438046,0.000047759637,0.00012160017,0.065751754,0.00070576393,0.000020677075,0.0001433599,0.0023534743],"study_design_scores_gemma":[0.00067150954,0.0006156016,0.43539393,0.000008129738,0.014152991,0.000006479736,0.00009112924,0.5477906,0.000035091733,0.000014102333,0.0010242698,0.00019615225],"about_ca_topic_score_codex":0.0017766394,"about_ca_topic_score_gemma":0.000300275,"teacher_disagreement_score":0.48203886,"about_ca_system_score_codex":0.00037100934,"about_ca_system_score_gemma":0.00021750997,"threshold_uncertainty_score":0.9253425},"labels":[],"label_agreement":null},{"id":"W3010692414","doi":"10.1111/biom.13261","title":"Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Markov chain Monte Carlo; Computer science; Covariate; Bayesian probability; Inference; Bayesian inference; Missing data; Data mining; Machine learning; Artificial intelligence; Econometrics; Mathematics","score_opus":0.24511680040900635,"score_gpt":0.41672022796744196,"score_spread":0.1716034275584356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010692414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014888516,0.0004594314,0.99558777,0.0013482416,0.0001647793,0.00055979786,0.00022802448,0.0001223668,0.00004071562],"genre_scores_gemma":[0.22218548,0.00020628418,0.7770748,0.00030771008,0.000100388585,0.00003467693,0.00001466672,0.00004230839,0.000033656153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769527,0.000093371644,0.00063211645,0.00046534036,0.0003491187,0.0007647656],"domain_scores_gemma":[0.9981456,0.0008188585,0.00020715281,0.0002282877,0.00020709635,0.00039299848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010118394,0.00022771103,0.00047874247,0.00039450708,0.00015769046,0.00008576863,0.00025815118,0.00007937106,0.000041218656],"category_scores_gemma":[0.0038490286,0.00020176361,0.00013213509,0.0024242096,0.000033165463,0.00006783161,0.000072965,0.00020862045,0.000011111264],"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":[0.000567817,0.00091671426,0.003030224,0.0027667962,0.00029367552,0.000036592566,0.0013778133,0.00011322966,0.00096471724,0.4196581,0.0052144523,0.56505984],"study_design_scores_gemma":[0.00084769743,0.0009657231,0.00011635106,0.000044736338,0.000037387315,0.000004138626,0.000038971408,0.8067457,0.0001704817,0.18955114,0.0011362169,0.00034144838],"about_ca_topic_score_codex":0.000051860647,"about_ca_topic_score_gemma":0.000016738219,"teacher_disagreement_score":0.80663246,"about_ca_system_score_codex":0.00022729035,"about_ca_system_score_gemma":0.00025280294,"threshold_uncertainty_score":0.82276815},"labels":[],"label_agreement":null},{"id":"W3010736969","doi":"10.1111/biom.13225","title":"Spatial data analysis in ecology and agriculture using R, Richard E.Plant, Boca Raton, FL: CRC Press, 2019.","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Land Use and Ecosystem Services","field":"Environmental Science","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":"Evolutionary ecology; Landscape ecology; Ecology; Citation; Library science; Sociology; Geography; Computer science; Biology","score_opus":0.04250103560400686,"score_gpt":0.24363625030732747,"score_spread":0.2011352147033206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010736969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99753076,0.0008833676,0.00040449438,0.00027300417,0.00010601211,0.00013826159,0.00041293202,0.000022026485,0.00022911627],"genre_scores_gemma":[0.9984476,0.00044268844,0.0005268231,0.00020275079,0.00008428924,0.0000015477717,0.00027829324,0.0000055344385,0.000010498274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998903,0.000050475992,0.00020666706,0.00042611628,0.00019943545,0.0002143577],"domain_scores_gemma":[0.9994695,0.000050134116,0.00009661537,0.00025328842,0.000005752956,0.00012467896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002004019,0.000112955444,0.00023949773,0.00020733202,0.00006407712,0.0000585959,0.00039064194,0.000108843946,0.00019098738],"category_scores_gemma":[0.000051714866,0.000081375125,0.000025922971,0.0034396376,0.000014383663,0.0002485976,0.0006094313,0.00007388232,0.000063712614],"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":[0.000014881819,0.000049110826,0.98963934,0.000031511066,0.00008247867,0.000027247013,0.0001628584,0.0007950971,0.0013061154,0.0000012478167,0.0072058258,0.0006842925],"study_design_scores_gemma":[0.00053132285,0.00007832933,0.79306024,0.0000062450763,0.00034946887,0.000007876087,0.000066360066,0.15427803,0.00047339688,0.000007147953,0.05082251,0.00031907723],"about_ca_topic_score_codex":0.005117218,"about_ca_topic_score_gemma":0.0073011974,"teacher_disagreement_score":0.1965791,"about_ca_system_score_codex":0.000040612093,"about_ca_system_score_gemma":0.0000059255153,"threshold_uncertainty_score":0.77357376},"labels":[],"label_agreement":null},{"id":"W3014840511","doi":"10.1111/biom.13247","title":"Discussion on “Predictively consistent prior effective sample sizes,” by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan","year":2020,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","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":"Université de Montréal","funders":"","keywords":"Multivariate statistics; Prior probability; Binary number; Dirichlet distribution; Mathematics; Statistics; Applied mathematics; Mathematical analysis","score_opus":0.28294551863920875,"score_gpt":0.45626916965863934,"score_spread":0.1733236510194306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014840511","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008040217,0.0019979302,0.26980534,0.67098576,0.008507956,0.00989799,0.03503719,0.0012735545,0.0016902563],"genre_scores_gemma":[0.0014947159,0.00085389544,0.71400934,0.2736802,0.006689266,0.00039889774,0.00072135695,0.0006524203,0.0014998771],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99110746,0.0024265,0.0016848121,0.0019749256,0.0018311098,0.00097516266],"domain_scores_gemma":[0.85506475,0.14221926,0.0009598061,0.0009182428,0.00028399576,0.00055396126],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0019653887,0.0011415384,0.002612801,0.0012712062,0.0003680532,0.00027940792,0.00066588377,0.0019609574,0.00024566991],"category_scores_gemma":[0.24413502,0.0007735371,0.00051406026,0.003260778,0.0008179226,0.000090694855,0.00060458074,0.0033519752,0.000077160956],"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":[0.00029618587,0.00029650662,0.00013022215,0.0011350296,0.00048012324,0.00036475228,0.0000858711,9.7697274e-8,0.00022694483,0.0005101298,0.92658323,0.06989091],"study_design_scores_gemma":[0.005470307,0.00416957,0.0007676046,0.0013408451,0.0017923379,0.000038579303,0.00007929499,0.00024462875,0.0014987928,0.2156957,0.7666428,0.0022595557],"about_ca_topic_score_codex":0.00003757442,"about_ca_topic_score_gemma":0.0000016162636,"teacher_disagreement_score":0.44420403,"about_ca_system_score_codex":0.00036582522,"about_ca_system_score_gemma":0.00015620892,"threshold_uncertainty_score":0.99947155},"labels":[],"label_agreement":null},{"id":"W3015662163","doi":"10.1111/biom.13277","title":"A powerful procedure that controls the false discovery rate with directional information","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":6,"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","keywords":"False discovery rate; Multiple comparisons problem; Computer science; Statistical hypothesis testing; Data mining; Control (management); Value (mathematics); Statistics; Computational biology; Machine learning; Artificial intelligence; Mathematics; Biology; Gene; Genetics","score_opus":0.3902658894803887,"score_gpt":0.46597937872472,"score_spread":0.0757134892443313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015662163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025673242,0.00013998956,0.95836,0.010230237,0.000684667,0.0015550228,0.0004762547,0.00024179235,0.0026387905],"genre_scores_gemma":[0.57800514,0.0000846176,0.41272566,0.007774999,0.0007594168,0.0001171141,0.000018161632,0.00005491314,0.00045998825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998173,0.0003107236,0.0005095459,0.00019465113,0.0005876894,0.00022440532],"domain_scores_gemma":[0.9701871,0.028883075,0.00036698286,0.00023861493,0.00018780389,0.00013642172],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0018145991,0.0001695297,0.00036764576,0.00020132778,0.000096075695,0.00024517786,0.00027037496,0.00011343603,0.000074508775],"category_scores_gemma":[0.13121147,0.00009193143,0.000092724826,0.0027855805,0.00014252577,0.00042513668,0.000079320904,0.00024132553,0.00009209076],"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":[0.0073496574,0.0012997768,0.023234395,0.0023328583,0.0017982188,0.00006158236,0.0037615981,0.00008051663,0.0023522442,0.67206806,0.17882888,0.10683219],"study_design_scores_gemma":[0.011272416,0.0027164712,0.03202861,0.00024224419,0.00085392065,0.000047958845,0.0016239051,0.0039685965,0.007876395,0.7914894,0.14629398,0.001586096],"about_ca_topic_score_codex":0.0000037327236,"about_ca_topic_score_gemma":9.1903223e-7,"teacher_disagreement_score":0.55233186,"about_ca_system_score_codex":0.00004115747,"about_ca_system_score_gemma":0.0000968636,"threshold_uncertainty_score":0.8761067},"labels":[],"label_agreement":null},{"id":"W3015708622","doi":"10.1111/biom.13278","title":"A Bayes factor approach with informative prior for rare genetic variant analysis from next generation sequencing data","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","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":"Princess Margaret Cancer Centre; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"False discovery rate; Bayes factor; Bayes' theorem; Prior probability; Sample size determination; Test statistic; Bayesian probability; Statistics; Null distribution; Genome-wide association study; Computer science; Beta-binomial distribution; Multiple comparisons problem; Statistical hypothesis testing; Context (archaeology); Binomial distribution; Negative binomial distribution; Computational biology; Biology; Mathematics; Genetics; Single-nucleotide polymorphism; Gene; Poisson distribution","score_opus":0.16315913533747634,"score_gpt":0.2944534867600373,"score_spread":0.13129435142256096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015708622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3530671,0.00056634215,0.644624,0.00013588935,0.000033824967,0.00025784818,0.0012605515,0.000010555191,0.000043875083],"genre_scores_gemma":[0.7654624,0.00013509818,0.22519565,0.0004884179,0.0003117996,0.000030980234,0.00833825,0.000014184325,0.000023216251],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871933,0.00007157172,0.00032764388,0.0005078413,0.00014153711,0.00023204321],"domain_scores_gemma":[0.99890345,0.00007244908,0.00023995641,0.000491129,0.00017208498,0.000120943034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017756606,0.00016354602,0.0002831048,0.00025091902,0.00011855675,0.00006594383,0.00036725387,0.00018412291,0.000014680415],"category_scores_gemma":[0.0008439193,0.0001343813,0.00008389094,0.0017608035,0.000032958247,0.000013926178,0.0001832645,0.000058192818,0.000003628313],"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":[0.00065617205,0.00047448862,0.23379095,0.0003230365,0.016935522,0.000016520326,0.0059317923,0.03692491,0.5432483,0.00017628018,0.03248623,0.12903583],"study_design_scores_gemma":[0.0016100232,0.00094097666,0.083724335,0.000004643473,0.0012063638,0.0000056032736,0.0011017302,0.88722545,0.006229328,0.000026905793,0.017199688,0.00072495075],"about_ca_topic_score_codex":0.0000944349,"about_ca_topic_score_gemma":0.00003621982,"teacher_disagreement_score":0.85030055,"about_ca_system_score_codex":0.00003605367,"about_ca_system_score_gemma":0.0001589052,"threshold_uncertainty_score":0.54799104},"labels":[],"label_agreement":null},{"id":"W3015831956","doi":"10.1111/biom.13270","title":"Retrospective versus prospective score tests for genetic association with case‐control data","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"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 Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Logistic regression; Genetic association; Statistics; Odds ratio; Score test; Contrast (vision); Random effects model; Association (psychology); Prospective cohort study; Computer science; Odds; Likelihood-ratio test; Econometrics; Medicine; Artificial intelligence; Mathematics; Biology; Internal medicine; Genetics; Psychology; Genotype; Meta-analysis; Single-nucleotide polymorphism","score_opus":0.05680102457148995,"score_gpt":0.30617925032025406,"score_spread":0.24937822574876412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015831956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90281755,0.0013005077,0.089703344,0.001705435,0.00040902567,0.0013245795,0.0022660159,0.00004716378,0.00042640656],"genre_scores_gemma":[0.98082113,0.000073599535,0.017567545,0.0003697422,0.00060351664,0.00006132818,0.00036651213,0.00002897244,0.00010764912],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99856514,0.000070128954,0.00023178423,0.00062865467,0.0001731926,0.0003311227],"domain_scores_gemma":[0.99861705,0.00016722365,0.00027153306,0.00042801225,0.0003917227,0.00012443322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039097917,0.00016559732,0.0002484525,0.00011308218,0.00013562688,0.00003120289,0.00027280854,0.00023711685,0.000007187513],"category_scores_gemma":[0.0057588997,0.00014943235,0.000058816233,0.0009981868,0.000036587127,0.0000059868125,0.00011911744,0.000089990215,0.000008797213],"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":[0.00040915742,0.00006448301,0.9777907,0.000016747703,0.0005011034,0.000019950212,0.000043027834,0.00010148537,0.0044828155,0.000040078932,0.014040689,0.0024897405],"study_design_scores_gemma":[0.0067053894,0.0041904603,0.9665388,0.000004867228,0.00037565446,0.00005089211,0.00013946611,0.003572001,0.0009391263,0.00007911554,0.016915634,0.000488623],"about_ca_topic_score_codex":0.000022875656,"about_ca_topic_score_gemma":0.000068539135,"teacher_disagreement_score":0.078003615,"about_ca_system_score_codex":0.00014108462,"about_ca_system_score_gemma":0.00012219422,"threshold_uncertainty_score":0.6894357},"labels":[],"label_agreement":null},{"id":"W3017527408","doi":"10.1111/biom.13286","title":"A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Endophenotype; Disease; Structural equation modeling; Medical diagnosis; Computer science; Neuroimaging; Psychology; Econometrics; Psychiatry; Clinical psychology; Medicine; Machine learning; Mathematics; Pathology; Cognition","score_opus":0.05774529328951429,"score_gpt":0.3219648040810188,"score_spread":0.2642195107915045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017527408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16045332,0.00062333763,0.83673847,0.0003250311,0.00017139733,0.0009504315,0.0007039569,0.000023408915,0.000010625124],"genre_scores_gemma":[0.5281174,0.000012768174,0.46818373,0.00042218354,0.00056908774,0.00018361771,0.0024640036,0.00003082274,0.000016384578],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985959,0.00008313899,0.00037070794,0.000537468,0.000114040275,0.0002987439],"domain_scores_gemma":[0.99877167,0.00034834043,0.00021393203,0.00014945377,0.0003776831,0.00013892532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043965367,0.00017488815,0.0002383319,0.00013318575,0.00022318873,0.000041643045,0.00014721378,0.00024244489,0.000014136535],"category_scores_gemma":[0.0028470235,0.0001674352,0.00014384034,0.00039707724,0.00001316433,0.0000079457195,0.000049421724,0.000055511777,6.1545404e-7],"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":[0.0027769073,0.000068117966,0.0017589291,0.00010490509,0.00039655127,8.061043e-8,0.00009109488,0.09116237,0.87124604,0.00058802636,0.0038882317,0.027918741],"study_design_scores_gemma":[0.0016852018,0.0005223177,0.0012211408,0.000010035975,0.00022575226,1.7281252e-7,0.000026193664,0.958016,0.028882995,0.0056208535,0.0035609493,0.00022839235],"about_ca_topic_score_codex":0.000023098639,"about_ca_topic_score_gemma":0.0000020628715,"teacher_disagreement_score":0.8668536,"about_ca_system_score_codex":0.000028579701,"about_ca_system_score_gemma":0.00016745247,"threshold_uncertainty_score":0.6827809},"labels":[],"label_agreement":null},{"id":"W3018281085","doi":"10.1111/biom.13285","title":"Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Confounding; Inverse probability; Outcome (game theory); Statistics; Context (archaeology); Observational study; Marginal structural model; Econometrics; Ordinary least squares; Generalized linear model; Computer science; Mathematics; Bayesian probability","score_opus":0.4591560821512847,"score_gpt":0.5072392278805445,"score_spread":0.04808314572925987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018281085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37185323,0.0020505216,0.62340486,0.00061816286,0.00017016602,0.0011117677,0.000040633717,0.0003592883,0.00039134728],"genre_scores_gemma":[0.87021506,0.00044525976,0.12912214,0.000059294758,0.000038457038,0.000064865446,0.00000542914,0.000012944347,0.00003658728],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905497,0.000022270548,0.00032448862,0.00020279473,0.00019973301,0.000195734],"domain_scores_gemma":[0.99809444,0.0013115031,0.00015112071,0.000118892465,0.00022174368,0.00010231644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027172163,0.00015493657,0.0004230805,0.0016236553,0.00006153602,0.000034848887,0.00011016356,0.000068040026,0.0000050888593],"category_scores_gemma":[0.0034800558,0.00012493512,0.00005638261,0.0065896395,0.00004862538,0.00024057331,0.00012240306,0.00008972473,0.0000024611938],"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":[0.0011484938,0.0004266727,0.61616385,0.0029906742,0.0049438635,0.000058211295,0.05692852,0.00015034559,0.004599172,0.041058403,0.01765586,0.25387594],"study_design_scores_gemma":[0.009425896,0.009762967,0.08172026,0.0025605948,0.0057501174,0.000024352916,0.09360167,0.04839812,0.32046053,0.3999169,0.021429406,0.006949217],"about_ca_topic_score_codex":0.000005442308,"about_ca_topic_score_gemma":0.000005320925,"teacher_disagreement_score":0.53444356,"about_ca_system_score_codex":0.00012756579,"about_ca_system_score_gemma":0.000015826137,"threshold_uncertainty_score":0.50947064},"labels":[],"label_agreement":null},{"id":"W3028120919","doi":"10.1111/biom.13307","title":"A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"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; HEC Montréal; Université Laval; Université du Québec à Montréal; Douglas Mental Health University Institute; McGill University; Jewish General Hospital; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Compute Canada; Genome Canada","keywords":"Covariate; DNA methylation; Computer science; Inference; Bisulfite sequencing; Confounding; Computational biology; Data mining; Algorithm; Statistics; Biology; Mathematics; Artificial intelligence; Machine learning; Genetics; Gene","score_opus":0.11339107913284373,"score_gpt":0.3402896786579435,"score_spread":0.22689859952509978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028120919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26701295,0.00089186773,0.73165244,0.0000411049,0.000057837293,0.00026350087,0.000057201323,0.0000058288592,0.000017278493],"genre_scores_gemma":[0.6723159,0.00007449981,0.3273609,0.00005315489,0.00005675817,0.000015633397,0.00010555022,0.000015935604,0.0000016975268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887174,0.000092533715,0.0003519809,0.000314952,0.00018538462,0.00018338546],"domain_scores_gemma":[0.99923325,0.00022595467,0.00012350605,0.00013139968,0.00019969676,0.00008621145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073580927,0.00012516986,0.00022181396,0.00032406324,0.000030472234,0.000016161113,0.0001042505,0.00016134129,0.0000011297332],"category_scores_gemma":[0.0030485543,0.0001275946,0.00006638921,0.0010362194,0.00001788618,0.0000043773903,0.000051091552,0.000055134595,4.992104e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008990622,0.000027470473,0.00023895754,0.00011098851,0.00003014241,4.648066e-7,0.000087204375,0.015204838,0.9672719,0.00030661412,0.000003435434,0.016628085],"study_design_scores_gemma":[0.0008392062,0.00029148042,0.0006551288,0.00001008688,0.000029127475,2.7545644e-7,0.000022052904,0.30928296,0.6881122,0.00042750686,0.00020285045,0.00012712722],"about_ca_topic_score_codex":0.00005596828,"about_ca_topic_score_gemma":0.000009853846,"teacher_disagreement_score":0.4053029,"about_ca_system_score_codex":0.000029298719,"about_ca_system_score_gemma":0.000091572336,"threshold_uncertainty_score":0.52031565},"labels":[],"label_agreement":null},{"id":"W3042637318","doi":"10.1111/biom.13334","title":"Maximum likelihood abundance estimation