{"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005885192,0.00037239888,0.9929957,0.00026369322,0.000019270006,0.000024191495,0.000040844352,0.00013506386,0.0002637176],"genre_scores_gemma":[0.35359386,0.0018762727,0.63460183,0.00058957277,0.00033179988,0.0010861955,0.00086607307,0.00034213875,0.0067122965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992516,0.005356359,0.0002760861,0.0009208108,0.0006824396,0.00024843382],"domain_scores_gemma":[0.97233623,0.023895577,0.0016547528,0.0009686861,0.0008674527,0.0002772634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018216835,0.001645564,0.0028071082,0.002133788,0.0008397911,0.0019909428,0.0034570473,0.0032609534,0.0016912105],"category_scores_gemma":[0.036448807,0.0020054586,0.0026334506,0.0018769831,0.0029667404,0.0023375824,0.002896204,0.003378844,0.0005331704],"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":[0.00016357687,0.00007784463,0.0024170517,0.00030437362,0.0003056643,0.00022358944,0.0002911167,0.8062044,0.0012523353,0.141443,0.0015774568,0.045739606],"study_design_scores_gemma":[0.00001529878,0.000018874383,0.00024861362,0.000021186035,0.000017125834,0.000025301555,0.000009590437,0.9653769,0.000109869936,0.03361261,0.00052899943,0.000015596475],"about_ca_topic_score_codex":0.0073958314,"about_ca_topic_score_gemma":0.006721439,"teacher_disagreement_score":0.018216835,"about_ca_system_score_codex":0.0018614872,"about_ca_system_score_gemma":0.0015989824,"threshold_uncertainty_score":0.096340954},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007810353,0.00017354004,0.9904475,0.0004093218,0.00004903051,0.00007111617,0.00007961132,0.0002811763,0.00067843834],"genre_scores_gemma":[0.34441876,0.00021451406,0.6523346,0.0010527316,0.00009073079,0.0004940724,0.00034185752,0.00039134448,0.0006614377],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9536031,0.034478236,0.0013717347,0.0039670505,0.005963393,0.0006164849],"domain_scores_gemma":[0.6716486,0.28888702,0.009452568,0.020288877,0.008743248,0.0009796536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08235553,0.0016230869,0.0017711408,0.0026422963,0.00085562427,0.0031727806,0.004649656,0.0031815097,0.0038930508],"category_scores_gemma":[0.38967115,0.00083524064,0.0015178272,0.0027359074,0.0044320542,0.0057499465,0.005223913,0.007642061,0.00077510584],"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":[0.0013446609,0.00048211758,0.040394228,0.0011879147,0.001192753,0.00117584,0.002303894,0.14778638,0.009354841,0.37612593,0.0082076965,0.41044378],"study_design_scores_gemma":[0.00025388715,0.00053381466,0.010221332,0.0003652532,0.0002605872,0.0010211474,0.00040403748,0.5372504,0.020205084,0.4203967,0.008897276,0.00019053608],"about_ca_topic_score_codex":0.0018956638,"about_ca_topic_score_gemma":0.0013645912,"teacher_disagreement_score":0.08235553,"about_ca_system_score_codex":0.0018429408,"about_ca_system_score_gemma":0.0025125807,"threshold_uncertainty_score":0.43554288},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2361958,0.0030385815,0.7564847,0.000846425,0.00027283485,0.0017173231,0.00048219014,0.00033503858,0.0006271463],"genre_scores_gemma":[0.77366775,0.0005144032,0.22354329,0.00025012981,0.00011022439,0.0013129058,0.00035339498,0.000034513254,0.00021338843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90925694,0.07990178,0.002932737,0.0044609453,0.0028918546,0.00055564847],"domain_scores_gemma":[0.5509534,0.39528167,0.019894438,0.02718297,0.0054360675,0.0012514526],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17213307,0.0014343638,0.0021738766,0.0019886673,0.00065578386,0.0019345961,0.003531291,0.0024374665,0.0011537618],"category_scores_gemma":[0.38757092,0.0013542037,0.003623739,0.0013552675,0.0016923235,0.0024171902,0.0020647803,0.0024267444,0.000139716],"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":[0.013830338,0.0019509528,0.3545667,0.0015025543,0.019516677,0.0013753419,0.0012310838,0.37515518,0.0038591127,0.053097896,0.001489767,0.17242433],"study_design_scores_gemma":[0.0011037178,0.0034014331,0.022968791,0.00014502065,0.0041278102,0.0004202932,0.0001355945,0.9079153,0.0024258925,0.055490416,0.001732874,0.00013284346],"about_ca_topic_score_codex":0.0030587865,"about_ca_topic_score_gemma":0.0019365443,"teacher_disagreement_score":0.8278669,"about_ca_system_score_codex":0.0011469235,"about_ca_system_score_gemma":0.001970406,"threshold_uncertainty_score":0.91033757},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20335568,0.0009813837,0.7893003,0.0020380416,0.00007620843,0.00032580175,0.0011828245,0.00040961415,0.002330165],"genre_scores_gemma":[0.87920064,0.00073522254,0.111157864,0.00040818888,0.00009738249,0.00062049524,0.0013237388,0.00010794936,0.0063484497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972862,0.001866528,0.00008750101,0.00035179371,0.00016739986,0.00024059876],"domain_scores_gemma":[0.9753635,0.021648768,0.0012728665,0.00069026585,0.00066783343,0.00035675298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012551283,0.0011027035,0.0015653096,0.0011377557,0.00047078446,0.0015156412,0.002482182,0.0017910396,0.002161571],"category_scores_gemma":[0.029912818,0.001206118,0.0022889553,0.001090121,0.0012710014,0.0009311471,0.0021195302,0.002069432,0.00037145644],"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":[0.0002875124,0.00011136414,0.023290716,0.00010668144,0.0003775302,0.00030462732,0.00031503846,0.9412846,0.00044128284,0.018281585,0.0008476554,0.014351437],"study_design_scores_gemma":[0.000026989199,0.000042742984,0.0016924173,0.00001966992,0.00004315491,0.0000343163,0.000026634754,0.98998183,0.000100671634,0.007672467,0.0003457213,0.000013393958],"about_ca_topic_score_codex":0.03230963,"about_ca_topic_score_gemma":0.02053123,"teacher_disagreement_score":0.03230963,"about_ca_system_score_codex":0.0015669275,"about_ca_system_score_gemma":0.0021579117,"threshold_uncertainty_score":0.066378295},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021171717,0.00037640147,0.97547877,0.00051845977,0.00018696753,0.0003131335,0.00049888925,0.00052479963,0.000930948],"genre_scores_gemma":[0.20443399,0.0004012859,0.79115397,0.00033113305,0.0004656934,0.0014516028,0.00075870985,0.00016695079,0.0008366908],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97113466,0.022007342,0.00133835,0.0019371697,0.0031351582,0.00044731738],"domain_scores_gemma":[0.91550547,0.065196775,0.005183746,0.008151714,0.0050032157,0.00095896696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025747338,0.00083673827,0.0020818808,0.0029103376,0.001055811,0.0018405231,0.0026943653,0.0018140415,0.0034668434],"category_scores_gemma":[0.103452794,0.0005644931,0.0020683391,0.0039679403,0.0023690637,0.0020617172,0.0026999176,0.0025097607,0.00064972084],"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":[0.0014349928,0.00033355338,0.061100084,0.0010281381,0.0025070996,0.0019154442,0.0012722344,0.10403729,0.005359768,0.11530302,0.013777109,0.6919312],"study_design_scores_gemma":[0.0006647516,0.0023218158,0.03288695,0.00034270404,0.00064045365,0.0019414637,0.00049041986,0.5630014,0.0030086213,0.3722965,0.021981087,0.00042389045],"about_ca_topic_score_codex":0.0018991096,"about_ca_topic_score_gemma":0.0016190013,"teacher_disagreement_score":0.025747338,"about_ca_system_score_codex":0.00069549086,"about_ca_system_score_gemma":0.0035278595,"threshold_uncertainty_score":0.13616657},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034262857,0.00036974516,0.99341017,0.000655706,0.00003969142,0.00002522406,0.00014275964,0.000062363506,0.0018680366],"genre_scores_gemma":[0.4344721,0.0017482924,0.5389868,0.0005819813,0.0003494626,0.00062183087,0.00090033223,0.00032036396,0.022018904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884003,0.00059299724,0.00005081899,0.00021393308,0.00021825428,0.000083962725],"domain_scores_gemma":[0.997331,0.0019916936,0.00017065842,0.000112211215,0.00029329737,0.000101115525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037225254,0.0008064482,0.0012130539,0.00091425906,0.00052426493,0.0016041449,0.0025649348,0.0020896888,0.0042667165],"category_scores_gemma":[0.008769296,0.0009785515,0.0013815837,0.0009573153,0.0017288958,0.0021448876,0.0016903444,0.0016633881,0.00057359197],"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":[0.00003796847,0.0000191476,0.0006641473,0.00007488592,0.00005585109,0.00010142322,0.00013370244,0.5826035,0.0007702259,0.39880547,0.0018346683,0.014898959],"study_design_scores_gemma":[0.000009018654,0.00000738257,0.00009857949,0.000007996068,0.0000058794008,0.000027031732,0.0000089658715,0.9235993,0.000060790204,0.075052105,0.0011142974,0.000008683592],"about_ca_topic_score_codex":0.013194947,"about_ca_topic_score_gemma":0.009023506,"teacher_disagreement_score":0.013194947,"about_ca_system_score_codex":0.0018625674,"about_ca_system_score_gemma":0.0016721213,"threshold_uncertainty_score":0.026236296},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026696294,0.00021710599,0.9954666,0.00012651464,0.00003284704,0.000027507302,0.00008890498,0.00015730457,0.0012135628],"genre_scores_gemma":[0.25970742,0.0024940805,0.7144384,0.00052110024,0.00039613474,0.00047085635,0.0011144435,0.0005391827,0.020318544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975212,0.0009154444,0.000114631686,0.00060833513,0.00064493675,0.00019537387],"domain_scores_gemma":[0.9946333,0.0028347801,0.00061399746,0.000915667,0.00081541983,0.00018694584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043965485,0.0014397777,0.0013887509,0.0022856677,0.00090258254,0.0026500446,0.0025449598,0.0019479026,0.007917196],"category_scores_gemma":[0.016221968,0.0010395077,0.0020906634,0.0030419424,0.0016737778,0.0045303563,0.0024536173,0.0033163826,0.0030437647],"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":[0.00010386053,0.00007575344,0.002663206,0.0001687671,0.000119383934,0.00031887187,0.0003342995,0.20897387,0.005626121,0.64714223,0.006464519,0.12800926],"study_design_scores_gemma":[0.000010749622,0.000029902945,0.00068785367,0.000039216146,0.000028163913,0.00032136528,0.000038968523,0.78905,0.0009871757,0.19967951,0.009077579,0.0000495359],"about_ca_topic_score_codex":0.0021438233,"about_ca_topic_score_gemma":0.0023480018,"teacher_disagreement_score":0.007917196,"about_ca_system_score_codex":0.0010539753,"about_ca_system_score_gemma":0.0008902077,"threshold_uncertainty_score":0.026485682},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024828708,0.00015486577,0.99635637,0.00022936186,0.000014213294,0.000046863788,0.000062829684,0.000063587286,0.0005890039],"genre_scores_gemma":[0.17879559,0.0013206874,0.814623,0.00032409068,0.00021172431,0.0011690375,0.00065095595,0.00017019504,0.002734707],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9838378,0.0133541785,0.0003936086,0.0008925228,0.0012079634,0.0003139567],"domain_scores_gemma":[0.93753654,0.056346226,0.0016699133,0.002782877,0.0013733627,0.00029108868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02640595,0.0014255343,0.0022047542,0.0025212958,0.0010669418,0.0021889429,0.003537703,0.0017604931,0.0049982583],"category_scores_gemma":[0.10295958,0.0014156698,0.0018413996,0.0031774412,0.0033048228,0.0034095629,0.0032861584,0.0045608026,0.0007411216],"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":[0.000088502704,0.00006228559,0.0017700805,0.00019434767,0.00017748779,0.00010930502,0.00036987817,0.23136346,0.00036676432,0.7042312,0.0015063636,0.059760306],"study_design_scores_gemma":[0.000031965064,0.00002385724,0.00040825343,0.000060768347,0.000031144446,0.000035544268,0.00003468134,0.43554384,0.00017633056,0.5620187,0.001612656,0.000022317849],"about_ca_topic_score_codex":0.010276587,"about_ca_topic_score_gemma":0.011287323,"teacher_disagreement_score":0.02640595,"about_ca_system_score_codex":0.0023573472,"about_ca_system_score_gemma":0.003478312,"threshold_uncertainty_score":0.13964963},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016822658,0.030187683,0.9555723,0.00064257864,0.0003194808,0.000111889894,0.0002205916,0.0006277239,0.010635532],"genre_scores_gemma":[0.017391643,0.032156054,0.9381025,0.00028492723,0.00042117026,0.00025882572,0.00089601503,0.00031078586,0.010178184],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981646,0.00039357462,0.00024545207,0.00035542465,0.0007620237,0.00007898917],"domain_scores_gemma":[0.99788505,0.00080392254,0.00012020786,0.00030827377,0.0008276832,0.000054770593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031707606,0.0018493138,0.0015229196,0.0044404995,0.0008303318,0.0048855217,0.0026224523,0.0013654415,0.008160624],"category_scores_gemma":[0.0061413185,0.0009756948,0.0013440709,0.007361096,0.0012985374,0.004557074,0.002280058,0.0035379922,0.005760883],"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":[0.000045907793,0.000079298334,0.00053995114,0.0010296479,0.000050112994,0.00009246579,0.0002570141,0.0069627753,0.003531271,0.17773712,0.011960886,0.7977135],"study_design_scores_gemma":[0.000029042048,0.00022790159,0.0014395323,0.0011774683,0.000071307186,0.0015010955,0.00041869454,0.22733451,0.007443459,0.4811355,0.27904955,0.00017196649],"about_ca_topic_score_codex":0.00073642854,"about_ca_topic_score_gemma":0.0010024799,"teacher_disagreement_score":0.008160624,"about_ca_system_score_codex":0.0010847378,"about_ca_system_score_gemma":0.0008571228,"threshold_uncertainty_score":0.0273},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0324045,0.00045310173,0.9654421,0.00040839854,0.000030704556,0.000040317675,0.00005000161,0.00013240526,0.0010384964],"genre_scores_gemma":[0.7732423,0.0012916068,0.2209275,0.0002503852,0.00023560152,0.00033760737,0.00033820645,0.00014390307,0.0032328719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971391,0.0020624315,0.00009911753,0.00025667166,0.00033915412,0.00010353318],"domain_scores_gemma":[0.97080034,0.026297074,0.0010766607,0.0004979829,0.0009394662,0.00038855325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075561893,0.0010406253,0.0019982061,0.0014761862,0.0007011951,0.0013384528,0.001433905,0.0011934731,0.0012387931],"category_scores_gemma":[0.03141068,0.0006784038,0.0012103543,0.0011471382,0.0016242919,0.0012802668,0.00247291,0.0013671219,0.00021672997],"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":[0.000051091014,0.00004200216,0.0018259251,0.00008449624,0.000083808314,0.00014413158,0.00010130899,0.93969077,0.00071062887,0.043143135,0.00030146417,0.013821149],"study_design_scores_gemma":[0.000003724915,0.000009520343,0.00010948212,0.0000051399165,0.00000401356,0.000010137881,0.0000043457935,0.99217904,0.00007617259,0.007485625,0.000106689266,0.0000060886227],"about_ca_topic_score_codex":0.0043451167,"about_ca_topic_score_gemma":0.0021096568,"teacher_disagreement_score":0.0075561893,"about_ca_system_score_codex":0.0008037691,"about_ca_system_score_gemma":0.0008347549,"threshold_uncertainty_score":0.039961398},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014676393,0.00011928017,0.99740016,0.00013248001,0.00003350932,0.000032511725,0.00013832982,0.00015554011,0.00052066037],"genre_scores_gemma":[0.1171502,0.00074155856,0.87594104,0.00060702633,0.00036793647,0.00068225793,0.0011996357,0.0004019932,0.0029083907],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890961,0.007079949,0.0005089745,0.0016226632,0.0013873215,0.00030494347],"domain_scores_gemma":[0.9700925,0.019119706,0.003137554,0.0053684725,0.0020337992,0.00024791886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01723352,0.0011497769,0.0018998482,0.0020906865,0.00052386627,0.001784737,0.003488453,0.001740643,0.0035812433],"category_scores_gemma":[0.07852123,0.00083039154,0.002165937,0.0026203976,0.0024450668,0.0026765245,0.0035152598,0.003483679,0.0013199917],"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":[0.00011467317,0.00007484589,0.007700647,0.00042245144,0.0007267612,0.00018779523,0.00027773032,0.12817311,0.0033041814,0.6959673,0.005583573,0.15746693],"study_design_scores_gemma":[0.0001271839,0.0001984881,0.003652902,0.00021789083,0.000183657,0.0003070376,0.00009597342,0.49593222,0.005546393,0.4656954,0.027902644,0.00014021136],"about_ca_topic_score_codex":0.0012131874,"about_ca_topic_score_gemma":0.0009372793,"teacher_disagreement_score":0.01723352,"about_ca_system_score_codex":0.00066958845,"about_ca_system_score_gemma":0.0017728026,"threshold_uncertainty_score":0.09114069},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004340432,0.0002600369,0.9925855,0.0004923936,0.000032956068,0.00023297755,0.00020800073,0.0001693865,0.001678404],"genre_scores_gemma":[0.14335112,0.00036558547,0.8510351,0.00034874628,0.00010138445,0.0022627164,0.0005786226,0.00013061111,0.001826185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9706651,0.023596857,0.0010149471,0.0022487051,0.0019229562,0.000551581],"domain_scores_gemma":[0.89745206,0.09006903,0.005100659,0.0047638933,0.0018368185,0.0007775073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037687667,0.0019181644,0.0030220828,0.0030776877,0.00082382327,0.0038137021,0.0035796256,0.0035069496,0.012052137],"category_scores_gemma":[0.092345804,0.0013548738,0.003344259,0.0028037133,0.0031215274,0.0063344,0.003286655,0.007839308,0.0012691553],"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":[0.0002522485,0.00025710554,0.0021590963,0.0003058442,0.00016916526,0.00012000064,0.00033119117,0.5111885,0.00044848942,0.3981345,0.0027967452,0.08383716],"study_design_scores_gemma":[0.00007634317,0.00012830537,0.00035261558,0.00010938919,0.000029870338,0.000051341754,0.000029206902,0.7295904,0.00029459465,0.266602,0.0026945793,0.00004131536],"about_ca_topic_score_codex":0.0016632987,"about_ca_topic_score_gemma":0.0013702981,"teacher_disagreement_score":0.037687667,"about_ca_system_score_codex":0.0043305233,"about_ca_system_score_gemma":0.0030571308,"threshold_uncertainty_score":0.19931388},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.116329834,0.0013166731,0.88017,0.00085578865,0.000059025402,0.00007245785,0.00016155516,0.00034774528,0.0006868836],"genre_scores_gemma":[0.8052372,0.0011657499,0.18915834,0.00020616385,0.00017577194,0.00020975445,0.00065578846,0.00012820773,0.0030631311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99466187,0.0045427284,0.00009747815,0.0002952926,0.00024191507,0.00016074281],"domain_scores_gemma":[0.96631294,0.030028086,0.0017286711,0.0007914682,0.00071497395,0.00042377293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012409442,0.00071989035,0.0012969607,0.0006670563,0.00029416347,0.00089291076,0.0012145396,0.000864313,0.0007896567],"category_scores_gemma":[0.030441225,0.0003879738,0.0006466977,0.0007689023,0.00084057776,0.00071607844,0.0010803327,0.0018442895,0.00026814442],"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":[0.0011565852,0.0001975853,0.01758696,0.00024307832,0.00021276936,0.00042507856,0.00013255018,0.85644203,0.0033649488,0.011595142,0.0026434085,0.105999865],"study_design_scores_gemma":[0.00002800916,0.00007858564,0.0021753728,0.000011036508,0.000013099379,0.000049092912,0.000013599263,0.9941322,0.0003109747,0.0029044913,0.00026588066,0.00001767638],"about_ca_topic_score_codex":0.0028388184,"about_ca_topic_score_gemma":0.0023861409,"teacher_disagreement_score":0.012409442,"about_ca_system_score_codex":0.000490367,"about_ca_system_score_gemma":0.0009383556,"threshold_uncertainty_score":0.06562823},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008411703,0.00056933175,0.9885308,0.0003162583,0.000039783168,0.0014618129,0.00011275623,0.000095010226,0.00046255055],"genre_scores_gemma":[0.12996294,0.0007797366,0.8609456,0.00027483414,0.000104974126,0.007008341,0.00023073427,0.000050359595,0.0006424882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7796742,0.20136867,0.0046674716,0.00749248,0.005678658,0.0011185749],"domain_scores_gemma":[0.6329204,0.3383114,0.009006985,0.013224266,0.005037738,0.0014992119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15291734,0.002040509,0.0051202932,0.0035125948,0.0010374227,0.002476372,0.003136,0.0037666338,0.0047942796],"category_scores_gemma":[0.29273355,0.0019361741,0.003642654,0.0024295938,0.004080676,0.004214837,0.0038473948,0.0028675895,0.00063015154],"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":[0.008432649,0.0008248926,0.029519385,0.0053029964,0.00519766,0.0013183292,0.0016971813,0.1496947,0.008900061,0.4146083,0.002768099,0.37173584],"study_design_scores_gemma":[0.0031288904,0.008146403,0.011309057,0.00088760303,0.0030360087,0.00094651326,0.00029963243,0.51920706,0.005645385,0.43931746,0.007830261,0.00024567117],"about_ca_topic_score_codex":0.0006460044,"about_ca_topic_score_gemma":0.0006315687,"teacher_disagreement_score":0.15291734,"about_ca_system_score_codex":0.0018079095,"about_ca_system_score_gemma":0.0041915383,"threshold_uncertainty_score":0.80871385},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014912167,0.0006671538,0.0010702544,0.9821212,0.01438512,0.000012433389,0.00012410023,0.00003285592,0.0014376686],"genre_scores_gemma":[0.0018806986,0.00028624714,0.0012623025,0.9709589,0.022178631,0.00007426502,0.000039889885,0.000051494688,0.0032675518],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9615644,0.015046338,0.0057315677,0.0061870357,0.009057279,0.002413