from capture‐recapture data when covariates are missing at random","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":6,"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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Statistics; Estimator; Covariate; Mathematics; Confidence interval; Missing data; Coverage probability; Restricted maximum likelihood; Mark and recapture; Imputation (statistics); Point estimation; Maximum likelihood; Population","score_opus":0.1102205926276703,"score_gpt":0.32194161168909713,"score_spread":0.21172101906142682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042637318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045220215,0.002650436,0.93950456,0.008786164,0.0009382079,0.00063875515,0.0014373567,0.0004113015,0.00041301455],"genre_scores_gemma":[0.5784954,0.000043193468,0.41657615,0.0008612064,0.00048624084,0.000007116099,0.0033770406,0.00006467408,0.000089013236],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981451,0.000083847146,0.00051142677,0.0004937154,0.0005117052,0.0002542145],"domain_scores_gemma":[0.9976748,0.0007999901,0.00050404633,0.0006726896,0.00015116317,0.00019728686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037239768,0.00023643486,0.00037861307,0.0002837004,0.0002005559,0.0001817029,0.0004504712,0.00020819172,0.000322984],"category_scores_gemma":[0.0042378595,0.00021936442,0.00006631644,0.0015312242,0.000031812007,0.0003641108,0.00025246685,0.00016233149,0.00013064637],"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":[0.0011355608,0.0008298243,0.052759036,0.00170517,0.00060113444,0.000095332696,0.013136986,0.0018168053,0.006293729,0.005527078,0.6196411,0.2964582],"study_design_scores_gemma":[0.0040566777,0.000048944177,0.0102021415,0.00018569318,0.0003394427,0.000010126703,0.00012724088,0.7924348,0.0006872782,0.12802094,0.06312315,0.00076357165],"about_ca_topic_score_codex":0.00015596347,"about_ca_topic_score_gemma":0.000027346423,"teacher_disagreement_score":0.790618,"about_ca_system_score_codex":0.00011575284,"about_ca_system_score_gemma":0.000041947962,"threshold_uncertainty_score":0.8945421},"labels":[],"label_agreement":null},{"id":"W3043464877","doi":"10.1111/biom.13329","title":"Approximate Bayesian inference for case‐crossover models","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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; Centre for Global Health Research; St. Michael's Hospital","funders":"","keywords":"Crossover; Inference; Flexibility (engineering); Computer science; Laplace's method; Bayesian probability; Econometrics; Statistics; Mathematics; Artificial intelligence","score_opus":0.2269703437760123,"score_gpt":0.41386899202038535,"score_spread":0.18689864824437305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043464877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072081125,0.00006351687,0.9963376,0.00032578356,0.00012237458,0.00044015513,0.00040985722,0.00013417118,0.0014457322],"genre_scores_gemma":[0.25765568,0.0000105389845,0.7417976,0.00032125978,0.00010457219,0.000041951756,0.0000062389754,0.000027859229,0.000034259134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855196,0.00005020191,0.0003893274,0.0003716907,0.00025759777,0.00037923185],"domain_scores_gemma":[0.9960658,0.0030642864,0.00013775032,0.00025820927,0.00019357189,0.00028037047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036032533,0.00019849915,0.00035206787,0.00025812903,0.00012600953,0.00012659706,0.00023397709,0.0001355474,0.000099325516],"category_scores_gemma":[0.008191547,0.00017156413,0.000100708065,0.0021361723,0.0000774649,0.00013904927,0.00009712625,0.00012572708,0.000011393178],"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":[0.000039128994,0.000075246644,0.000065212276,0.0003796442,0.000026080665,0.0001047703,0.00027939372,0.0000028856537,0.00018539808,0.93365526,0.002541492,0.06264549],"study_design_scores_gemma":[0.0004875146,0.00016822343,0.000009031626,0.000013483527,0.00004344465,0.000038865113,0.000057168967,0.19498108,0.00052916416,0.8023452,0.0010744234,0.00025237395],"about_ca_topic_score_codex":0.000011485415,"about_ca_topic_score_gemma":0.0000012078533,"teacher_disagreement_score":0.25693485,"about_ca_system_score_codex":0.000033793047,"about_ca_system_score_gemma":0.00005329336,"threshold_uncertainty_score":0.9806639},"labels":[],"label_agreement":null},{"id":"W3044906356","doi":"10.1111/biom.13331","title":"Analysis of noisy survival data with graphical proportional hazards measurement error models","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Proportional hazards model; Inference; Survival analysis; Computer science; Statistics; Data mining; Flexibility (engineering); Graphical model; Observational error; Econometrics; Mathematics; Machine learning; Artificial intelligence","score_opus":0.4814915038068301,"score_gpt":0.4180228145676048,"score_spread":0.06346868923922527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044906356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010896718,0.000064163796,0.987162,0.0004003885,0.00004383573,0.00014710161,0.0007347657,0.000032683645,0.000518365],"genre_scores_gemma":[0.64421326,0.000009319498,0.35562822,0.00004799158,0.000028028948,0.0000043348005,0.000054337674,0.000010589509,0.0000038968824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973724,0.00010395838,0.00045188778,0.0003684838,0.0015256741,0.00017758379],"domain_scores_gemma":[0.9979582,0.00054476236,0.00020621983,0.00052726426,0.0005841566,0.0001793981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001349645,0.0001324097,0.0004813705,0.00075642014,0.000035472556,0.000022473158,0.00044011112,0.00006724916,0.000116805735],"category_scores_gemma":[0.005968907,0.00009410004,0.000078937315,0.009987851,0.00011618759,0.0000765242,0.00017152017,0.00011131197,0.0000015980837],"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":[0.000423701,0.0017053178,0.042741906,0.0007902286,0.006145168,0.000038886286,0.00036945005,0.000460243,0.0015047181,0.89577913,0.003477498,0.046563774],"study_design_scores_gemma":[0.0008353734,0.0007039189,0.036800236,0.00003778859,0.003899547,0.000001572188,0.00012724324,0.89395726,0.00039632744,0.0620394,0.0006891433,0.00051219214],"about_ca_topic_score_codex":0.000027065209,"about_ca_topic_score_gemma":0.000014306223,"teacher_disagreement_score":0.893497,"about_ca_system_score_codex":0.000026706395,"about_ca_system_score_gemma":0.00014489208,"threshold_uncertainty_score":0.714577},"labels":[],"label_agreement":null},{"id":"W3046149928","doi":"10.1111/biom.13346","title":"A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Pairwise comparison; Inference; Estimator; Computer science; Statistical inference; Feature selection; Selection (genetic algorithm); Variable (mathematics); Latent variable; Statistical hypothesis testing; Statistics; Machine learning; Artificial intelligence; Econometrics; Data mining; Mathematics","score_opus":0.15376964275093327,"score_gpt":0.384751510100338,"score_spread":0.2309818673494047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046149928","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008833788,0.000014640311,0.98970705,0.000086133274,0.000019217787,0.00046965585,0.00036527333,0.00009064438,0.00041361677],"genre_scores_gemma":[0.1770086,0.00000401075,0.8226853,0.00014654087,0.000040132778,0.000049296254,0.00003363208,0.000016836015,0.000015627016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988755,0.00006322468,0.00029150033,0.00025970704,0.00025070162,0.00025939028],"domain_scores_gemma":[0.99598473,0.0031535833,0.00013689046,0.00008957028,0.00036783895,0.00026736024],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00033372236,0.0001644974,0.00028261173,0.00021979239,0.00012422996,0.00011746719,0.000104147635,0.00009689899,0.00010013942],"category_scores_gemma":[0.009093971,0.00012521255,0.000013774166,0.0023880606,0.00007289147,0.00019368708,0.00003514551,0.00013584181,0.000002992613],"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":[0.0003142709,0.0001551297,0.002264064,0.00077654584,0.000053591113,0.0000011497297,0.00062762265,0.0000029145112,0.0005775501,0.95991755,0.0011048882,0.034204755],"study_design_scores_gemma":[0.0033920263,0.011700701,0.025074707,0.00015275595,0.00036171707,0.000044441353,0.001216223,0.3504687,0.002048829,0.59519935,0.009143011,0.0011975573],"about_ca_topic_score_codex":0.000020855314,"about_ca_topic_score_gemma":0.000006839845,"teacher_disagreement_score":0.36471817,"about_ca_system_score_codex":0.00003308111,"about_ca_system_score_gemma":0.00015396763,"threshold_uncertainty_score":0.99925286},"labels":[],"label_agreement":null},{"id":"W3081731064","doi":"10.1111/biom.13362","title":"Nonparametric matrix response regression with application to brain imaging data analysis","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":15,"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":"Simons Foundation","keywords":"Computer science; Nonparametric statistics; Neuroimaging; Covariance matrix; Algorithm; Artificial intelligence; Regression; Pattern recognition (psychology); Regularization (linguistics); Machine learning; Data mining; Mathematics; Econometrics; Statistics","score_opus":0.07158497471767379,"score_gpt":0.34431798504057726,"score_spread":0.2727330103229035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081731064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21315843,0.0003294346,0.65450907,0.13022488,0.0001398635,0.0007133821,0.00037626462,0.00035863137,0.00019004713],"genre_scores_gemma":[0.9831773,0.00001217958,0.0064223176,0.010076504,0.000084040046,0.000025127394,0.000028540193,0.000022647413,0.00015131054],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974008,0.00022591624,0.00021659398,0.0011482746,0.0007489264,0.0002594667],"domain_scores_gemma":[0.98769027,0.0108610485,0.00014717114,0.0009922443,0.00009895641,0.00021031298],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0008661804,0.00017391973,0.00025833305,0.0037377733,0.00021942565,0.000114285365,0.000759073,0.000036986523,0.000014786409],"category_scores_gemma":[0.07830092,0.00013989712,0.0000530028,0.065587215,0.000068045774,0.0002844632,0.0006651204,0.00012476988,0.00013283145],"study_design_candidate":"bench_or_experimental","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":[0.004394028,0.00035814755,0.07082364,0.00007394101,0.00023926306,0.00011424079,0.0005407008,0.0029234197,0.72788364,0.00044165528,0.12389209,0.06831525],"study_design_scores_gemma":[0.0017539808,0.001054115,0.120630965,0.000029256948,0.00064821495,0.000034283745,0.00036492705,0.38174203,0.06935786,0.00009107119,0.42291793,0.0013753654],"about_ca_topic_score_codex":0.0000268821,"about_ca_topic_score_gemma":0.000004091885,"teacher_disagreement_score":0.7700189,"about_ca_system_score_codex":0.000098858785,"about_ca_system_score_gemma":0.000056263856,"threshold_uncertainty_score":0.9542742},"labels":[],"label_agreement":null},{"id":"W3119638420","doi":"10.1111/biom.13569","title":"Bayesian multiple index models for environmental mixtures","year":2021,"lang":"en","type":"preprint","venue":"Biometrics","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","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":"University of Waterloo","funders":"National Institute of Environmental Health Sciences; Environmental Protection Agency","keywords":"Interpretability; Linear model; Index (typography); Bayesian probability; Curse of dimensionality; Additive model; Econometrics; Range (aeronautics); Statistics; Bayesian inference; Computer science; Flexibility (engineering); Mathematics; Data mining; Artificial intelligence","score_opus":0.051360451980163216,"score_gpt":0.26809554329143714,"score_spread":0.21673509131127394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119638420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3497373,0.0014483302,0.6432091,0.00010516635,0.0021537964,0.0008619726,0.0004700865,0.00015355609,0.0018607216],"genre_scores_gemma":[0.97047865,0.000096543714,0.02770997,0.000062124665,0.0003111166,0.00009121626,0.0002986471,0.00005176946,0.0008999729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774516,0.00005768954,0.0003831996,0.0007827315,0.0005865642,0.00044464102],"domain_scores_gemma":[0.99876267,0.00028194976,0.0002277107,0.0005366682,0.000007422172,0.00018360416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044128753,0.00032506956,0.0003294982,0.00031138613,0.00019656714,0.00014274947,0.00046685952,0.0004783946,0.00018740984],"category_scores_gemma":[0.0002790943,0.00034053245,0.00033098314,0.00069633074,0.00014173012,0.00012698566,0.0014765676,0.00037772962,0.000024931449],"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":[0.00007261794,0.00092779094,0.23308133,0.00042287307,0.00024304437,0.00003463731,0.0018159569,0.3066946,0.0059107444,0.000012990611,0.00469683,0.4460866],"study_design_scores_gemma":[0.00079484633,0.0001106948,0.02008783,0.00010036126,0.00010316875,0.000008524594,0.00049691735,0.9594656,0.004393563,0.0014156837,0.011789339,0.00123342],"about_ca_topic_score_codex":0.00024435198,"about_ca_topic_score_gemma":0.000008228702,"teacher_disagreement_score":0.65277106,"about_ca_system_score_codex":0.00050747656,"about_ca_system_score_gemma":0.000021132762,"threshold_uncertainty_score":0.9999047},"labels":[],"label_agreement":null},{"id":"W3138410997","doi":"10.1111/biom.13460","title":"A Bayesian spatial model for imaging genetics","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging genetics; Alzheimer's Disease Neuroimaging Initiative; Bayesian probability; Bivariate analysis; Computer science; Neuroimaging; Artificial intelligence; Gibbs sampling; Multivariate statistics; Bayes' theorem; Correlation; Pattern recognition (psychology); Bayesian inference; Data set; Machine learning; Mathematics; Biology; Neuroscience","score_opus":0.08789597052750568,"score_gpt":0.33307147029377143,"score_spread":0.24517549976626574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138410997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039070607,0.00031516748,0.99389195,0.0003271463,0.00016090278,0.00013589657,0.000087781686,0.000051446852,0.0011226165],"genre_scores_gemma":[0.48747993,0.000024369816,0.5114339,0.00019023441,0.0001035136,0.000014963766,0.000023473376,0.000016290169,0.00071336055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991522,0.000018959236,0.00022570901,0.00021621365,0.0001743026,0.00021261752],"domain_scores_gemma":[0.9990174,0.00038801372,0.00007435668,0.0002532461,0.00019249797,0.00007449592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021795729,0.000096074546,0.00015259339,0.00033074606,0.0000883419,0.000058612946,0.00011327277,0.00006932189,0.00007973992],"category_scores_gemma":[0.0014203965,0.000087466986,0.00010031174,0.0015600132,0.000017095772,0.000028988985,0.00007489596,0.00005759928,0.0000071474774],"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":[0.000040758023,0.0024887926,0.014777243,0.000583881,0.0002096758,0.00014161509,0.000350412,0.00068487914,0.023199094,0.4169173,0.11959486,0.4210115],"study_design_scores_gemma":[0.00046806727,0.000021121326,0.0004606356,0.000006964088,0.000047762984,0.000010152538,0.000030071787,0.9094299,0.002366273,0.0798009,0.007170477,0.00018765137],"about_ca_topic_score_codex":0.0000034480345,"about_ca_topic_score_gemma":0.000002862721,"teacher_disagreement_score":0.90874505,"about_ca_system_score_codex":0.000035529945,"about_ca_system_score_gemma":0.00006267448,"threshold_uncertainty_score":0.35668},"labels":[],"label_agreement":null},{"id":"W3138924051","doi":"10.1111/biom.13456","title":"A generalized robust allele‐based genetic association test","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Covariate; Test statistic; Allele; Statistics; Allele frequency; Sample size determination; Mathematics; Type I and type II errors; Statistic; Econometrics; Statistical hypothesis testing; Genetics; Biology","score_opus":0.026408524701805814,"score_gpt":0.2660775755313801,"score_spread":0.2396690508295743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138924051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95847964,0.0037632189,0.03392542,0.0015987355,0.00044669388,0.0001679757,0.000115490875,0.0000363961,0.0014664494],"genre_scores_gemma":[0.90944225,0.0009774055,0.079501376,0.0020807893,0.0005344661,0.00003834536,0.0010161827,0.000039644812,0.0063695693],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99852943,0.00016174474,0.00032147943,0.0004025992,0.0002137393,0.00037097337],"domain_scores_gemma":[0.9987555,0.00016283791,0.0002102863,0.00036337724,0.00040149144,0.00010651632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042597306,0.00015117433,0.00021165567,0.0002141651,0.00010639182,0.00003721836,0.000152412,0.00035681325,0.00010477456],"category_scores_gemma":[0.005108809,0.00016062101,0.00014751521,0.0013600605,0.00002203375,0.0000016640878,0.00008756541,0.00007851538,0.0000523334],"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":[0.000010446726,0.00032241907,0.7106699,0.000016950204,0.00013628205,0.000012939408,0.000009682335,0.0022820705,0.20353524,0.000032639353,0.07844578,0.0045256414],"study_design_scores_gemma":[0.0017477542,0.00029343236,0.65279806,0.000005092413,0.00008930942,0.000015516278,0.00003361402,0.0037761617,0.02988583,0.00010164299,0.31075573,0.000497882],"about_ca_topic_score_codex":0.000022977252,"about_ca_topic_score_gemma":0.000036782763,"teacher_disagreement_score":0.23230994,"about_ca_system_score_codex":0.00010760754,"about_ca_system_score_gemma":0.00023106317,"threshold_uncertainty_score":0.6549935},"labels":[],"label_agreement":null},{"id":"W3148552882","doi":"10.1111/biom.13468","title":"Bayesian analysis of coupled cellular and nuclear trajectories for cell migration","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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 Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Bivariate analysis; Motility; Nucleus; Cell migration; Markov chain; Process (computing); Cell; Econometrics; Biological system; Artificial intelligence; Biology; Machine learning; Mathematics; Neuroscience; Cell biology","score_opus":0.010529687781070152,"score_gpt":0.2381988552052882,"score_spread":0.22766916742421803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148552882","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89064753,0.0017207192,0.107148215,0.000045402314,0.0001367603,0.00007693778,0.000089898116,0.0000040777036,0.00013049069],"genre_scores_gemma":[0.99592924,0.0004994553,0.0025801368,0.00003256239,0.00004292142,0.000003937746,0.00055443833,0.000010790075,0.0003464939],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994859,0.000013811132,0.0001520758,0.00018894953,0.0000727023,0.00008653883],"domain_scores_gemma":[0.999504,0.000022299122,0.00007460658,0.00017164763,0.00018727567,0.000040166957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087706874,0.0000675457,0.00013335505,0.00035602984,0.00004768925,0.00002296835,0.000046263092,0.00008701004,0.000034238787],"category_scores_gemma":[0.00011682143,0.000071082,0.00013605434,0.0014641811,0.000015676396,0.0000026298524,0.00003012972,0.00002244856,4.085007e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014776804,0.00007729885,0.0003930193,0.000021314823,0.00018684893,5.777259e-7,0.000034120814,0.000008293739,0.99839085,0.000275791,0.00023196536,0.0003651304],"study_design_scores_gemma":[0.0002842305,0.00021457349,0.0005099865,0.000001990735,0.00045011437,0.0000010765228,0.00023122986,0.02206727,0.9122691,0.00002552902,0.06382784,0.000117024254],"about_ca_topic_score_codex":0.000021932501,"about_ca_topic_score_gemma":0.000043904183,"teacher_disagreement_score":0.10528176,"about_ca_system_score_codex":0.0000069410867,"about_ca_system_score_gemma":0.000026806338,"threshold_uncertainty_score":0.289864},"labels":[],"label_agreement":null},{"id":"W3156920115","doi":"10.1111/biom.13479","title":"Feature screening with large‐scale and high‐dimensional survival data","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Western University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Covariate; Computer science; Sample size determination; Dimension (graph theory); Big data; Variable (mathematics); Scale (ratio); Data mining; Feature (linguistics); Sample (material); Computation; Variables; Machine learning; Statistics; Mathematics; Algorithm","score_opus":0.15670479044302615,"score_gpt":0.380795179429857,"score_spread":0.22409038898683087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156920115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045755226,0.0006660839,0.9505816,0.0008250578,0.00022469448,0.00008429518,0.0010188277,0.000051413463,0.0007927899],"genre_scores_gemma":[0.022538923,0.000028791703,0.97641695,0.000111040456,0.00008145599,0.0000014024065,0.00012797205,0.00001693091,0.00067654654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99886906,0.000089216424,0.00011349623,0.0003410971,0.00037289815,0.00021424472],"domain_scores_gemma":[0.99739975,0.0017577158,0.000056053712,0.000495306,0.00017484467,0.00011634491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056593533,0.00011235288,0.00022167087,0.00017695855,0.00009172771,0.00006517753,0.00016113388,0.00008329062,0.00011171734],"category_scores_gemma":[0.003531234,0.000083630985,0.000012907406,0.001814598,0.000052122927,0.000067053996,0.0003833643,0.00014860801,0.000004518683],"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":[0.00017139345,0.0009830212,0.050183192,0.0006091147,0.00037545586,0.00062473555,0.00027152826,0.0000017899365,0.002706462,0.55275476,0.080181755,0.31113678],"study_design_scores_gemma":[0.010876916,0.001344741,0.3868775,0.00088319776,0.0011359015,0.0006979583,0.0021373592,0.073665425,0.0078010224,0.26788068,0.24309693,0.0036023525],"about_ca_topic_score_codex":0.000009006069,"about_ca_topic_score_gemma":0.000018417655,"teacher_disagreement_score":0.33669433,"about_ca_system_score_codex":0.0000085395795,"about_ca_system_score_gemma":0.00004851906,"threshold_uncertainty_score":0.42274722},"labels":[],"label_agreement":null},{"id":"W3176022367","doi":"10.1111/biom.13513","title":"Another look at regression analysis using ranked set samples with application to an osteoporosis study","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":3,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Regression analysis; Osteoporosis; Regression; Set (abstract data type); Mathematics; Linear regression; Medicine; Computer science; Internal medicine","score_opus":0.22734864388675521,"score_gpt":0.46310500800984244,"score_spread":0.23575636412308723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176022367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36268976,0.000038040725,0.6368185,0.000014607097,0.000015895901,0.0002776901,0.00007912949,0.000032982905,0.000033362976],"genre_scores_gemma":[0.3519387,0.000004181356,0.6477555,0.000054413904,0.000021171454,0.00002420075,0.00003458497,0.000025396152,0.00014187304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983691,0.00019214429,0.00029907154,0.0004930831,0.0004305772,0.00021602413],"domain_scores_gemma":[0.9981132,0.00067419367,0.00015539974,0.00064076856,0.0002747209,0.00014170256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004752323,0.00016846403,0.0004073282,0.0008253189,0.00015372029,0.000044857603,0.00012163283,0.00006585887,0.0000688892],"category_scores_gemma":[0.0007537633,0.0001250704,0.000060610415,0.007819152,0.00002697753,0.000080152495,0.00010508881,0.000057058955,0.0000046853734],"study_design_candidate":"bench_or_experimental","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":[0.0015770328,0.01034456,0.19483548,0.000671869,0.0052460595,0.00027436548,0.020727653,0.008354735,0.28847164,0.02016689,0.00088375114,0.44844598],"study_design_scores_gemma":[0.020665752,0.009487227,0.14255184,0.00057354453,0.03391139,0.00013387328,0.051948495,0.28191164,0.19191039,0.24024725,0.016825188,0.009833399],"about_ca_topic_score_codex":0.00004471825,"about_ca_topic_score_gemma":0.00015616453,"teacher_disagreement_score":0.43861255,"about_ca_system_score_codex":0.00013656386,"about_ca_system_score_gemma":0.000028744,"threshold_uncertainty_score":0.5100222},"labels":[],"label_agreement":null},{"id":"W3208943075","doi":"10.1111/biom.13596","title":"Sample size considerations for stepped wedge designs with subclusters","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute on Aging","keywords":"Sample size determination; CRTS; Eigenvalues and eigenvectors; Gaussian; Mathematics; Statistics; Cluster analysis; Sample (material); Computer science; Correlation; Algorithm; Physics; Geometry","score_opus":0.23704033998928054,"score_gpt":0.3989707747949734,"score_spread":0.16193043480569286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208943075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011913143,0.000040963634,0.99685067,0.0003914824,0.00014815286,0.00034335992,0.00037278217,0.000052898584,0.00060837955],"genre_scores_gemma":[0.035930417,0.000008248711,0.9634808,0.00023070758,0.000049595674,0.000051480514,0.0000062227778,0.000021585687,0.00022098956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898976,0.000092522496,0.00024343617,0.00023511183,0.0001965464,0.00024261152],"domain_scores_gemma":[0.9479064,0.05126119,0.00008350122,0.00025270818,0.000385573,0.00011062444],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00032354888,0.00012237483,0.00024090758,0.00020036638,0.00014796022,0.00010948972,0.000066729146,0.00007245605,0.00037550658],"category_scores_gemma":[0.065091565,0.000100006764,0.000052425603,0.0016189129,0.000067359026,0.000045815326,0.00003057573,0.00006562457,0.0000071178074],"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":[0.000041791187,0.00025016314,0.0008579111,0.000226796,0.0000860613,0.000034820918,0.00017280846,4.1957577e-7,0.0021295603,0.9711881,0.011298499,0.013713048],"study_design_scores_gemma":[0.0009863373,0.00019774075,0.0007770024,0.000025932359,0.00010147717,0.000023789384,0.00015001191,0.00054366625,0.005218922,0.99017286,0.0015722886,0.00022997241],"about_ca_topic_score_codex":0.000008968068,"about_ca_topic_score_gemma":0.0000149164525,"teacher_disagreement_score":0.06476802,"about_ca_system_score_codex":0.000043025633,"about_ca_system_score_gemma":0.00019044332,"threshold_uncertainty_score":0.94278353},"labels":[],"label_agreement":null},{"id":"W3210110199","doi":"10.1111/biom.13776","title":"Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","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":"Observational study; Nonparametric statistics; Econometrics; Parametric statistics; Statistics; Semiparametric model; Semiparametric regression; Mathematics; Computer science","score_opus":0.49491503928947006,"score_gpt":0.49405273154595414,"score_spread":0.0008623077435159221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210110199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9339429,0.0024910886,0.061567083,0.00015181361,0.00017376828,0.001116273,0.000029844621,0.0002703312,0.00025688118],"genre_scores_gemma":[0.7752811,0.00018123945,0.22367875,0.00009743234,0.000009694009,0.0006205888,0.000008580881,0.000026883887,0.00009577777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983234,0.00011508668,0.0003890773,0.00037125463,0.00047073502,0.00033047248],"domain_scores_gemma":[0.99395853,0.005424869,0.00014631909,0.00026811485,0.00010199698,0.00010016684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000666517,0.0002249587,0.00047360637,0.0051299287,0.00016613005,0.000034153567,0.00017914924,0.00004925158,0.0000056036247],"category_scores_gemma":[0.003812195,0.00021288838,0.00004405224,0.016580185,0.00004034791,0.00016297908,0.00036779096,0.00014932526,0.0000028081638],"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":[0.00015367355,0.0029272693,0.054997817,0.000650696,0.00035723083,0.0002497906,0.004265597,0.02300282,0.0009735962,0.68780404,0.0017157466,0.2229017],"study_design_scores_gemma":[0.0015501796,0.0029587904,0.011118283,0.000070737915,0.000084624226,0.000025197307,0.00051574386,0.03484055,0.0015000816,0.94572663,0.00091734255,0.00069181906],"about_ca_topic_score_codex":0.000033845274,"about_ca_topic_score_gemma":0.0000064022574,"teacher_disagreement_score":0.2579226,"about_ca_system_score_codex":0.0009260611,"about_ca_system_score_gemma":0.00004467998,"threshold_uncertainty_score":0.8681336},"labels":[],"label_agreement":null},{"id":"W3214826453","doi":"10.1111/biom.13608","title":"Variable Selection in Regression-Based Estimation of Dynamic Treatment Regimes","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada","keywords":"Estimation; Regression; Statistics; Selection (genetic algorithm); Feature selection; Regression analysis; Econometrics; Variable (mathematics); Computer science; Mathematics; Artificial intelligence; Economics","score_opus":0.0786703818476228,"score_gpt":0.39944019643331174,"score_spread":0.32076981458568893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214826453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036524747,0.00015297373,0.9619572,0.000053384247,0.00008449287,0.000100605306,0.000022530174,0.000024085462,0.0010799599],"genre_scores_gemma":[0.19830039,0.000014978373,0.801346,0.000008076099,0.0000050993217,0.000009298727,0.000009793288,0.0000067031892,0.00029967166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992446,0.00010176568,0.0002469724,0.00014421051,0.0001539162,0.00010854668],"domain_scores_gemma":[0.9978259,0.0017879738,0.00010959823,0.00012279172,0.00012220528,0.000031503067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002592708,0.00007792438,0.00019422793,0.00050728425,0.000022520824,0.0000120263785,0.00003996823,0.00007114124,0.000104808605],"category_scores_gemma":[0.0058344877,0.00006181052,0.000027988744,0.003645383,0.00001904321,0.000024569677,0.000010726506,0.000040542633,0.0000028088843],"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":[0.000047492147,0.0014740784,0.0025439416,0.00043678738,0.000036889774,0.000017417091,0.00011128276,0.00036673856,0.012114461,0.3382669,0.00045463344,0.6441294],"study_design_scores_gemma":[0.00096905814,0.0003625094,0.0070485733,0.00023437176,0.000054891378,0.000004918965,0.000045247063,0.5399693,0.039982054,0.41077977,0.00036923232,0.00018002176],"about_ca_topic_score_codex":0.000044764263,"about_ca_topic_score_gemma":0.000003966635,"teacher_disagreement_score":0.64394933,"about_ca_system_score_codex":0.00016597784,"about_ca_system_score_gemma":0.00018032889,"threshold_uncertainty_score":0.69848484},"labels":[],"label_agreement":null},{"id":"W3216069892","doi":"10.1111/biom.13611","title":"Supervised Two-Dimensional Functional Principal Component Analysis with Time-to-Event Outcomes and Mammogram Imaging Data","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"AI in cancer detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Simon Fraser University","funders":"National Cancer Institute; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Breast Cancer Research Foundation","keywords":"Principal component analysis; Breast cancer; Mammography; Censoring (clinical trials); Computer science; Artificial intelligence; Population; Data set; Medicine; Event (particle physics); Pattern recognition (psychology); Data mining; Machine learning; Statistics; Cancer; Mathematics; Pathology; Internal medicine","score_opus":0.03557363428132682,"score_gpt":0.27811177480463467,"score_spread":0.24253814052330785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216069892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16315198,0.00045843917,0.83300024,0.0024252161,0.0004204194,0.00017927038,0.00007531931,0.00017583664,0.000113272676],"genre_scores_gemma":[0.7658154,0.000008147907,0.232133,0.0009894454,0.00010917882,0.000018184752,0.0003118591,0.000022354592,0.0005924305],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772227,0.00007061693,0.0002525212,0.00082787813,0.00084933377,0.00027737982],"domain_scores_gemma":[0.9981346,0.00022039415,0.00008471104,0.0011021062,0.00025234974,0.00020583764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046956737,0.00017339105,0.00026453807,0.0016486287,0.0001623638,0.00026044346,0.000560251,0.00003167456,0.0001105021],"category_scores_gemma":[0.00009266285,0.00014965437,0.00006680948,0.010906326,0.00004669391,0.00038087356,0.0013482091,0.00010664241,0.00007008226],"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":[0.0001120782,0.0008526732,0.548186,0.000056888253,0.0024498971,0.0003337135,0.00016998984,0.028425694,0.007391555,0.0005748289,0.005397693,0.40604898],"study_design_scores_gemma":[0.00088779774,0.00006873774,0.40755278,0.000009785799,0.00022493087,0.00007804562,0.000010516468,0.58039784,0.0006979465,0.000033208216,0.009690989,0.00034743696],"about_ca_topic_score_codex":0.00005876692,"about_ca_topic_score_gemma":0.00002345131,"teacher_disagreement_score":0.6026634,"about_ca_system_score_codex":0.00014624723,"about_ca_system_score_gemma":0.0001506672,"threshold_uncertainty_score":0.6102728},"labels":[],"label_agreement":null},{"id":"W347239352","doi":"10.1111/j.1541-0420.2008.01005.x","title":"Discussion of \"Simple Defensible Sample Sizes Based on Cost Efficiency\" by Peter Bacchetti, Charles E. McCulloch, and Mark R. Segal","year":2008,"lang":"en","type":"article","venue":"Biometrics","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":"McGill University","funders":"","keywords":"Biostatistics; Epidemiology; Library science; Sample (material); Citation; Gerontology; Sociology; Medicine; Computer science; Pathology; Physics","score_opus":0.06517663664116934,"score_gpt":0.3341665048636557,"score_spread":0.26898986822248633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W347239352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044656277,0.00012991276,0.95295304,0.0001619384,0.00010690462,0.0003148531,0.00071712706,0.000048169826,0.0009117685],"genre_scores_gemma":[0.49894226,0.000071785886,0.5006784,0.00011735031,0.000031109656,0.0000136384815,0.000032250973,0.00002638942,0.000086772794],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99848294,0.00011590116,0.0003570165,0.0003054629,0.0004435905,0.00029507832],"domain_scores_gemma":[0.9947363,0.004552306,0.00015423089,0.00030056984,0.00010680638,0.00014979467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004399424,0.00019094799,0.000362833,0.0005172373,0.00014189487,0.0000284753,0.00014995896,0.000111453024,0.00018839163],"category_scores_gemma":[0.0067441035,0.000121315075,0.000070787784,0.0016685871,0.00021231489,0.000045657827,0.00006356128,0.000113186616,0.000006168454],"study_design_candidate":"design_other","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":[0.0006863409,0.0044234064,0.097767785,0.0018807232,0.00012326933,0.00008248587,0.0007681613,0.000006652129,0.03683465,0.08027116,0.11136466,0.6657907],"study_design_scores_gemma":[0.012900779,0.0073607364,0.2584208,0.0010427624,0.0005875869,0.0001259269,0.000472609,0.06231728,0.19281544,0.37216568,0.08688167,0.004908742],"about_ca_topic_score_codex":0.000029998804,"about_ca_topic_score_gemma":0.0000018196257,"teacher_disagreement_score":0.66088194,"about_ca_system_score_codex":0.00003413905,"about_ca_system_score_gemma":0.000045930152,"threshold_uncertainty_score":0.80738086},"labels":[],"label_agreement":null},{"id":"W4205150711","doi":"10.1111/biom.13625","title":"Ultra-High Dimensional Variable Selection for Doubly Robust Causal Inference","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":21,"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 Toronto","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Causal inference; Covariate; Estimator; Feature selection; Propensity score matching; Computer science; Confounding; Econometrics; Outcome (game theory); Robustness (evolution); Causal model; Inference; Statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.16438772303541557,"score_gpt":0.38276504830221764,"score_spread":0.21837732526680206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205150711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020734578,0.00005263868,0.9763577,0.0000893155,0.00049064244,0.00071143545,0.00023707861,0.0005885129,0.00073812215],"genre_scores_gemma":[0.4981327,0.000005212873,0.49990505,0.00012977261,0.00009766224,0.0004431507,0.00009513865,0.00004471446,0.0011466044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998305,0.000063953514,0.00035547503,0.0003586047,0.0005272406,0.0003897144],"domain_scores_gemma":[0.99753094,0.0015849047,0.00021757999,0.0002638648,0.00031337776,0.000089312685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074272725,0.00019457082,0.00026155057,0.0010400369,0.0004046092,0.00004234606,0.0002746245,0.00010056534,0.00046073162],"category_scores_gemma":[0.0022640065,0.00020074549,0.0000634025,0.004624246,0.000042888303,0.00018607646,0.00013610037,0.00027061318,0.0000067092806],"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":[0.0001374288,0.0007364746,0.0010894994,0.00015075276,0.00008100255,0.0000042529173,0.00009069569,0.005868729,0.04987502,0.8976478,0.03821912,0.0060992306],"study_design_scores_gemma":[0.0013029319,0.0016310317,0.00031745463,0.000025936513,0.00011429107,0.000058154725,0.00005332794,0.008647087,0.041784886,0.9007427,0.044430826,0.00089133554],"about_ca_topic_score_codex":0.000088019915,"about_ca_topic_score_gemma":0.000005875601,"teacher_disagreement_score":0.47739813,"about_ca_system_score_codex":0.00040632614,"about_ca_system_score_gemma":0.00016759345,"threshold_uncertainty_score":0.8186164},"labels":[],"label_agreement":null},{"id":"W4205614259","doi":"10.1111/biom.13623","title":"Generalized Network Structured Models with Mixed Responses Subject to Measurement Error and Misclassification","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; Graphical model; Computer science; Binary number; Gaussian; Binary data; Observational error; Sample (material); Inference; Data mining; Machine learning; Algorithm; Artificial intelligence; Statistics; Mathematics","score_opus":0.055716182139514354,"score_gpt":0.2569420407276194,"score_spread":0.20122585858810504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205614259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94341373,0.0025299063,0.0529715,0.00019926052,0.00025496114,0.0003766515,0.000055241224,0.000016443966,0.00018231617],"genre_scores_gemma":[0.9339591,0.000020652164,0.06507915,0.00021774936,0.00012264427,0.000082039194,0.000060264312,0.000021730282,0.00043665405],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99870336,0.00015216351,0.00015738037,0.00037542215,0.00038134604,0.00023031047],"domain_scores_gemma":[0.99938166,0.000013427925,0.00007085376,0.0003089281,0.00010966224,0.00011545806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039696155,0.00014107126,0.000122998,0.00021017823,0.0002185341,0.000025359057,0.00018126507,0.00005842361,0.000017236065],"category_scores_gemma":[0.00007307016,0.00012642538,0.000029849147,0.0009989004,0.000040874445,0.0000020123289,0.00014444438,0.00006481451,7.8475404e-7],"study_design_candidate":"bench_or_experimental","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":[0.0070503787,0.00042978002,0.013057248,0.00008631629,0.00059567834,0.0000050670137,0.0007397803,0.19747826,0.66739386,0.01875797,0.0505591,0.04384656],"study_design_scores_gemma":[0.0058873715,0.008784782,0.6122451,0.000024847424,0.00027246732,0.00012847333,0.0007963465,0.0019786668,0.04850688,0.007502047,0.31188732,0.0019857055],"about_ca_topic_score_codex":0.000014541392,"about_ca_topic_score_gemma":0.000020552452,"teacher_disagreement_score":0.618887,"about_ca_system_score_codex":0.00003932896,"about_ca_system_score_gemma":0.00010967981,"threshold_uncertainty_score":0.51554775},"labels":[],"label_agreement":null},{"id":"W4214891115","doi":"10.1111/biom.13652","title":"A Time-Heterogeneous D-Vine Copula Model for Unbalanced and Unequally Spaced Longitudinal Data","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","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":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vine copula; Copula (linguistics); Computer science; Homogeneous; Gaussian; Econometrics; Longitudinal data; Statistics; Mathematics; Data mining","score_opus":0.2507653103219556,"score_gpt":0.4159281288095117,"score_spread":0.1651628184875561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214891115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011031339,0.00020197882,0.98501104,0.00015702446,0.00010675759,0.00037292324,0.0029733991,0.000060372302,0.00008516765],"genre_scores_gemma":[0.12408395,0.000019075796,0.87513465,0.00007449196,0.000043722852,0.00005744907,0.00015565762,0.000031834497,0.00039918427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985705,0.00007768495,0.00028879248,0.0004360233,0.00033794,0.00028904754],"domain_scores_gemma":[0.9973485,0.0017171539,0.00013765263,0.000599516,0.00008239054,0.00011478835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008508587,0.0001538174,0.00031942927,0.00039886226,0.00020442904,0.000051676972,0.0004370505,0.000049396618,0.000120855606],"category_scores_gemma":[0.0033613257,0.00014511407,0.000036587986,0.0012943324,0.00006717561,0.00005193112,0.0006426665,0.000109788874,0.000003878417],"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":[0.0010854664,0.002066625,0.0025385213,0.0015817201,0.0005554854,0.0001842571,0.00074741524,0.00053758844,0.007755338,0.45710343,0.07452103,0.45132312],"study_design_scores_gemma":[0.00068996905,0.00030333948,0.00013097916,0.000007945501,0.00008307377,0.000028566468,0.000017294222,0.8244551,0.00006528608,0.17279121,0.0011849017,0.00024232137],"about_ca_topic_score_codex":0.000008715893,"about_ca_topic_score_gemma":0.0000029943046,"teacher_disagreement_score":0.8239175,"about_ca_system_score_codex":0.000057348054,"about_ca_system_score_gemma":0.00006313763,"threshold_uncertainty_score":0.591758},"labels":[],"label_agreement":null},{"id":"W4220713636","doi":"10.1111/biom.13657","title":"Zero-Inflated