355],"domain_scores_gemma":[0.8827094,0.090679266,0.004862222,0.0051146033,0.0126242,0.0040103025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040100902,0.0013238512,0.003915502,0.0016579743,0.0049375235,0.008729538,0.006137551,0.0915792,0.0071368366],"category_scores_gemma":[0.21689053,0.002090957,0.0030011954,0.0016676323,0.014536337,0.007198005,0.0060154274,0.125303,0.009809229],"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":[0.00008080913,0.000025513124,0.00041288754,0.000047426696,0.000042837786,0.0002504961,0.00022741378,0.00010451834,0.00013196025,0.025162099,0.9649679,0.008546199],"study_design_scores_gemma":[0.00024714626,0.00006508087,0.001443463,0.000586624,0.00013174246,0.0006620824,0.00039137588,0.0010049951,0.00049961737,0.109541796,0.8852072,0.00021888928],"about_ca_topic_score_codex":0.011061077,"about_ca_topic_score_gemma":0.01081375,"teacher_disagreement_score":0.0915792,"about_ca_system_score_codex":0.0051959353,"about_ca_system_score_gemma":0.010426124,"threshold_uncertainty_score":0.21207637},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63575864,0.0010619896,0.35663614,0.0012615883,0.0000739366,0.000102631035,0.0027401065,0.0012772158,0.0010877319],"genre_scores_gemma":[0.9692468,0.00020567008,0.026773445,0.00006587034,0.00004023859,0.00009046028,0.002498932,0.00004831519,0.0010302508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866784,0.00069732696,0.00008264588,0.0003226125,0.00011866463,0.000110879344],"domain_scores_gemma":[0.9904308,0.007248457,0.0008770981,0.00075581745,0.00041059472,0.00027719067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005720347,0.00059218705,0.0009863272,0.00095337915,0.00025843308,0.0011360343,0.0009920318,0.0008125857,0.0015374954],"category_scores_gemma":[0.014893024,0.0005294628,0.0012003463,0.00094784744,0.0003736492,0.0008902997,0.0008618761,0.0012965791,0.00065862964],"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":[0.0003266101,0.00014382903,0.04934834,0.00003523543,0.00017155902,0.00008406926,0.000091113965,0.9067744,0.00040780738,0.0015037089,0.0013909759,0.039722353],"study_design_scores_gemma":[0.000008343958,0.000033783497,0.0037522758,0.000004334014,0.000013251058,0.000022903592,0.000009125394,0.99415475,0.00014020962,0.0016979035,0.00015387667,0.000009246072],"about_ca_topic_score_codex":0.010538907,"about_ca_topic_score_gemma":0.0110349795,"teacher_disagreement_score":0.010538907,"about_ca_system_score_codex":0.00074609614,"about_ca_system_score_gemma":0.0007376897,"threshold_uncertainty_score":0.030252457},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5838189,0.00064655737,0.41194254,0.00041995844,0.00008448383,0.0002583588,0.0004923332,0.00058698,0.0017497686],"genre_scores_gemma":[0.8579913,0.000119891374,0.14004919,0.00015874427,0.000041677795,0.00018775255,0.0005060654,0.00007147526,0.0008740366],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9862736,0.009020761,0.000732064,0.00250454,0.00087145093,0.0005976331],"domain_scores_gemma":[0.95151615,0.02949316,0.009404014,0.0067721363,0.0023069417,0.0005076598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020543363,0.0006440188,0.0007748116,0.001392961,0.00041164632,0.00097218546,0.0014313932,0.00090550346,0.001491682],"category_scores_gemma":[0.08977341,0.0004726235,0.0010165118,0.0026695037,0.00073609146,0.0012522968,0.0011631093,0.00071910734,0.00031084396],"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":[0.0005550394,0.0002558441,0.7042998,0.00022585441,0.0009873646,0.00015303891,0.00046048046,0.042004842,0.0037591425,0.0018977332,0.0012664265,0.24413446],"study_design_scores_gemma":[0.00015240387,0.0010768657,0.7698634,0.000091180154,0.0007545384,0.00032464022,0.0005045231,0.21389887,0.005592543,0.0031879558,0.004472776,0.00008035769],"about_ca_topic_score_codex":0.012577046,"about_ca_topic_score_gemma":0.025413398,"teacher_disagreement_score":0.020543363,"about_ca_system_score_codex":0.00072387524,"about_ca_system_score_gemma":0.0010258133,"threshold_uncertainty_score":0.10864502},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03275372,0.0009315325,0.9650194,0.00021787046,0.00002373648,0.00010404297,0.00032118507,0.0002950681,0.00033339058],"genre_scores_gemma":[0.5810661,0.0014466152,0.41398513,0.00020837587,0.0001183206,0.0005748248,0.0015241622,0.00013058519,0.00094592356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991154,0.0063944715,0.00053311104,0.0009705604,0.00073261914,0.00021516236],"domain_scores_gemma":[0.94547856,0.042132106,0.0048380475,0.0050517637,0.0021863263,0.0003132032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022392564,0.0011223841,0.0017513793,0.0026627749,0.0002647687,0.0013639715,0.0018190484,0.0017581459,0.00085147633],"category_scores_gemma":[0.09578321,0.0005452904,0.0017763095,0.0021961785,0.0009714659,0.0018560305,0.0014072425,0.0014158492,0.00032779988],"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":[0.0009394313,0.00024122636,0.05980811,0.0008856116,0.0018963999,0.0009935145,0.00044625346,0.5557294,0.0067194914,0.042323526,0.0032080496,0.326809],"study_design_scores_gemma":[0.00007065835,0.00033223114,0.016603379,0.00010613228,0.00028013805,0.0007335499,0.00007869995,0.92072785,0.003847168,0.053528972,0.0035924464,0.00009882696],"about_ca_topic_score_codex":0.001246838,"about_ca_topic_score_gemma":0.0008900635,"teacher_disagreement_score":0.022392564,"about_ca_system_score_codex":0.00057721575,"about_ca_system_score_gemma":0.0008661209,"threshold_uncertainty_score":0.118424594},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019186467,0.00068663765,0.97248966,0.0015181476,0.00015836328,0.00014946108,0.001246616,0.0003984534,0.004166164],"genre_scores_gemma":[0.5818292,0.0026420443,0.32803565,0.0008083118,0.00067435316,0.001964769,0.0036026745,0.00033564257,0.080107324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967874,0.0016628591,0.00014266938,0.000758332,0.00032002796,0.00032872078],"domain_scores_gemma":[0.9898958,0.0076147844,0.0009328045,0.0005869255,0.0005998532,0.00036980122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009555775,0.0019351626,0.0030995128,0.0025404403,0.0013278067,0.0032060866,0.0077654948,0.004596313,0.018441098],"category_scores_gemma":[0.016160687,0.0013986189,0.002901477,0.0027869362,0.0028618155,0.004579869,0.0027174177,0.0045843776,0.0037168264],"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":[0.00013119406,0.00009284156,0.0021499938,0.000116252566,0.00015889175,0.0003530724,0.0003288918,0.24444494,0.0005813094,0.73705596,0.0031233386,0.011463256],"study_design_scores_gemma":[0.00005917628,0.0000546635,0.0005735527,0.000022958455,0.00004857515,0.00009754157,0.000046823767,0.87037706,0.000076409095,0.12559003,0.0030074816,0.0000456938],"about_ca_topic_score_codex":0.02270922,"about_ca_topic_score_gemma":0.016009161,"teacher_disagreement_score":0.02270922,"about_ca_system_score_codex":0.0024459509,"about_ca_system_score_gemma":0.0018224613,"threshold_uncertainty_score":0.06169164},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004787686,0.000108710476,0.99450785,0.00006076573,0.00000667067,0.000014921947,0.000027725571,0.00008648016,0.00039921285],"genre_scores_gemma":[0.5915232,0.0010633244,0.40317884,0.00021238386,0.00018807026,0.00032316378,0.00047485426,0.00029206852,0.0027441364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960128,0.0023975328,0.00013316788,0.0006233244,0.0006668931,0.00016621716],"domain_scores_gemma":[0.9708671,0.021213269,0.0020904203,0.0040816823,0.0014971666,0.00025032766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009857072,0.0009125111,0.0014110081,0.0020631547,0.00061434286,0.001672849,0.0027472498,0.0015053786,0.001849556],"category_scores_gemma":[0.052007657,0.0009636424,0.001605265,0.0020550506,0.0018311676,0.002738205,0.0024432787,0.0029808911,0.00045338448],"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":[0.000066996006,0.00009090401,0.0057214596,0.00017076734,0.00028244703,0.00033571897,0.00030950867,0.60588986,0.0028332316,0.2777596,0.001699425,0.10484011],"study_design_scores_gemma":[0.000005655734,0.00001919125,0.0010790803,0.000019835108,0.00001670583,0.000087443186,0.000020558264,0.92303395,0.0005003101,0.074339226,0.0008539334,0.000024204759],"about_ca_topic_score_codex":0.002237309,"about_ca_topic_score_gemma":0.0014917537,"teacher_disagreement_score":0.009857072,"about_ca_system_score_codex":0.0009505321,"about_ca_system_score_gemma":0.0010502452,"threshold_uncertainty_score":0.052129805},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34792694,0.00034872568,0.65040696,0.00007326318,0.000011486539,0.0000344885,0.000114656956,0.00023009937,0.0008533671],"genre_scores_gemma":[0.88410676,0.00035612442,0.11425927,0.000025991843,0.00001742138,0.00004719284,0.00027048655,0.000054825836,0.0008619436],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799466,0.0010382672,0.0001313742,0.00039236931,0.00032631046,0.00011705452],"domain_scores_gemma":[0.98145753,0.013595105,0.0020726146,0.0017365154,0.0009933271,0.00014482054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062662493,0.00039331248,0.0010615971,0.0012031788,0.00022091514,0.00064473023,0.00085840886,0.00046402702,0.00052679406],"category_scores_gemma":[0.03193629,0.0004501388,0.00060074107,0.0013727929,0.00068813993,0.001387039,0.0012411571,0.00056016777,0.00022064864],"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":[0.00065574533,0.00008798442,0.43069744,0.00032407677,0.00058464805,0.0009792582,0.0010106863,0.22060987,0.015204166,0.013401666,0.0008823757,0.31556204],"study_design_scores_gemma":[0.000041634245,0.00045642644,0.15934297,0.000048171445,0.0001715261,0.0012381348,0.00043570768,0.8016645,0.009533773,0.02523172,0.0017470586,0.00008849749],"about_ca_topic_score_codex":0.0020901433,"about_ca_topic_score_gemma":0.0019331449,"teacher_disagreement_score":0.0062662493,"about_ca_system_score_codex":0.00036081907,"about_ca_system_score_gemma":0.00033857525,"threshold_uncertainty_score":0.033139527},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049543753,0.00038506836,0.9916612,0.0004999784,0.00004043546,0.00002624189,0.00015261727,0.000060575967,0.0022195214],"genre_scores_gemma":[0.39312458,0.0029739866,0.5837635,0.00061934127,0.0006416001,0.0005769161,0.00086979783,0.00014929482,0.017280955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99786925,0.0012281364,0.000081738915,0.00036086887,0.0003323633,0.00012760748],"domain_scores_gemma":[0.9962288,0.002816811,0.00029070606,0.00025677288,0.00030081748,0.000106097556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055307937,0.0007383874,0.0014328021,0.0014680561,0.0008478412,0.001566207,0.003355671,0.0016675077,0.0040510353],"category_scores_gemma":[0.012185393,0.00089680904,0.0012070581,0.0016157351,0.001451247,0.0023543302,0.0017914796,0.002635167,0.0008142208],"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":[0.000034829438,0.000051460433,0.002076762,0.00008964303,0.00010166095,0.00019864195,0.00030324198,0.30418852,0.00064296805,0.657657,0.002198845,0.032456372],"study_design_scores_gemma":[0.000011462159,0.000028074357,0.000840419,0.00003080399,0.00003155705,0.000119493474,0.0000331624,0.75033146,0.00011413135,0.24391834,0.0045044166,0.000036749323],"about_ca_topic_score_codex":0.008549729,"about_ca_topic_score_gemma":0.011006494,"teacher_disagreement_score":0.008549729,"about_ca_system_score_codex":0.0013164713,"about_ca_system_score_gemma":0.0015487869,"threshold_uncertainty_score":0.029249966},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04833388,0.0005499383,0.9485818,0.0005260646,0.0001547201,0.000311724,0.00053142954,0.00035992992,0.0006504455],"genre_scores_gemma":[0.5826158,0.0005893856,0.40875474,0.0010449131,0.00022053227,0.0016406652,0.00142093,0.00015007159,0.0035630313],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93970275,0.04161746,0.0044407947,0.009179152,0.0042005866,0.0008592727],"domain_scores_gemma":[0.84558475,0.09382856,0.017573584,0.03847066,0.0040624905,0.00048005284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10061963,0.0012649478,0.0028190236,0.0016356177,0.0009911583,0.0027553535,0.004156968,0.0027580776,0.0021991262],"category_scores_gemma":[0.18706045,0.0010465792,0.0028954518,0.0028711967,0.0025639615,0.002754942,0.0022033176,0.003600023,0.0006976653],"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":[0.0037610123,0.0006787934,0.31350636,0.0013262855,0.0049025165,0.0015252765,0.003299197,0.14133146,0.0042009815,0.17787613,0.0053936453,0.3421983],"study_design_scores_gemma":[0.00067963346,0.0013850355,0.08981314,0.00049802155,0.0023712944,0.0016580056,0.00030579235,0.64419514,0.0059192935,0.23867635,0.014224137,0.00027412656],"about_ca_topic_score_codex":0.003762837,"about_ca_topic_score_gemma":0.002410942,"teacher_disagreement_score":0.10061963,"about_ca_system_score_codex":0.0012629451,"about_ca_system_score_gemma":0.0014854529,"threshold_uncertainty_score":0.5321338},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001553472,0.00028258632,0.99751925,0.00012012231,0.000031246283,0.000037760587,0.00014790875,0.00014771774,0.00015997235],"genre_scores_gemma":[0.15517743,0.0019445273,0.83185613,0.0002531542,0.00036156824,0.0019707999,0.0023888585,0.00030807423,0.005739377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9885183,0.008825994,0.0003600643,0.0009539903,0.0010327132,0.00030883134],"domain_scores_gemma":[0.96904254,0.024216184,0.0022573892,0.0025535212,0.0015463184,0.000384091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01840216,0.0013896147,0.0022850283,0.0024072144,0.00084990944,0.0017681102,0.005236181,0.0020692989,0.0060737384],"category_scores_gemma":[0.04322596,0.0012154435,0.003081642,0.004343415,0.0015430386,0.002044131,0.0022705477,0.004468053,0.0015929003],"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":[0.00018871507,0.00013679748,0.003929587,0.00040271372,0.0005890944,0.00036603824,0.00042628183,0.47035763,0.0010960008,0.44773236,0.0041885185,0.07058633],"study_design_scores_gemma":[0.000031636577,0.00007147665,0.00072315545,0.000043085984,0.00005157474,0.00006527484,0.000030189161,0.8363557,0.00018227659,0.15900107,0.003405739,0.000038839324],"about_ca_topic_score_codex":0.004829937,"about_ca_topic_score_gemma":0.0048559057,"teacher_disagreement_score":0.01840216,"about_ca_system_score_codex":0.0014746119,"about_ca_system_score_gemma":0.0021883203,"threshold_uncertainty_score":0.09732109},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007102413,0.000644302,0.99747354,0.00021053491,0.00007593995,0.0002201198,0.00023213105,0.0001806401,0.00025265611],"genre_scores_gemma":[0.040732812,0.0026343386,0.9480858,0.0003191393,0.00041356488,0.0042152107,0.0015734435,0.00021361854,0.0018120399],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9610635,0.031626586,0.0012403645,0.0023086602,0.0034398774,0.00032110995],"domain_scores_gemma":[0.87066,0.11134826,0.0056303786,0.008891116,0.0030800218,0.0003902553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.075834386,0.0016226356,0.0024765157,0.0058649713,0.00087054697,0.0016563043,0.005568771,0.0019461523,0.009183527],"category_scores_gemma":[0.20490474,0.0012267558,0.003550808,0.006692889,0.0021222692,0.0026478362,0.0032046484,0.0057864236,0.0020635987],"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":[0.00026559684,0.00021160147,0.010680203,0.0023624038,0.0022854684,0.00084053143,0.00106705,0.13215668,0.001025809,0.553708,0.01668433,0.27871224],"study_design_scores_gemma":[0.00018757585,0.0002490806,0.0039019634,0.00046793267,0.00031411665,0.0004556396,0.00015121166,0.33662927,0.00067884027,0.632249,0.024598788,0.00011664357],"about_ca_topic_score_codex":0.0046708407,"about_ca_topic_score_gemma":0.0033775128,"teacher_disagreement_score":0.075834386,"about_ca_system_score_codex":0.0017998161,"about_ca_system_score_gemma":0.0037590833,"threshold_uncertainty_score":0.40105534},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024731029,0.0009873129,0.97266686,0.00007186728,0.000095294075,0.00008860065,0.00021592151,0.00020748499,0.0009356319],"genre_scores_gemma":[0.37280515,0.0012414512,0.6206612,0.00015444255,0.0001853367,0.0008264975,0.0013477497,0.00029541968,0.002482681],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99395823,0.0038790666,0.0003138954,0.0007341617,0.0009376837,0.00017695894],"domain_scores_gemma":[0.9620504,0.028121483,0.0031152405,0.0046090567,0.0017749699,0.00032890553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012517312,0.00082559296,0.0009976218,0.002549166,0.0005478752,0.0010668153,0.0017005306,0.00081308454,0.0023828028],"category_scores_gemma":[0.07339116,0.00041311598,0.0012029547,0.003110009,0.00083219993,0.0018908764,0.0012345138,0.002180163,0.00058873865],"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":[0.00073293154,0.00035460378,0.05913206,0.0011630703,0.0016378902,0.0006344077,0.0011013744,0.1163278,0.007535344,0.22559983,0.0067677638,0.5790129],"study_design_scores_gemma":[0.00007228245,0.00078024296,0.052406155,0.00039097812,0.00046563646,0.0010583444,0.000553582,0.62855333,0.007124167,0.28238228,0.025921261,0.00029170385],"about_ca_topic_score_codex":0.0016495688,"about_ca_topic_score_gemma":0.0019114737,"teacher_disagreement_score":0.012517312,"about_ca_system_score_codex":0.0005021883,"about_ca_system_score_gemma":0.0010217017,"threshold_uncertainty_score":0.06619871},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00812952,0.00032675598,0.98975956,0.00031682418,0.000050300067,0.00009211768,0.0003318836,0.0003325188,0.00066045497],"genre_scores_gemma":[0.44595182,0.0030749727,0.5310709,0.0011060071,0.0008497619,0.0044156825,0.0027721748,0.00031818482,0.010440429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918223,0.0040411116,0.00034405474,0.0018148462,0.0014076597,0.00056997774],"domain_scores_gemma":[0.97487724,0.021291781,0.0015749616,0.0012574736,0.0007725556,0.00022603538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013240814,0.002291313,0.002775317,0.0034605446,0.00095786684,0.002263692,0.0044895415,0.0042573777,0.0036012186],"category_scores_gemma":[0.027888155,0.0014181553,0.002923792,0.0036932197,0.004131001,0.0034395794,0.001608539,0.004196858,0.0017526813],"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":[0.00020960961,0.00023525883,0.0045160907,0.000305048,0.00030231592,0.0003079038,0.00043019155,0.61620665,0.008015157,0.3264929,0.0020143606,0.04096441],"study_design_scores_gemma":[0.00002943794,0.0001718826,0.0009775291,0.00002110587,0.000052560896,0.00011116228,0.000029041335,0.88482916,0.0011527285,0.11095145,0.0016186346,0.000055210567],"about_ca_topic_score_codex":0.0056627537,"about_ca_topic_score_gemma":0.0033329923,"teacher_disagreement_score":0.013240814,"about_ca_system_score_codex":0.0022950012,"about_ca_system_score_gemma":0.001820222,"threshold_uncertainty_score":0.07002497},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028091406,0.00027365886,0.9964485,0.00019560855,0.000022192542,0.000044382017,0.000027674565,0.000051181843,0.00012762635],"genre_scores_gemma":[0.12007328,0.0012800089,0.87608165,0.00028119085,0.00017917524,0.00081421004,0.00020795398,0.000083723455,0.0009987651],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95606273,0.034437258,0.0015526073,0.0027465667,0.004552361,0.0006484203],"domain_scores_gemma":[0.79198897,0.18872927,0.0070063276,0.0073033343,0.0042754877,0.0006966365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07719703,0.0014338174,0.0031106554,0.0038995596,0.0013920201,0.0019463254,0.0032240425,0.0028592069,0.0026221268],"category_scores_gemma":[0.22434138,0.0011365126,0.0044830567,0.0037673158,0.0032453046,0.0034864778,0.0050374684,0.0034071996,0.00039776423],"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":[0.0005545168,0.000204648,0.0136733195,0.0013587003,0.0020204342,0.0008562844,0.0018890322,0.13001595,0.0032270164,0.57063293,0.0022916594,0.27327555],"study_design_scores_gemma":[0.00015303378,0.00023104524,0.0022732562,0.00018676465,0.00046952386,0.00043409033,0.00013509899,0.4607939,0.0020077906,0.52981776,0.0034251963,0.00007256287],"about_ca_topic_score_codex":0.0025994368,"about_ca_topic_score_gemma":0.002206828,"teacher_disagreement_score":0.07719703,"about_ca_system_score_codex":0.0015452213,"about_ca_system_score_gemma":0.0047569806,"threshold_uncertainty_score":0.40826178},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015719502,0.000167051,0.9977223,0.00005941636,0.000024402523,0.000053850075,0.00005435603,0.000105202125,0.00024148135],"genre_scores_gemma":[0.06771958,0.0007027412,0.9253527,0.00021100398,0.0002151072,0.00086660596,0.0007492618,0.0001197285,0.0040632677],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99033797,0.006420055,0.0004389234,0.0012875908,0.0012666674,0.00024880664],"domain_scores_gemma":[0.95442885,0.035463348,0.0031918236,0.0041420353,0.0024188603,0.00035501795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018015597,0.0010521751,0.0018512248,0.003062609,0.0015172289,0.0021363217,0.005357337,0.0021460853,0.005133251],"category_scores_gemma":[0.051161595,0.0010380074,0.0018409974,0.0033582905,0.0014980128,0.0055623315,0.0036964987,0.0023303197,0.0019601963],"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":[0.00023033221,0.00019062022,0.008911343,0.00071530865,0.00059021835,0.00023229151,0.0009985942,0.31341562,0.0015850456,0.24545544,0.004807829,0.4228674],"study_design_scores_gemma":[0.000065167056,0.00017976444,0.0015032305,0.00012539304,0.0000986453,0.00015177843,0.00013648605,0.8624676,0.0011373593,0.12577285,0.008270793,0.00009094333],"about_ca_topic_score_codex":0.003471281,"about_ca_topic_score_gemma":0.004216078,"teacher_disagreement_score":0.018015597,"about_ca_system_score_codex":0.0009874165,"about_ca_system_score_gemma":0.0015330184,"threshold_uncertainty_score":0.09527671},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980683,0.00008606839,0.0002154113,0.00003785321,0.000002346882,0.00000823535,0.00064918405,0.000007367329,0.00092519046],"genre_scores_gemma":[0.9964592,0.00015446873,0.00042847177,0.000033205684,0.0000014544049,0.00001583062,0.0013702606,0.00000749667,0.0015297466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997335,0.000020157924,0.000011865634,0.00008980142,0.00007850717,0.00006607455],"domain_scores_gemma":[0.99925715,0.000083251,0.00009997221,0.000032903823,0.0004335686,0.00009314525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022900112,0.00028397568,0.00033377192,0.001220035,0.0013921128,0.0008039549,0.00057276903,0.0003069993,0.0006404967],"category_scores_gemma":[0.0007504978,0.00023903826,0.00024239365,0.0024133883,0.00045474965,0.00017314895,0.0005596309,0.0004256729,0.00013385531],"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":[0.00016752677,0.000045029014,0.9683242,0.000047164987,0.00010404225,0.00036378545,0.0023001458,0.0007593369,0.015790842,0.00006641105,0.00056148163,0.01147001],"study_design_scores_gemma":[0.0000010401341,0.0000064598275,0.99809104,0.0000049775686,0.000010673567,0.000026738928,0.0009602108,0.00031017067,0.00021532708,0.0000089983705,0.0003594789,0.0000049174955],"about_ca_topic_score_codex":0.9475461,"about_ca_topic_score_gemma":0.97930795,"teacher_disagreement_score":0.052453876,"about_ca_system_score_codex":0.0046775537,"about_ca_system_score_gemma":0.0031991028,"threshold_uncertainty_score":0.10552549},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02133956,0.00017760346,0.97462475,0.00008363496,0.00024392689,0.0006410992,0.000365035,0.0005886102,0.0019356965],"genre_scores_gemma":[0.19371119,0.00017512539,0.8005341,0.00020979662,0.00013419059,0.0031638113,0.0006813171,0.00017174552,0.0012187999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94154227,0.03546304,0.0018185992,0.00623239,0.0141251525,0.00081849145],"domain_scores_gemma":[0.8816802,0.09788821,0.004788265,0.009025885,0.0059797233,0.00063774106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03773869,0.0012364585,0.0031328022,0.0036073546,0.0011507581,0.0014811173,0.0039215717,0.0018261629,0.010054277],"category_scores_gemma":[0.1010953,0.0005936451,0.0021460056,0.0023215495,0.002676671,0.0025449162,0.0017185637,0.0030869716,0.0009176243],"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":[0.0056556514,0.0022731775,0.0124059515,0.0028013678,0.0022812707,0.0006680976,0.000999841,0.031743355,0.058315147,0.15119393,0.0072304914,0.72443175],"study_design_scores_gemma":[0.0024102414,0.04015674,0.0684915,0.0006163789,0.0015745845,0.0023983915,0.001130333,0.44957972,0.117237516,0.2789519,0.036260325,0.0011924029],"about_ca_topic_score_codex":0.0005746328,"about_ca_topic_score_gemma":0.000708684,"teacher_disagreement_score":0.03773869,"about_ca_system_score_codex":0.0013157174,"about_ca_system_score_gemma":0.0024147332,"threshold_uncertainty_score":0.19958371},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014990579,0.0001260087,0.9979972,0.00005963326,0.000011803675,0.000010791862,0.000029711136,0.000050897506,0.00021477732],"genre_scores_gemma":[0.22224179,0.00264205,0.77044356,0.0003113088,0.0003091788,0.00046590538,0.00057642243,0.0002529142,0.0027568534],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890631,0.00676279,0.00047846092,0.0016495595,0.001692929,0.00035324928],"domain_scores_gemma":[0.98065805,0.013319145,0.002065431,0.0025976223,0.001166626,0.0001931145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012087971,0.0016359752,0.0026642722,0.002334856,0.00058817613,0.0027791045,0.0023642764,0.0013447643,0.0018632148],"category_scores_gemma":[0.037440225,0.0008984734,0.00279588,0.0037860384,0.0021242576,0.0039187046,0.0028823758,0.0030501815,0.0006728153],"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":[0.00004671584,0.00006647301,0.0031539092,0.00026022905,0.0005623966,0.0004010193,0.0004386993,0.3039729,0.002146562,0.60422873,0.0017344443,0.08298792],"study_design_scores_gemma":[0.000008112502,0.000035156187,0.0008627757,0.0000375599,0.000077042925,0.000096424235,0.00003897768,0.8285445,0.00058105,0.16749153,0.0021850553,0.000041835017],"about_ca_topic_score_codex":0.002806337,"about_ca_topic_score_gemma":0.0019190776,"teacher_disagreement_score":0.012087971,"about_ca_system_score_codex":0.0011173089,"about_ca_system_score_gemma":0.0018324762,"threshold_uncertainty_score":0.06392807},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014098076,0.00010486222,0.9843336,0.0004260599,0.000011749573,0.000059934795,0.0002520857,0.00014336902,0.00057033997],"genre_scores_gemma":[0.63014305,0.0004026796,0.3654166,0.0002857683,0.000104576124,0.00074858696,0.0013548258,0.00008105907,0.0014628707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99282986,0.0047338544,0.00026247863,0.0012400433,0.0007615742,0.00017219038],"domain_scores_gemma":[0.96269155,0.03230427,0.0018717701,0.0017926331,0.0010537506,0.0002860437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014292444,0.0014684406,0.0011057046,0.0030526184,0.00078892236,0.0023381293,0.0024612057,0.001967885,0.001648086],"category_scores_gemma":[0.05867317,0.00089896144,0.0019567702,0.00184269,0.002052199,0.0032786855,0.0016620861,0.002516464,0.00031406706],"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":[0.00008606906,0.00008862175,0.0050942493,0.00010823436,0.00015022527,0.00020647603,0.00024043022,0.8268142,0.0010974873,0.13820662,0.00055875187,0.027348613],"study_design_scores_gemma":[0.000007646352,0.000008747403,0.00029170126,0.000006512332,0.000012638159,0.000018971092,0.000013887134,0.92088455,0.00017788945,0.078307465,0.00026273663,0.0000072801095],"about_ca_topic_score_codex":0.005998771,"about_ca_topic_score_gemma":0.0041699,"teacher_disagreement_score":0.014292444,"about_ca_system_score_codex":0.0026568398,"about_ca_system_score_gemma":0.0013272004,"threshold_uncertainty_score":0.07558662},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044248244,0.00051909074,0.9938852,0.00010960776,0.00002479195,0.00002770087,0.000047446338,0.0001708051,0.0007905133],"genre_scores_gemma":[0.2716702,0.0018208204,0.7236955,0.0002598886,0.00024656637,0.0005726925,0.00057737244,0.00022948117,0.0009275216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9817785,0.009764323,0.0009513576,0.0019684222,0.005097641,0.00043969546],"domain_scores_gemma":[0.7439771,0.21697076,0.01095705,0.015449369,0.01174009,0.000905476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034708798,0.0012840068,0.0019322819,0.005749606,0.00066843757,0.003159555,0.0044416524,0.00294446,0.0030228328],"category_scores_gemma":[0.30706212,0.00058508385,0.0016068567,0.0041039614,0.0029308286,0.003686203,0.0032996032,0.0037405465,0.0012335306],"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":[0.0003216935,0.00015999736,0.014603478,0.0007109108,0.00050722534,0.00042876045,0.0011555514,0.13840325,0.0029018717,0.44780397,0.0043095243,0.38869378],"study_design_scores_gemma":[0.00006196012,0.00018473969,0.005934228,0.00046168742,0.00016686738,0.00073770265,0.00011904939,0.6616204,0.0033134862,0.3241364,0.0031159297,0.00014761399],"about_ca_topic_score_codex":0.0014446073,"about_ca_topic_score_gemma":0.0008233194,"teacher_disagreement_score":0.034708798,"about_ca_system_score_codex":0.0010996206,"about_ca_system_score_gemma":0.0014371859,"threshold_uncertainty_score":0.18355983},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07272688,0.00095713837,0.9231634,0.00042018446,0.000039309376,0.000114259485,0.00034925697,0.00025480945,0.0019746996],"genre_scores_gemma":[0.7628995,0.0017111432,0.22806405,0.0002653335,0.00022903366,0.00046566263,0.0021175721,0.00011228705,0.004135477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609166,0.002230303,0.0001866338,0.00071411807,0.00057242083,0.00020480479],"domain_scores_gemma":[0.9721829,0.023485,0.0017661997,0.0011876264,0.0010419147,0.000336343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009715433,0.0007925141,0.0019863113,0.0021997907,0.00059061585,0.0020490969,0.0023828775,0.0015466823,0.0025998075],"category_scores_gemma":[0.05062686,0.0010310576,0.0011997552,0.0016918515,0.001672677,0.0023785932,0.0014846949,0.0023674886,0.0006881911],"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":[0.0007643358,0.00034729004,0.04570404,0.00043734963,0.00066661305,0.0003560043,0.00063411397,0.6076732,0.0033438907,0.12698503,0.0055028605,0.20758528],"study_design_scores_gemma":[0.000067731555,0.00006899238,0.01126864,0.0001196802,0.00007368911,0.00014501206,0.00007116615,0.89526653,0.00076320505,0.0908458,0.0012495606,0.00006000425],"about_ca_topic_score_codex":0.008793968,"about_ca_topic_score_gemma":0.008742173,"teacher_disagreement_score":0.009715433,"about_ca_system_score_codex":0.0010580034,"about_ca_system_score_gemma":0.0013381998,"threshold_uncertainty_score":0.051380754},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014242693,0.0002829834,0.9975073,0.00015685926,0.000030718216,0.00003858399,0.0000872261,0.0001081505,0.00036384785],"genre_scores_gemma":[0.20193604,0.0023148404,0.7825186,0.00047673177,0.000435392,0.0014474778,0.0017741213,0.00031648297,0.008780272],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921112,0.005244627,0.00025148795,0.0010095198,0.0010941948,0.00028896387],"domain_scores_gemma":[0.9814586,0.0144631285,0.0010616282,0.0015386436,0.0012626373,0.0002153965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015428672,0.0012957834,0.0027997259,0.0028633142,0.0008944925,0.0023193106,0.0044558104,0.00288021,0.004574051],"category_scores_gemma":[0.048825137,0.0012222164,0.0027618338,0.003479244,0.0020461164,0.0038441357,0.002523195,0.0042380965,0.0017784644],"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":[0.0002070261,0.00010598462,0.0039122873,0.00036011718,0.0003670544,0.00022300695,0.00034943837,0.42063263,0.0009154744,0.42387772,0.0054714764,0.14357786],"study_design_scores_gemma":[0.000032321823,0.000047525053,0.0007335446,0.000047903413,0.000056179153,0.00017791914,0.000029612353,0.86663675,0.00021660802,0.12656328,0.0054062763,0.00005213954],"about_ca_topic_score_codex":0.005438573,"about_ca_topic_score_gemma":0.0035645121,"teacher_disagreement_score":0.015428672,"about_ca_system_score_codex":0.0014263816,"about_ca_system_score_gemma":0.0018586888,"threshold_uncertainty_score":0.0815956},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002388516,0.000103645296,0.9972379,0.000085427295,0.000007831093,0.000012538899,0.000023995135,0.00004837222,0.000091742804],"genre_scores_gemma":[0.19592644,0.00060091994,0.8000463,0.00023254013,0.00013208407,0.00063335476,0.00040035587,0.00009396976,0.0019340505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98872024,0.009082866,0.0003351593,0.00094274484,0.000738561,0.00018044842],"domain_scores_gemma":[0.95310587,0.041651588,0.0018957688,0.0021071625,0.00091297104,0.0003266676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01686518,0.0011997335,0.0019594165,0.0013680297,0.00053536653,0.0015438322,0.0032649687,0.0018252503,0.002532678],"category_scores_gemma":[0.06760753,0.0013484071,0.001989954,0.0015285711,0.00210767,0.0032647962,0.0034227609,0.002440549,0.00043384964],"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":[0.00053539436,0.00017084715,0.0052336347,0.0007303889,0.0008414999,0.00046551792,0.0009693053,0.34090683,0.002074204,0.43922296,0.0024812235,0.20636815],"study_design_scores_gemma":[0.00006231609,0.000053483094,0.000728764,0.00003856489,0.00007370342,0.00010220272,0.000037132366,0.80469406,0.0004614563,0.19222984,0.001482342,0.000036185473],"about_ca_topic_score_codex":0.0018761094,"about_ca_topic_score_gemma":0.0028678374,"teacher_disagreement_score":0.01686518,"about_ca_system_score_codex":0.0010322772,"about_ca_system_score_gemma":0.0015441417,"threshold_uncertainty_score":0.08919263},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020368226,0.00039732028,0.97458583,0.0010331398,0.00006972839,0.00009190902,0.0014080083,0.00032038955,0.0017254815],"genre_scores_gemma":[0.7370489,0.001952726,0.23175673,0.0006336083,0.00043435337,0.0014767343,0.0053259134,0.0002234393,0.021147572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723536,0.0011655503,0.00013035083,0.0008042082,0.00032573522,0.00033888844],"domain_scores_gemma":[0.9836347,0.012838994,0.0013810139,0.0007775808,0.00094160077,0.00042605025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007909794,0.0011180536,0.0019032748,0.0017955774,0.0009332889,0.0020733676,0.0035070695,0.0024764345,0.0093426015],"category_scores_gemma":[0.0199728,0.00093286834,0.0017197864,0.002081647,0.0022524516,0.0035516587,0.0018297082,0.0033172371,0.0014623536],"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":[0.00023905312,0.00008042611,0.0046488643,0.00014922414,0.00013491875,0.00031806354,0.00041612794,0.45331794,0.0007862664,0.51517606,0.003542418,0.0211906],"study_design_scores_gemma":[0.000041316445,0.000041939034,0.001011249,0.00003360759,0.00003966904,0.00007963125,0.000030548334,0.8729035,0.00012103978,0.124105826,0.001553251,0.00003844551],"about_ca_topic_score_codex":0.020978369,"about_ca_topic_score_gemma":0.02027469,"teacher_disagreement_score":0.020978369,"about_ca_system_score_codex":0.0026353023,"about_ca_system_score_gemma":0.002351655,"threshold_uncertainty_score":0.041831493},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0124118,0.0010767446,0.9851311,0.0001869343,0.000044933895,0.0003027387,0.00020383616,0.00033382548,0.00030805473],"genre_scores_gemma":[0.08399221,0.0014251717,0.91143185,0.00013373778,0.0000648493,0.0017715561,0.0003927111,0.00026071488,0.0005272625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8884966,0.09874683,0.003082356,0.0036981003,0.005545101,0.00043098684],"domain_scores_gemma":[0.7635992,0.21036313,0.0055765286,0.014306807,0.0055285953,0.00062564795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1111693,0.0011786192,0.002244969,0.0027317903,0.00059387315,0.0014652349,0.0038093897,0.0023226233,0.0023614573],"category_scores_gemma":[0.1707894,0.0012422262,0.0020542273,0.0038009556,0.0011489432,0.0029351264,0.0025965022,0.0025518674,0.00073995645],"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":[0.0043213284,0.0010314707,0.05449469,0.0026378944,0.00490533,0.00021415202,0.0015974329,0.10257876,0.0038369906,0.04345922,0.0026894836,0.77823323],"study_design_scores_gemma":[0.002095246,0.003107981,0.07131265,0.001233516,0.0015526336,0.0009376495,0.0006084304,0.80777365,0.0070425137,0.087701306,0.015757928,0.0008764389],"about_ca_topic_score_codex":0.0026328259,"about_ca_topic_score_gemma":0.0034752158,"teacher_disagreement_score":0.1111693,"about_ca_system_score_codex":0.000963809,"about_ca_system_score_gemma":0.0015650658,"threshold_uncertainty_score":0.5879265},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026610302,0.00064407126,0.9690345,0.00046805284,0.000067890825,0.00021764175,0.00034310098,0.0005102278,0.002104239],"genre_scores_gemma":[0.46992856,0.00086455717,0.5229834,0.00083399477,0.0006213889,0.0011046418,0.0016186903,0.00035012828,0.0016946614],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9616413,0.026441779,0.0014077368,0.004831503,0.004890932,0.0007866442],"domain_scores_gemma":[0.83352125,0.14262426,0.006615166,0.013106488,0.0028142594,0.0013186432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03904256,0.0018109116,0.0031144754,0.004406704,0.0014530744,0.0025204134,0.0034155808,0.0022963514,0.005321478],"category_scores_gemma":[0.16503814,0.0008159132,0.0030955414,0.0030187787,0.0054032984,0.004856689,0.003193278,0.0038673405,0.0015413808],"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":[0.0021299568,0.00046711406,0.02528618,0.0012926568,0.0020521944,0.0021359208,0.0007974962,0.058543377,0.015314205,0.2335354,0.008741533,0.649704],"study_design_scores_gemma":[0.00074479746,0.0021643376,0.020986782,0.00021543074,0.0007940624,0.0027732898,0.00037341227,0.2172609,0.011114402,0.7322635,0.011001915,0.0003071087],"about_ca_topic_score_codex":0.0002842238,"about_ca_topic_score_gemma":0.0003736547,"teacher_disagreement_score":0.03904256,"about_ca_system_score_codex":0.000616192,"about_ca_system_score_gemma":0.0015820474,"threshold_uncertainty_score":0.20647925},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005480512,0.00022684212,0.9932955,0.00013324809,0.000036332174,0.00006934107,0.000083352,0.00006688561,0.0006078862],"genre_scores_gemma":[0.18176557,0.0007428197,0.8120249,0.00051226094,0.000182074,0.0004800718,0.00039724985,0.000082284496,0.0038126642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947042,0.002825616,0.0003368241,0.000854964,0.0010831264,0.00019531455],"domain_scores_gemma":[0.9703123,0.021361642,0.0020377485,0.0042963736,0.001687443,0.00030457295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020116556,0.0005299692,0.0011298298,0.001499702,0.00041783004,0.0010583869,0.002669849,0.0014478877,0.0033844619],"category_scores_gemma":[0.059737027,0.00046674663,0.001481142,0.0015121727,0.0012841924,0.0023644678,0.0019399464,0.0020005438,0.00068378873],"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":[0.00014960088,0.00011771998,0.022641335,0.00052824564,0.00040148332,0.00039013443,0.0006084334,0.12442553,0.0037376047,0.62343436,0.004234706,0.2193309],"study_design_scores_gemma":[0.00008812645,0.00025142662,0.009038881,0.00019371194,0.00014959909,0.0006519206,0.00008738338,0.6864645,0.0029730299,0.28734508,0.012625426,0.00013085238],"about_ca_topic_score_codex":0.00211487,"about_ca_topic_score_gemma":0.0020382924,"teacher_disagreement_score":0.020116556,"about_ca_system_score_codex":0.0006782008,"about_ca_system_score_gemma":0.0019358738,"threshold_uncertainty_score":0.106387794},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002647869,0.00010423845,0.9968688,0.000050629194,0.000007439259,0.000011113711,0.00004090922,0.00011493972,0.00015409969],"genre_scores_gemma":[0.13664515,0.00035978743,0.860464,0.000067783585,0.00013436185,0.0002607762,0.0005467118,0.000096107186,0.0014253914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99780935,0.001178245,0.000117120326,0.00037083006,0.00045490722,0.00006958726],"domain_scores_gemma":[0.9918886,0.0057729604,0.0009282372,0.0006698105,0.0005847735,0.00015556898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028980318,0.0008902976,0.001215982,0.00243309,0.00058689597,0.001481901,0.0028008407,0.0011579324,0.0011879009],"category_scores_gemma":[0.021432126,0.0011867298,0.0010032108,0.0020383922,0.0010548417,0.0019747645,0.0017489434,0.0014840057,0.0007311309],"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":[0.00027646197,0.00018502193,0.017267225,0.00047399322,0.00041259435,0.00039024214,0.00054228766,0.53244823,0.011318276,0.050129794,0.0021402247,0.38441563],"study_design_scores_gemma":[0.000021094553,0.000042225798,0.0021570756,0.000024224288,0.000029982806,0.00023215641,0.000031011066,0.9757923,0.0011338094,0.019325215,0.001162574,0.00004839019],"about_ca_topic_score_codex":0.003483507,"about_ca_topic_score_gemma":0.0033768967,"teacher_disagreement_score":0.003483507,"about_ca_system_score_codex":0.0005588892,"about_ca_system_score_gemma":0.0007716932,"threshold_uncertainty_score":0.01532644},"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":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027727874,0.957415,0.028089378,0.0039737863,0.0031484603,0.00088488776,0.0020942308,0.00010306041,0.0015184272],"genre_scores_gemma":[0.11634187,0.78142,0.08530813,0.0062215356,0.0034733315,0.0037600645,0.0027169157,0.00009475943,0.000663351],"study_design_codex":"systematic_review","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.856022,0.08408557,0.03550667,0.008601138,0.014938715,0.00084597024],"domain_scores_gemma":[0.34256074,0.5955221,0.035847917,0.012568834,0.0122414045,0.0012590691],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12552808,0.00353442,0.0123414295,0.037717525,0.0009689032,0.008584937,0.0040998785,0.0061923647,0.008381809],"category_scores_gemma":[0.5215324,0.0022763493,0.009695644,0.018994387,0.0035543612,0.0043701036,0.004906162,0.004567152,0.0010961232],"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":[0.0030579537,0.00012856368,0.0056782058,0.6180166,0.078700565,0.00075513887,0.00068011286,0.0061990544,0.0007290116,0.016284926,0.0065306127,0.2632393],"study_design_scores_gemma":[0.0031328837,0.0020377256,0.021926662,0.48006803,0.18829831,0.0013291589,0.0012028937,0.0041805436,0.0017555307,0.15114442,0.14455198,0.00037188074],"about_ca_topic_score_codex":0.0031295028,"about_ca