Poisson Models with Measurement Error in the Response","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Count data; Poisson distribution; Observational error; Computer science; Identifiability; Estimator; Inference; Statistics; Errors-in-variables models; Bayesian probability; Algorithm; Zero (linguistics); Zero-inflated model; Data mining; Mathematics; Poisson regression; Artificial intelligence; Population","score_opus":0.25211076889629314,"score_gpt":0.37749203842077766,"score_spread":0.12538126952448453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220713636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05986197,0.0001309083,0.9373392,0.00092893257,0.00009445401,0.000450281,0.000057198715,0.00004862067,0.0010884495],"genre_scores_gemma":[0.740761,0.0000028651548,0.25891697,0.00017146087,0.000008682603,0.00007916302,0.000001651477,0.000015226084,0.000042969932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99726737,0.0008868334,0.00026539364,0.00020594348,0.0011244059,0.0002500294],"domain_scores_gemma":[0.99731827,0.002069416,0.00010122718,0.00034967938,0.00011257319,0.0000488392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053838436,0.00012038013,0.00018201116,0.0007481435,0.00015346693,0.00004177623,0.00034479197,0.00003556375,0.00009093848],"category_scores_gemma":[0.003180118,0.00007614086,0.00003186689,0.005235205,0.000040256407,0.00004300251,0.00008341854,0.00023960305,0.0000041953986],"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":[0.0029129484,0.0014757427,0.0010556657,0.00013178312,0.00008875851,0.00035138812,0.004437689,0.00014680214,0.0031391485,0.91649663,0.009020093,0.060743373],"study_design_scores_gemma":[0.0013039573,0.0011983197,0.008228163,0.00003650061,0.00006203299,0.00004616834,0.0009799671,0.010454437,0.00027709827,0.9723372,0.0046953205,0.00038086585],"about_ca_topic_score_codex":0.00003839092,"about_ca_topic_score_gemma":0.0000051800375,"teacher_disagreement_score":0.680899,"about_ca_system_score_codex":0.00021433005,"about_ca_system_score_gemma":0.0000966907,"threshold_uncertainty_score":0.3807128},"labels":[],"label_agreement":null},{"id":"W4226487945","doi":"10.1111/biom.13692","title":"Power Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","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":"Ottawa Hospital; University of Ottawa","funders":"National Institute on Aging; Patient-Centered Outcomes Research Institute","keywords":"Sample size determination; Estimator; Intraclass correlation; Statistics; Cluster randomised controlled trial; Statistical power; Cluster (spacecraft); Computer science; Mathematics; Econometrics; Data mining; Randomized controlled trial; Medicine; Psychometrics","score_opus":0.18790196610096394,"score_gpt":0.42572147913694564,"score_spread":0.2378195130359817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226487945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019901518,0.00023207275,0.9742696,0.00016974326,0.00015067866,0.002807312,0.0002975916,0.00034315966,0.0018282937],"genre_scores_gemma":[0.6042337,0.0000394153,0.39182284,0.00046334596,0.000050360355,0.0015207779,0.00012279623,0.00007239709,0.001674384],"study_design_codex":"randomized_trial","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99704874,0.000765967,0.0009069537,0.00035264535,0.000600714,0.00032496135],"domain_scores_gemma":[0.98081124,0.01752872,0.0008017423,0.0005041121,0.0002711504,0.00008306488],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008947801,0.00023035244,0.0020083697,0.0029173465,0.00017005793,0.000053479507,0.0003225098,0.00007860744,0.00072635984],"category_scores_gemma":[0.016746426,0.0001692466,0.000685419,0.0068463013,0.00010414034,0.000099680474,0.00018844452,0.00016724711,0.0000032615824],"study_design_candidate":"randomized_trial","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":[0.45187917,0.005764812,0.00463519,0.0006956744,0.04048727,0.0001570756,0.0026932685,0.00040126665,0.007060844,0.28164917,0.1724043,0.03217196],"study_design_scores_gemma":[0.41153142,0.0035941703,0.00023851682,0.00004682264,0.016981604,0.00005686699,0.0013888814,0.0030255087,0.014611384,0.4903996,0.055568773,0.0025564583],"about_ca_topic_score_codex":0.0000090942,"about_ca_topic_score_gemma":0.000002417891,"teacher_disagreement_score":0.58433217,"about_ca_system_score_codex":0.00023778886,"about_ca_system_score_gemma":0.00006533556,"threshold_uncertainty_score":0.99153596},"labels":[],"label_agreement":null},{"id":"W4229035405","doi":"10.1111/biom.13687","title":"Coherent Modeling of Longitudinal Causal Effects on Binary Outcomes","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","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":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Multiplicative function; Binary number; Variation (astronomy); Computer science; Mathematics; Econometrics","score_opus":0.2122266913199617,"score_gpt":0.4176687021793165,"score_spread":0.20544201085935482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229035405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9043606,0.00019375401,0.0930683,0.00010430671,0.0003955234,0.0005464248,0.00006672831,0.00036865333,0.00089574023],"genre_scores_gemma":[0.97753096,0.000014793502,0.02208141,0.00006526238,0.00002475454,0.00009858258,0.000010142292,0.000034231813,0.00013984073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99839777,0.00008730412,0.00035478172,0.0002482814,0.0006623257,0.00024956837],"domain_scores_gemma":[0.99806494,0.0011804717,0.00019294221,0.0004103386,0.00008861541,0.00006270534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045494933,0.00018181486,0.00036893148,0.0013935786,0.00011764952,0.000010054519,0.00029543127,0.000056292152,0.00008192689],"category_scores_gemma":[0.0010624368,0.00016465364,0.000108520915,0.0024492494,0.000028725666,0.00006278684,0.00031680267,0.00024539354,0.000006105295],"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":[0.00075668056,0.009789383,0.1422328,0.0036128676,0.0010634969,0.0007466433,0.0017169315,0.017625112,0.057456147,0.6530367,0.032812297,0.07915093],"study_design_scores_gemma":[0.00456454,0.015105711,0.014722013,0.00034317447,0.0005382439,0.000074030584,0.00085475453,0.08444582,0.101813555,0.77013713,0.004278315,0.0031227148],"about_ca_topic_score_codex":0.000019195983,"about_ca_topic_score_gemma":0.0000010597182,"teacher_disagreement_score":0.12751079,"about_ca_system_score_codex":0.00023949458,"about_ca_system_score_gemma":0.000035034234,"threshold_uncertainty_score":0.6714381},"labels":[],"label_agreement":null},{"id":"W4233521617","doi":"10.1111/j.1541-0420.2008.01004_2.x","title":"Discussions","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Biostatistics; Epidemiology; Library science; Public health; Citation; Medicine; Gerontology; Sociology; Computer science; Pathology","score_opus":0.5836070243354328,"score_gpt":0.43811985529992914,"score_spread":0.14548716903550363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233521617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92235595,0.0005656756,0.024494788,0.00468814,0.0018047999,0.00027778404,0.00002213844,0.00009202185,0.045698714],"genre_scores_gemma":[0.9911416,0.00007718634,0.0021305697,0.00020483488,0.00007800186,0.0000043642385,0.0000012649832,0.0000034064192,0.0063587544],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996762,0.00009652505,0.0003958584,0.00029143374,0.0022699179,0.00018431462],"domain_scores_gemma":[0.9983129,0.00067108334,0.00009032523,0.0005622668,0.00023665873,0.00012678117],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024720258,0.000074203555,0.00013462569,0.0010916196,0.00027100657,0.000063950916,0.000600274,0.000046733676,0.0008673998],"category_scores_gemma":[0.007998812,0.00004045896,0.00010413724,0.008111205,0.000108802546,0.00013603909,0.00011331339,0.00006788965,0.0019308558],"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":[0.0000148325125,0.0006293344,0.32344332,0.00000397742,0.000017130065,0.00003221468,0.0005076907,0.00005109456,0.0035283247,0.0031923922,0.43676358,0.23181611],"study_design_scores_gemma":[0.00019877292,0.00007419814,0.30577105,0.000003071902,0.0000034517066,0.00000806462,0.00020247351,0.0001408326,0.0007253501,0.0076720016,0.6850672,0.00013356359],"about_ca_topic_score_codex":0.000008739492,"about_ca_topic_score_gemma":0.0000014492093,"teacher_disagreement_score":0.2483036,"about_ca_system_score_codex":0.000038198024,"about_ca_system_score_gemma":0.000034805595,"threshold_uncertainty_score":0.99884623},"labels":[],"label_agreement":null},{"id":"W4235266065","doi":"10.4018/978-1-5225-0983-7.ch066","title":"Veillance","year":2016,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":1,"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":"Hypocrisy; Computer security; Political science; Computer science; Internet privacy; Law","score_opus":0.0560344253582679,"score_gpt":0.3067235366788441,"score_spread":0.2506891113205762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235266065","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000060381844,0.00392373,0.0007901641,0.0010190997,0.0018492782,0.000241796,0.0002468558,0.00015878293,0.99176425],"genre_scores_gemma":[0.0040663993,0.016466346,0.00038883233,0.00019610355,0.0032782175,0.000008561805,0.000042113035,0.0000438982,0.9755095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998599,0.000026624146,0.00018297821,0.00031425705,0.0006078662,0.00026927152],"domain_scores_gemma":[0.9989297,0.00015202041,0.00018462018,0.000448539,0.00014622693,0.000138929],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056899857,0.00015806165,0.00019880799,0.0010754694,0.0003187751,0.00008696709,0.0006508282,0.0005113226,0.0008227535],"category_scores_gemma":[0.0012922589,0.00013832333,0.000096370226,0.00050521066,0.00024951267,0.00013673547,0.0002303915,0.00016573742,0.0016801414],"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":[0.000007281672,0.00001251645,0.000040605802,0.000025323123,0.000024945713,0.000009237377,0.00021348782,2.3026283e-9,0.000017668594,0.72535706,0.046052825,0.22823903],"study_design_scores_gemma":[0.00008296011,0.000024531419,0.000035045057,0.00003046097,0.000008725995,5.56325e-7,0.000009587683,1.4940223e-7,0.000007353041,0.08761655,0.91198665,0.00019745402],"about_ca_topic_score_codex":0.00015554101,"about_ca_topic_score_gemma":0.00010538157,"teacher_disagreement_score":0.8659338,"about_ca_system_score_codex":0.000268418,"about_ca_system_score_gemma":0.00018032223,"threshold_uncertainty_score":0.99909717},"labels":[],"label_agreement":null},{"id":"W4238323837","doi":"10.1111/j.0006-341x.2004.238_2.x","title":"BOOK REVIEWS: 2","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Citation; Library science; Population; Medicine; Family medicine; Computer science; Environmental health","score_opus":0.018636784794667408,"score_gpt":0.26241413073813835,"score_spread":0.24377734594347095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238323837","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14446746,0.57599396,0.2520304,0.0008374255,0.0017109653,0.0009283156,0.00004817389,0.00007490146,0.023908405],"genre_scores_gemma":[0.56753784,0.024230005,0.35536715,0.018725006,0.0027387005,0.00011326516,0.00033577852,0.00012355395,0.030828705],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993696,0.000015742546,0.00014742286,0.00020870306,0.00009245943,0.00016606128],"domain_scores_gemma":[0.9995608,0.0000044650683,0.00004634042,0.0002786212,0.000032686756,0.00007711043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010823918,0.00009900545,0.00009569456,0.00012937086,0.00004278458,0.000011833061,0.0001712072,0.00009982336,0.00006232669],"category_scores_gemma":[0.0001409354,0.00008813541,0.00006690494,0.0005473237,0.0000490701,0.0000014084434,0.000056255718,0.000041493957,0.00015110972],"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":[0.00008281202,0.00071703736,0.002016058,0.000160808,0.00013544505,0.0000038516855,0.00017102188,0.00057946047,0.22104375,0.022215566,0.62867695,0.124197245],"study_design_scores_gemma":[0.000334489,0.00026727354,0.007899623,0.0000068770864,0.0000088172455,0.00000754399,0.0000048530524,1.3858741e-7,0.016784422,0.00068713556,0.9738655,0.00013331781],"about_ca_topic_score_codex":0.0000036127483,"about_ca_topic_score_gemma":6.8901676e-7,"teacher_disagreement_score":0.55176395,"about_ca_system_score_codex":0.000011413582,"about_ca_system_score_gemma":0.00004394477,"threshold_uncertainty_score":0.3594058},"labels":[],"label_agreement":null},{"id":"W4239567217","doi":"10.1111/biom.12794","title":"Issue Information - Masthead","year":2018,"lang":"en","type":"paratext","venue":"Biometrics","topic":"Corporate Taxation and Avoidance","field":"Business, Management and Accounting","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":"Citation; Computer science; World Wide Web; Information retrieval; Library science","score_opus":0.02616060814303882,"score_gpt":0.24452599739878209,"score_spread":0.21836538925574328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239567217","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007154753,0.0005942041,0.0038170144,0.00079097156,0.016326817,0.0003990291,0.00008851335,0.00016281666,0.97710514],"genre_scores_gemma":[0.017916055,0.0006057136,0.0010167004,0.017005239,0.034143563,0.00006951382,0.0083903875,0.00013353916,0.92071927],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987028,0.0000050167628,0.0003988654,0.00020434061,0.00043195544,0.00025701776],"domain_scores_gemma":[0.9981355,0.000023666913,0.0009633611,0.00031492757,0.0005480496,0.000014490278],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025943833,0.0002576939,0.0002526113,0.0039019806,0.00014893264,0.00083050295,0.000378186,0.00027083437,0.016693637],"category_scores_gemma":[0.0002649031,0.00024214014,0.00008456729,0.006824881,0.00005544733,0.001674276,0.00018460039,0.00016270483,0.46830627],"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":[0.000007192697,0.000018706545,0.000052129148,0.00028716083,0.000009201774,4.6147073e-7,0.0000074522245,0.0000024940841,0.000009363695,0.00046276994,0.96746284,0.031680256],"study_design_scores_gemma":[0.00020473811,0.000008533441,0.00033179898,0.000048476923,0.000019032741,4.3980754e-7,0.000027006789,0.00031665893,0.000021436643,0.000084816194,0.99862444,0.0003126031],"about_ca_topic_score_codex":0.00017098681,"about_ca_topic_score_gemma":0.0000060618318,"teacher_disagreement_score":0.45161262,"about_ca_system_score_codex":0.00006023625,"about_ca_system_score_gemma":0.000048402202,"threshold_uncertainty_score":0.98741883},"labels":[],"label_agreement":null},{"id":"W4240317170","doi":"10.1111/biom.12806","title":"Issue Information - Masthead","year":2018,"lang":"en","type":"paratext","venue":"Biometrics","topic":"Human auditory perception and evaluation","field":"Engineering","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":"Citation; Computer science; Information retrieval; World Wide Web; Library science","score_opus":0.032115206464297254,"score_gpt":0.2849195679720704,"score_spread":0.25280436150777313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240317170","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011190238,0.0010013559,0.02854209,0.000046812373,0.056523357,0.0004183091,0.00036390775,0.0005179269,0.9114672],"genre_scores_gemma":[0.0048942096,0.004420311,0.0025377641,0.0006229447,0.020301446,0.000097535645,0.009292704,0.00020298458,0.9576301],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900055,0.000020253156,0.00031271172,0.00009992375,0.00037352095,0.00019301541],"domain_scores_gemma":[0.99944,0.000019780013,0.00007362985,0.00024422913,0.00014847136,0.0000738962],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022180149,0.00019780586,0.00018211002,0.0023661845,0.000057444686,0.00012819417,0.0001733028,0.0004000176,0.2297926],"category_scores_gemma":[0.000053514814,0.00020719247,0.00006155017,0.001723433,0.00003127449,0.0002997203,0.000022587144,0.00017182705,0.8419546],"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":[0.0000010445609,0.0000041480407,6.644789e-7,0.00014987799,0.000014166579,6.574648e-8,0.00014632718,0.0003586135,0.000055870587,7.3368597e-7,0.97215086,0.0271176],"study_design_scores_gemma":[0.000121847486,0.000032407163,0.00016577255,0.000025506848,0.000012427349,9.155393e-7,0.000025048323,0.0040735635,0.000073181094,0.0000038188036,0.9952305,0.0002350315],"about_ca_topic_score_codex":8.2108096e-7,"about_ca_topic_score_gemma":7.7851945e-7,"teacher_disagreement_score":0.612162,"about_ca_system_score_codex":0.00024219556,"about_ca_system_score_gemma":0.000029267832,"threshold_uncertainty_score":0.8449064},"labels":[],"label_agreement":null},{"id":"W4240886303","doi":"10.1111/biom.12802","title":"Issue Information - Masthead","year":2018,"lang":"en","type":"paratext","venue":"Biometrics","topic":"Corporate Taxation and Avoidance","field":"Business, Management and Accounting","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":"Citation; Computer science; World Wide Web; Information retrieval; Data science","score_opus":0.02616060814303882,"score_gpt":0.24452599739878209,"score_spread":0.21836538925574328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240886303","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007154753,0.0005942041,0.0038170144,0.00079097156,0.016326817,0.0003990291,0.00008851335,0.00016281666,0.97710514],"genre_scores_gemma":[0.017916055,0.0006057136,0.0010167004,0.017005239,0.034143563,0.00006951382,0.0083903875,0.00013353916,0.92071927],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987028,0.0000050167628,0.0003988654,0.00020434061,0.00043195544,0.00025701776],"domain_scores_gemma":[0.9981355,0.000023666913,0.0009633611,0.00031492757,0.0005480496,0.000014490278],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025943833,0.0002576939,0.0002526113,0.0039019806,0.00014893264,0.00083050295,0.000378186,0.00027083437,0.016693637],"category_scores_gemma":[0.0002649031,0.00024214014,0.00008456729,0.006824881,0.00005544733,0.001674276,0.00018460039,0.00016270483,0.46830627],"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":[0.000007192697,0.000018706545,0.000052129148,0.00028716083,0.000009201774,4.6147073e-7,0.0000074522245,0.0000024940841,0.000009363695,0.00046276994,0.96746284,0.031680256],"study_design_scores_gemma":[0.00020473811,0.000008533441,0.00033179898,0.000048476923,0.000019032741,4.3980754e-7,0.000027006789,0.00031665893,0.000021436643,0.000084816194,0.99862444,0.0003126031],"about_ca_topic_score_codex":0.00017098681,"about_ca_topic_score_gemma":0.0000060618318,"teacher_disagreement_score":0.45161262,"about_ca_system_score_codex":0.00006023625,"about_ca_system_score_gemma":0.000048402202,"threshold_uncertainty_score":0.98741883},"labels":[],"label_agreement":null},{"id":"W4246331866","doi":"10.1111/j.1541-0420.2007.00741_1.x","title":"PRESIDENTIAL ADDRESS","year":2007,"lang":"fr","type":"article","venue":"Biometrics","topic":"Canadian Identity and History","field":"Social Sciences","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":"Humanities; Presidential address; Philosophy; Political science; Art","score_opus":0.032701077800657424,"score_gpt":0.2889254195598466,"score_spread":0.2562243417591892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246331866","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046914544,0.18842782,0.0026028992,0.0053907507,0.04680971,0.00040385113,0.00024038064,0.00013716616,0.7090729],"genre_scores_gemma":[0.181386,0.0060868515,0.00051003654,0.00044831407,0.0049145943,0.000002156591,0.000010514348,0.000028278215,0.80661327],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827665,0.000068251786,0.0002443301,0.00021826528,0.0006171267,0.0005753565],"domain_scores_gemma":[0.9989746,0.0001985283,0.00010606355,0.00017137497,0.00012139622,0.00042800544],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012806642,0.000089406254,0.00013253593,0.0041024447,0.0008037719,0.0004488812,0.00032699085,0.00028938218,0.0045260885],"category_scores_gemma":[0.0004991048,0.0001424524,0.00012346938,0.009455047,0.0012406721,0.00028590517,0.000049691716,0.00015684769,0.0020284595],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010887393,0.00015784807,0.000692152,0.00007028648,0.000038291488,0.00016372965,0.0040354934,7.9626363e-7,0.0000764539,0.08408049,0.49989802,0.41077554],"study_design_scores_gemma":[0.00013738584,0.000029336516,0.012012166,0.000015560716,0.000040456966,0.0000028615873,0.0004740347,0.0000020257826,0.000026134814,0.00029697345,0.98679155,0.00017150777],"about_ca_topic_score_codex":0.5951638,"about_ca_topic_score_gemma":0.75236565,"teacher_disagreement_score":0.48689353,"about_ca_system_score_codex":0.0015926189,"about_ca_system_score_gemma":0.0006054875,"threshold_uncertainty_score":0.9987486},"labels":[],"label_agreement":null},{"id":"W4281492300","doi":"10.1111/biom.13702","title":"Semiparametric