_topic_score_gemma":0.0027292725,"teacher_disagreement_score":0.8744719,"about_ca_system_score_codex":0.004536581,"about_ca_system_score_gemma":0.0058235894,"threshold_uncertainty_score":0.6638639},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047556185,0.00025840872,0.9939277,0.00009495036,0.00003794396,0.000025671181,0.000055342556,0.00008713409,0.00075729523],"genre_scores_gemma":[0.1627824,0.00073609245,0.83062565,0.00022692503,0.00017316312,0.00029459185,0.00049780856,0.00013368623,0.004529719],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966691,0.0016430153,0.00013141835,0.0003728681,0.0010391135,0.00014447555],"domain_scores_gemma":[0.9885408,0.006461519,0.0010756577,0.0016340013,0.0021362794,0.0001517526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007392324,0.00053206674,0.0012533846,0.0016013256,0.00050069275,0.0010885653,0.0019437437,0.001166524,0.0046563162],"category_scores_gemma":[0.03522332,0.00046448104,0.00070199615,0.001657628,0.00088400097,0.0016242014,0.0017841442,0.0013755917,0.0022236141],"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":[0.0002196499,0.00016348997,0.012434501,0.0005595747,0.0002548201,0.0002831945,0.00042094145,0.16840643,0.014897577,0.24139504,0.0073725577,0.5535922],"study_design_scores_gemma":[0.00007028891,0.00015638671,0.0063579245,0.00020344397,0.000108695036,0.00096229115,0.000076274904,0.80183107,0.008971324,0.16538857,0.015757155,0.0001165973],"about_ca_topic_score_codex":0.0011558083,"about_ca_topic_score_gemma":0.0010368056,"teacher_disagreement_score":0.007392324,"about_ca_system_score_codex":0.0007532089,"about_ca_system_score_gemma":0.0013284044,"threshold_uncertainty_score":0.039094806},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38182575,0.00089121354,0.6139149,0.00021164562,0.00003107145,0.00004100066,0.00024382611,0.00031588503,0.0025247617],"genre_scores_gemma":[0.954745,0.0002601768,0.044472046,0.000046806796,0.000016158958,0.000041046147,0.00013918971,0.000024808955,0.00025470715],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954555,0.0027323666,0.00017010082,0.00073551555,0.00069913303,0.00020734133],"domain_scores_gemma":[0.96395975,0.030333437,0.0019611549,0.0029123763,0.00054642395,0.0002869449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008257004,0.00056593097,0.00088301266,0.0015634475,0.0003445085,0.0011579181,0.0008769183,0.0007314504,0.0015235976],"category_scores_gemma":[0.04068325,0.00036470772,0.00052295404,0.0010748345,0.0024048313,0.0019362009,0.0021858998,0.00085384544,0.00015072283],"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":[0.000902416,0.0001547759,0.21853814,0.00094015076,0.0012763712,0.0015353,0.0009334712,0.23041084,0.15280066,0.10536015,0.00055066636,0.2865971],"study_design_scores_gemma":[0.00008258929,0.0007185278,0.1724554,0.00010585477,0.0004102852,0.0025108256,0.00033976455,0.4652827,0.09618353,0.2591209,0.00253856,0.00025100342],"about_ca_topic_score_codex":0.00053415546,"about_ca_topic_score_gemma":0.0005436533,"teacher_disagreement_score":0.008257004,"about_ca_system_score_codex":0.00037369248,"about_ca_system_score_gemma":0.00037754053,"threshold_uncertainty_score":0.043667734},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048386417,0.00020903214,0.99427336,0.00024402907,0.000013610666,0.000031274616,0.00008457832,0.00006966524,0.00023579634],"genre_scores_gemma":[0.26437598,0.00092222675,0.7300674,0.00042176415,0.000159496,0.00060643634,0.00067453476,0.00017362121,0.0025984324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98966765,0.008547714,0.00030828768,0.00051837804,0.0008093654,0.00014863654],"domain_scores_gemma":[0.9449247,0.048621815,0.0017495408,0.0028469083,0.0014251542,0.00043175678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019648012,0.00066803,0.0015395324,0.0016044665,0.0005960015,0.0015306583,0.0022887613,0.0015854917,0.0026173089],"category_scores_gemma":[0.088797286,0.00071069744,0.0014215688,0.0020685724,0.0021795253,0.00294926,0.0022842165,0.0019965721,0.00068286824],"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":[0.00051081803,0.00019287202,0.011613605,0.0007730056,0.00041149088,0.0010910772,0.00084160804,0.20718208,0.0027978977,0.5501631,0.004585601,0.21983683],"study_design_scores_gemma":[0.00009989788,0.000144384,0.0033887497,0.00011603241,0.00007956119,0.000731809,0.000100422854,0.7268413,0.0014580113,0.2622755,0.004666706,0.00009772157],"about_ca_topic_score_codex":0.0015318095,"about_ca_topic_score_gemma":0.0015195402,"teacher_disagreement_score":0.019648012,"about_ca_system_score_codex":0.0007996678,"about_ca_system_score_gemma":0.0023006257,"threshold_uncertainty_score":0.10390985},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09046799,0.00040462372,0.9019048,0.001351289,0.000030500607,0.00016545985,0.0018313348,0.00038698266,0.0034569467],"genre_scores_gemma":[0.8360415,0.00088047725,0.15154144,0.00023232917,0.000077359604,0.0007121806,0.0021612896,0.00010686709,0.008246484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965318,0.0019072286,0.00012819316,0.00058754254,0.0004931409,0.0003522024],"domain_scores_gemma":[0.99285054,0.0048708934,0.0009956051,0.00043840968,0.00069103856,0.0001535204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073100887,0.00076836394,0.0014567343,0.0018598763,0.00079872843,0.0015858028,0.0036138322,0.001472205,0.0044691605],"category_scores_gemma":[0.020669151,0.0009782601,0.0016419627,0.002908835,0.0015413389,0.0018882916,0.0017305245,0.00159266,0.0006598158],"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":[0.00007476355,0.000060511888,0.013213558,0.000059703187,0.00015242108,0.00013773722,0.00047496505,0.80750847,0.00031119026,0.15223725,0.0017695738,0.023999726],"study_design_scores_gemma":[0.000018033285,0.000012669006,0.00329458,0.000017064145,0.000036865968,0.000035054447,0.000051622268,0.9536771,0.00006644247,0.042056967,0.0007140793,0.000019449619],"about_ca_topic_score_codex":0.1415654,"about_ca_topic_score_gemma":0.11885622,"teacher_disagreement_score":0.1415654,"about_ca_system_score_codex":0.0034578126,"about_ca_system_score_gemma":0.0030976348,"threshold_uncertainty_score":0.28148276},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03312692,0.0002415455,0.9648354,0.000036767782,0.000018513056,0.000027618064,0.0007014297,0.0003152607,0.0006966059],"genre_scores_gemma":[0.383831,0.0010026423,0.6080224,0.000062653795,0.00011608963,0.00018505834,0.0035809653,0.00015360805,0.003045638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999223,0.0003127917,0.00006525212,0.00018935317,0.00016721996,0.000042375697],"domain_scores_gemma":[0.9935994,0.004307946,0.0007044609,0.0008778155,0.00039394083,0.00011637497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022798553,0.00047017273,0.0006275603,0.0016930108,0.00026318687,0.0005105,0.001172102,0.0004797916,0.0021570504],"category_scores_gemma":[0.012489234,0.000593382,0.00052399887,0.0009365446,0.00028802865,0.0011222102,0.0007287154,0.0012127581,0.00074156316],"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":[0.00016516275,0.00017712753,0.17996001,0.00037654032,0.00094635464,0.00022055316,0.0004769908,0.15356399,0.017324593,0.022760954,0.0049431208,0.61908466],"study_design_scores_gemma":[0.000049475828,0.00016448747,0.21364112,0.00007125205,0.00024941066,0.0009931985,0.00007250142,0.72058487,0.012466147,0.03937205,0.012187302,0.00014819912],"about_ca_topic_score_codex":0.0030962597,"about_ca_topic_score_gemma":0.0046976744,"teacher_disagreement_score":0.0030962597,"about_ca_system_score_codex":0.0002456122,"about_ca_system_score_gemma":0.00039536256,"threshold_uncertainty_score":0.012057185},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69178665,0.015898725,0.2728113,0.0065467274,0.0012783384,0.0011071413,0.002334184,0.00046245835,0.0077744727],"genre_scores_gemma":[0.955602,0.0012576497,0.039650563,0.0008829308,0.00021208535,0.00047606096,0.00061222207,0.00008759349,0.001218792],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9020299,0.07698783,0.003341708,0.008607309,0.0074125673,0.0016206662],"domain_scores_gemma":[0.42882746,0.52193105,0.02100325,0.023522355,0.003618684,0.0010972978],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.114085026,0.0016236354,0.002155137,0.0025292002,0.0007757254,0.0026654631,0.0019316265,0.0024654076,0.003487903],"category_scores_gemma":[0.33277074,0.00052482716,0.004682089,0.0026309055,0.0059538367,0.0031840375,0.0048518106,0.0037967744,0.0004071273],"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":[0.03072046,0.0013252185,0.39034566,0.004947214,0.024993647,0.0020851556,0.005248508,0.1057457,0.008017477,0.10925801,0.0056087812,0.31170407],"study_design_scores_gemma":[0.0010748197,0.012941602,0.530821,0.0024285126,0.016372072,0.0015852397,0.004125983,0.11776407,0.02657733,0.26558128,0.020071961,0.000656214],"about_ca_topic_score_codex":0.0024379373,"about_ca_topic_score_gemma":0.001569679,"teacher_disagreement_score":0.885915,"about_ca_system_score_codex":0.0015540288,"about_ca_system_score_gemma":0.0013474977,"threshold_uncertainty_score":0.60334647},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017104903,0.0010479677,0.97954065,0.0002097797,0.00006757459,0.00027949255,0.0005101846,0.0004899034,0.0007495582],"genre_scores_gemma":[0.45909184,0.0012088487,0.5318083,0.0005130072,0.00033978143,0.0033479733,0.0021202909,0.0003124934,0.0012574007],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95964277,0.030711308,0.001745001,0.0030069405,0.004309625,0.0005842889],"domain_scores_gemma":[0.64099264,0.3094883,0.02341006,0.02216242,0.0028848983,0.0010616967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049701322,0.0012735145,0.0038619877,0.0031823353,0.00033011087,0.001747419,0.0040988955,0.0018944751,0.005754482],"category_scores_gemma":[0.24601948,0.00072681677,0.0036750906,0.002479388,0.002210099,0.002385072,0.002221789,0.003256558,0.0007875008],"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":[0.0036254013,0.0008286261,0.018301457,0.0025257235,0.012456513,0.0008115016,0.00068230316,0.15595672,0.0046941955,0.24822468,0.004393154,0.5474997],"study_design_scores_gemma":[0.0012473604,0.0043435055,0.025131695,0.0005017638,0.002341777,0.00084059517,0.00014878363,0.5498307,0.0054652006,0.40399194,0.0058850576,0.00027169185],"about_ca_topic_score_codex":0.0005283555,"about_ca_topic_score_gemma":0.00037009484,"teacher_disagreement_score":0.049701322,"about_ca_system_score_codex":0.00083174935,"about_ca_system_score_gemma":0.0017706772,"threshold_uncertainty_score":0.26284885},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047866493,0.014849426,0.97390175,0.0025349811,0.0018848768,0.00007134804,0.00017567063,0.0003978696,0.0013974791],"genre_scores_gemma":[0.21894324,0.018294742,0.7322881,0.00095446897,0.003761807,0.00066736306,0.0016878352,0.0011710704,0.0222313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99533623,0.003186611,0.00026893045,0.00061546406,0.00045788512,0.00013481609],"domain_scores_gemma":[0.9923645,0.005754,0.00025670303,0.0007432118,0.0007699824,0.000111558984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0098739145,0.0025652777,0.0034398932,0.00244456,0.0018101489,0.0026778015,0.0033164206,0.0022737223,0.0047539747],"category_scores_gemma":[0.022054931,0.0020327643,0.0038172945,0.005082713,0.0023085044,0.0034341249,0.0021976149,0.005186607,0.0019430897],"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":[0.0004661203,0.00017472904,0.0023950767,0.00091429404,0.002524933,0.00049460266,0.0005267795,0.4055711,0.0019484228,0.28869694,0.05788897,0.23839809],"study_design_scores_gemma":[0.000057359663,0.00010859289,0.0006925468,0.00009902651,0.0003649194,0.0001441623,0.00004110808,0.7448436,0.0016027186,0.23343818,0.018481215,0.00012656422],"about_ca_topic_score_codex":0.011651215,"about_ca_topic_score_gemma":0.011813888,"teacher_disagreement_score":0.011651215,"about_ca_system_score_codex":0.0014548219,"about_ca_system_score_gemma":0.0020013102,"threshold_uncertainty_score":0.052218854},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025280155,0.00010614215,0.9948519,0.00029405588,0.0000151864315,0.000069715556,0.00006967401,0.00006803393,0.0019974024],"genre_scores_gemma":[0.16641548,0.00031879527,0.82904506,0.0008076608,0.00010468781,0.0011979525,0.00029183755,0.00012265045,0.0016959086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98679775,0.007809052,0.0006262315,0.0017904409,0.0025236576,0.00045297784],"domain_scores_gemma":[0.98540664,0.010107711,0.001076404,0.0012598883,0.0018431557,0.00030624107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018407492,0.001954222,0.0024602683,0.0018662909,0.0008488371,0.0019095169,0.0021714175,0.003585534,0.002696329],"category_scores_gemma":[0.037277717,0.0010080434,0.0016451802,0.0010550427,0.003472677,0.002393996,0.0024475115,0.002357383,0.00093002134],"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":[0.00031284525,0.00020272355,0.0012820278,0.00039250648,0.00026182874,0.000104421175,0.00016397532,0.30537552,0.007736282,0.6016287,0.0038763871,0.07866276],"study_design_scores_gemma":[0.00012279008,0.00037500003,0.0006388353,0.00011109008,0.0000772878,0.00010766142,0.000037071954,0.5898151,0.0039218757,0.39964128,0.0050937734,0.00005827305],"about_ca_topic_score_codex":0.0012578082,"about_ca_topic_score_gemma":0.00072137854,"teacher_disagreement_score":0.018407492,"about_ca_system_score_codex":0.002111381,"about_ca_system_score_gemma":0.003965169,"threshold_uncertainty_score":0.097349286},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040859305,0.00026349045,0.9931693,0.00029107745,0.00003460481,0.00014409515,0.00062567816,0.0004439561,0.0009417626],"genre_scores_gemma":[0.24004164,0.0012820655,0.7422491,0.00097355933,0.00021044922,0.0026482593,0.0039734174,0.00038377478,0.008237793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99591273,0.0019688185,0.00016173907,0.0009349823,0.0007494768,0.00027225577],"domain_scores_gemma":[0.9917026,0.0059115128,0.0005790861,0.0007257363,0.00083165267,0.00024946046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0094041545,0.0013954252,0.0029923283,0.0018566533,0.0014860712,0.002229938,0.00602926,0.002571345,0.005049954],"category_scores_gemma":[0.017185332,0.0018877536,0.0022236356,0.003088724,0.002176346,0.0027229788,0.0018907342,0.0044073695,0.0016565898],"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":[0.00031712983,0.00012970176,0.0031048525,0.00036522243,0.0002710763,0.00027376585,0.00024846432,0.76905596,0.005457033,0.16196036,0.005348701,0.053467747],"study_design_scores_gemma":[0.00004906025,0.0000247879,0.00062832463,0.000018953233,0.000039879313,0.00005016461,0.000013858206,0.93792635,0.00049216463,0.0582464,0.0024695054,0.000040501025],"about_ca_topic_score_codex":0.019028861,"about_ca_topic_score_gemma":0.025582055,"teacher_disagreement_score":0.019028861,"about_ca_system_score_codex":0.0029732143,"about_ca_system_score_gemma":0.0036194234,"threshold_uncertainty_score":0.049734533},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06429127,0.0001324473,0.93422717,0.00029618942,0.00003221958,0.00006305828,0.00014440517,0.00013050623,0.00068268634],"genre_scores_gemma":[0.74246377,0.00016562534,0.2551292,0.00018806977,0.00013271665,0.00027663878,0.0006330208,0.00003916346,0.0009719357],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99101526,0.0048407065,0.00039464375,0.001994678,0.001428123,0.000326468],"domain_scores_gemma":[0.9278668,0.0584411,0.0056108343,0.0045868997,0.0028331063,0.0006613427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018529035,0.00061351986,0.0010765356,0.0028678295,0.0006058784,0.0014789377,0.003073201,0.0012539965,0.0031895814],"category_scores_gemma":[0.07902375,0.00035854243,0.001152958,0.0018424664,0.0037238505,0.0018316532,0.0026235576,0.0022958312,0.00028179053],"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":[0.00050314557,0.000253186,0.086543426,0.00036982808,0.0010871231,0.00056814146,0.0009817593,0.1827798,0.002511722,0.6100742,0.0027752758,0.11155245],"study_design_scores_gemma":[0.000119321805,0.00010904409,0.011417327,0.000058244827,0.00011924729,0.00018510202,0.00014445157,0.6630742,0.0016597797,0.32198521,0.0010898428,0.000038263956],"about_ca_topic_score_codex":0.0020475646,"about_ca_topic_score_gemma":0.0013670686,"teacher_disagreement_score":0.018529035,"about_ca_system_score_codex":0.0011040244,"about_ca_system_score_gemma":0.0009916584,"threshold_uncertainty_score":0.09799212},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006029059,0.001345942,0.989899,0.00030687414,0.00013957241,0.0004784914,0.0001913826,0.0004761262,0.0011335189],"genre_scores_gemma":[0.2254706,0.0015630628,0.76393867,0.0006746348,0.00040887683,0.0052680075,0.00096183154,0.00031166655,0.0014026806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8573429,0.12175229,0.003840283,0.0073102894,0.0087787,0.00097550865],"domain_scores_gemma":[0.29978544,0.6564494,0.013215894,0.019102652,0.010225318,0.0012212773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1639754,0.0024964274,0.0044650068,0.005804618,0.0009674605,0.003448748,0.006627845,0.0043591065,0.0075989733],"category_scores_gemma":[0.5220496,0.0014919583,0.0043900516,0.0039213817,0.0038558817,0.0044868975,0.004182369,0.007752675,0.0013817422],"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":[0.0035903128,0.0003635854,0.01061682,0.0031806182,0.0030140423,0.001617568,0.0015183889,0.14335218,0.0034362732,0.5431831,0.0056524314,0.2804746],"study_design_scores_gemma":[0.0008310983,0.0018172713,0.0041255644,0.0009847616,0.0007381786,0.0009058299,0.00018515381,0.6800331,0.00370685,0.2979263,0.008573747,0.00017215428],"about_ca_topic_score_codex":0.0011742208,"about_ca_topic_score_gemma":0.00058721297,"teacher_disagreement_score":0.1639754,"about_ca_system_score_codex":0.001924647,"about_ca_system_score_gemma":0.0025006367,"threshold_uncertainty_score":0.8671952},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025376895,0.00009099454,0.9739405,0.0000733523,0.000009920746,0.000048576672,0.00006578299,0.00008694118,0.00030697975],"genre_scores_gemma":[0.63553935,0.00042605528,0.36141643,0.00008442554,0.000054309097,0.00037932902,0.00036590884,0.00009146312,0.0016427132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998694,0.0005854706,0.00010817048,0.0003134354,0.00023258047,0.000066235756],"domain_scores_gemma":[0.9960758,0.0027534836,0.0005141541,0.00033279145,0.00024406592,0.00007970725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039128996,0.0009345707,0.0011334221,0.0012246246,0.0004128113,0.0008249627,0.0013805743,0.0011185654,0.0010113221],"category_scores_gemma":[0.012333312,0.00079696754,0.0015201103,0.0006137187,0.0008199825,0.0019910662,0.0017118492,0.0016843764,0.0002771922],"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":[0.00007991126,0.00005001372,0.0046432214,0.00009368197,0.00013839648,0.00014908091,0.0001295325,0.9314365,0.0045002135,0.02725571,0.00036178136,0.03116189],"study_design_scores_gemma":[0.000004396432,0.0000149394255,0.00042220726,0.00000612321,0.000010010601,0.000022529715,0.000007697178,0.9914477,0.000497837,0.00733289,0.00022035887,0.000013402781],"about_ca_topic_score_codex":0.00448433,"about_ca_topic_score_gemma":0.0020777825,"teacher_disagreement_score":0.00448433,"about_ca_system_score_codex":0.0007907076,"about_ca_system_score_gemma":0.0011105848,"threshold_uncertainty_score":0.0206936},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010878704,0.2668152,0.5992583,0.034732327,0.01340402,0.0008370652,0.0105296625,0.011335864,0.06199973],"genre_scores_gemma":[0.015686681,0.2073198,0.66094124,0.018786991,0.019354945,0.0038434684,0.010233596,0.00375295,0.060080405],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961333,0.0017011026,0.0006361505,0.00031626114,0.0011337331,0.00007943637],"domain_scores_gemma":[0.96538645,0.026142092,0.0011374484,0.0022665255,0.004519258,0.0005480817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009990769,0.0033443116,0.0031720179,0.008141738,0.00088333705,0.0026602736,0.0026555716,0.0038508482,0.029433148],"category_scores_gemma":[0.036875635,0.0034517942,0.0016184895,0.0043261894,0.003136061,0.0031942513,0.0019496424,0.010183674,0.03698318],"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":[0.000060847997,0.00011128377,0.00050941017,0.0016243714,0.00010156088,0.00025769984,0.0002828068,0.0024786214,0.0006902604,0.0325486,0.78159755,0.1797371],"study_design_scores_gemma":[0.00010118252,0.00014243474,0.0015150082,0.0016998474,0.00009946119,0.0019126976,0.0002373617,0.0061295456,0.00050031993,0.21976866,0.7678015,0.00009206607],"about_ca_topic_score_codex":0.0025198727,"about_ca_topic_score_gemma":0.00435632,"teacher_disagreement_score":0.029433148,"about_ca_system_score_codex":0.0009116677,"about_ca_system_score_gemma":0.0030604259,"threshold_uncertainty_score":0.098463714},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028749218,0.00005705318,0.9700772,0.00007974972,0.00001563348,0.00007325158,0.00011542934,0.00043672998,0.00039572027],"genre_scores_gemma":[0.546069,0.000118714546,0.451084,0.00006767506,0.000049523227,0.00028184528,0.0010117346,0.00020414496,0.0011133513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934006,0.0048257764,0.00026813216,0.0006058426,0.0006945533,0.00020515788],"domain_scores_gemma":[0.9832129,0.0133050885,0.0008452423,0.0011231732,0.0012184373,0.00029516628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0115818335,0.0014922459,0.0017354973,0.0034690301,0.0011823659,0.0016091242,0.0024953126,0.0009266938,0.0022383565],"category_scores_gemma":[0.034370422,0.00062769937,0.0024776938,0.002061923,0.0008895479,0.0020685694,0.0025193635,0.0016214503,0.0006405144],"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":[0.00035185262,0.00017136,0.03700868,0.00021348074,0.00096400984,0.00042003128,0.0003655751,0.6988729,0.0038483285,0.02967475,0.0018757206,0.22623336],"study_design_scores_gemma":[0.000023152861,0.00010177442,0.0026348804,0.0000129886685,0.00006992546,0.000061070976,0.000037998347,0.9832403,0.00068905536,0.012620955,0.00047212947,0.00003580569],"about_ca_topic_score_codex":0.004489171,"about_ca_topic_score_gemma":0.0075641787,"teacher_disagreement_score":0.0115818335,"about_ca_system_score_codex":0.00073840143,"about_ca_system_score_gemma":0.0017475679,"threshold_uncertainty_score":0.061251342},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016669208,0.000108821405,0.99738616,0.0003911579,0.000024570114,0.000049819835,0.000027671513,0.000072720075,0.000272118],"genre_scores_gemma":[0.06785452,0.00034472384,0.9287945,0.0004297758,0.00015574803,0.00049262267,0.00011216547,0.000118816024,0.001697198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96356887,0.027447412,0.0010889759,0.00385784,0.003421754,0.0006151526],"domain_scores_gemma":[0.89406586,0.086510085,0.0059018834,0.0073147374,0.0052243304,0.0009831417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06711338,0.0016481276,0.0029024389,0.0023277646,0.0014949541,0.002964087,0.0061241826,0.004580182,0.0044861864],"category_scores_gemma":[0.16169281,0.002003077,0.002898521,0.002856041,0.003436327,0.004148193,0.0050018146,0.006879789,0.0009251023],"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":[0.0002840628,0.00021258566,0.005903226,0.00045174384,0.00068691024,0.0005340169,0.0010103867,0.33587402,0.0025111234,0.4941632,0.004278785,0.15408997],"study_design_scores_gemma":[0.00015740338,0.00018931812,0.001810864,0.00012215768,0.00019005728,0.0002643631,0.00008019506,0.7135556,0.0011276356,0.27523005,0.007134337,0.00013810975],"about_ca_topic_score_codex":0.00746785,"about_ca_topic_score_gemma":0.00909703,"teacher_disagreement_score":0.06711338,"about_ca_system_score_codex":0.0022883224,"about_ca_system_score_gemma":0.004815581,"threshold_uncertainty_score":0.35493368},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011511123,0.00008218994,0.99765235,0.00007696337,0.00003113794,0.00035323843,0.000049638853,0.00016783144,0.00043552555],"genre_scores_gemma":[0.023723813,0.00010284247,0.97321963,0.000102578095,0.00008544866,0.0023816107,0.00012450668,0.00008963341,0.0001700327],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6355671,0.27729884,0.021945398,0.019881198,0.043229107,0.0020784491],"domain_scores_gemma":[0.26398894,0.62777704,0.031344682,0.039254308,0.0363327,0.001302217],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3137946,0.002474973,0.0032275596,0.0127914585,0.0036082682,0.0042681657,0.008994936,0.00434158,0.004996895],"category_scores_gemma":[0.66430575,0.0023067938,0.0038974744,0.0079578115,0.006298745,0.007081587,0.0065880087,0.00793683,0.00228904],"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":[0.0007740678,0.00035168475,0.017661769,0.0016791597,0.0015594678,0.00049887475,0.006548977,0.023866387,0.0033534307,0.27150992,0.0066649,0.66553134],"study_design_scores_gemma":[0.00058680464,0.0011572525,0.019794693,0.0014940882,0.000978157,0.0014979638,0.0013148692,0.2538045,0.013575442,0.6868187,0.018306728,0.00067076896],"about_ca_topic_score_codex":0.0025975236,"about_ca_topic_score_gemma":0.0024075692,"teacher_disagreement_score":0.6862054,"about_ca_system_score_codex":0.0027285109,"about_ca_system_score_gemma":0.005126656,"threshold_uncertainty_score":0.8462134},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019373868,0.000049130493,0.9798846,0.000030310042,0.0000074009545,0.00001650574,0.00007170286,0.000394603,0.00017177443],"genre_scores_gemma":[0.35487467,0.00013312502,0.6436224,0.0000395152,0.000008761074,0.0001262928,0.000466556,0.00009291134,0.0006357521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890935,0.00058191275,0.00006475827,0.00014030874,0.00022400988,0.00007961112],"domain_scores_gemma":[0.99728394,0.0018281705,0.00020456103,0.00038772912,0.00026555886,0.000029926472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028234706,0.00049627834,0.00071056845,0.00088245963,0.0005345346,0.00063150853,0.00083857647,0.0006052136,0.00070236984],"category_scores_gemma":[0.011879831,0.00040526208,0.0008893564,0.0018308469,0.00051648344,0.0007195248,0.0006112689,0.0007897512,0.00024126226],"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":[0.00007610754,0.00003713971,0.006978522,0.00007154566,0.00008794755,0.00013959606,0.00013127374,0.8632411,0.0039955843,0.018225042,0.0006202979,0.10639588],"study_design_scores_gemma":[0.000013064555,0.000021334003,0.0020089082,0.000007796881,0.000012613439,0.00002873524,0.000019643874,0.9855812,0.0016616184,0.009754745,0.0008733059,0.000016961716],"about_ca_topic_score_codex":0.030675774,"about_ca_topic_score_gemma":0.05415607,"teacher_disagreement_score":0.030675774,"about_ca_system_score_codex":0.0010783015,"about_ca_system_score_gemma":0.0018270932,"threshold_uncertainty_score":0.060994446},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024007678,0.00018683277,0.9989831,0.00007641563,0.000011777154,0.00002094737,0.000048141035,0.00010320297,0.00032954823],"genre_scores_gemma":[0.049834263,0.0014767529,0.94226146,0.00018301835,0.00020958319,0.00095802534,0.00070788147,0.00038544755,0.003983596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993404,0.0048447084,0.00021622825,0.00046270387,0.00089927117,0.00017304151],"domain_scores_gemma":[0.986997,0.010940522,0.0005646797,0.0008460878,0.00051507517,0.00013657508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00980003,0.0013568837,0.0018175521,0.0021732138,0.00078157603,0.0019044997,0.0034748705,0.001672923,0.008109532],"category_scores_gemma":[0.031155674,0.0011865196,0.0021303785,0.0028266408,0.0012038391,0.0023637007,0.003565332,0.0037651327,0.002440057],"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":[0.00009497612,0.00008928855,0.0012397488,0.0004808639,0.00029512343,0.00029201884,0.00035815887,0.29339874,0.0008902535,0.46566352,0.007131061,0.2300661],"study_design_scores_gemma":[0.00004816632,0.00003069584,0.00028719392,0.00009136456,0.0000470654,0.00017080108,0.000044446373,0.5941013,0.00039173974,0.39182797,0.012916789,0.000042437285],"about_ca_topic_score_codex":0.0021821007,"about_ca_topic_score_gemma":0.0023714337,"teacher_disagreement_score":0.00980003,"about_ca_system_score_codex":0.001045496,"about_ca_system_score_gemma":0.0022962256,"threshold_uncertainty_score":0.051828146},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05357168,0.000111503134,0.94563746,0.00006992676,0.000007140546,0.000022852102,0.000052868276,0.00041825362,0.000108411434],"genre_scores_gemma":[0.65639037,0.0001407172,0.34244224,0.00004028921,0.000038656108,0.00015127954,0.00034167504,0.0000876584,0.00036715824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753475,0.0013945544,0.000120070465,0.0005050744,0.00034844398,0.00009705361],"domain_scores_gemma":[0.97089416,0.025074175,0.0012943895,0.0018180694,0.00073900365,0.00018021988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008917223,0.00034305625,0.000971596,0.0015295888,0.0005794866,0.00066854496,0.0009319001,0.00092821283,0.0005673316],"category_scores_gemma":[0.036410652,0.00045472776,0.000796878,0.0015236319,0.0009797273,0.00080197316,0.00078794523,0.0013184035,0.00017871398],"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":[0.00057593384,0.00016750579,0.024843361,0.00021786017,0.0003711334,0.00040307865,0.00046795278,0.6112911,0.025885988,0.031867813,0.00087906065,0.30302918],"study_design_scores_gemma":[0.000013864541,0.000033656073,0.0051853387,0.000005112367,0.00001182226,0.000048295184,0.000028513514,0.97856504,0.001687626,0.014124257,0.00027663738,0.00001976843],"about_ca_topic_score_codex":0.0025597992,"about_ca_topic_score_gemma":0.0026780956,"teacher_disagreement_score":0.008917223,"about_ca_system_score_codex":0.00058570155,"about_ca_system_score_gemma":0.000759304,"threshold_uncertainty_score":0.047159374},"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":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012551968,0.0069204974,0.94660807,0.018248146,0.00090307824,0.0038540761,0.00034499136,0.00035494464,0.01021419],"genre_scores_gemma":[0.14879356,0.0033299034,0.8249967,0.0042475406,0.0012649283,0.015240977,0.00020312621,0.0004062102,0.0015170862],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.30590937,0.63951474,0.020003844,0.0056378227,0.027497781,0.0014365469],"domain_scores_gemma":[0.09202483,0.85361,0.012889376,0.025372451,0.014955524,0.0011477591],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4983711,0.0028223766,0.005892395,0.004508917,0.0017474215,0.0059090266,0.0054234443,0.0043800324,0.0049617435],"category_scores_gemma":[0.7693245,0.0028048253,0.0025303662,0.006600522,0.009526574,0.00708704,0.0058882125,0.0075250682,0.0017334545],"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":[0.0042355903,0.0005877258,0.006619938,0.0045669368,0.0015293302,0.00072294753,0.0041409745,0.035458542,0.0023021183,0.44532967,0.01380974,0.48069653],"study_design_scores_gemma":[0.0024439304,0.0035645845,0.015043608,0.00463152,0.0011130328,0.0018581923,0.0015995307,0.06950344,0.0037791159,0.8512144,0.044779267,0.0004694071],"about_ca_topic_score_codex":0.002034694,"about_ca_topic_score_gemma":0.00273443,"teacher_disagreement_score":0.5016289,"about_ca_system_score_codex":0.005658102,"about_ca_system_score_gemma":0.008433389,"threshold_uncertainty_score":0.61859775},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03826125,0.0000861109,0.9594867,0.00024153969,0.000032154287,0.00023038208,0.000489501,0.000623912,0.00054840336],"genre_scores_gemma":[0.5652907,0.00015602072,0.4312119,0.00012182419,0.00006666737,0.00043853655,0.0012934053,0.00006394668,0.0013570617],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958698,0.0017071981,0.00030009542,0.0010488848,0.000867888,0.00020606384],"domain_scores_gemma":[0.98589104,0.009702442,0.0013813676,0.0011843033,0.0016225422,0.00021821108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00775735,0.0004581882,0.0009448666,0.002655362,0.0007336648,0.0014632047,0.0021356957,0.00086229184,0.0014538219],"category_scores_gemma":[0.022695696,0.00044771325,0.0012035255,0.0022775484,0.00070269185,0.0011991868,0.001771119,0.00090970326,0.00031765716],"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":[0.0005379729,0.000285429,0.22501072,0.00037990062,0.00067338895,0.0010610407,0.0009518122,0.3199449,0.0044236807,0.065365285,0.010255073,0.37111074],"study_design_scores_gemma":[0.000026047033,0.000039612358,0.0063761286,0.00001511369,0.000030401145,0.00022334381,0.0000894101,0.9768446,0.0012619401,0.013296288,0.0017704603,0.000026660398],"about_ca_topic_score_codex":0.0107494425,"about_ca_topic_score_gemma":0.0072107846,"teacher_disagreement_score":0.0107494425,"about_ca_system_score_codex":0.0011959574,"about_ca_system_score_gemma":0.00180926,"threshold_uncertainty_score":0.04102528},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019958585,0.00010448019,0.9972101,0.00014201595,0.000024969839,0.00008948604,0.00008133892,0.000072542265,0.00027923417],"genre_scores_gemma":[0.11899529,0.00063616916,0.87494355,0.0005232135,0.00029749022,0.0017353536,0.0012958199,0.00015610797,0.001417015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94905573,0.03891173,0.001703262,0.0045700762,0.005025577,0.0007335891],"domain_scores_gemma":[0.7748626,0.19679956,0.004724875,0.019023618,0.0037331474,0.00085624837],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.078671284,0.0014522162,0.0028899396,0.0038734989,0.001407652,0.0020301368,0.005324985,0.0022421628,0.0081967125],"category_scores_gemma":[0.2619121,0.0014592613,0.0025694757,0.0044763605,0.0057437234,0.0059281653,0.0045014475,0.005516738,0.00094249926],"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":[0.00029879477,0.00012799668,0.0044966023,0.00040374222,0.00037782366,0.0004735539,0.000514965,0.07564457,0.00051575777,0.82505757,0.003195264,0.088893294],"study_design_scores_gemma":[0.000117420794,0.00006079065,0.0009728137,0.0000717935,0.0000511914,0.00019187744,0.00003989601,0.38108185,0.0006250977,0.6145782,0.0021792091,0.00002992616],"about_ca_topic_score_codex":0.0054076617,"about_ca_topic_score_gemma":0.0034165923,"teacher_disagreement_score":0.9213287,"about_ca_system_score_codex":0.002235179,"about_ca_system_score_gemma":0.0029438497,"threshold_uncertainty_score":0.41605848},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04916099,0.0003701775,0.9457337,0.00091779145,0.000079075835,0.00021481392,0.0013210841,0.00038478881,0.0018175227],"genre_scores_gemma":[0.7669144,0.00089988636,0.21404685,0.00038764934,0.00016431029,0.0020869588,0.0037642166,0.00013115071,0.0116046425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970252,0.0016369354,0.00014909961,0.0005187151,0.00039015323,0.0002799131],"domain_scores_gemma":[0.98641205,0.010447835,0.0010871957,0.0010045274,0.00074762775,0.00030075785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013148709,0.00087465975,0.0016219744,0.0014698502,0.0006042839,0.0017746153,0.0031914955,0.002038854,0.0077170874],"category_scores_gemma":[0.019114867,0.0006961438,0.001706391,0.0019478848,0.0012695863,0.0021850856,0.0017844551,0.0031058842,0.0016246916],"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":[0.00094917975,0.00039622223,0.023009945,0.0005320492,0.00047742252,0.0006347114,0.0009219589,0.61724067,0.0028481137,0.2917167,0.005287383,0.055985693],"study_design_scores_gemma":[0.00006994547,0.00013371988,0.0033475033,0.00005350823,0.000084677944,0.00015448812,0.000037165406,0.939364,0.00039261734,0.054438483,0.0018704877,0.000053416352],"about_ca_topic_score_codex":0.0039719585,"about_ca_topic_score_gemma":0.0038580163,"teacher_disagreement_score":0.013148709,"about_ca_system_score_codex":0.0012588341,"about_ca_system_score_gemma":0.0013783337,"threshold_uncertainty_score":0.06953788},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026215086,0.0011975159,0.0006085131,0.88924056,0.10357708,0.000042480187,0.00019523042,0.00007706824,0.004799518],"genre_scores_gemma":[0.0022099635,0.0007900926,0.0006887795,0.90462035,0.07584862,0.00009954552,0.00005459271,0.0000525565,0.015635535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9937348,0.0021200387,0.00076114526,0.0008053266,0.0020251817,0.0005534995],"domain_scores_gemma":[0.9757652,0.015954614,0.0011585746,0.00075654103,0.0049972325,0.0013677798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0107193235,0.0011998621,0.003098863,0.0012349096,0.0031318823,0.005550604,0.002903764,0.037253603,0.007193199],"category_scores_gemma":[0.051760767,0.0009714567,0.0016646516,0.0010853616,0.002619057,0.0021802564,0.0008275865,0.029307622,0.0075735706],"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":[0.000054710676,0.000014321843,0.00012582476,0.000029805053,0.000010224511,0.0001638003,0.000021457336,0.000033582062,0.000055834134,0.0010259034,0.99501246,0.0034520335],"study_design_scores_gemma":[0.0001405769,0.00008113597,0.0012628512,0.0002596975,0.00006452778,0.00055271573,0.00018466348,0.0008038121,0.0004061355,0.0061849444,0.99000615,0.000052738316],"about_ca_topic_score_codex":0.0047739893,"about_ca_topic_score_gemma":0.009389166,"teacher_disagreement_score":0.037253603,"about_ca_system_score_codex":0.0044141244,"about_ca_system_score_gemma":0.0042540072,"threshold_uncertainty_score":0.056689918},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043651536,0.00076492626,0.952761,0.00025172092,0.00012537965,0.000056351855,0.000391648,0.0008400787,0.00115734],"genre_scores_gemma":[0.2948713,0.0020393718,0.6993711,0.0001560099,0.00037772715,0.00014159163,0.0011344173,0.00028267654,0.0016258559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.000102704085,0.00004858463,0.00016265365,0.00027996438,0.00005619809],"domain_scores_gemma":[0.9990061,0.0003535837,0.0001899258,0.00020658251,0.00019309654,0.000050818613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009544475,0.0005886245,0.00087456306,0.002322252,0.0003025984,0.0012384292,0.00053737726,0.00046015801,0.0016863493],"category_scores_gemma":[0.0031870068,0.0003113915,0.0008686797,0.001999648,0.00046764108,0.0010604868,0.0010451971,0.00066677446,0.00082175655],"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":[0.00032197553,0.000107328604,0.00332528,0.0006748186,0.00025082653,0.00076548755,0.00039628075,0.051639356,0.42189887,0.024742665,0.003258618,0.4926184],"study_design_scores_gemma":[0.00003338251,0.00034562315,0.021876676,0.00007390851,0.00015847725,0.001187425,0.00030244444,0.79041165,0.09780187,0.068925366,0.018739764,0.00014346899],"about_ca_topic_score_codex":0.00049615203,"about_ca_topic_score_gemma":0.00049446063,"teacher_disagreement_score":0.002322252,"about_ca_system_score_codex":0.0002920754,"about_ca_system_score_gemma":0.00029857355,"threshold_uncertainty_score":0.005641341},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029976452,0.00006495564,0.9669738,0.000119381155,0.000035317757,0.000603798,0.00057561503,0.0009259873,0.000724607],"genre_scores_gemma":[0.6281681,0.00026895737,0.35765192,0.0001888425,0.000061372004,0.0060497816,0.0024671173,0.00040203746,0.0047417786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9893579,0.00546492,0.0006317461,0.0021983094,0.0018410977,0.0005062037],"domain_scores_gemma":[0.9672125,0.022543063,0.0022642347,0.006052256,0.00175201,0.00017589644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013965878,0.0015210573,0.0020707685,0.0012815258,0.00042775008,0.0017116911,0.0033876223,0.002037216,0.0042416817],"category_scores_gemma":[0.056802146,0.0007951857,0.0024665582,0.001494145,0.0014471648,0.0018617494,0.0012986091,0.0034822493,0.0018351688],"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":[0.002290588,0.0010681031,0.01461164,0.0008713901,0.0009104385,0.00061848614,0.0015799699,0.6779421,0.03343952,0.08442417,0.0035651894,0.17867848],"study_design_scores_gemma":[0.000069116635,0.00046367612,0.007064673,0.000026949012,0.00007700685,0.0002567191,0.00004608896,0.9547389,0.00481252,0.030232072,0.0021028605,0.00010940414],"about_ca_topic_score_codex":0.0019586112,"about_ca_topic_score_gemma":0.0013509169,"teacher_disagreement_score":0.013965878,"about_ca_system_score_codex":0.001032804,"about_ca_system_score_gemma":0.0010186281,"threshold_uncertainty_score":0.07385951},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021958356,0.00062705355,0.9957099,0.0002593245,0.00007628716,0.00001903668,0.00016301715,0.00019878532,0.0007508116],"genre_scores_gemma":[0.15223813,0.0035855852,0.834325,0.00021885478,0.00049814564,0.00060609315,0.0017605547,0.0003497643,0.0064178556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9915658,0.0058095595,0.00040022793,0.0009555402,0.000980719,0.0002881401],"domain_scores_gemma":[0.98782235,0.007813028,0.0013618289,0.0017088029,0.0011165516,0.00017744904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011993675,0.0012148237,0.0013671545,0.0031247297,0.00081540947,0.0023385389,0.0029572798,0.0017968392,0.0039878213],"category_scores_gemma":[0.043213617,0.0009945584,0.0035751164,0.0055520623,0.0012411902,0.0026601055,0.0019111098,0.0025266563,0.0014173756],"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":[0.00009344686,0.000054134136,0.004887865,0.00023616083,0.00039821697,0.00015858765,0.00036364273,0.23370688,0.00085061335,0.6117109,0.0047098,0.14282984],"study_design_scores_gemma":[0.000022431042,0.000054930566,0.002033131,0.000102617254,0.000097641394,0.00013309516,0.00008106313,0.61464673,0.0005459015,0.36897936,0.013224989,0.0000780138],"about_ca_topic_score_codex":0.011733007,"about_ca_topic_score_gemma":0.009999948,"teacher_disagreement_score":0.011993675,"about_ca_system_score_codex":0.0014480313,"about_ca_system_score_gemma":0.0023621258,"threshold_uncertainty_score":0.063429356},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920419,0.00028255733,0.00111348,0.00041029754,0.0000065722675,0.00005426699,0.0029601902,0.000026871847,0.0031037708],"genre_scores_gemma":[0.9946301,0.00020429725,0.0017395376,0.000024353263,0.000002973562,0.00003117439,0.0016124989,0.0000052515056,0.0017497458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99941206,0.00013769555,0.000029834151,0.00009622696,0.00020625889,0.00011798511],"domain_scores_gemma":[0.9986539,0.0002558097,0.00037929154,0.0001221834,0.0004551134,0.00013371851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007372188,0.00015806923,0.00018383574,0.000971137,0.0010864452,0.0006373379,0.00064712774,0.00020619783,0.0010500016],"category_scores_gemma":[0