Distributed Lag Quantile Regression for Modeling Time-Dependent Exposure Mixtures","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":9,"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":"National Institute of Environmental Health Sciences; National Institutes of Health; York University","keywords":"Quantile regression; Lag; Distributed lag; Econometrics; Quantile; Semiparametric model; Statistics; Semiparametric regression; Regression; Regression analysis; Time lag; Computer science; Mathematics; Nonparametric statistics","score_opus":0.06087264841277221,"score_gpt":0.3202755744316261,"score_spread":0.25940292601885384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281492300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8509543,0.0023045787,0.13943845,0.002334939,0.0008748602,0.0013948166,0.0016997781,0.000273457,0.0007248088],"genre_scores_gemma":[0.9929214,0.000056125686,0.00452997,0.0009126578,0.00006578969,0.00008927937,0.0002898782,0.000026745869,0.0011081387],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978322,0.00013117505,0.00035184203,0.00038091897,0.00081211067,0.000491786],"domain_scores_gemma":[0.99893826,0.00033477583,0.00016714004,0.0003079708,0.00001863735,0.00023320505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013159998,0.00015710288,0.00021536453,0.00054483936,0.00071895315,0.000041294552,0.00037139058,0.000101528545,0.001294732],"category_scores_gemma":[0.0007224196,0.00014144786,0.00008929873,0.0049651586,0.000039063714,0.00013186794,0.00040424187,0.00021339998,0.00014588718],"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":[0.00083276903,0.0023015486,0.028961949,0.00034376353,0.00007456908,0.000041067116,0.0016780128,0.31331697,0.012840671,0.00030901938,0.49576223,0.14353743],"study_design_scores_gemma":[0.0018728677,0.0014566747,0.0040558893,0.000020397294,0.00005626842,0.000025104016,0.00046373755,0.74743706,0.0021289806,0.0013100634,0.2403902,0.0007827341],"about_ca_topic_score_codex":0.00018911176,"about_ca_topic_score_gemma":0.0000038362996,"teacher_disagreement_score":0.43412012,"about_ca_system_score_codex":0.0005334061,"about_ca_system_score_gemma":0.00003554731,"threshold_uncertainty_score":0.99961823},"labels":[],"label_agreement":null},{"id":"W4281956449","doi":"10.1111/biom.13683","title":"Reader reaction to “Outcome‐adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017)","year":2022,"lang":"en","type":"letter","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Causal inference; Propensity score matching; Estimator; Statistics; Confounding; Collinearity; Lasso (programming language); Econometrics; Outcome (game theory); Computer science; Inference; Mathematics; Artificial intelligence","score_opus":0.2340422227013879,"score_gpt":0.4155220545397822,"score_spread":0.1814798318383943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281956449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004377463,0.00020204175,0.95864594,0.029729312,0.0012219037,0.0037741505,0.0020685154,0.0013407818,0.0025795987],"genre_scores_gemma":[0.003675528,0.00029676475,0.81851554,0.13191687,0.0031292874,0.004744676,0.0041010524,0.0007518912,0.03286843],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966851,0.00013236499,0.000759321,0.0009196414,0.00077497045,0.0007286287],"domain_scores_gemma":[0.9952251,0.00302179,0.000579402,0.000544646,0.00048630717,0.00014276562],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008516604,0.0005972543,0.0008544382,0.0026707267,0.00028504568,0.0001338602,0.00037046205,0.0011407326,0.00015057261],"category_scores_gemma":[0.0042214566,0.0006128406,0.00011468798,0.004200074,0.000067343266,0.00028855682,0.000254422,0.0015145697,0.000010977039],"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":[0.0000530338,0.000074844684,0.00018106849,0.00022351513,0.00011190482,0.000008127246,0.000055128898,7.278375e-7,0.0026060857,0.0042147874,0.9904167,0.0020540853],"study_design_scores_gemma":[0.00029619387,0.0011895008,0.000030300847,0.000048560738,0.00024725558,0.000028390023,0.00005753447,0.0001517626,0.00096881186,0.046121538,0.9500311,0.00082902604],"about_ca_topic_score_codex":0.00028237325,"about_ca_topic_score_gemma":0.0000255199,"teacher_disagreement_score":0.14013045,"about_ca_system_score_codex":0.0011294218,"about_ca_system_score_gemma":0.00014970297,"threshold_uncertainty_score":0.9996323},"labels":[],"label_agreement":null},{"id":"W4304890846","doi":"10.1111/biom.13770","title":"Spatial Dependence Modeling of Latent Susceptibility and Time to Joint Damage in Psoriatic Arthritis","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Spondyloarthritis Studies and Treatments","field":"Medicine","cited_by":0,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psoriatic arthritis; Psoriasis; Latent variable; Joint (building); Arthritis; Latent class model; Medicine; Econometrics; Dermatology; Statistics; Mathematics; Immunology; Engineering; Structural engineering","score_opus":0.03263825552620557,"score_gpt":0.26161379936008183,"score_spread":0.22897554383387625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304890846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996865,0.0019889942,0.0001978813,0.00016873651,0.000093571296,0.00039439436,0.00006883051,0.000013166549,0.00020945637],"genre_scores_gemma":[0.9986946,0.0002448979,0.00084765995,0.00005270955,0.00001910022,0.00002875677,0.00001642702,0.000009185856,0.000086684086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989327,0.00003358024,0.0002807579,0.00023881413,0.00033836937,0.00017578264],"domain_scores_gemma":[0.99959517,0.00003959299,0.000046561476,0.00017626307,0.000055583667,0.00008681772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003069235,0.00008907596,0.00029465219,0.00074016216,0.00006919803,0.0000061767896,0.000041854375,0.000025454381,0.0003399007],"category_scores_gemma":[0.00026223954,0.00008880249,0.000041348107,0.0016720606,0.000021857044,0.000024137345,0.00022248326,0.00008734441,0.000015515283],"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":[0.0007825218,0.002683261,0.52330935,0.00025355694,0.00020024682,0.0008162457,0.001616869,0.0015173721,0.014088041,0.00008474514,0.00044863866,0.45419914],"study_design_scores_gemma":[0.012421382,0.005846127,0.9053557,0.00024431653,0.00012665463,0.00011813525,0.00059636997,0.07227469,0.0014687366,0.00031379727,0.00076354307,0.00047056793],"about_ca_topic_score_codex":0.0009734703,"about_ca_topic_score_gemma":0.00012843564,"teacher_disagreement_score":0.45372856,"about_ca_system_score_codex":0.0001452251,"about_ca_system_score_gemma":0.00003631011,"threshold_uncertainty_score":0.3721674},"labels":[],"label_agreement":null},{"id":"W4307967006","doi":"10.1111/biom.13789","title":"Latent Multinomial Models for Extended Batch-Mark Data","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial distribution; Data set; Computer science; Mark and recapture; Set (abstract data type); Latent variable; Synthetic data; Statistics; Transformation (genetics); Econometrics; Artificial intelligence; Mathematics; Population; Biology","score_opus":0.3146186061967376,"score_gpt":0.3923351724051062,"score_spread":0.07771656620836859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307967006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15509392,0.00045668462,0.83122,0.0011829982,0.0027300175,0.0021896148,0.0049984287,0.00036324761,0.0017651261],"genre_scores_gemma":[0.87071073,0.000010790386,0.12659785,0.00008299753,0.00014837178,0.00006880058,0.0013351691,0.000031279025,0.0010139982],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892265,0.000038501617,0.00029047497,0.0002470779,0.00032863778,0.0001726872],"domain_scores_gemma":[0.9986761,0.00049001345,0.00015829843,0.00054593646,0.00007906861,0.000050539442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076593016,0.00009099971,0.00014043022,0.0005216078,0.00024197203,0.000027805761,0.00034747188,0.000040371127,0.000116197654],"category_scores_gemma":[0.00074454624,0.00009211787,0.000050897976,0.0012847213,0.000010846205,0.00013617997,0.00033623376,0.00007564654,0.00000531841],"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":[0.0003917485,0.0021134887,0.0036527351,0.0005130096,0.00019301787,0.000010864288,0.0011512046,0.012932222,0.0009461679,0.27467296,0.39680675,0.30661586],"study_design_scores_gemma":[0.00072356354,0.00007412283,0.0013143445,0.000002834024,0.000046650322,0.0000049055056,0.000036241945,0.885708,0.00003654559,0.056987744,0.05488508,0.00017999105],"about_ca_topic_score_codex":0.000028703062,"about_ca_topic_score_gemma":0.0000037438053,"teacher_disagreement_score":0.87277573,"about_ca_system_score_codex":0.00010741543,"about_ca_system_score_gemma":0.000036416986,"threshold_uncertainty_score":0.3756458},"labels":[],"label_agreement":null},{"id":"W4309098329","doi":"10.1111/biom.13792","title":"Instrumental Variable Estimation of the Causal Hazard Ratio","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":21,"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 Toronto","funders":"Division of Mathematical Sciences; London School of Hygiene and Tropical Medicine; National Institutes of Health; University of Toronto Scarborough; Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Instrumental variable; Estimation; Statistics; Variable (mathematics); Hazard ratio; Econometrics; Mathematics; Computer science; Economics; Confidence interval","score_opus":0.08625165975155138,"score_gpt":0.35299662854257935,"score_spread":0.26674496879102794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309098329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17998871,0.0000365208,0.814148,0.00013044881,0.00085559813,0.0002780955,0.00026059372,0.000031105294,0.004270924],"genre_scores_gemma":[0.64155656,0.0000010480458,0.3581592,0.000043638145,0.000012899857,0.000015795069,0.000004057939,0.0000063083376,0.00020047826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990963,0.000122029225,0.00021874366,0.0000926054,0.00037032305,0.00010005021],"domain_scores_gemma":[0.9988412,0.0007678876,0.00013006432,0.0001956848,0.00004004594,0.000025101073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005329435,0.00005662222,0.00011163721,0.00019716639,0.0001459414,0.0000142630115,0.00018324271,0.000022070502,0.0005573237],"category_scores_gemma":[0.0032177905,0.00004188868,0.00003028008,0.002536026,0.000043930937,0.000027728498,0.0001921385,0.00009646628,0.0000027753981],"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":[0.000008515084,0.0001684023,0.0012912548,0.000053948905,0.000017301634,6.109436e-7,0.00011299783,0.000100835474,0.0022197806,0.9568754,0.0030692408,0.03608173],"study_design_scores_gemma":[0.00078887056,0.00045017427,0.011754048,0.000021619338,0.00009589979,0.00002137877,0.0003067367,0.09688168,0.012718532,0.8706155,0.00606185,0.00028375097],"about_ca_topic_score_codex":0.000022762142,"about_ca_topic_score_gemma":3.7080096e-7,"teacher_disagreement_score":0.46156782,"about_ca_system_score_codex":0.00007580945,"about_ca_system_score_gemma":0.00006539555,"threshold_uncertainty_score":0.6102304},"labels":[],"label_agreement":null},{"id":"W4310461430","doi":"10.1111/biom.13793","title":"Rejoinder to Discussions on “Instrumental Variable Estimation of the Causal Hazard Ratio”","year":2022,"lang":"en","type":"letter","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","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 Toronto","funders":"","keywords":"Instrumental variable; Estimation; Econometrics; Statistics; Hazard ratio; Hazard; Psychology; Mathematics; Economics; Confidence interval; Biology","score_opus":0.12956008541337877,"score_gpt":0.3820493137410032,"score_spread":0.25248922832762444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310461430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033577324,0.000063198706,0.5561031,0.41188088,0.004686159,0.0060368655,0.0032703322,0.0010708462,0.013530862],"genre_scores_gemma":[0.034219522,0.000039252856,0.5993813,0.33547026,0.0017408123,0.0013175289,0.0013937814,0.00055055955,0.025886979],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973062,0.00017910886,0.00058436365,0.00040651398,0.0011754391,0.00034836],"domain_scores_gemma":[0.99734384,0.0009341166,0.0005125884,0.0010506379,0.00010164559,0.000057176283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042907213,0.00033524958,0.00044699587,0.001495658,0.00022030332,0.0000482322,0.0006996716,0.00042798877,0.0006735159],"category_scores_gemma":[0.0027035417,0.00022451772,0.00014993518,0.004741797,0.00007921906,0.00010164011,0.00052467815,0.001184539,0.000020611462],"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":[0.000009709722,0.00009630925,0.000016481952,0.00013553166,0.000037844788,0.000007938227,0.000075044896,0.00009810929,0.00044557315,0.01611597,0.9810992,0.0018622819],"study_design_scores_gemma":[0.0003827542,0.0009847946,0.00012158856,0.00044046616,0.00023917675,0.00002648297,0.000103078055,0.0006959122,0.012155097,0.16798829,0.8158862,0.00097614655],"about_ca_topic_score_codex":0.000021060865,"about_ca_topic_score_gemma":0.0000018854344,"teacher_disagreement_score":0.16521299,"about_ca_system_score_codex":0.00063228613,"about_ca_system_score_gemma":0.00015034316,"threshold_uncertainty_score":0.91555667},"labels":[],"label_agreement":null},{"id":"W4311284239","doi":"10.1111/biom.13813","title":"Bayesian Sample Size Calculations for Comparing Two Strategies in SMART Studies","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Statistics Canada","funders":"","keywords":"Sample size determination; Bayesian probability; Computer science; Sample (material); Statistics; Econometrics; Mathematics; Chemistry","score_opus":0.3304240056312043,"score_gpt":0.533945866941269,"score_spread":0.20352186131006472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311284239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9477616,0.007439843,0.020504398,0.0023384024,0.004078088,0.0031558566,0.0006237974,0.00017997883,0.01391806],"genre_scores_gemma":[0.99033856,0.000021982594,0.008035233,0.00014292901,0.000074638156,0.0005429775,0.000034726705,0.000016140752,0.0007928258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985188,0.00018272862,0.00031011706,0.00025232972,0.00030039178,0.00043560806],"domain_scores_gemma":[0.9965999,0.0029502802,0.00006872991,0.00023659381,0.00006099879,0.0000834827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009209209,0.00009058045,0.00021001046,0.001116881,0.00033774532,0.000029371813,0.00020638086,0.000028619614,0.00050252],"category_scores_gemma":[0.0009947857,0.000097763215,0.000044672594,0.003747517,0.00005527638,0.000056772187,0.00018360496,0.00019307813,0.00001237911],"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":[0.0003994391,0.0017361392,0.719651,0.00066147145,0.00030133338,0.000054250242,0.01036561,0.0013922047,0.00025010674,0.18859573,0.024469677,0.052123062],"study_design_scores_gemma":[0.006923456,0.0013764899,0.59935087,0.00002633676,0.000033854038,0.000017182314,0.056715928,0.008617233,0.000040173185,0.03477864,0.29147613,0.000643701],"about_ca_topic_score_codex":0.001543478,"about_ca_topic_score_gemma":0.00067640026,"teacher_disagreement_score":0.26700643,"about_ca_system_score_codex":0.00043028287,"about_ca_system_score_gemma":0.000088120825,"threshold_uncertainty_score":0.5502242},"labels":[],"label_agreement":null},{"id":"W4319294609","doi":"10.1111/biom.13836","title":"Spatial Modeling of <i>Mycobacterium Tuberculosis</i> Transmission with Dyadic Genetic Relatedness Data","year":2023,"lang":"en","type":"article","venue":"Biometrics","topic":"Mycobacterium research and diagnosis","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Diseases; United States Agency for International Development","keywords":"Transmission (telecommunications); Correlation; Bayesian probability; Random effects model; Spatial correlation; Computer science; Mycobacterium tuberculosis; Statistics; Econometrics; Biology; Data mining; Computational biology; Tuberculosis; Artificial intelligence; Mathematics; Medicine; Pathology","score_opus":0.06383473750126213,"score_gpt":0.31037638486079355,"score_spread":0.24654164735953144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319294609","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821072,0.0012129729,0.014581412,0.0006427007,0.00016578096,0.00044285678,0.00024473283,0.00015555452,0.0004467776],"genre_scores_gemma":[0.99187326,0.00418677,0.0026698054,0.000058914895,0.00013180937,0.000015315902,0.0009058933,0.000045171913,0.00011303104],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787974,0.00004880829,0.00037426356,0.00045028157,0.0008126583,0.0004342425],"domain_scores_gemma":[0.99825984,0.00017236563,0.00007023607,0.00089886494,0.0002288715,0.0003698334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041092857,0.00017289522,0.00035878923,0.0017817388,0.00006138082,0.0000310787,0.00032741553,0.00015560171,0.00012412829],"category_scores_gemma":[0.0003734241,0.00012932415,0.00006290932,0.0070952335,0.0000647578,0.00014428239,0.00017549905,0.00019247274,0.00007767404],"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":[0.003355528,0.001731545,0.42945188,0.0051380116,0.00085998274,0.0012518576,0.00055037724,0.00087332673,0.23291045,0.000023223312,0.018734045,0.30511978],"study_design_scores_gemma":[0.0069857524,0.0030205634,0.3114766,0.0012578864,0.0009209816,0.00021645737,0.0001715195,0.63731104,0.01706428,0.000051453633,0.020710707,0.00081276096],"about_ca_topic_score_codex":0.0005147276,"about_ca_topic_score_gemma":0.000006683093,"teacher_disagreement_score":0.6364377,"about_ca_system_score_codex":0.00004664242,"about_ca_system_score_gemma":0.00022294738,"threshold_uncertainty_score":0.5273686},"labels":[],"label_agreement":null},{"id":"W4366082911","doi":"10.1111/biom.13870","title":"Sparse Estimation in Semiparametric Finite Mixture of Varying Coefficient Regression Models","year":2023,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; University of Nevada, Las Vegas","keywords":"Covariate; Parametric statistics; Mathematics; Statistics; Regression analysis; Sample size determination; Regression; Feature selection; Computer science; Artificial intelligence","score_opus":0.05419783994451265,"score_gpt":0.3119173921996076,"score_spread":0.25771955225509496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366082911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012781884,0.0006582475,0.9850953,0.00014337558,0.00039489515,0.00018632997,0.000007665099,0.00013866123,0.0005936187],"genre_scores_gemma":[0.5400022,0.00012199473,0.45970306,0.00004425333,0.00001159133,0.000007430321,0.000008743025,0.000009190011,0.00009150922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998222,0.0001290854,0.00040200254,0.00040037677,0.00052245654,0.00032410235],"domain_scores_gemma":[0.99832857,0.0007356571,0.0001946021,0.0005371772,0.000113112466,0.000090904425],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0013455608,0.00015293898,0.0002693809,0.005978226,0.000054302087,0.000060936516,0.0006104608,0.00016241998,0.00000223869],"category_scores_gemma":[0.0008233938,0.00012913525,0.000074302574,0.035183627,0.000029020595,0.0003307815,0.0002796744,0.0001590792,0.000018372097],"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":[0.000013841775,0.00023522413,0.0003451848,0.00014743302,0.000012749904,0.000053987962,0.0012916432,0.18046674,0.0024505623,0.025541387,0.0015693835,0.78787184],"study_design_scores_gemma":[0.00027215361,0.000048488862,0.00055434956,0.00007899415,0.000004304666,0.0000029130144,0.0000046643568,0.97667813,0.0028127537,0.019261237,0.00013943984,0.00014259138],"about_ca_topic_score_codex":0.000019351171,"about_ca_topic_score_gemma":4.136772e-7,"teacher_disagreement_score":0.79621136,"about_ca_system_score_codex":0.000063162384,"about_ca_system_score_gemma":0.00006264607,"threshold_uncertainty_score":0.985324},"labels":[],"label_agreement":null},{"id":"W4377690683","doi":"10.1111/biom.13881","title":"Instability of Inverse Probability Weighting Methods and a Remedy for Nonignorable Missing Data","year":2023,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Inverse probability weighting; Missing data; Weighting; Instability; Inverse probability; Statistics; Inverse; Mathematics; Econometrics; Computer science; Medicine; Bayesian probability; Posterior probability; Physics; Propensity score matching; Radiology; Geometry","score_opus":0.4313019177408205,"score_gpt":0.5069443664946836,"score_spread":0.07564244875386306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377690683","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026525777,0.00005928225,0.9718853,0.00013347513,0.00012930536,0.00047288218,0.00032553583,0.00007888123,0.000389525],"genre_scores_gemma":[0.0058043185,0.000020414009,0.9940589,0.000012989911,0.000027547203,0.000015670419,0.000022483022,0.000015250259,0.00002244896],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982669,0.00031213646,0.00050757494,0.0004372426,0.00020882617,0.00026735148],"domain_scores_gemma":[0.9859424,0.012717895,0.00020505574,0.0008068032,0.00021883512,0.000108987275],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008123866,0.0001226364,0.00038047772,0.00047964166,0.00010717183,0.00004013239,0.00030959726,0.000101019425,0.000017805056],"category_scores_gemma":[0.0783097,0.000103809056,0.00003861883,0.0037569064,0.00017819247,0.000121732846,0.00037966968,0.00009075799,0.0000011868153],"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":[0.000039293613,0.0001899981,0.0052745743,0.0028388218,0.000039444163,0.0000018773885,0.0002718871,1.2687691e-7,0.0076915435,0.10904814,0.0014497206,0.8731546],"study_design_scores_gemma":[0.00031300567,0.000089141344,0.0023540598,0.000055554985,0.000058985606,0.0000013835906,0.00010784494,0.031882506,0.0036382803,0.9589539,0.0023935176,0.0001518312],"about_ca_topic_score_codex":0.0000336124,"about_ca_topic_score_gemma":0.00000663336,"teacher_disagreement_score":0.8730027,"about_ca_system_score_codex":0.000037527156,"about_ca_system_score_gemma":0.00009459226,"threshold_uncertainty_score":0.9294541},"labels":[],"label_agreement":null},{"id":"W4385715310","doi":"10.1111/biom.13915","title":"A Proportional Incidence Rate Model for Aggregated Data to Study the Vaccine Effectiveness Against COVID-19 Hospital and ICU Admissions","year":2023,"lang":"en","type":"article","venue":"Biometrics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; University of Ottawa; Actua; Public Health Agency of Canada; University of Toronto; University of Waterloo","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Incidence (geometry); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Emergency medicine; Statistics; Intensive care medicine; Virology; Mathematics; Internal medicine; Outbreak","score_opus":0.5299358810654573,"score_gpt":0.513278885910717,"score_spread":0.016656995154740284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385715310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89289916,0.00022487337,0.09465922,0.007328682,0.00012856848,0.0039500343,0.0005260395,0.00028022513,0.0000031761767],"genre_scores_gemma":[0.9942217,0.00013878387,0.003555353,0.0012007224,0.00005777215,0.00062408904,0.00009165537,0.000025636851,0.00008432237],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977899,0.0003184357,0.00045934154,0.0007035481,0.00035517782,0.000373589],"domain_scores_gemma":[0.9804209,0.017994601,0.0002165567,0.0007665987,0.00022417128,0.00037718192],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0064241,0.00022922011,0.00043737248,0.000572729,0.0005957273,0.000051750118,0.00070975866,0.00008421329,0.0000063383723],"category_scores_gemma":[0.22632371,0.00013544768,0.000057029847,0.0057579135,0.0000512228,0.000100356905,0.0018059468,0.0001280755,0.000011773108],"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":[0.0018854357,0.007228885,0.63463575,0.002992209,0.0024484098,0.0003270174,0.008284147,0.010666481,0.0036604959,0.010387123,0.2736107,0.043873325],"study_design_scores_gemma":[0.0036873482,0.0015750302,0.54961276,0.00009908562,0.00034229044,0.0000038660764,0.0011620098,0.36887407,0.000071959825,0.06724992,0.0064625638,0.0008590943],"about_ca_topic_score_codex":0.000044899036,"about_ca_topic_score_gemma":0.000035523826,"teacher_disagreement_score":0.35820758,"about_ca_system_score_codex":0.00017104723,"about_ca_system_score_gemma":0.00022737247,"threshold_uncertainty_score":0.78019327},"labels":[],"label_agreement":null},{"id":"W4392565704","doi":"10.1093/biomtc/ujad038","title":"Two-phase designs with failure time processes subject to nonsusceptibility","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Covariate; Accelerated failure time model; Bivariate analysis; Proportional hazards model; Computer science; Logistic regression; Statistics; Fraction (chemistry); Phase (matter); Multivariate statistics; Econometrics; Mathematics; Machine learning","score_opus":0.1900245702038443,"score_gpt":0.48801193031734763,"score_spread":0.29798736011350335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392565704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3747536,0.0027963256,0.6094777,0.00088025036,0.00069166545,0.0014840678,0.00023334526,0.0006777167,0.009005362],"genre_scores_gemma":[0.648712,0.00000475344,0.34804565,0.00015441627,0.0001121564,0.00004240145,0.0000065681625,0.000040081064,0.002881964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99532276,0.00035762417,0.000586462,0.0011619296,0.0020949072,0.00047632187],"domain_scores_gemma":[0.9936448,0.0043709055,0.00009294442,0.0008429327,0.00063849514,0.0004099746],"candidate_categories":["metaresearch","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0050921915,0.0002994994,0.00041168183,0.003809037,0.00014411613,0.0011989346,0.0009974712,0.00010561733,0.0011822729],"category_scores_gemma":[0.010549194,0.00019433587,0.00010598033,0.04135369,0.00016822036,0.00055762345,0.00021246153,0.00017329643,0.0041066837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007537545,0.0012304966,0.0027628178,0.00017258569,0.000120587385,0.0003778357,0.001786819,0.00019120605,0.5676083,0.0006456804,0.051317245,0.3730327],"study_design_scores_gemma":[0.0037905308,0.011430477,0.0020520738,0.00031372329,0.00017970422,0.00028551323,0.0025632887,0.011713754,0.6886192,0.005254316,0.2710436,0.002753801],"about_ca_topic_score_codex":0.000023137605,"about_ca_topic_score_gemma":0.000011155293,"teacher_disagreement_score":0.3702789,"about_ca_system_score_codex":0.00018353651,"about_ca_system_score_gemma":0.0004718147,"threshold_uncertainty_score":0.99983793},"labels":[],"label_agreement":null},{"id":"W4394748865","doi":"10.1093/biomtc/ujad019","title":"A flexible framework for spatial capture-recapture with unknown identities","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Wildlife Ecology and Conservation","field":"Environmental 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":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Innotech Alberta","keywords":"Mark and recapture; Identity (music); Computer science; Poisson distribution; Camera trap; Process (computing); Density estimation; Wildlife; Statistics; Acoustics; Ecology; Biology; Mathematics; Physics; Population","score_opus":0.01603632825477516,"score_gpt":0.25386443054338004,"score_spread":0.23782810228860488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394748865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3090784,0.0011869227,0.678673,0.0026573457,0.0019385194,0.00052406493,0.000052744002,0.00029929855,0.005589687],"genre_scores_gemma":[0.97317106,0.000028992377,0.01684314,0.0008362254,0.00021569159,0.000074013755,0.000028544533,0.000020286903,0.008782031],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992689,0.000013283386,0.00010406867,0.00023675786,0.00018561768,0.00019138277],"domain_scores_gemma":[0.99953276,0.0002531479,0.000028983726,0.00012698988,0.000011094744,0.000047016674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016912492,0.00009690823,0.000087328975,0.00023664223,0.00010577337,0.000085932945,0.00013316596,0.00015763624,0.0006856189],"category_scores_gemma":[0.00015398083,0.000075811105,0.000042300755,0.0021243598,0.00011114318,0.00017497061,0.000044850975,0.00011526741,0.0002963908],"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":[0.00023671392,0.00027876295,0.54535246,0.00029741946,0.00021700432,0.00007991107,0.0017510866,0.0005798858,0.000604184,0.10062577,0.25942233,0.090554476],"study_design_scores_gemma":[0.0005059499,0.00054654444,0.34110764,0.00010127656,0.00014128065,0.000035925525,0.00019664309,0.0038699121,0.0011988423,0.054055728,0.5976085,0.0006317671],"about_ca_topic_score_codex":0.00014364878,"about_ca_topic_score_gemma":0.000107699525,"teacher_disagreement_score":0.66409266,"about_ca_system_score_codex":0.00009266133,"about_ca_system_score_gemma":0.00002438122,"threshold_uncertainty_score":0.75070465},"labels":[],"label_agreement":null},{"id":"W4395015241","doi":"10.1093/biomtc/ujae029","title":"Addressing age measurement errors in fish growth estimation from length-stratified samples","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Estimator; Statistics; Computer science; Discretization; Stock assessment; Small area estimation; Stratified sampling; Estimation; Sample size determination; Observational error; Econometrics; Mathematics; Ecology","score_opus":0.1835899347180771,"score_gpt":0.3214557153678229,"score_spread":0.1378657806497458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395015241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8561091,0.00032318512,0.015841883,0.0018171737,0.0008003862,0.00070469396,0.000120482364,0.00042749158,0.123855636],"genre_scores_gemma":[0.9946068,0.00006959058,0.004955808,0.00006262007,0.00003889009,0.000016339807,0.00005183199,0.000017861268,0.00018027396],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99841934,0.000047802492,0.00020200794,0.000295303,0.0008016635,0.00023390425],"domain_scores_gemma":[0.99961704,0.0001216839,0.000022994856,0.00015372691,0.000011069297,0.000073480456],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055117096,0.000098813834,0.00010325655,0.00046070988,0.000047191472,0.00021660075,0.00019204561,0.00007117595,0.0015453827],"category_scores_gemma":[0.00056613353,0.00009047703,0.000033917833,0.0034428511,0.000082245584,0.00025932473,0.00015572814,0.00015023796,0.000083794475],"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":[0.000022064685,0.0001589204,0.17079157,0.00010528301,0.00002672666,0.00017196948,0.0006644922,0.000092240334,0.011435662,0.00018882226,0.013762367,0.8025799],"study_design_scores_gemma":[0.00045842028,0.00012340136,0.83457917,0.0000845351,0.000021040225,0.0000025429297,0.00022041179,0.04015786,0.0043074274,0.0029926552,0.116544574,0.00050795625],"about_ca_topic_score_codex":0.004918037,"about_ca_topic_score_gemma":0.0014148026,"teacher_disagreement_score":0.8020719,"about_ca_system_score_codex":0.00036220963,"about_ca_system_score_gemma":0.000022650387,"threshold_uncertainty_score":0.99936736},"labels":[],"label_agreement":null},{"id":"W4399207768","doi":"10.1093/biomtc/ujae044","title":"Discussion on “Bayesian meta-analysis of penetrance for cancer risk” by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Penetrance; Bayesian probability; Statistics; Mathematics; Biology; Genetics","score_opus":0.06272181320364839,"score_gpt":0.349570307719786,"score_spread":0.2868484945161376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399207768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5225854,0.43169123,0.0080477,0.009506854,0.0007575668,0.0034300145,0.022363063,0.00022632122,0.0013918526],"genre_scores_gemma":[0.98952293,0.00802673,0.00038709003,0.00020826586,0.00005203409,0.00019665553,0.00031965296,0.000031952794,0.0012546754],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9983976,0.00004580715,0.00033879082,0.0005416073,0.00043998993,0.00023619471],"domain_scores_gemma":[0.9987803,0.00042656658,0.00012490914,0.00035524947,0.00009551767,0.00021746542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002589885,0.00024186737,0.0008494032,0.0015310561,0.00008176388,0.00005455163,0.00008612863,0.00010255926,0.0002730714],"category_scores_gemma":[0.000111857364,0.0001298629,0.0008597565,0.0048811766,0.00006394686,0.000074905445,0.000031223622,0.000085588166,0.0000056030512],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005984577,0.005385739,0.27818832,0.0022526118,0.4976551,0.00015374774,0.0013768289,0.00017129707,0.0017852103,0.0018609408,0.10449243,0.10607933],"study_design_scores_gemma":[0.0024580809,0.0016000246,0.17388551,0.0001599274,0.67681164,0.0000028205363,0.00019017085,0.006539628,0.023465047,0.00022575434,0.11401405,0.0006473582],"about_ca_topic_score_codex":0.00051162904,"about_ca_topic_score_gemma":0.00006341385,"teacher_disagreement_score":0.46693754,"about_ca_system_score_codex":0.00009196079,"about_ca_system_score_gemma":0.00006829439,"threshold_uncertainty_score":0.5295656},"labels":[],"label_agreement":null},{"id":"W4400692562","doi":"10.1093/biomtc/ujae065","title":"Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; National Institute on Aging; Eunice Kennedy Shriver National Institute of Child Health and Human Development; North Carolina State University; Fonds de Recherche du Québec - Santé; University of North Carolina; National Institutes of Health; National Science Foundation","keywords":"Covariate; Causal inference; Estimator; Observational study; Confounding; Econometrics; Marginal structural model; Inference; Computer science; Estimation; Specification; Statistics; Data mining; Mathematics; Artificial intelligence; Engineering","score_opus":0.649482041297089,"score_gpt":0.4852762919118487,"score_spread":0.1642057493852403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400692562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42350447,0.0003332431,0.5747349,0.00033948536,0.00019638502,0.0004617987,0.00012118345,0.00025044905,0.000058063448],"genre_scores_gemma":[0.5404299,0.000030873074,0.45934173,0.000024237952,0.000020305482,0.000048370795,0.00003173297,0.000016743294,0.000056115372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848574,0.000045695007,0.0005638037,0.00027069487,0.00041249482,0.0002215787],"domain_scores_gemma":[0.99765176,0.0015763483,0.00011635817,0.00022985971,0.00037637987,0.000049327773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045677,0.0001757572,0.0003171738,0.0018013858,0.000046447458,0.00004233348,0.00020241023,0.000096501746,0.0000119553],"category_scores_gemma":[0.0031429154,0.00016203195,0.00005861576,0.006037173,0.000056386278,0.00054817885,0.000113786096,0.00014267817,0.0000047934445],"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":[0.000032898828,0.000090974754,0.004636462,0.0007036398,0.00011077592,0.000014168193,0.0016849769,0.18949905,0.0043794946,0.79015124,0.0014992343,0.0071971044],"study_design_scores_gemma":[0.00016184342,0.00010134696,0.015918754,0.0002573206,0.000031860203,0.000003173782,0.00014569778,0.5744086,0.0013778213,0.40731037,0.00006327838,0.00021991586],"about_ca_topic_score_codex":0.000045121033,"about_ca_topic_score_gemma":0.00004442687,"teacher_disagreement_score":0.38490957,"about_ca_system_score_codex":0.00033607686,"about_ca_system_score_gemma":0.0001032284,"threshold_uncertainty_score":0.6607471},"labels":[],"label_agreement":null},{"id":"W4402582001","doi":"10.1093/biomtc/ujae098","title":"Semi-parametric benchmark dose analysis with monotone additive models","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Prenatal Substance Exposure Effects","field":"Medicine","cited_by":1,"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":"Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers; National Institute on Drug Abuse; National Institute on Aging; National Institute on Alcohol Abuse and Alcoholism; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Monotone polygon; Parametric statistics; Benchmark (surveying); Mathematics; Parametric model; Additive model; Statistics; Econometrics; Applied mathematics; Computer science; Geography","score_opus":0.01780650800748596,"score_gpt":0.27095714150071887,"score_spread":0.2531506334932329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402582001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84805495,0.03157783,0.10516824,0.0002498539,0.0005104558,0.0011720068,0.00039043993,0.0006809866,0.012195261],"genre_scores_gemma":[0.99402475,0.0004723283,0.003959258,0.00010356209,0.00015793384,0.000061453604,0.0003128667,0.00004418609,0.0008636483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776995,0.000047989433,0.00029191605,0.00061847136,0.0008504422,0.00042125536],"domain_scores_gemma":[0.99814206,0.0007675754,0.00007278193,0.00051953655,0.00019348193,0.00030457045],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0004002601,0.0002740511,0.0005561964,0.009616456,0.000052493593,0.00009941581,0.0001568923,0.00017678498,0.00016891406],"category_scores_gemma":[0.00042915274,0.00020373003,0.00025351893,0.06998296,0.000093415816,0.0002958838,0.00006250173,0.00031628847,0.00012179971],"study_design_candidate":"design_other","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":[0.0037359267,0.0038489085,0.08004081,0.0032627282,0.047909275,0.016259031,0.0031220682,0.009166284,0.019732615,0.010949997,0.06608008,0.7358923],"study_design_scores_gemma":[0.0065065753,0.0057438174,0.2731532,0.0011617877,0.019178683,0.0004218473,0.00035173103,0.615594,0.037613485,0.0015647666,0.036001146,0.002708932],"about_ca_topic_score_codex":0.000062426814,"about_ca_topic_score_gemma":0.000009132903,"teacher_disagreement_score":0.7331833,"about_ca_system_score_codex":0.00034587926,"about_ca_system_score_gemma":0.00014945006,"threshold_uncertainty_score":0.949785},"labels":[],"label_agreement":null},{"id":"W4402928408","doi":"10.1093/biomtc/ujae084","title":"Discussion on “LEAP: the latent exchangeability prior for borrowing information from historical data” by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","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":"Extrapolation; Key (lock); Control (management); Computer science; Econometrics; History; Mathematical economics; Operations research; Statistics; Mathematics; Artificial intelligence; Computer security","score_opus":0.04409951354962683,"score_gpt":0.2956402483527383,"score_spread":0.25154073480311145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402928408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3850479,0.04676067,0.38021958,0.11982549,0.0041378336,0.008705745,0.030058805,0.006426635,0.01881733],"genre_scores_gemma":[0.98735684,0.0009325934,0.006103168,0.00029264245,0.00063514087,0.00038304424,0.0029627231,0.00005986432,0.0012739588],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99832606,0.000022197764,0.00040948455,0.0005109538,0.00041348548,0.00031782556],"domain_scores_gemma":[0.99843407,0.00044610168,0.00014331951,0.0007939596,0.000047702008,0.0001348772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005560174,0.00023106193,0.00022459032,0.0005053144,0.00027805247,0.0002840513,0.0005168456,0.0002370196,0.00022924246],"category_scores_gemma":[0.0003388097,0.00014564687,0.000094170195,0.001726842,0.000043382504,0.00042562472,0.00035055247,0.00031278032,0.000016151844],"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":[0.00006977291,0.00033555678,0.0011911177,0.0009682531,0.000108941684,0.0000023608482,0.0009910415,9.25276e-7,0.046496775,0.004281198,0.17281161,0.77274245],"study_design_scores_gemma":[0.00023928726,0.00006379116,0.00037124744,0.000098578166,0.00007001025,0.0000020380364,0.00011609477,0.007189787,0.015180796,0.0014249752,0.97495943,0.00028397332],"about_ca_topic_score_codex":0.0003369866,"about_ca_topic_score_gemma":0.000008670913,"teacher_disagreement_score":0.8021478,"about_ca_system_score_codex":0.0005841031,"about_ca_system_score_gemma":0.00003132128,"threshold_uncertainty_score":0.5939307},"labels":[],"label_agreement":null},{"id":"W4403587533","doi":"10.1093/biomtc/ujae117","title":"Case-crossover designs and overdispersion with application to air pollution epidemiology","year":2024,"lang":"en","type":"article","venue":"Biometrics","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":true,"ca_institutions":"Health Canada; University of Waterloo; University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; Institut de Valorisation des Données","keywords":"Overdispersion; Crossover; Econometrics; Poisson distribution; Conditional independence; Statistics; Computer science; Mathematics; Count data; Machine