.004155776,0.00013303204,0.00028270064,0.0027563088,0.00046790863,0.00021154118,0.0006914671,0.0001949393,0.000087997025],"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":[0.00015290974,0.000021934778,0.9674518,0.000039653805,0.00005795967,0.00029181037,0.00217221,0.0077216164,0.00056288386,0.0009590065,0.0023301179,0.018238103],"study_design_scores_gemma":[0.000013464189,0.000014393729,0.9900572,0.00001616609,0.00001937021,0.000066649554,0.001535826,0.0057458407,0.00012656937,0.00024829808,0.0021447511,0.000011446279],"about_ca_topic_score_codex":0.99085355,"about_ca_topic_score_gemma":0.99532205,"teacher_disagreement_score":0.018359805,"about_ca_system_score_codex":0.018359805,"about_ca_system_score_gemma":0.013430693,"threshold_uncertainty_score":0.1332103},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011602799,0.00084551016,0.9829699,0.0021721974,0.00009205402,0.00033004492,0.00009068799,0.00016011448,0.001736657],"genre_scores_gemma":[0.6375496,0.002808181,0.347865,0.0018647568,0.0006426917,0.002828456,0.0002550869,0.00016702976,0.0060192905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9369837,0.05577388,0.0009944878,0.0021694493,0.0030644126,0.0010140752],"domain_scores_gemma":[0.6842909,0.29032356,0.007920131,0.014699846,0.0020592262,0.0007062593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15579577,0.0023392665,0.005652724,0.0030567849,0.000868936,0.0035083264,0.0069802133,0.0044737817,0.00415594],"category_scores_gemma":[0.31303933,0.0010861441,0.0035694675,0.0027325763,0.007529389,0.0057536117,0.004861171,0.0057338746,0.0006659432],"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":[0.00060655916,0.00013915302,0.012320303,0.00042612525,0.0014378335,0.00091451843,0.0008447631,0.23746258,0.0004255147,0.65560585,0.0030500463,0.08676679],"study_design_scores_gemma":[0.00019037635,0.00021498284,0.001636253,0.0001419441,0.0002242741,0.0002834709,0.000098428194,0.38663515,0.00031632377,0.6081569,0.002044884,0.00005697342],"about_ca_topic_score_codex":0.00386972,"about_ca_topic_score_gemma":0.0024330923,"teacher_disagreement_score":0.15579577,"about_ca_system_score_codex":0.002391153,"about_ca_system_score_gemma":0.0026848272,"threshold_uncertainty_score":0.82393664},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077361106,0.04728223,0.004862015,0.5205557,0.012809502,0.000033160297,0.00030736104,0.00023995014,0.40617397],"genre_scores_gemma":[0.50912476,0.080883846,0.009952248,0.08550783,0.014107255,0.00011988032,0.0004540954,0.0004175228,0.29943258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965642,0.0015081846,0.00010244408,0.0004148064,0.0007132334,0.0006970819],"domain_scores_gemma":[0.9939804,0.0007886165,0.00037984116,0.0006543361,0.0011987957,0.0029979427],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0047399625,0.00040468475,0.0005610834,0.0011665362,0.0107550975,0.014462796,0.001115382,0.0030020871,0.03418019],"category_scores_gemma":[0.0064927647,0.00020467817,0.00039049366,0.0013148837,0.019829022,0.020359041,0.0057526273,0.005593547,0.0059486832],"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":[0.000025321197,0.00002415094,0.0011095462,0.00009105704,0.000008635264,0.0001430239,0.011911218,0.00008421536,0.00023272439,0.74445367,0.18152273,0.060393754],"study_design_scores_gemma":[0.0000018802862,0.000009267945,0.00034150432,0.00011054551,0.0000029041375,0.00009627387,0.003891061,0.000026559503,0.00004831771,0.064584136,0.9308752,0.000012405489],"about_ca_topic_score_codex":0.013501148,"about_ca_topic_score_gemma":0.023043044,"teacher_disagreement_score":0.9892449,"about_ca_system_score_codex":0.008728754,"about_ca_system_score_gemma":0.01359547,"threshold_uncertainty_score":0.11434412},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17157045,0.000405999,0.8255486,0.00054151966,0.000041181305,0.00004608684,0.00041563393,0.00025260623,0.001177949],"genre_scores_gemma":[0.9554117,0.00042490056,0.038045723,0.000056642057,0.00007439092,0.00015636376,0.00046924007,0.00005936631,0.0053018113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980823,0.0006552287,0.000089937326,0.0006713727,0.00026154827,0.00023962],"domain_scores_gemma":[0.98994654,0.006168015,0.002030197,0.0009810018,0.0005985741,0.0002758032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005810465,0.00080845784,0.0012040432,0.0011935942,0.0006438384,0.0016193228,0.0029839594,0.0015751362,0.0018250515],"category_scores_gemma":[0.014888316,0.0009870313,0.0015222222,0.0016343264,0.0024052763,0.0027521753,0.0018887555,0.0016365974,0.0003180132],"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":[0.00007721457,0.000063374784,0.026263285,0.000060463935,0.00012441038,0.00028463863,0.00039714354,0.8680079,0.0011641632,0.089241706,0.00045251547,0.013863135],"study_design_scores_gemma":[0.000006639469,0.000021581704,0.002400941,0.0000073523697,0.000022422402,0.0000412879,0.00003312224,0.98057336,0.00013648736,0.016384717,0.00035860282,0.0000134267875],"about_ca_topic_score_codex":0.036696788,"about_ca_topic_score_gemma":0.025937214,"teacher_disagreement_score":0.036696788,"about_ca_system_score_codex":0.0018032985,"about_ca_system_score_gemma":0.001867087,"threshold_uncertainty_score":0.0729664},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0872114,0.001260544,0.9059143,0.00088266225,0.00028362902,0.00055580406,0.0007557882,0.000768589,0.0023673812],"genre_scores_gemma":[0.7165742,0.00056544965,0.27785653,0.00047230156,0.00042258945,0.0013078451,0.0009883691,0.00020996925,0.001602821],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95888805,0.029599173,0.0019893066,0.0034821928,0.0050402447,0.0010009886],"domain_scores_gemma":[0.6973504,0.2613944,0.017288856,0.016032377,0.005906866,0.0020270664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042855147,0.0024181677,0.0029580675,0.0039234087,0.0009288848,0.0021802078,0.0035262308,0.0025699914,0.0054925634],"category_scores_gemma":[0.25275996,0.00058315095,0.0019509307,0.0044089453,0.004098582,0.0050363573,0.0029378487,0.0038744744,0.0011523394],"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":[0.005089611,0.0005878558,0.06512726,0.0011552266,0.001997318,0.0016935666,0.0005517362,0.17141113,0.007737263,0.16030204,0.010424984,0.5739221],"study_design_scores_gemma":[0.00070271286,0.0040475316,0.033962745,0.00029582507,0.00042195537,0.0017177262,0.0005056192,0.6709745,0.0057688025,0.27360088,0.0076264325,0.00037532384],"about_ca_topic_score_codex":0.0005015961,"about_ca_topic_score_gemma":0.0005190092,"teacher_disagreement_score":0.042855147,"about_ca_system_score_codex":0.0008630925,"about_ca_system_score_gemma":0.0016794804,"threshold_uncertainty_score":0.22664237},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005695738,0.00050324446,0.99699223,0.0010477534,0.0000652272,0.000038797756,0.00012622852,0.00017669199,0.0004803023],"genre_scores_gemma":[0.05243231,0.0021449549,0.940196,0.0011232727,0.00059975934,0.0009496031,0.00047056258,0.00023090432,0.0018527807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9672841,0.027821595,0.0008486513,0.001781311,0.001996503,0.0002678633],"domain_scores_gemma":[0.943787,0.04619705,0.0032418906,0.0041250265,0.0023044827,0.00034451493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027539618,0.0018187187,0.0027435003,0.0027620003,0.00092116103,0.0031634578,0.004963314,0.0037090553,0.0036553373],"category_scores_gemma":[0.09367185,0.001437123,0.0024829463,0.003419542,0.0023800035,0.003288605,0.0034315437,0.0051554493,0.0019259857],"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":[0.0001836838,0.00005907478,0.0023125822,0.00061220845,0.0009175445,0.0007853385,0.000524325,0.08993181,0.00079044723,0.6888112,0.013209208,0.20186266],"study_design_scores_gemma":[0.000049846385,0.000066525296,0.0004582628,0.00011147523,0.0000928954,0.00024980927,0.000054466836,0.28154096,0.00029499977,0.70248383,0.014540398,0.00005652846],"about_ca_topic_score_codex":0.003812438,"about_ca_topic_score_gemma":0.0052884556,"teacher_disagreement_score":0.027539618,"about_ca_system_score_codex":0.0024588339,"about_ca_system_score_gemma":0.0031234093,"threshold_uncertainty_score":0.1456452},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012208805,0.00023332733,0.99638015,0.00016791551,0.000087704604,0.0004705087,0.00036547804,0.00038818375,0.0006858408],"genre_scores_gemma":[0.065481976,0.0008667943,0.9105894,0.00064621423,0.0002617361,0.0144790765,0.0011792866,0.00028398482,0.006211506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94896847,0.038565937,0.0016843467,0.005499157,0.004111528,0.0011704997],"domain_scores_gemma":[0.90675324,0.073166154,0.007876587,0.008040056,0.0036541505,0.00050998415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0746392,0.0037317274,0.0033925343,0.0016824979,0.0010312309,0.003460326,0.0065625114,0.004536859,0.018267224],"category_scores_gemma":[0.10539326,0.0019288935,0.0037610822,0.0027994239,0.0039052574,0.004855712,0.003348908,0.00680504,0.0043447907],"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":[0.0011072194,0.00032123906,0.002735076,0.0012037805,0.0005677316,0.00020127247,0.0007616248,0.1510057,0.0025113216,0.74558955,0.006614041,0.08738144],"study_design_scores_gemma":[0.00053606665,0.00091355137,0.001755041,0.00020251347,0.00022764503,0.00010757528,0.00007704316,0.52941304,0.0015857975,0.444776,0.020230602,0.00017511821],"about_ca_topic_score_codex":0.003475987,"about_ca_topic_score_gemma":0.0028873328,"teacher_disagreement_score":0.0746392,"about_ca_system_score_codex":0.0047102054,"about_ca_system_score_gemma":0.0036116678,"threshold_uncertainty_score":0.39473456},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033052884,0.00084304897,0.95551264,0.002454949,0.0004165983,0.00047968875,0.00040431885,0.0007114258,0.0061243116],"genre_scores_gemma":[0.55424184,0.0004811218,0.43703273,0.0017549322,0.00070258736,0.0018030924,0.00048720298,0.00051446725,0.0029819026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.8873968,0.086011246,0.0038816028,0.011940624,0.009489258,0.0012805612],"domain_scores_gemma":[0.5353155,0.41402557,0.011251691,0.032785863,0.005533481,0.0010879576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.095011346,0.0014324064,0.003587572,0.0032364891,0.0013649962,0.0038042068,0.0038214873,0.0037262253,0.012655287],"category_scores_gemma":[0.37511137,0.0010093166,0.0038590773,0.00466117,0.0045827003,0.007976411,0.005208115,0.005277913,0.001779938],"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":[0.003971162,0.00071421434,0.07448006,0.0019309707,0.0065400163,0.001735232,0.0024982875,0.042115003,0.010559904,0.19236009,0.019930426,0.6431646],"study_design_scores_gemma":[0.001617709,0.0052045058,0.07084541,0.0006207904,0.0029605057,0.003743395,0.0010655345,0.25193033,0.018322537,0.59034204,0.05255274,0.00079454755],"about_ca_topic_score_codex":0.00084232044,"about_ca_topic_score_gemma":0.00069035427,"teacher_disagreement_score":0.095011346,"about_ca_system_score_codex":0.0010356357,"about_ca_system_score_gemma":0.0016876342,"threshold_uncertainty_score":0.50247407},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024130823,0.00008147702,0.975239,0.000030080679,0.0000140206275,0.0000360481,0.00007360421,0.00025569694,0.0001392638],"genre_scores_gemma":[0.33028355,0.0003239695,0.6666778,0.00007322329,0.00004096915,0.00052325556,0.0008702823,0.00017912209,0.0010277629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954744,0.0029988775,0.00026242135,0.0004580459,0.0006972403,0.00010911479],"domain_scores_gemma":[0.9682819,0.024642536,0.0018225915,0.0035137285,0.0015556971,0.00018342788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01983728,0.0005535466,0.0013058592,0.0020799935,0.00046644924,0.0007525213,0.0018804327,0.0011655161,0.0013273724],"category_scores_gemma":[0.06013906,0.0006936008,0.001459487,0.0015920085,0.0009621489,0.0014908505,0.0011780991,0.0019122043,0.00036194563],"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":[0.00053136447,0.00022250743,0.025380624,0.00030606927,0.0007657058,0.00014315944,0.0006701933,0.65590334,0.014262595,0.0873461,0.0017724442,0.212696],"study_design_scores_gemma":[0.000041630916,0.000080703176,0.011520669,0.000035332305,0.00006676334,0.00005550077,0.000035582398,0.9533631,0.0013755615,0.032213006,0.0011501753,0.000061781924],"about_ca_topic_score_codex":0.0064568147,"about_ca_topic_score_gemma":0.008543253,"teacher_disagreement_score":0.01983728,"about_ca_system_score_codex":0.0008274071,"about_ca_system_score_gemma":0.0009254268,"threshold_uncertainty_score":0.10491079},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002865831,0.016714888,0.844991,0.10182559,0.016153555,0.000568697,0.0004390357,0.0005580483,0.015883368],"genre_scores_gemma":[0.054602545,0.010306704,0.8441336,0.063355766,0.018771425,0.0018075127,0.00021991838,0.00050798955,0.0062945574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.75028694,0.19049218,0.011771686,0.011892463,0.03434355,0.0012131468],"domain_scores_gemma":[0.2579198,0.6911269,0.0078079603,0.027879585,0.014140415,0.0011253374],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2561111,0.0027637384,0.004864603,0.0028575652,0.0027920578,0.0046341834,0.008951227,0.011384039,0.007660305],"category_scores_gemma":[0.47982404,0.0013654375,0.0051274565,0.00537163,0.024291314,0.016794896,0.006546251,0.040632203,0.0028284208],"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":[0.00074835354,0.00022871996,0.003450257,0.0017956133,0.00055731175,0.0011957783,0.0012004058,0.0059563546,0.0012693802,0.6520394,0.09261649,0.2389419],"study_design_scores_gemma":[0.00038310018,0.00096926524,0.002306216,0.0016380993,0.00027290863,0.0011521786,0.0002600602,0.015620123,0.0023893055,0.8089218,0.16573997,0.0003470261],"about_ca_topic_score_codex":0.0024356248,"about_ca_topic_score_gemma":0.0026518423,"teacher_disagreement_score":0.2561111,"about_ca_system_score_codex":0.0029451307,"about_ca_system_score_gemma":0.0051557943,"threshold_uncertainty_score":0.9173475},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003203426,0.0030432022,0.7820195,0.0028738556,0.0025213421,0.0021391273,0.07950957,0.111300565,0.013389547],"genre_scores_gemma":[0.01544964,0.0021724484,0.86922485,0.0007375255,0.0005550322,0.0062207156,0.035903804,0.050056096,0.019679891],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9866933,0.004887253,0.002394164,0.0021707003,0.0035168077,0.00033766084],"domain_scores_gemma":[0.95190006,0.025793048,0.0030454942,0.008889512,0.009622163,0.00074971723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015498655,0.00363537,0.005654745,0.008438857,0.001357106,0.0043078023,0.0043038484,0.0015349253,0.13135369],"category_scores_gemma":[0.075515285,0.0027986916,0.004413898,0.008738505,0.002840918,0.004352355,0.0033269802,0.0063398704,0.08403188],"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":[0.0006967807,0.000121257515,0.0017703365,0.0038855674,0.0005682599,0.00033674037,0.00064350665,0.0035891992,0.005770081,0.011701555,0.7817508,0.18916588],"study_design_scores_gemma":[0.00038357606,0.0003089277,0.0077549363,0.0014011734,0.0005195677,0.0012281786,0.00035504563,0.023491768,0.014987664,0.05983713,0.88915205,0.0005800061],"about_ca_topic_score_codex":0.0045333505,"about_ca_topic_score_gemma":0.0045789992,"teacher_disagreement_score":0.13135369,"about_ca_system_score_codex":0.0009942552,"about_ca_system_score_gemma":0.004209501,"threshold_uncertainty_score":0.4394219},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024003336,0.00042404607,0.99891293,0.00010118304,0.00004041465,0.000048236732,0.000032084605,0.00007492992,0.00012626164],"genre_scores_gemma":[0.01726492,0.0010946327,0.97933173,0.00018298432,0.0002858533,0.00087135873,0.00021507982,0.00012079414,0.000632703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9575986,0.035118166,0.0013376963,0.0020988998,0.003465061,0.00038161298],"domain_scores_gemma":[0.8861505,0.09712942,0.005315404,0.0063219154,0.004528582,0.0005542566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04599294,0.0017809075,0.003310945,0.0038627544,0.0012592791,0.001767183,0.0065729,0.0031958078,0.0064377664],"category_scores_gemma":[0.12254364,0.0010744501,0.003257829,0.006112877,0.0022447566,0.003298178,0.0027629447,0.006183049,0.0024031983],"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":[0.00025355752,0.00018950706,0.0036748487,0.0016402347,0.0016448974,0.0005978117,0.0006583142,0.07228767,0.0012673421,0.32371655,0.012185766,0.58188355],"study_design_scores_gemma":[0.00024686818,0.00023305835,0.0017074101,0.0005510278,0.0003570708,0.0009745453,0.00013176037,0.45524123,0.0018628316,0.5208435,0.017667431,0.0001832334],"about_ca_topic_score_codex":0.0008504235,"about_ca_topic_score_gemma":0.0011662091,"teacher_disagreement_score":0.04599294,"about_ca_system_score_codex":0.0010799123,"about_ca_system_score_gemma":0.002391831,"threshold_uncertainty_score":0.24323684},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012066607,0.00005614967,0.9868608,0.000032033782,0.000016706068,0.00018360902,0.00010137784,0.0003696863,0.0003129857],"genre_scores_gemma":[0.18420379,0.00013225547,0.81224686,0.00009883076,0.00005869002,0.0010984584,0.0006594972,0.00009874418,0.0014028477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9666326,0.025359886,0.0012113001,0.0036825838,0.0024751963,0.00063840713],"domain_scores_gemma":[0.97040784,0.015799949,0.004008349,0.0073522395,0.0021719297,0.00025968812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01974519,0.00087555323,0.0017064745,0.001614487,0.000474035,0.0011716874,0.0021441937,0.0013716735,0.0022548146],"category_scores_gemma":[0.08000182,0.0010106311,0.0014802804,0.0020520273,0.0013938553,0.0027485865,0.0022156874,0.0010245313,0.0014576113],"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":[0.0024165544,0.00058427325,0.0780648,0.00049386243,0.00086275744,0.0003621575,0.00057423004,0.21008845,0.0119870575,0.1747722,0.005925979,0.5138677],"study_design_scores_gemma":[0.00043971598,0.0010439206,0.019819994,0.000078972946,0.00021767769,0.00085049134,0.00006670437,0.89180696,0.0063457084,0.07280399,0.0063229706,0.00020294708],"about_ca_topic_score_codex":0.0020329857,"about_ca_topic_score_gemma":0.0018028738,"teacher_disagreement_score":0.01974519,"about_ca_system_score_codex":0.0007176279,"about_ca_system_score_gemma":0.0010530856,"threshold_uncertainty_score":0.10442376},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14113775,0.0043204017,0.8442637,0.0043370654,0.00050665543,0.0008315602,0.00078439224,0.00023832708,0.0035801968],"genre_scores_gemma":[0.8460893,0.001651567,0.14661178,0.0010586745,0.0002868539,0.0015255137,0.00075581233,0.000023654424,0.0019968466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95732594,0.030961787,0.0020698588,0.00495864,0.0037942256,0.0008895206],"domain_scores_gemma":[0.84656453,0.13041486,0.00884561,0.011802547,0.0019648878,0.0004075369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.075451344,0.0015331287,0.0027301605,0.002570988,0.001279408,0.002357585,0.00416427,0.00364479,0.003930532],"category_scores_gemma":[0.22735807,0.00092599215,0.0024050018,0.0025830325,0.003399078,0.0026922687,0.0019843783,0.002493649,0.0004067784],"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":[0.004702695,0.0012311083,0.17467384,0.0015499016,0.008865745,0.006400168,0.0015645593,0.1642486,0.0025180331,0.45163578,0.005426745,0.17718288],"study_design_scores_gemma":[0.00095567893,0.0014433069,0.03972478,0.00037056283,0.004063763,0.0025822083,0.00035381565,0.4506072,0.0029309657,0.4883893,0.008362315,0.0002160669],"about_ca_topic_score_codex":0.0071150726,"about_ca_topic_score_gemma":0.0032068028,"teacher_disagreement_score":0.075451344,"about_ca_system_score_codex":0.0013050528,"about_ca_system_score_gemma":0.0012559873,"threshold_uncertainty_score":0.3990296},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021931694,0.00037188653,0.9928052,0.001111052,0.000117549804,0.0001120244,0.0009158424,0.000250259,0.0021231428],"genre_scores_gemma":[0.18573028,0.0018067714,0.79289585,0.0008511475,0.00055149256,0.002533957,0.003187469,0.00024635263,0.012196725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99219066,0.0048520714,0.00035934825,0.0010121062,0.0011177046,0.00046814803],"domain_scores_gemma":[0.98830324,0.008163331,0.0008729362,0.0010287665,0.0013023965,0.00032938816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009494001,0.00077171926,0.002132823,0.002170715,0.0010822342,0.0025850674,0.005359385,0.002303219,0.012129359],"category_scores_gemma":[0.030237691,0.00076940475,0.0029776872,0.0044243457,0.0011302416,0.0028975564,0.0029642603,0.004882481,0.002906547],"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":[0.00017007194,0.00011500655,0.009102341,0.00030939883,0.00046645134,0.00028894533,0.00062728213,0.12263945,0.00054726657,0.7662916,0.014092713,0.08534947],"study_design_scores_gemma":[0.000095463874,0.00015179737,0.0026739815,0.00013642045,0.0001732881,0.00018280142,0.00011749719,0.6511184,0.00023290618,0.31787676,0.027162908,0.00007780301],"about_ca_topic_score_codex":0.0132477945,"about_ca_topic_score_gemma":0.0166444,"teacher_disagreement_score":0.0132477945,"about_ca_system_score_codex":0.0025196318,"