learning","score_opus":0.1341484707150464,"score_gpt":0.4184271989247,"score_spread":0.2842787282096536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403587533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014826996,0.0002570275,0.9835956,0.00057045097,0.00009005066,0.0002423411,0.00007427043,0.00007786506,0.00026537554],"genre_scores_gemma":[0.4786718,0.000013658871,0.520988,0.00020845288,0.000044496264,0.000016087557,0.0000029550438,0.000010750877,0.000043860367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99911904,0.0000915037,0.00019552988,0.00029329062,0.00010661161,0.00019401743],"domain_scores_gemma":[0.996835,0.0027859926,0.000039220275,0.00016562211,0.00004957449,0.00012461861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007472236,0.00010630244,0.00018605393,0.0005136045,0.00007763782,0.000029149105,0.00005015624,0.00009441462,0.000025621292],"category_scores_gemma":[0.0024949205,0.000076001714,0.00002295811,0.0020951382,0.00006694998,0.000055734334,0.000042633015,0.00008416479,0.00003077375],"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":[0.00003213554,0.000034839108,0.0005752931,0.00017931688,0.000021685299,0.000120288634,0.00011908759,0.0000019453914,0.00083707226,0.74250436,0.003550957,0.25202304],"study_design_scores_gemma":[0.00044895548,0.0008442685,0.01108563,0.00015630777,0.00017307454,0.0021169914,0.0001455435,0.014724971,0.0008891005,0.94677144,0.0220795,0.00056421955],"about_ca_topic_score_codex":0.000053038457,"about_ca_topic_score_gemma":0.0000055193495,"teacher_disagreement_score":0.46384478,"about_ca_system_score_codex":0.000073663876,"about_ca_system_score_gemma":0.00002350101,"threshold_uncertainty_score":0.309926},"labels":[],"label_agreement":null},{"id":"W4405366937","doi":"10.1093/biomtc/ujae141","title":"An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; McGill University","funders":"Canadian Statistical Sciences Institute; National Institute of Mental Health; National Institutes of Health","keywords":"Bayesian probability; Computer science; Feature selection; Personalized medicine; Machine learning; Clinical study design; Precision medicine; Identification (biology); Medicine; Population; Clinical trial; Artificial intelligence; Bioinformatics; Internal medicine","score_opus":0.5389790871329021,"score_gpt":0.5280648069140222,"score_spread":0.010914280218879857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405366937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005330765,0.00016776205,0.9930931,0.000012659831,0.0003085447,0.0008053536,0.00017379204,0.00008530889,0.000022719421],"genre_scores_gemma":[0.4044468,0.00002399673,0.5953885,0.0000044268836,0.00007030809,0.00003110074,0.0000016099435,0.000019837411,0.000013421754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825585,0.00021846191,0.0006689061,0.0003885319,0.0002925777,0.00017567398],"domain_scores_gemma":[0.9842842,0.015002618,0.00020874287,0.00017296708,0.00025095473,0.000080489925],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0041921707,0.00013264672,0.00030649753,0.00080228294,0.00008364633,0.000088844936,0.00010331642,0.00014406112,0.000005772994],"category_scores_gemma":[0.012756351,0.00012215479,0.000055924924,0.0018529579,0.00005812486,0.00016144155,0.000030658553,0.00009734104,3.1777603e-7],"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":[0.00093462033,0.0010157942,0.0004268335,0.0020377254,0.00077397143,0.000004036787,0.0016725436,0.04236111,0.15530191,0.7127302,0.00058490963,0.08215628],"study_design_scores_gemma":[0.00013488076,0.00021306265,0.000025602127,0.000045174154,0.00015298405,0.0000011255809,0.000033068496,0.5559145,0.0074220146,0.43598717,0.0000018516071,0.000068566005],"about_ca_topic_score_codex":0.0000060472494,"about_ca_topic_score_gemma":2.1923718e-7,"teacher_disagreement_score":0.5135534,"about_ca_system_score_codex":0.0001460552,"about_ca_system_score_gemma":0.00010019254,"threshold_uncertainty_score":0.99555963},"labels":[],"label_agreement":null},{"id":"W4405367701","doi":"10.1093/biomtc/ujae143","title":"A Bayesian joint model for mediation analysis with matrix-valued mediators","year":2024,"lang":"en","type":"article","venue":"Biometrics","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 Alberta; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bayesian probability; Matrix (chemical analysis); Mediation; Varimax rotation; Mathematical optimization; Mathematics; Data mining; Artificial intelligence; Algorithm; Statistics; Chemistry","score_opus":0.08331043200215418,"score_gpt":0.39239376102404017,"score_spread":0.30908332902188596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405367701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009612355,0.00022738532,0.99746513,0.00016607331,0.00016999632,0.00033035086,0.0002989896,0.00017168268,0.00020915233],"genre_scores_gemma":[0.16120851,0.000021705353,0.8383331,0.000026039324,0.000098698976,0.00007169942,0.000033132303,0.000032087144,0.0001750306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984201,0.00005181204,0.00039626067,0.0003610417,0.00048617471,0.00028458712],"domain_scores_gemma":[0.9974075,0.0018977011,0.00010151407,0.00026788242,0.00016755241,0.00015786599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008930572,0.00018084917,0.00039831107,0.0026014352,0.0000661389,0.00014508939,0.00013416354,0.00012438197,0.00007624224],"category_scores_gemma":[0.0030657582,0.00012888305,0.00017852281,0.008970678,0.000050921957,0.00008993855,0.000027954758,0.000103992315,0.000009776233],"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":[0.00003650192,0.00011003303,0.00037176642,0.00067645655,0.00081576133,0.000014725303,0.00047005908,0.000073417745,0.00019984425,0.95016253,0.0023685677,0.044700313],"study_design_scores_gemma":[0.00015881093,0.00007707143,0.00011316453,0.000016553518,0.0006911205,0.0000012884773,0.000024079121,0.6969495,0.00014935856,0.30155763,0.00010427926,0.00015719475],"about_ca_topic_score_codex":0.0000070480055,"about_ca_topic_score_gemma":0.000010259892,"teacher_disagreement_score":0.69687605,"about_ca_system_score_codex":0.00010182478,"about_ca_system_score_gemma":0.00012284362,"threshold_uncertainty_score":0.5255698},"labels":[],"label_agreement":null},{"id":"W4405449519","doi":"10.1093/biomtc/ujae151","title":"Graphical model inference with external network data","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"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 Toronto","funders":"Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Banco Bilbao Vizcaya Argentaria; H2020 European Research Council; China Scholarship Council","keywords":"Graphical model; Computer science; Inference; Sample (material); Data mining; Statistical inference; Interpretation (philosophy); Variance (accounting); Probabilistic logic; Enhanced Data Rates for GSM Evolution; Statistical model; Artificial intelligence; Machine learning; Theoretical computer science; Statistics; Mathematics; Programming language","score_opus":0.3294630722262854,"score_gpt":0.5174496955438369,"score_spread":0.1879866233175515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405449519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07762053,0.030994816,0.8427683,0.0021410114,0.0038546273,0.00093549944,0.0005308358,0.0005443274,0.04061006],"genre_scores_gemma":[0.97444725,0.00043764096,0.018894332,0.00039900167,0.0006557483,0.000032463056,0.000099172976,0.000037322974,0.0049970746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821824,0.00007292791,0.00019706227,0.00048214255,0.00051010784,0.00051952683],"domain_scores_gemma":[0.9984376,0.00037911057,0.00002553032,0.00087827654,0.000041576586,0.00023793403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075124257,0.00011107629,0.00012362316,0.00081731204,0.00007168266,0.000104179824,0.0007015623,0.00011417018,0.0004413982],"category_scores_gemma":[0.00010652812,0.00008432979,0.000020339648,0.005528212,0.00010499444,0.00013197784,0.00032492707,0.000388602,0.00040476807],"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":[0.0002426312,0.00040624698,0.035077833,0.00046697722,0.00016067007,0.00069667434,0.0003477662,0.00017619791,0.00008491497,0.28185013,0.1467554,0.53373456],"study_design_scores_gemma":[0.0012448943,0.0013222492,0.06171833,0.00044086238,0.00007323866,0.00017292188,0.00009282794,0.5727641,0.000015144678,0.022726597,0.33864307,0.00078577345],"about_ca_topic_score_codex":0.00010404914,"about_ca_topic_score_gemma":0.000020515261,"teacher_disagreement_score":0.89682674,"about_ca_system_score_codex":0.00005355464,"about_ca_system_score_gemma":0.00014571343,"threshold_uncertainty_score":0.520261},"labels":[],"label_agreement":null},{"id":"W4405832592","doi":"10.1093/biomtc/ujae150","title":"Time-dependent prognostic accuracy measures for recurrent event data","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and 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":"McGill University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"Estimator; Event (particle physics); Baseline (sea); Statistics; Biomarker; Computer science; Econometrics; Medicine; Mathematics","score_opus":0.3317464096993471,"score_gpt":0.47238405777511067,"score_spread":0.14063764807576357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405832592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010896885,0.0037119687,0.99062485,0.00029331289,0.001228819,0.0008150152,0.0014928485,0.00021567578,0.00052781525],"genre_scores_gemma":[0.10009645,0.00041659857,0.8966859,0.00007737287,0.0006642622,0.00020474869,0.00025044783,0.00010176122,0.001502443],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982705,0.00008654619,0.0003942669,0.00046158148,0.0004912259,0.00029586794],"domain_scores_gemma":[0.98789454,0.01111114,0.000074239746,0.0006358356,0.00016223262,0.00012202731],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0018018949,0.00016359074,0.00023584363,0.00054609834,0.00006443058,0.00017419884,0.00054964697,0.00008275364,0.00016670798],"category_scores_gemma":[0.056676354,0.00012547772,0.00006562746,0.0016495914,0.000046365552,0.00010367558,0.00026541378,0.00013031828,0.00018042808],"study_design_candidate":"design_other","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":[0.000021800988,0.00021721421,0.000032738644,0.0006541782,0.00008196137,0.000010655275,0.000059404774,4.635765e-7,0.0002862502,0.051476054,0.05896887,0.8881904],"study_design_scores_gemma":[0.00062058965,0.0010695858,0.0004339071,0.00065939524,0.000600531,0.000027222073,0.000063682906,0.12723503,0.0012162875,0.5173007,0.34994012,0.00083291036],"about_ca_topic_score_codex":0.0000032961154,"about_ca_topic_score_gemma":0.000001185232,"teacher_disagreement_score":0.8873575,"about_ca_system_score_codex":0.0000708883,"about_ca_system_score_gemma":0.00011711682,"threshold_uncertainty_score":0.9512697},"labels":[],"label_agreement":null},{"id":"W4406465845","doi":"10.1093/biomtc/ujae165","title":"Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"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 de Montréal; McGill University Health Centre","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Environmental Health Sciences; Fonds de recherche du Québec – Nature et technologies; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada","keywords":"Model selection; Statistics; Selection (genetic algorithm); Estimation; Mathematics; Nested set model; Random effects model; Econometrics; Computer science; Medicine; Internal medicine; Data mining; Machine learning; Meta-analysis; Engineering","score_opus":0.1170136167091732,"score_gpt":0.4549681931347914,"score_spread":0.3379545764256182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406465845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16778314,0.000015077447,0.83069044,0.000058425896,0.00012667199,0.001123941,0.0000616358,0.00006179932,0.00007889653],"genre_scores_gemma":[0.43512595,0.0000010061577,0.5642282,0.00003632152,0.0000078130515,0.00020204471,0.000020547968,0.000011754689,0.00036638835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888974,0.00008815494,0.00040486464,0.00024394445,0.00014919351,0.00022412027],"domain_scores_gemma":[0.9944128,0.0050950553,0.00011373902,0.00012573742,0.000214884,0.000037793543],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00088131696,0.00015056487,0.00037035334,0.0010829138,0.00007976303,0.000049128703,0.000101996964,0.00013005263,0.000005812167],"category_scores_gemma":[0.019103838,0.00011848117,0.000093108625,0.0023945493,0.000020681702,0.00006168116,0.000021286405,0.0000699847,5.6544616e-7],"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":[0.0009909758,0.00020345396,0.010853754,0.002316156,0.00018344144,9.392474e-7,0.0004474063,0.002199726,0.0056998455,0.7126656,0.0015944412,0.2628442],"study_design_scores_gemma":[0.0013490103,0.00010347006,0.004024366,0.000025798941,0.000056235283,3.506302e-7,0.000004545034,0.7225252,0.0010240235,0.27077946,0.000012111079,0.00009545646],"about_ca_topic_score_codex":0.000027137958,"about_ca_topic_score_gemma":0.0000127326175,"teacher_disagreement_score":0.72032547,"about_ca_system_score_codex":0.00013284612,"about_ca_system_score_gemma":0.00005147009,"threshold_uncertainty_score":0.9891587},"labels":[],"label_agreement":null},{"id":"W4408533419","doi":"10.1093/biomtc/ujaf018","title":"Jointly modeling means and variances for nonlinear mixed effects models with measurement errors and outliers","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Bayesian Methods and Mixture Models","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 British Columbia","funders":"","keywords":"Outlier; Inference; Random effects model; Variance (accounting); Computer science; Mixed model; Statistical inference; Statistics; Statistical model; Nonlinear system; Human immunodeficiency virus (HIV); Econometrics; Data mining; Mathematics; Machine learning; Artificial intelligence","score_opus":0.041374461524431166,"score_gpt":0.2666058924521734,"score_spread":0.22523143092774225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408533419","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022166222,0.0028953156,0.993341,0.000469173,0.00030467234,0.00046543064,0.0000052904115,0.00010503683,0.00019742432],"genre_scores_gemma":[0.17173217,0.00012336734,0.8278375,0.00021166101,0.000022075821,0.000031221494,7.920683e-7,0.000012519548,0.000028645805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861383,0.000068618814,0.00019985443,0.0005074006,0.00032782197,0.00028246178],"domain_scores_gemma":[0.9990931,0.00018672261,0.000063124746,0.00030106338,0.000237168,0.00011880003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011966819,0.00018527551,0.00027520314,0.00086965977,0.00013890318,0.00018263068,0.00027620327,0.000091666305,5.555018e-8],"category_scores_gemma":[0.00019618229,0.00014315225,0.000039851588,0.0020344583,0.000047030917,0.00028378313,0.0001311719,0.000085683176,1.1847338e-7],"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":[0.00008384455,0.00016325813,0.00013093835,0.000782685,0.00024952568,0.000008582785,0.0009028526,0.003554826,0.0013128815,0.33778307,0.00035965565,0.6546679],"study_design_scores_gemma":[0.00079671585,0.0001683766,0.000041828178,0.000084088366,0.000047946185,0.000002948856,0.000016785365,0.9517626,0.0008264708,0.045704182,0.00036521105,0.00018285585],"about_ca_topic_score_codex":0.000018830131,"about_ca_topic_score_gemma":0.0000068464933,"teacher_disagreement_score":0.94820774,"about_ca_system_score_codex":0.000049408318,"about_ca_system_score_gemma":0.00009397584,"threshold_uncertainty_score":0.583758},"labels":[],"label_agreement":null},{"id":"W4409802238","doi":"10.1093/biomtc/ujaf041","title":"Optimal dynamic treatment regime estimation in the presence of nonadherence","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Estimator; Robustness (evolution); Estimation; Reliability (semiconductor); Computer science; Population; Outcome (game theory); Econometrics; Process (computing); Precision medicine; Medicine; Mathematics; Statistics; Economics","score_opus":0.11847246443711536,"score_gpt":0.4363638841518298,"score_spread":0.31789141971471446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409802238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26302177,0.00037913152,0.72982675,0.00033848576,0.00008093154,0.00087614165,0.00001968197,0.00012282093,0.005334311],"genre_scores_gemma":[0.81581897,0.00009908765,0.18352176,0.000015333346,0.0000023120501,0.000070463364,0.0000041512085,0.000005059787,0.00046287788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991724,0.000058545924,0.00027772266,0.00015008415,0.00020043863,0.00014081511],"domain_scores_gemma":[0.9979867,0.0014036243,0.00013071833,0.00039069392,0.00007447333,0.000013792674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034696242,0.000105468054,0.00017186356,0.00080666295,0.000026845957,0.00001623203,0.00029930726,0.0000698147,0.0000099556455],"category_scores_gemma":[0.001412052,0.000072352115,0.000037210073,0.0037438795,0.00007805011,0.00009391188,0.000044749107,0.0000700399,0.0000046807804],"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":[0.000112074755,0.002520196,0.0047143223,0.00065493205,0.000107934466,0.000040393767,0.0025005764,0.0011471984,0.013101238,0.6308491,0.0037037258,0.34054828],"study_design_scores_gemma":[0.0015221849,0.0016187044,0.01534501,0.0006760112,0.00015275089,0.000015693882,0.0011448992,0.1235145,0.06081808,0.791655,0.0028933492,0.0006437891],"about_ca_topic_score_codex":0.000054907174,"about_ca_topic_score_gemma":0.000014194006,"teacher_disagreement_score":0.5527972,"about_ca_system_score_codex":0.00016796544,"about_ca_system_score_gemma":0.00006397083,"threshold_uncertainty_score":0.29504335},"labels":[],"label_agreement":null},{"id":"W4410419022","doi":"10.1093/biomtc/ujaf060","title":"Robust and efficient semi-supervised learning for Ising model","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Machine Learning in Healthcare","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 Toronto","funders":"","keywords":"Computer science; Leverage (statistics); Estimator; Machine learning; Inference; Artificial intelligence; Ising model; Key (lock); Supervised learning; Data mining; Mathematics; Statistics","score_opus":0.05574743264278325,"score_gpt":0.3103747464432825,"score_spread":0.25462731380049924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410419022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047901522,0.0011187936,0.9483235,0.0013358521,0.0002952473,0.00023324857,0.0000017795076,0.00019587853,0.0005942087],"genre_scores_gemma":[0.7719729,0.000030299383,0.22686356,0.00035722845,0.000029361552,0.000015693178,0.0000035817154,0.000010224191,0.0007171428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987966,0.000056908648,0.00021000428,0.0004196889,0.00021096025,0.0003058281],"domain_scores_gemma":[0.9988532,0.0005357672,0.00006579944,0.0002998485,0.00016002728,0.00008538012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066938315,0.000117906064,0.0001566536,0.0014429698,0.0003171066,0.00019685875,0.00042222627,0.00009124889,0.0000011048634],"category_scores_gemma":[0.0011851303,0.000116706964,0.00004494671,0.0042619337,0.000025258862,0.000080579906,0.0003189088,0.00020116262,0.0000025754027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008231121,0.000049714134,0.010495275,0.00039581998,0.000014309415,0.0000020171778,0.0006333053,0.72470677,0.00043297277,0.02558741,0.0005914825,0.23708269],"study_design_scores_gemma":[0.00028541856,0.000048419453,0.0018209852,0.000035803525,0.0000050607946,0.0000016371595,0.000021786516,0.9945189,0.000091343834,0.00034107597,0.0027147615,0.000114838],"about_ca_topic_score_codex":0.000029135052,"about_ca_topic_score_gemma":8.8662017e-7,"teacher_disagreement_score":0.7240714,"about_ca_system_score_codex":0.0000832461,"about_ca_system_score_gemma":0.000108438995,"threshold_uncertainty_score":0.4759172},"labels":[],"label_agreement":null},{"id":"W4411505827","doi":"10.1093/biomtc/ujaf073","title":"Design of platform trials with a change in the control treatment arm","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","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":"Ottawa Hospital","funders":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Department of Health and Social Care; Medical Research Council; National Institute for Health and Care Research","keywords":"Frequentist inference; Type I and type II errors; Control (management); Conditional probability; Statistical power; Computer science; Power (physics); Statistics; Mathematics; Bayesian probability; Artificial intelligence; Bayesian