about_ca_system_score_gemma":0.0029469696,"threshold_uncertainty_score":0.0502097},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096005954,0.0003638652,0.90147877,0.00030899854,0.00003252753,0.00008387044,0.00017726136,0.0006916093,0.0008572409],"genre_scores_gemma":[0.8337484,0.00028520863,0.16439396,0.00013306599,0.00005599101,0.00022289714,0.0006958498,0.00023554533,0.00022905992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98093075,0.012779077,0.0010958365,0.0016732116,0.0030601008,0.0004610752],"domain_scores_gemma":[0.78653306,0.19183065,0.007079643,0.00899021,0.0044843857,0.0010821328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032578826,0.0014604103,0.0019479786,0.0062460103,0.0012347467,0.003051745,0.0023385333,0.0038059202,0.0025401684],"category_scores_gemma":[0.22299956,0.0010894772,0.0017194591,0.0021155998,0.002462242,0.0050114538,0.003009877,0.0031898194,0.0007830663],"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":[0.0011235907,0.00030181537,0.095260054,0.00061905844,0.0013767846,0.000834294,0.0015714058,0.670007,0.0067708674,0.071413,0.0017915042,0.14893065],"study_design_scores_gemma":[0.000059508864,0.00023293441,0.011913038,0.00013400607,0.00008770717,0.00048561706,0.00029090213,0.919544,0.0020746451,0.06398256,0.0010535622,0.00014144741],"about_ca_topic_score_codex":0.0029751628,"about_ca_topic_score_gemma":0.00222258,"teacher_disagreement_score":0.032578826,"about_ca_system_score_codex":0.00097277976,"about_ca_system_score_gemma":0.0014920109,"threshold_uncertainty_score":0.17229533},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006123617,0.00019095223,0.99306595,0.00011248253,0.000019553358,0.00007043173,0.00010915445,0.00019457715,0.00011326272],"genre_scores_gemma":[0.22907129,0.0007498771,0.7656045,0.00019134625,0.00012974301,0.0011510539,0.0012774543,0.00018781894,0.0016368984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9742131,0.021929985,0.00067888194,0.0016267113,0.0012436232,0.0003077039],"domain_scores_gemma":[0.9240642,0.06498411,0.0037792057,0.005252159,0.0015846593,0.0003357177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041723616,0.0015417389,0.0030561453,0.0025304817,0.00053224224,0.0020699352,0.0034575022,0.0015438315,0.0017507541],"category_scores_gemma":[0.08754955,0.0013773883,0.0031519858,0.0017524703,0.0014433442,0.0025306589,0.0023914818,0.0034860573,0.0006687046],"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":[0.00076559826,0.0002648991,0.020500388,0.0005274862,0.0015468046,0.00036016677,0.0006567621,0.61807793,0.0016386088,0.12049682,0.0022331967,0.23293138],"study_design_scores_gemma":[0.000054297983,0.00011502899,0.0015324771,0.000039485636,0.00008088161,0.00010794901,0.000037790163,0.94280434,0.0006081193,0.053331383,0.0012444012,0.00004390593],"about_ca_topic_score_codex":0.0033787023,"about_ca_topic_score_gemma":0.004216871,"teacher_disagreement_score":0.041723616,"about_ca_system_score_codex":0.001320806,"about_ca_system_score_gemma":0.002019229,"threshold_uncertainty_score":0.22065824},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033534851,0.00030782833,0.9948955,0.00028947485,0.000024060535,0.00006441864,0.00037095754,0.00013112421,0.0005631063],"genre_scores_gemma":[0.40207788,0.0025258528,0.576092,0.00069509464,0.00038392027,0.0027509637,0.0036057134,0.00023034525,0.011638259],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99258184,0.005103826,0.0002519492,0.0010369581,0.00071671937,0.0003087673],"domain_scores_gemma":[0.9745063,0.020548657,0.00205564,0.0017587569,0.0008384664,0.00029214376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01208044,0.0013524753,0.0024143828,0.0016623512,0.0006576226,0.0020081487,0.0041250866,0.0021557584,0.0062561943],"category_scores_gemma":[0.030036757,0.0011645472,0.0022216844,0.0021703949,0.0019504349,0.0027383533,0.0021544923,0.0035988654,0.001966423],"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":[0.00017978183,0.00016051685,0.004557523,0.0004270981,0.00055221573,0.0004642754,0.00044241006,0.3612987,0.001516106,0.57425636,0.0038334315,0.05231154],"study_design_scores_gemma":[0.000043463486,0.00011700857,0.0013714812,0.000045246976,0.0000838572,0.00016170328,0.00004254546,0.6846427,0.00031018068,0.30808082,0.005039459,0.00006161292],"about_ca_topic_score_codex":0.004119091,"about_ca_topic_score_gemma":0.004020853,"teacher_disagreement_score":0.01208044,"about_ca_system_score_codex":0.0014911729,"about_ca_system_score_gemma":0.0017286933,"threshold_uncertainty_score":0.06388825},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013589211,0.001024581,0.98278266,0.0008911049,0.00009357514,0.00009103791,0.0003890336,0.00027474063,0.0008639426],"genre_scores_gemma":[0.5408614,0.004129472,0.4341271,0.00083680137,0.00066412095,0.0024099408,0.0028602108,0.00033413828,0.013776859],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9830025,0.012470994,0.0006711893,0.0018104067,0.001425937,0.00061900314],"domain_scores_gemma":[0.92517865,0.062335692,0.005430613,0.0034445056,0.0030814193,0.00052919483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046579313,0.0013075536,0.0028986463,0.0021773558,0.0008726286,0.0022938105,0.004879086,0.0028281356,0.007961573],"category_scores_gemma":[0.10942648,0.0009815969,0.0024907722,0.0034049256,0.0021116533,0.004959077,0.0032387422,0.004535537,0.0015343853],"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":[0.00044557216,0.00019963074,0.015349197,0.00061091525,0.0008697786,0.00060853205,0.00096842914,0.40995976,0.00066693424,0.45263755,0.0059317932,0.11175185],"study_design_scores_gemma":[0.00006552635,0.00009934443,0.001684129,0.00011059401,0.00010810724,0.00010062393,0.00008863094,0.7421393,0.0002488375,0.25142798,0.003881758,0.000045230925],"about_ca_topic_score_codex":0.005474594,"about_ca_topic_score_gemma":0.0042807604,"teacher_disagreement_score":0.046579313,"about_ca_system_score_codex":0.0017360101,"about_ca_system_score_gemma":0.0018805903,"threshold_uncertainty_score":0.24633789},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114447005,0.0004825363,0.88184094,0.00028055246,0.000027323833,0.00005843087,0.00012159437,0.00031561987,0.0024260222],"genre_scores_gemma":[0.90684426,0.0002536699,0.09139139,0.00009560113,0.000074553354,0.00017754806,0.00027267233,0.00015308637,0.00073720736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98973656,0.0062799705,0.00053807464,0.0010341128,0.0021095756,0.00030170908],"domain_scores_gemma":[0.7541177,0.21599641,0.013405786,0.009354879,0.006008226,0.0011170382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023695858,0.0005808854,0.00070492167,0.0027175273,0.00053149014,0.0015706553,0.0017238377,0.0012711006,0.0022312298],"category_scores_gemma":[0.21312161,0.00049640134,0.0014742968,0.0013267178,0.0029161286,0.0026768986,0.0022131526,0.0017987055,0.00035808794],"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":[0.00030194226,0.00008984443,0.0784401,0.00031440443,0.00032472517,0.00057976256,0.0015881101,0.6504658,0.0030552957,0.18008035,0.0012757641,0.083483815],"study_design_scores_gemma":[0.000014953885,0.000113819435,0.015499828,0.00005159595,0.00004866667,0.00041224025,0.00014694085,0.91164786,0.0018037658,0.06931148,0.0008639299,0.000084945736],"about_ca_topic_score_codex":0.0036252283,"about_ca_topic_score_gemma":0.0029103595,"teacher_disagreement_score":0.023695858,"about_ca_system_score_codex":0.0016732902,"about_ca_system_score_gemma":0.0007430557,"threshold_uncertainty_score":0.12531716},"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":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013254034,0.0059254626,0.9681309,0.0023690844,0.0004562747,0.006944667,0.00045322828,0.0006016798,0.0018647359],"genre_scores_gemma":[0.24668172,0.0031666458,0.7216294,0.0015551571,0.00037113746,0.024991892,0.00065933604,0.0001760544,0.00076878595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.41549298,0.5626676,0.0077349083,0.0049952706,0.008431726,0.0006774569],"domain_scores_gemma":[0.5016468,0.46056703,0.017369555,0.013092173,0.0058177584,0.0015067899],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3460635,0.004080488,0.008130259,0.004641315,0.000844335,0.0048463224,0.0051097325,0.005572239,0.0053473045],"category_scores_gemma":[0.52811044,0.0019351626,0.006364686,0.0028926355,0.0028847177,0.0045259693,0.005258592,0.0051368373,0.0006091067],"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":[0.024783418,0.0012297686,0.00874717,0.014084033,0.032092076,0.00057727785,0.00085838296,0.32330602,0.0011940642,0.16901368,0.005368963,0.41874507],"study_design_scores_gemma":[0.012591627,0.009341632,0.0021620025,0.002025739,0.0078122187,0.00018491979,0.00015599155,0.6462734,0.0022054557,0.30891758,0.008092581,0.00023697692],"about_ca_topic_score_codex":0.0015258626,"about_ca_topic_score_gemma":0.0010763643,"teacher_disagreement_score":0.6539365,"about_ca_system_score_codex":0.0033834758,"about_ca_system_score_gemma":0.006438246,"threshold_uncertainty_score":0.80642015},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042473266,0.09184111,0.6868371,0.05797636,0.03300797,0.0006480242,0.013681999,0.029006971,0.08657571],"genre_scores_gemma":[0.007829155,0.060513634,0.7692744,0.03108327,0.013647408,0.0032979688,0.01082407,0.023276709,0.080253445],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99349713,0.0029930437,0.00074161665,0.0010559922,0.0015229493,0.00018920423],"domain_scores_gemma":[0.9805822,0.012511945,0.001092589,0.0020263668,0.0030201934,0.00076667056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076962337,0.0024089944,0.0021028325,0.0046015144,0.0014920358,0.0045796623,0.002856458,0.0036026253,0.10144514],"category_scores_gemma":[0.03256848,0.0025213296,0.0026364883,0.005592404,0.00302259,0.007537051,0.0030468064,0.01298655,0.10466326],"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":[0.000042180036,0.00002924653,0.0002685391,0.00086835236,0.000050324365,0.00014713353,0.0002701406,0.000695352,0.0006401993,0.03644125,0.8268839,0.13366343],"study_design_scores_gemma":[0.000012853153,0.000017010585,0.00024198015,0.00042195403,0.000012392555,0.0002230731,0.000036925216,0.0004447416,0.00019076087,0.028278809,0.9700799,0.000039641578],"about_ca_topic_score_codex":0.0032515314,"about_ca_topic_score_gemma":0.0036158275,"teacher_disagreement_score":0.10144514,"about_ca_system_score_codex":0.0015185117,"about_ca_system_score_gemma":0.0042823628,"threshold_uncertainty_score":0.3393678},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010330142,0.003403269,0.97624683,0.0030036736,0.00016770713,0.000112525624,0.0001312714,0.00014325613,0.006461242],"genre_scores_gemma":[0.5405486,0.0060087503,0.44062042,0.00243839,0.0011281718,0.0009546865,0.0007366671,0.00062767096,0.0069366056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8494675,0.12139772,0.005789112,0.0074641365,0.014182932,0.0016986658],"domain_scores_gemma":[0.2796394,0.6702057,0.008905575,0.025250498,0.015461222,0.000537502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14775568,0.001676585,0.003847773,0.0040019085,0.0011836517,0.00448171,0.004075081,0.00451419,0.0030386988],"category_scores_gemma":[0.57260543,0.0014729636,0.0022013509,0.0040709204,0.007820079,0.008780014,0.0069038873,0.0067811944,0.0013093419],"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":[0.00051320245,0.00012831872,0.022134691,0.0007433607,0.00070635637,0.00052979,0.0016154109,0.11136883,0.00052313384,0.7267454,0.0041329265,0.13085856],"study_design_scores_gemma":[0.00014213959,0.00018974594,0.0057914886,0.0006088603,0.00021181746,0.0003794487,0.00030953722,0.35906857,0.00132988,0.62005365,0.011826591,0.000088262794],"about_ca_topic_score_codex":0.0043702736,"about_ca_topic_score_gemma":0.0014483857,"teacher_disagreement_score":0.14775568,"about_ca_system_score_codex":0.0030584403,"about_ca_system_score_gemma":0.0025331455,"threshold_uncertainty_score":0.78141606},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021946298,0.00057650974,0.9738565,0.0005929383,0.000090765425,0.0002185246,0.0013055303,0.00035396984,0.0010588878],"genre_scores_gemma":[0.5627096,0.002629522,0.4062105,0.0006648318,0.00052835385,0.003191766,0.005669331,0.00034172446,0.01805435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9853839,0.01006499,0.0005739451,0.0024310078,0.00092347583,0.0006225887],"domain_scores_gemma":[0.94590753,0.04299793,0.0034147415,0.004981127,0.0020816722,0.00061701494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0298714,0.001943779,0.002823033,0.002481717,0.00080202054,0.0032521172,0.005860872,0.0026542498,0.010292244],"category_scores_gemma":[0.07743174,0.0014171489,0.0032785514,0.0031670071,0.00320317,0.0052324072,0.0034189764,0.004487873,0.0020596853],"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":[0.00050276564,0.00022299636,0.019724812,0.00033649232,0.0009016293,0.00049337925,0.0015462233,0.20511034,0.0004809382,0.7017054,0.0046944325,0.06428065],"study_design_scores_gemma":[0.0000952986,0.00013430582,0.0038760025,0.000076173324,0.00013641106,0.00020933138,0.00022237077,0.54590493,0.00021958766,0.44359222,0.0054501896,0.000083160245],"about_ca_topic_score_codex":0.009381244,"about_ca_topic_score_gemma":0.006327869,"teacher_disagreement_score":0.0298714,"about_ca_system_score_codex":0.0017937092,"about_ca_system_score_gemma":0.0014791666,"threshold_uncertainty_score":0.15797698},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069402237,0.0004622901,0.99045724,0.0003205407,0.000023612156,0.00013511915,0.00009516979,0.00010577834,0.0014601477],"genre_scores_gemma":[0.35282317,0.001005334,0.6413636,0.00048572902,0.00009307671,0.0011255019,0.0005046442,0.000081937964,0.0025169603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9872722,0.008741596,0.00040697816,0.0015097657,0.0018514802,0.00021794028],"domain_scores_gemma":[0.97579044,0.020542456,0.0012798032,0.000958166,0.0012696537,0.00015943359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018555662,0.0011776513,0.002353009,0.001796905,0.00051269314,0.00165536,0.0019854712,0.0020404002,0.0038237134],"category_scores_gemma":[0.059355922,0.00075300684,0.0016248255,0.0012426511,0.001832348,0.0018829968,0.0015213717,0.0024640488,0.00080041034],"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":[0.00067330094,0.0002310511,0.0060588876,0.00070770987,0.0004904277,0.00016130091,0.00026083467,0.6749916,0.00414086,0.11942012,0.0034212498,0.18944268],"study_design_scores_gemma":[0.00012904494,0.00021451536,0.0030951684,0.0001576886,0.0001632171,0.00019247767,0.000035543584,0.86919314,0.0028846331,0.1202023,0.003644736,0.000087543965],"about_ca_topic_score_codex":0.002739919,"about_ca_topic_score_gemma":0.002003568,"teacher_disagreement_score":0.018555662,"about_ca_system_score_codex":0.0018847381,"about_ca_system_score_gemma":0.001857601,"threshold_uncertainty_score":0.09813285},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035645373,0.00058146426,0.994194,0.0005719973,0.000036047135,0.00011157615,0.00015511643,0.000108018794,0.0006772413],"genre_scores_gemma":[0.17670444,0.002286134,0.8148187,0.0005447227,0.00042581782,0.0015774783,0.0007527748,0.000117005344,0.002772939],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9641676,0.026277972,0.0014431367,0.0040128673,0.0032580183,0.00084042014],"domain_scores_gemma":[0.84886235,0.13595703,0.0064912913,0.00431951,0.003613015,0.0007568446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.058398787,0.0023607656,0.0041756877,0.005932287,0.002080322,0.004380092,0.008801395,0.0043998538,0.0048335507],"category_scores_gemma":[0.16254541,0.0030962052,0.0031963747,0.005377399,0.005042858,0.005930139,0.0042485027,0.0067333877,0.00085456384],"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":[0.00020705229,0.00014238167,0.006875453,0.00046830787,0.0009068243,0.00051938946,0.0010773346,0.33824605,0.00046549688,0.5559733,0.002866089,0.09225226],"study_design_scores_gemma":[0.000074809774,0.00004912976,0.0012470346,0.00011535055,0.00016071938,0.00018866168,0.000059116453,0.47892463,0.00019769181,0.5164448,0.002463679,0.00007443161],"about_ca_topic_score_codex":0.019391337,"about_ca_topic_score_gemma":0.02009287,"teacher_disagreement_score":0.058398787,"about_ca_system_score_codex":0.004084016,"about_ca_system_score_gemma":0.0035533851,"threshold_uncertainty_score":0.308846},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.541659,0.00033357766,0.44398794,0.00008997776,0.000019506211,0.0004903213,0.0032752494,0.0009110496,0.009233438],"genre_scores_gemma":[0.62654966,0.0005864331,0.36222282,0.00004889663,0.00003818521,0.00077201263,0.005567226,0.00008184241,0.00413285],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970445,0.0012850467,0.00026049357,0.0004361336,0.000888686,0.000085114996],"domain_scores_gemma":[0.98843086,0.0036383562,0.004408333,0.0014499908,0.0019266856,0.000145784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004631008,0.00064359664,0.00043454437,0.003634232,0.00023926787,0.00072322023,0.0006981089,0.00032495992,0.0009962954],"category_scores_gemma":[0.017743463,0.0004857247,0.00047587606,0.0031924634,0.0003669693,0.0014090027,0.0006933992,0.000338476,0.0007076278],"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":[0.00022785517,0.00021208217,0.6107956,0.00024910367,0.00029462643,0.00008744137,0.00031568765,0.07704114,0.0078677,0.0036496902,0.0015294838,0.2977295],"study_design_scores_gemma":[0.000040120256,0.0004202981,0.72231007,0.00006354757,0.00007589288,0.0001842563,0.00022938855,0.26255268,0.006038738,0.0042596906,0.0037527343,0.00007250125],"about_ca_topic_score_codex":0.007868089,"about_ca_topic_score_gemma":0.022901986,"teacher_disagreement_score":0.007868089,"about_ca_system_score_codex":0.00087312557,"about_ca_system_score_gemma":0.00081698835,"threshold_uncertainty_score":0.02449143},"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":"editorial","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015742006,0.003151781,0.012926196,0.1138437,0.86077976,0.00017478937,0.0032350859,0.002901359,0.0028299503],"genre_scores_gemma":[0.0123371845,0.012149992,0.08742095,0.34730792,0.3927016,0.0030489403,0.011060785,0.017825352,0.11614731],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9444444,0.025004407,0.0076981694,0.005487279,0.015557391,0.0018082627],"domain_scores_gemma":[0.65996057,0.18039408,0.010565044,0.02259773,0.12092846,0.0055540516],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.041756168,0.007990481,0.0067530414,0.009948763,0.0063854395,0.008218496,0.01114538,0.015084235,0.09563419],"category_scores_gemma":[0.47905192,0.0050964975,0.0064612483,0.009957968,0.006950098,0.008654359,0.0062466674,0.026494928,0.07021539],"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":[0.00002140922,0.0000070935057,0.000049058093,0.000059291615,0.000021368445,0.000038028866,0.00001571458,0.000047234986,0.000016784605,0.0004102248,0.99622214,0.0030916014],"study_design_scores_gemma":[0.00026709988,0.00007181441,0.0017816632,0.000998215,0.000143104,0.00041601123,0.00008656187,0.0016598994,0.00042193284,0.008683717,0.9852529,0.00021711092],"about_ca_topic_score_codex":0.027863069,"about_ca_topic_score_gemma":0.02248928,"teacher_disagreement_score":0.95824385,"about_ca_system_score_codex":0.010584567,"about_ca_system_score_gemma":0.013313774,"threshold_uncertainty_score":0.31992823},"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":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009063298,0.00035001122,0.9878606,0.0006249129,0.00025921947,0.00018922934,0.00023217477,0.00046876204,0.0009517037],"genre_scores_gemma":[0.27863896,0.0005910352,0.71306986,0.0013247256,0.0008952444,0.0028002698,0.00088890933,0.00033589682,0.0014551381],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95859134,0.025865145,0.0018059709,0.0038479315,0.009130848,0.0007586791],"domain_scores_gemma":[0.76484436,0.20448452,0.010289278,0.011100954,0.0077267713,0.0015541177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039816037,0.0009482151,0.003067201,0.0038588592,0.0011714319,0.002006078,0.004082176,0.0024717683,0.007709949],"category_scores_gemma":[0.21144456,0.0005609906,0.0020215644,0.003799533,0.0035537197,0.0038485313,0.002917338,0.0040797903,0.0013852874],"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":[0.0011886979,0.00046927348,0.040491227,0.0012605421,0.0019362656,0.0010646307,0.0009327955,0.023224045,0.006139076,0.16744179,0.016340928,0.7395108],"study_design_scores_gemma":[0.0011615767,0.004729692,0.03578779,0.0006466816,0.0009851746,0.00511416,0.0006776344,0.46372193,0.012677332,0.42964008,0.04429124,0.0005667195],"about_ca_topic_score_codex":0.0006745517,"about_ca_topic_score_gemma":0.0005221228,"teacher_disagreement_score":0.039816037,"about_ca_system_score_codex":0.0010139777,"about_ca_system_score_gemma":0.0036892812,"threshold_uncertainty_score":0.21056986},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011434831,0.0017732194,0.9815159,0.001643492,0.00018259246,0.0013351559,0.0001514653,0.00010777425,0.0018555182],"genre_scores_gemma":[0.18390971,0.0011412792,0.8029433,0.001041796,0.0003056133,0.009746349,0.00029350727,0.00007981296,0.00053865765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.62851024,0.32597458,0.012688