inference","score_opus":0.8586986399465512,"score_gpt":0.6110839896227614,"score_spread":0.2476146503237897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411505827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00539376,0.00047831642,0.98813206,0.00065819774,0.00034555205,0.0038352506,0.00013971275,0.000033441887,0.000983714],"genre_scores_gemma":[0.30878124,0.00019476676,0.68993396,0.00036591195,0.00012278794,0.000498108,0.0000010263843,0.00001578848,0.000086420165],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99675876,0.0013811026,0.0010656925,0.00020900824,0.00036396793,0.00022146318],"domain_scores_gemma":[0.79818857,0.2009708,0.00035503908,0.0003710877,0.00008199713,0.000032510598],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012850553,0.0001583175,0.0010580185,0.0009333649,0.000028738408,0.000024383267,0.00027517322,0.00013598337,0.00004813238],"category_scores_gemma":[0.12269376,0.00007703288,0.00011217612,0.004330538,0.000101965394,0.00002944119,0.000020123764,0.00009445944,0.0000035468656],"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":[0.003834874,0.0049241325,0.0028666812,0.00040259233,0.0008820963,0.000060034436,0.0014883677,0.000008444417,0.00035221153,0.38408944,0.0012248001,0.59986633],"study_design_scores_gemma":[0.015994117,0.004179534,0.003684591,0.00024753643,0.0009332497,0.000003275072,0.00027552395,0.0011395654,0.0026534146,0.9694498,0.0011691063,0.00027027904],"about_ca_topic_score_codex":0.000051018385,"about_ca_topic_score_gemma":0.0000054443476,"teacher_disagreement_score":0.5995961,"about_ca_system_score_codex":0.00009942469,"about_ca_system_score_gemma":0.00008232087,"threshold_uncertainty_score":0.8846962},"labels":[],"label_agreement":null},{"id":"W4411654307","doi":"10.1093/biomtc/ujaf074","title":"Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","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":"Ottawa Hospital; University of Ottawa","funders":"National Center for Advancing Translational Sciences; Claude Pepper Older Americans Independence Center, Wake Forest School of Medicine; National Institute on Aging; National Institutes of Health; Georgia Clinical and Translational Science Alliance","keywords":"CRTS; Sample size determination; Context (archaeology); Computer science; Statistics; Mathematics","score_opus":0.41889502217972463,"score_gpt":0.574672411786123,"score_spread":0.15577738960639842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411654307","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00832228,0.000047128553,0.98130226,0.0005161872,0.0012818824,0.006327925,0.00040654233,0.000110789275,0.0016850203],"genre_scores_gemma":[0.01733142,0.000004295097,0.9674325,0.0005954091,0.0003091515,0.00089787226,0.00002142111,0.000050877577,0.013357027],"study_design_codex":"randomized_trial","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939363,0.0021147958,0.0023796286,0.0005561427,0.00064152194,0.00037159034],"domain_scores_gemma":[0.7574495,0.24077359,0.0006040124,0.00040588007,0.0006087107,0.00015827632],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03171262,0.00027170702,0.0019467796,0.0014262944,0.00013074097,0.00017743997,0.0002517553,0.00031237426,0.0009885837],"category_scores_gemma":[0.5591775,0.00018708465,0.00063371257,0.0030391102,0.00016900957,0.00005708572,0.00011918309,0.0001595638,0.00012907598],"study_design_candidate":"randomized_trial","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":[0.62569684,0.0022561313,0.0020128444,0.0010898183,0.003500546,0.000011150001,0.00017093832,0.00019954427,0.0013805714,0.23626782,0.10555893,0.021854844],"study_design_scores_gemma":[0.30586016,0.0005892481,0.006413018,0.00014939893,0.0007462008,0.0000030143046,0.000009206094,0.00434937,0.0015504116,0.6679866,0.0117382975,0.0006050862],"about_ca_topic_score_codex":0.000003962265,"about_ca_topic_score_gemma":5.105528e-7,"teacher_disagreement_score":0.52746487,"about_ca_system_score_codex":0.0002081639,"about_ca_system_score_gemma":0.0001532326,"threshold_uncertainty_score":0.99992466},"labels":[],"label_agreement":null},{"id":"W4411668491","doi":"10.1093/biomtc/ujaf076","title":"Regularized principal spline functions to mitigate spatial confounding","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"NextGenerationEU; Natural Sciences and Engineering Research Council of Canada","keywords":"Confounding; Econometrics; Statistics; Parametric statistics; Bayesian probability; Prior probability; Spline (mechanical); Mathematics; Computer science; Engineering","score_opus":0.03815688523402972,"score_gpt":0.2641277123255591,"score_spread":0.22597082709152938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411668491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20686132,0.0010488699,0.7463834,0.0026438502,0.003358662,0.00047381647,0.0017326778,0.00015328184,0.03734408],"genre_scores_gemma":[0.97744375,0.0000688524,0.003020982,0.0007424397,0.00025068026,0.000035523593,0.00038113928,0.000018005336,0.018038629],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985123,0.000013187103,0.00062148034,0.00047157155,0.00006559178,0.00031585488],"domain_scores_gemma":[0.9990216,0.00010687829,0.00017379744,0.00047628937,0.00007566774,0.00014576458],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005533885,0.00015858364,0.00041434006,0.0036365462,0.00018884009,0.00015301169,0.00030799315,0.00012138708,0.00073279656],"category_scores_gemma":[0.0009077217,0.00018120826,0.00016134851,0.0075209606,0.000040038827,0.00011031209,0.00016311753,0.000111382265,0.0022066722],"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":[0.00025525218,0.0007039433,0.47711107,0.00020666474,0.001345821,0.000032516553,0.00033966397,0.0005563427,0.0029853892,0.39105564,0.038933683,0.08647402],"study_design_scores_gemma":[0.0009557137,0.00009011955,0.10128761,0.000022584813,0.00006383203,0.0000015027379,0.00004156153,0.008466643,0.00029267592,0.003890622,0.88447154,0.00041559292],"about_ca_topic_score_codex":0.0020527418,"about_ca_topic_score_gemma":0.00018517164,"teacher_disagreement_score":0.84553784,"about_ca_system_score_codex":0.00014395989,"about_ca_system_score_gemma":0.000033970093,"threshold_uncertainty_score":0.9985702},"labels":[],"label_agreement":null},{"id":"W4412629587","doi":"10.1093/biomtc/ujaf082","title":"Sparse 2-stage Bayesian meta-analysis for individualized treatments","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods in Clinical Trials","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":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Covariate; Bayesian probability; Computer science; Identification (biology); Meta-analysis; Data mining; Machine learning; Econometrics; Artificial intelligence; Medicine; Mathematics; Internal medicine","score_opus":0.8274700279161862,"score_gpt":0.6240568146491524,"score_spread":0.20341321326703377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412629587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056494056,0.00043853172,0.9920029,0.0003907674,0.0003939816,0.0011257342,0.0020155318,0.00011591619,0.0029516693],"genre_scores_gemma":[0.0055126124,0.000031183223,0.9781676,0.00035087994,0.00004927863,0.00031526355,0.000025085283,0.000027899361,0.015520198],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968152,0.00055695174,0.0011799572,0.0005687383,0.00049139815,0.00038775732],"domain_scores_gemma":[0.9303925,0.06803736,0.00039221268,0.0008181665,0.00021161023,0.0001481514],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004390157,0.0002848514,0.0021058477,0.0029982878,0.00011650073,0.00009601451,0.00043161566,0.00026193447,0.0011231455],"category_scores_gemma":[0.12923926,0.0002121326,0.0020377159,0.012911589,0.00009251439,0.0000434926,0.00012776181,0.00011109809,0.000015733893],"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":[0.00018707271,0.0015127029,0.0028561193,0.00034241998,0.4995585,0.000014180708,0.00006527703,0.000006777908,0.00005435956,0.46100932,0.012645866,0.0217474],"study_design_scores_gemma":[0.0016911285,0.000116367126,0.00066712307,0.0000026037883,0.34457737,1.2952808e-7,0.000018754505,0.00030809428,0.00084859546,0.63726974,0.014268414,0.00023170197],"about_ca_topic_score_codex":0.000019647541,"about_ca_topic_score_gemma":0.000005711332,"teacher_disagreement_score":0.1762604,"about_ca_system_score_codex":0.00009445532,"about_ca_system_score_gemma":0.0000656166,"threshold_uncertainty_score":0.99978995},"labels":[],"label_agreement":null},{"id":"W4414346850","doi":"10.1093/biomtc/ujaf116","title":"Estimating associations between cumulative exposure and health via generalized distributed lag non-linear models using penalized splines","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; Health Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized additive model; Lag; Distributed lag; Scale (ratio); Laplace's method; Generalized linear model; Generalized linear mixed model; Additive model","score_opus":0.17209255252378394,"score_gpt":0.41472748434148343,"score_spread":0.2426349318176995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414346850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33634856,0.00018679969,0.6601851,0.0024179,0.000107769876,0.00033030048,0.0002978188,0.000045270914,0.00008051259],"genre_scores_gemma":[0.7820421,0.00004035047,0.2159908,0.0014201577,0.00009693345,0.0000061368232,0.0002874971,0.000014269084,0.00010174807],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982335,0.00013457515,0.00055980764,0.00030435383,0.0003506417,0.00041714797],"domain_scores_gemma":[0.99893373,0.00027876918,0.0003366785,0.00017727315,0.00004160231,0.00023197297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013119442,0.00016298737,0.0003877927,0.0003394621,0.000578718,0.000049845617,0.00013566944,0.0001347564,0.000031236657],"category_scores_gemma":[0.0004514423,0.00015717577,0.00004742737,0.0036897713,0.00008960618,0.00027060555,0.0001897988,0.0001438533,0.000008262449],"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":[0.00004513119,0.00036490124,0.7604969,0.0003741982,0.0001807633,0.000004182562,0.0027046413,0.17259943,0.00055330986,0.0008266356,0.0072625615,0.054587316],"study_design_scores_gemma":[0.0008114999,0.000060733637,0.18874484,0.000038879112,0.000031508393,5.3736846e-7,0.000030077692,0.80646384,0.000056404333,0.003287101,0.000312287,0.00016227622],"about_ca_topic_score_codex":0.0045590736,"about_ca_topic_score_gemma":0.00006545069,"teacher_disagreement_score":0.6338644,"about_ca_system_score_codex":0.0006220271,"about_ca_system_score_gemma":0.00009886627,"threshold_uncertainty_score":0.68919855},"labels":[],"label_agreement":null},{"id":"W4414966744","doi":"10.1093/biomtc/ujaf128","title":"Inverse-intensity weighted generalized estimating equations for longitudinal data subject to irregular observation: which variables should be included in the visit rate model?","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Outcome (game theory); Conditional independence; Regression analysis; Inverse probability weighting; Generalized estimating equation; Regression; Linear regression; Weighting","score_opus":0.5402143993276196,"score_gpt":0.47497695751935703,"score_spread":0.06523744180826258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414966744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03236782,0.00003769993,0.9616798,0.003838146,0.00015621366,0.0013028913,0.00024561872,0.00023325939,0.00013851134],"genre_scores_gemma":[0.13838623,0.000011665112,0.8593042,0.0013230403,0.000058918282,0.00021633855,0.00052639464,0.000026443986,0.00014677848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785686,0.00014800348,0.00066357636,0.0005554381,0.0003997283,0.0003764246],"domain_scores_gemma":[0.99403274,0.0033424376,0.00023319665,0.001433822,0.0008948657,0.000062964995],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003430187,0.00025810002,0.00044464477,0.0014059361,0.0002906458,0.00015713989,0.0011317458,0.00018639154,0.000008264589],"category_scores_gemma":[0.019679254,0.00021119614,0.000053398195,0.010370125,0.000044533583,0.00044640293,0.0006257869,0.00022116509,0.0000019068452],"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":[0.00025863553,0.00069222593,0.0035976227,0.0006315801,0.00022698726,0.000011076182,0.0012788658,0.009938299,0.013579228,0.90457624,0.06134997,0.0038592587],"study_design_scores_gemma":[0.0003812117,0.000043053602,0.00033856483,0.00007018479,0.00008739023,9.463327e-7,0.000073076386,0.729502,0.002090242,0.26660138,0.00061181455,0.00020012146],"about_ca_topic_score_codex":0.00014920425,"about_ca_topic_score_gemma":0.0003409476,"teacher_disagreement_score":0.7195637,"about_ca_system_score_codex":0.0002504855,"about_ca_system_score_gemma":0.00022384885,"threshold_uncertainty_score":0.9885784},"labels":[],"label_agreement":null},{"id":"W4415614733","doi":"10.1093/biomtc/ujaf143","title":"Adaptive stratified sampling design in two-phase studies for average causal effect estimation","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","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":"National Natural Science Foundation of China; City University of Hong Kong","keywords":"Confounding; Estimator; Sampling design; Sampling (signal processing); Stratified sampling; Sample size determination; Causal inference; Observational study; Stratification (seeds)","score_opus":0.39395430355975664,"score_gpt":0.544197749972689,"score_spread":0.15024344641293236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415614733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022412501,0.0003697107,0.9751312,0.000028954497,0.00014736895,0.0015167827,0.000024340283,0.00023583435,0.00013331142],"genre_scores_gemma":[0.5152464,0.000026200309,0.48434034,0.000019401657,0.00001572628,0.00026961422,0.000008426944,0.000014141992,0.000059736143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872184,0.00012279119,0.0004273464,0.00028152013,0.00017720327,0.00026932592],"domain_scores_gemma":[0.990438,0.008932608,0.00015878127,0.00023656136,0.00020033587,0.000033744316],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0011835864,0.00020786539,0.00041442615,0.0017196662,0.00008817026,0.000039780287,0.00014795183,0.000088046225,0.0000027908202],"category_scores_gemma":[0.010308376,0.00018839713,0.000057119854,0.003126745,0.000057800284,0.00017451298,0.00005933993,0.00013415125,0.000002193842],"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":[0.0011895854,0.0011378783,0.00030843014,0.0018092806,0.0004727642,0.000034996727,0.0013372102,0.0055784895,0.037824288,0.48859885,0.00307628,0.45863193],"study_design_scores_gemma":[0.0030906878,0.0012482156,0.000071825176,0.00036189027,0.00010177957,0.0000014715955,0.00019919896,0.06450508,0.11005022,0.8199382,0.00006975491,0.0003616944],"about_ca_topic_score_codex":0.000009337251,"about_ca_topic_score_gemma":0.0000121262765,"teacher_disagreement_score":0.4928339,"about_ca_system_score_codex":0.00034727537,"about_ca_system_score_gemma":0.00006989942,"threshold_uncertainty_score":0.9980282},"labels":[],"label_agreement":null},{"id":"W4415615039","doi":"10.1093/biomtc/ujaf141","title":"A Bayesian collocation integral method for system identification of ordinary differential equations","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"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","keywords":"Ode; Ordinary differential equation; Frequentist inference; Collocation (remote sensing); Collocation method; Identification (biology); Trajectory; Bayesian probability; System identification","score_opus":0.013096959576466546,"score_gpt":0.3031745698066669,"score_spread":0.29007761023020034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415615039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04520554,0.00060927385,0.9534821,0.00004013071,0.0002576775,0.00028036846,0.00004129454,0.000011549037,0.000072099225],"genre_scores_gemma":[0.9849618,0.000016471986,0.01353412,0.000009241422,0.00007961783,0.00007367576,0.00040309696,0.000009441293,0.0009125514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991,0.000075444856,0.0003579312,0.0002426697,0.00011021796,0.00011372243],"domain_scores_gemma":[0.99907804,0.00006165916,0.00018751777,0.00032475227,0.0003183781,0.000029673198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003597979,0.000090527814,0.0001564133,0.0007840535,0.00007433997,0.000019457637,0.00016305946,0.00013307255,0.0000021231035],"category_scores_gemma":[0.00030693103,0.00009121247,0.0001516384,0.0023539208,0.000027424772,0.0000025078243,0.00005022786,0.000023043336,8.639685e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005223351,0.00009624068,0.0007598445,0.00019714244,0.00026105525,7.497783e-8,0.000011815381,0.00054990966,0.9706885,0.004307797,0.0016964155,0.021378955],"study_design_scores_gemma":[0.0009080093,0.00021630684,0.008297973,0.00004523503,0.00062695215,0.0000017500224,0.00029895332,0.2812969,0.7015807,0.00026631556,0.0061976374,0.00026321964],"about_ca_topic_score_codex":0.000013828296,"about_ca_topic_score_gemma":0.0000075748358,"teacher_disagreement_score":0.93994796,"about_ca_system_score_codex":0.000052758194,"about_ca_system_score_gemma":0.00008583973,"threshold_uncertainty_score":0.37195364},"labels":[],"label_agreement":null},{"id":"W7117551031","doi":"10.1093/biomtc/ujaf170","title":"Maximized sequential probability ratio test regression","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Manitoba Health","funders":"National Institute of General Medical Sciences; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Poisson regression; Sequential probability ratio test; Poisson distribution; Regression analysis; Regression; Confounding; Conditional probability; Linear regression; Sample size determination","score_opus":0.18732251411507986,"score_gpt":0.4830547194125379,"score_spread":0.29573220529745803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117551031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8185692,0.0035316187,0.011511725,0.011307449,0.016859852,0.0028751,0.00076845265,0.0013127975,0.13326381],"genre_scores_gemma":[0.9873962,0.0005906967,0.00066206965,0.0026612177,0.00024430352,0.00008116557,0.00007752273,0.000013876499,0.00827294],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99843526,0.00022631804,0.0003943557,0.00037058105,0.00018675493,0.0003867024],"domain_scores_gemma":[0.99827784,0.000982574,0.00014067606,0.00026775306,0.0001656232,0.00016552716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079808774,0.00020742032,0.00026407687,0.00092126406,0.0003791725,0.00003755867,0.0002888334,0.00030685918,0.0008458819],"category_scores_gemma":[0.0012230851,0.00018171365,0.00014038711,0.0038377417,0.00017727092,0.0002324815,0.00012276712,0.0005818533,0.00026657202],"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":[0.00060787983,0.0026622547,0.07051144,0.00026538738,0.0003435343,0.00007094904,0.00016133758,0.00030620786,0.68296546,0.0056051817,0.16008295,0.07641745],"study_design_scores_gemma":[0.002984949,0.0000468339,0.00437274,0.000026144447,0.00020681693,0.000007905825,0.000044776658,0.0028552462,0.1527271,0.001751694,0.8347056,0.00027021882],"about_ca_topic_score_codex":0.000013310373,"about_ca_topic_score_gemma":0.000002360846,"teacher_disagreement_score":0.67462265,"about_ca_system_score_codex":0.00021574639,"about_ca_system_score_gemma":0.00023662482,"threshold_uncertainty_score":0.9261814},"labels":[],"label_agreement":null},{"id":"W7124436614","doi":"10.1093/biomtc/ujaf174","title":"Estimating optimal dynamic treatment regimes with Gaussian process emulation","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperparameter optimization; Estimator; Emulation; Grid; Gaussian process; Process (computing); Parametric statistics; Noise (video); Gaussian; Estimation theory","score_opus":0.08512936259223094,"score_gpt":0.4365562607013978,"score_spread":0.35142689810916683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124436614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15994292,0.00009749755,0.83552724,0.00013059059,0.000059615973,0.00042910106,0.000007430882,0.00050778064,0.003297837],"genre_scores_gemma":[0.5549725,0.0000074090603,0.44411567,0.000011837404,0.0000113794185,0.000055135308,0.000009198634,0.000015800195,0.00080104347],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905634,0.00002025622,0.00024309444,0.00025536199,0.00020511853,0.0002198064],"domain_scores_gemma":[0.99903804,0.00031385256,0.00016415198,0.0003042551,0.00013581714,0.00004389751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012804096,0.00018623055,0.00023038503,0.0010952785,0.00010121532,0.000052310723,0.00013566554,0.000090246474,0.000010923775],"category_scores_gemma":[0.00051609124,0.00013852012,0.000034132157,0.0034594678,0.000054157386,0.00016842404,0.000028418433,0.00007208777,0.000004594587],"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":[0.00033081783,0.0023558666,0.015077315,0.0020836543,0.0006251932,0.00010866244,0.0026155796,0.0065604197,0.0037437764,0.2694924,0.0011300273,0.6958763],"study_design_scores_gemma":[0.002973466,0.0028835386,0.0056876913,0.001418127,0.0005606976,0.000050070266,0.0010750438,0.42192286,0.055702034,0.5047301,0.0013507903,0.0016455505],"about_ca_topic_score_codex":0.000008717531,"about_ca_topic_score_gemma":0.0000047768544,"teacher_disagreement_score":0.69423074,"about_ca_system_score_codex":0.0003347691,"about_ca_system_score_gemma":0.00009457126,"threshold_uncertainty_score":0.5648686},"labels":[],"label_agreement":null}]}