573,0.012810454,0.01880417,0.0012119571],"domain_scores_gemma":[0.1991173,0.7536234,0.017601915,0.019516915,0.008899377,0.0012411623],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.34295285,0.0017868538,0.0051295394,0.00658926,0.0018256598,0.003893737,0.005681218,0.007948594,0.0020552354],"category_scores_gemma":[0.7365388,0.002468145,0.002652382,0.003735129,0.00992616,0.006257683,0.0052553923,0.0064721955,0.00039952318],"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":[0.0028859144,0.00026985497,0.029923454,0.0034795566,0.0016744573,0.0012313232,0.0033568607,0.066923775,0.0031733068,0.69255245,0.004444686,0.19008428],"study_design_scores_gemma":[0.0012627383,0.0013689703,0.009661353,0.0017676275,0.00074265234,0.0010301055,0.0003736563,0.21022019,0.003886343,0.7561161,0.013387255,0.0001830957],"about_ca_topic_score_codex":0.0019124238,"about_ca_topic_score_gemma":0.0018109496,"teacher_disagreement_score":0.34295285,"about_ca_system_score_codex":0.0033074045,"about_ca_system_score_gemma":0.004532027,"threshold_uncertainty_score":0.8102561},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011832201,0.00016758201,0.9981846,0.00006810079,0.00003185503,0.000015741152,0.000038256127,0.00009047901,0.00022019816],"genre_scores_gemma":[0.19123466,0.0017648417,0.79753405,0.00028940503,0.00053221406,0.0006698993,0.0009190092,0.00027820983,0.0067777936],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969664,0.001819389,0.000110829336,0.00042139814,0.0005669337,0.00011506121],"domain_scores_gemma":[0.99537224,0.0028167076,0.00037155178,0.00045268048,0.0008681644,0.00011868975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042870864,0.0007806226,0.0013183539,0.0014866677,0.00044100336,0.0009745767,0.0018198367,0.0010963901,0.0042268606],"category_scores_gemma":[0.013636554,0.00051307376,0.0016093219,0.001409053,0.00068853557,0.0017819282,0.0016548353,0.0026225958,0.0008947802],"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":[0.0002595193,0.00016614224,0.0057099806,0.0006179318,0.00055846624,0.00038083765,0.00042344903,0.27418122,0.008231305,0.28516075,0.010461242,0.41384923],"study_design_scores_gemma":[0.00006358223,0.000101242375,0.0012730778,0.000042176416,0.000105618834,0.00037960478,0.000025066654,0.91186875,0.0014809405,0.07273858,0.0118546095,0.00006676681],"about_ca_topic_score_codex":0.0016743013,"about_ca_topic_score_gemma":0.0010931419,"teacher_disagreement_score":0.0042870864,"about_ca_system_score_codex":0.0005532,"about_ca_system_score_gemma":0.001640543,"threshold_uncertainty_score":0.022672534},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18606289,0.0011327268,0.7877969,0.0046092165,0.00038055293,0.00029271928,0.0017059648,0.0009854379,0.017033674],"genre_scores_gemma":[0.83207244,0.0015351023,0.056265067,0.0012260292,0.0003406496,0.0008470068,0.0012510597,0.000334326,0.10612835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981025,0.00045983953,0.00009744198,0.0007488366,0.00027553178,0.00031582825],"domain_scores_gemma":[0.99303436,0.0034182821,0.0016564719,0.00093872903,0.000638427,0.000313764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067970846,0.0016540688,0.0029234083,0.0014153342,0.0013649084,0.002582133,0.0065385406,0.00474891,0.0058832043],"category_scores_gemma":[0.012839157,0.0012618154,0.0020652893,0.0015593183,0.0039625545,0.0054600285,0.002730746,0.0034835567,0.0029191386],"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":[0.0004839872,0.00015530518,0.010734434,0.00023258696,0.00023967504,0.0006742281,0.0008785529,0.40914032,0.0076530813,0.53959715,0.008512812,0.0216978],"study_design_scores_gemma":[0.00017534771,0.00017708873,0.0027483262,0.00004148343,0.00011375953,0.00043973446,0.00009154428,0.86891717,0.0008328054,0.12101367,0.0053322017,0.000116909614],"about_ca_topic_score_codex":0.01190334,"about_ca_topic_score_gemma":0.0075043878,"teacher_disagreement_score":0.01190334,"about_ca_system_score_codex":0.0021529347,"about_ca_system_score_gemma":0.001485583,"threshold_uncertainty_score":0.035946906},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056243623,0.0010341466,0.9903058,0.00014129494,0.00016643644,0.00018605757,0.00017561915,0.0006372098,0.0017290311],"genre_scores_gemma":[0.13739689,0.00072631397,0.85860914,0.00018771351,0.00021087371,0.001428156,0.000518573,0.00038912543,0.0005332498],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90288043,0.05897373,0.0063200765,0.009172386,0.021532027,0.0011214077],"domain_scores_gemma":[0.6689631,0.2766858,0.013466389,0.019364776,0.02043458,0.0010853293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07155238,0.0016844237,0.0026405312,0.010157761,0.0012272332,0.0035905219,0.0043223156,0.0028963636,0.0046258215],"category_scores_gemma":[0.3717094,0.00082601793,0.0026316077,0.004594629,0.0024329918,0.004701082,0.0045425133,0.0050262073,0.0016816917],"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":[0.0019419501,0.00034977603,0.02036106,0.0030446742,0.0019882843,0.0004114165,0.0027553912,0.032659598,0.007779424,0.168248,0.01080405,0.74965626],"study_design_scores_gemma":[0.000782556,0.003058058,0.05143866,0.0025933993,0.0015101088,0.0038525003,0.0015608706,0.3658221,0.02132258,0.50559676,0.041394267,0.0010681176],"about_ca_topic_score_codex":0.000826223,"about_ca_topic_score_gemma":0.0005720568,"teacher_disagreement_score":0.07155238,"about_ca_system_score_codex":0.0010901785,"about_ca_system_score_gemma":0.0016256603,"threshold_uncertainty_score":0.37840968},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15496156,0.00057927624,0.84237444,0.0006589063,0.000045527442,0.00009873669,0.00037504412,0.00021052179,0.0006959378],"genre_scores_gemma":[0.75370836,0.0010431019,0.23846307,0.00037135524,0.00017960957,0.0006176662,0.0010456147,0.000067835055,0.0045034336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9927246,0.0045812167,0.00025838974,0.0014747527,0.0006029573,0.0003581848],"domain_scores_gemma":[0.9561159,0.03674897,0.003526328,0.0024585896,0.0006528088,0.00049728336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018121654,0.0010882864,0.0026436888,0.002551434,0.0006210956,0.0017163402,0.0030299632,0.0022755512,0.0020248871],"category_scores_gemma":[0.035901345,0.0009959068,0.0016933688,0.002647301,0.0024067631,0.0022930554,0.002392936,0.0025354393,0.000669354],"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":[0.00057336607,0.00036373315,0.0545222,0.00022719138,0.0005874844,0.0014272952,0.00095402985,0.6460534,0.0010860292,0.2118903,0.0015004994,0.08081441],"study_design_scores_gemma":[0.000044250006,0.00013007846,0.0033318433,0.000027274018,0.000060290873,0.00021776005,0.00006585502,0.90278465,0.000206557,0.09246874,0.00062397844,0.000038725542],"about_ca_topic_score_codex":0.0060814666,"about_ca_topic_score_gemma":0.004073984,"teacher_disagreement_score":0.018121654,"about_ca_system_score_codex":0.0014640933,"about_ca_system_score_gemma":0.0016327656,"threshold_uncertainty_score":0.09583765},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004196164,0.0004928996,0.99367225,0.000264666,0.000044253087,0.000028971692,0.00005728534,0.00007898443,0.001164424],"genre_scores_gemma":[0.19896236,0.0017926802,0.793554,0.0005259213,0.0006983051,0.0006281903,0.00041601498,0.00023308545,0.0031894164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9887427,0.0065910197,0.00042040195,0.0017408599,0.002204513,0.00030045045],"domain_scores_gemma":[0.88297474,0.098285824,0.0049961912,0.00872944,0.004258063,0.00075573195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01773748,0.000779859,0.0016816021,0.003330801,0.00063564995,0.0013638118,0.0024923163,0.0017142394,0.0033644887],"category_scores_gemma":[0.113732636,0.0006698004,0.0017658074,0.002402734,0.0024912744,0.002909784,0.003584716,0.0027902208,0.0005920951],"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":[0.00022786642,0.00015971133,0.012799055,0.00042396592,0.00022313454,0.00019555929,0.00071931095,0.13098057,0.0047910754,0.53005177,0.003136868,0.3162911],"study_design_scores_gemma":[0.00006206462,0.0003683084,0.008174892,0.00018430117,0.00008232394,0.00043354894,0.00009793493,0.5713421,0.0036721537,0.39728367,0.018128956,0.00016973635],"about_ca_topic_score_codex":0.0016148516,"about_ca_topic_score_gemma":0.0015257036,"teacher_disagreement_score":0.01773748,"about_ca_system_score_codex":0.0016933986,"about_ca_system_score_gemma":0.0016167971,"threshold_uncertainty_score":0.09380585},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07981671,0.000098551856,0.9187544,0.00011879679,0.000012872483,0.00009224565,0.0003344825,0.0003413783,0.00043054082],"genre_scores_gemma":[0.74610883,0.00019475115,0.24865288,0.00021520158,0.00003605242,0.0007911548,0.0015495061,0.00015060474,0.0023010923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9907499,0.005978254,0.0005476563,0.0013871114,0.0010221122,0.0003149395],"domain_scores_gemma":[0.83460903,0.11836829,0.019486995,0.023861727,0.0029535266,0.0007203195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03761349,0.000837163,0.0018936456,0.0013778015,0.00042473924,0.0016315745,0.0031125855,0.0018560676,0.0021089711],"category_scores_gemma":[0.11899908,0.0009140196,0.001969787,0.0010172722,0.0021470187,0.003189107,0.0020725983,0.002673644,0.00062139845],"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":[0.002059295,0.0005984093,0.088490345,0.00076741737,0.00090459187,0.00039442483,0.0010947399,0.5550527,0.026272094,0.09016627,0.0015546443,0.23264511],"study_design_scores_gemma":[0.000056057317,0.00031945598,0.021470325,0.000056369372,0.00014163436,0.00028326965,0.00008373906,0.92639637,0.007366294,0.04272432,0.00097338815,0.0001286651],"about_ca_topic_score_codex":0.0011516178,"about_ca_topic_score_gemma":0.0012560041,"teacher_disagreement_score":0.03761349,"about_ca_system_score_codex":0.0010051874,"about_ca_system_score_gemma":0.00072791235,"threshold_uncertainty_score":0.19892156},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003079652,0.0003154204,0.9953962,0.00023087944,0.00011058463,0.00016398383,0.00006580992,0.0002382624,0.00039916934],"genre_scores_gemma":[0.22626664,0.0008341667,0.76677763,0.00075208186,0.0006474558,0.0031086565,0.00048338843,0.0002533296,0.000876688],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9164057,0.069750994,0.0018354557,0.0041091344,0.007346677,0.0005521704],"domain_scores_gemma":[0.5581245,0.4172416,0.0083343815,0.011085689,0.0040754355,0.0011383755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.080679424,0.0016156781,0.0038177518,0.004673377,0.0011291681,0.0032202182,0.004752637,0.0030727643,0.008160593],"category_scores_gemma":[0.45258212,0.0008856154,0.0024180422,0.002968442,0.005537714,0.004183579,0.0031931628,0.0058911243,0.0014305953],"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":[0.003171958,0.00060527574,0.01566876,0.0015599915,0.0023183734,0.0011131153,0.0008391657,0.096940026,0.0036653357,0.37933242,0.0094069755,0.4853786],"study_design_scores_gemma":[0.00092436076,0.0011777811,0.005835294,0.00026896407,0.00032899086,0.0008910939,0.00012274951,0.5218263,0.003061725,0.45935038,0.0060294606,0.00018286728],"about_ca_topic_score_codex":0.0009928684,"about_ca_topic_score_gemma":0.0006888883,"teacher_disagreement_score":0.080679424,"about_ca_system_score_codex":0.0014282867,"about_ca_system_score_gemma":0.0025260686,"threshold_uncertainty_score":0.42667866},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7588504,0.0014984489,0.23617165,0.0005868735,0.00008381877,0.0001588218,0.0004266695,0.00025569758,0.0019675149],"genre_scores_gemma":[0.9711349,0.00014482952,0.027942719,0.000117665986,0.00004404121,0.00007571431,0.000185113,0.000025237732,0.00032980953],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.88052374,0.10375448,0.0021993343,0.007696521,0.0039288523,0.0018969757],"domain_scores_gemma":[0.6801876,0.29265156,0.011682521,0.012211278,0.0019293806,0.0013375942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.069495335,0.0010202297,0.0025064049,0.004801805,0.001575266,0.002308177,0.0023343428,0.0030774157,0.0015367563],"category_scores_gemma":[0.14735702,0.00091440236,0.0034037705,0.005319267,0.0047692005,0.002659512,0.0036645702,0.0019078109,0.00029971034],"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":[0.0015460803,0.00024230963,0.8800918,0.00036791616,0.0035268064,0.0013018762,0.0012618566,0.02924343,0.0040446026,0.0122446595,0.0004036239,0.065725215],"study_design_scores_gemma":[0.00014370437,0.0026168628,0.7886015,0.00010806971,0.0031012543,0.0021490334,0.0013309121,0.13964392,0.004570604,0.054687504,0.0026860253,0.00036063418],"about_ca_topic_score_codex":0.0053233635,"about_ca_topic_score_gemma":0.006473529,"teacher_disagreement_score":0.069495335,"about_ca_system_score_codex":0.00091868185,"about_ca_system_score_gemma":0.0010228915,"threshold_uncertainty_score":0.36753088},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014615958,0.0025017397,0.97962165,0.00066334085,0.00010519822,0.00006507824,0.00008606835,0.0002939429,0.0020470682],"genre_scores_gemma":[0.3467424,0.0030372394,0.6454975,0.0006951031,0.00023645202,0.00021453369,0.00039460327,0.00031583168,0.0028663995],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9310949,0.05963478,0.0010055194,0.00404263,0.0036297033,0.00059258525],"domain_scores_gemma":[0.7515092,0.21664324,0.008657838,0.017879358,0.004928042,0.00038223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0926158,0.0015865111,0.0019569704,0.0030902706,0.00089274615,0.0025023073,0.0022051528,0.0025099302,0.0019004043],"category_scores_gemma":[0.29814777,0.000785142,0.0022144734,0.0041215234,0.002879439,0.004238243,0.002303232,0.002489036,0.0012705209],"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":[0.00063870323,0.00012860031,0.06028661,0.0006079682,0.0024290392,0.00035634928,0.0014062022,0.25414494,0.0019563749,0.25233954,0.005124488,0.42058128],"study_design_scores_gemma":[0.00020896141,0.0006843339,0.028255248,0.00048815738,0.00085964886,0.0011939538,0.00035346224,0.6293565,0.0059887604,0.31606373,0.0161756,0.00037158647],"about_ca_topic_score_codex":0.006055085,"about_ca_topic_score_gemma":0.0034026804,"teacher_disagreement_score":0.0926158,"about_ca_system_score_codex":0.0012739713,"about_ca_system_score_gemma":0.0015684868,"threshold_uncertainty_score":0.48980498},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010195579,0.69671196,0.06795203,0.10168152,0.019501306,0.00009771303,0.0017343836,0.0007291002,0.110572435],"genre_scores_gemma":[0.042025506,0.5727443,0.07776462,0.049063705,0.034074716,0.00060271734,0.0023239253,0.0019983216,0.21940216],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99668735,0.0012422351,0.00018682414,0.00077094627,0.0009840276,0.00012873144],"domain_scores_gemma":[0.9942947,0.0040243207,0.00029864543,0.00027314262,0.0009175166,0.00019164833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004337925,0.0018709867,0.0015747225,0.0028472983,0.0013319091,0.0051459176,0.0015433802,0.003158393,0.012996351],"category_scores_gemma":[0.01484651,0.0011071918,0.0012486326,0.0049985144,0.0046734773,0.0059381127,0.001671947,0.0094356565,0.015741955],"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":[0.000042398024,0.000030489378,0.00059741637,0.0007788911,0.00006456165,0.00008607469,0.00063059555,0.0019012362,0.00016343597,0.2870257,0.59444445,0.11423472],"study_design_scores_gemma":[0.000008771076,0.000027102984,0.0008020587,0.0007240339,0.000016363947,0.00019409561,0.00010882753,0.00082867534,0.000078493395,0.13968602,0.8574939,0.00003164414],"about_ca_topic_score_codex":0.006608659,"about_ca_topic_score_gemma":0.005118287,"teacher_disagreement_score":0.012996351,"about_ca_system_score_codex":0.0031887386,"about_ca_system_score_gemma":0.004314497,"threshold_uncertainty_score":0.043477118},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13077617,0.00084728823,0.8319053,0.0011417123,0.0017631791,0.004373464,0.010329833,0.0035331126,0.015329983],"genre_scores_gemma":[0.50001067,0.0003835804,0.48391432,0.0006504581,0.00069505017,0.0064198496,0.0049090404,0.00027227085,0.0027447697],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9407298,0.030383645,0.005855703,0.007891553,0.014371403,0.00076785975],"domain_scores_gemma":[0.5358943,0.41127455,0.018542496,0.017117813,0.015510317,0.0016606183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032447767,0.0010479286,0.002674444,0.007032336,0.0015696873,0.0030157964,0.0029804248,0.0018634665,0.02141011],"category_scores_gemma":[0.3125676,0.0005569461,0.0023635142,0.010311974,0.0022824784,0.0057764472,0.0024692605,0.0022925308,0.0025469118],"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":[0.0033328724,0.0014729949,0.26062474,0.0034043216,0.0057414197,0.0014695888,0.003010559,0.0063707912,0.006253542,0.07916458,0.03861671,0.590538],"study_design_scores_gemma":[0.001947878,0.0120717,0.34418297,0.001523144,0.002684855,0.011039788,0.0051805982,0.19799902,0.0133426655,0.3166894,0.092196755,0.0011411879],"about_ca_topic_score_codex":0.0006602955,"about_ca_topic_score_gemma":0.000680398,"teacher_disagreement_score":0.032447767,"about_ca_system_score_codex":0.0007681476,"about_ca_system_score_gemma":0.0019836277,"threshold_uncertainty_score":0.17160225},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003497758,0.015630137,0.8432762,0.12843865,0.0040738946,0.00030763587,0.0006366854,0.00061709457,0.003521895],"genre_scores_gemma":[0.0892094,0.0120848175,0.7738434,0.10240106,0.0143033005,0.0016162976,0.00028109446,0.0007736171,0.00548702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.77165127,0.19161043,0.009912213,0.0101045435,0.015548267,0.0011731691],"domain_scores_gemma":[0.4594537,0.48860914,0.008583185,0.031585727,0.010890326,0.00087789027],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.22325009,0.0014940042,0.005932866,0.003281838,0.0021388095,0.0046264273,0.011286903,0.008548156,0.0024507397],"category_scores_gemma":[0.48190185,0.0013858969,0.0060926457,0.005888048,0.014237664,0.009826436,0.0055896416,0.027386015,0.0012079686],"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":[0.00048626686,0.000079786325,0.01377515,0.0026461445,0.0018969629,0.0031638443,0.004546942,0.011092474,0.00068498275,0.59492284,0.12900315,0.23770148],"study_design_scores_gemma":[0.0001456558,0.0001393228,0.00478777,0.0023206535,0.00037246977,0.0023467403,0.0005385383,0.037983753,0.0009835963,0.8764084,0.07376289,0.00021011331],"about_ca_topic_score_codex":0.008284222,"about_ca_topic_score_gemma":0.0071403855,"teacher_disagreement_score":0.7767499,"about_ca_system_score_codex":0.0024042684,"about_ca_system_score_gemma":0.0036247754,"threshold_uncertainty_score":0.9578709},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006940244,0.0012013059,0.9906657,0.000153432,0.00018143927,0.000068017805,0.0001584514,0.0002333244,0.00039813403],"genre_scores_gemma":[0.036665987,0.0011615152,0.95884806,0.000073597104,0.00017849448,0.00025403776,0.00061704987,0.00011236922,0.0020889665],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959053,0.0025188685,0.0002507722,0.0005466457,0.0006890073,0.0000893819],"domain_scores_gemma":[0.9926212,0.0052146222,0.00028049113,0.0011080032,0.00060580246,0.00016991698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007514189,0.00089854875,0.0010237854,0.0035962602,0.0007459835,0.0007718211,0.002222926,0.00084278645,0.003453179],"category_scores_gemma":[0.018007126,0.0008766079,0.0016260651,0.003932261,0.0013273685,0.0017298037,0.0019728811,0.0029737952,0.0016426663],"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":[0.00033408398,0.00015569052,0.009320698,0.0005997824,0.00042723797,0.0001895839,0.00044093572,0.08421882,0.007776906,0.057715673,0.011897576,0.8269231],"study_design_scores_gemma":[0.000108502696,0.00029457844,0.012656967,0.00013329588,0.0001748779,0.00062118383,0.00010781718,0.83017796,0.007266646,0.10643936,0.041888826,0.000130078],"about_ca_topic_score_codex":0.00684821,"about_ca_topic_score_gemma":0.009831397,"teacher_disagreement_score":0.007514189,"about_ca_system_score_codex":0.00064653315,"about_ca_system_score_gemma":0.0013592837,"threshold_uncertainty_score":0.03973931},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52014196,0.00023749023,0.46837974,0.00063954137,0.00010437324,0.00011217238,0.0007118747,0.0004467325,0.009226095],"genre_scores_gemma":[0.9120828,0.00008172387,0.08539619,0.000095660755,0.00006944836,0.00013960153,0.00059512764,0.000059499675,0.0014798541],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99049133,0.0054170573,0.00040595728,0.0012133755,0.0021330933,0.00033921655],"domain_scores_gemma":[0.87195134,0.1161596,0.0034590075,0.0031065184,0.0037833236,0.0015403274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013229624,0.0006866542,0.0009246776,0.0023495583,0.0010891794,0.001214594,0.0016162752,0.0011772655,0.0050993855],"category_scores_gemma":[0.096772715,0.00021989635,0.00090677215,0.0019759405,0.0035143795,0.0021189111,0.0024414465,0.0011090713,0.000679285],"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":[0.0019892142,0.0004701961,0.44892582,0.00037205894,0.0014084376,0.0014814141,0.00150501,0.05449936,0.011095693,0.2223082,0.003918895,0.25202575],"study_design_scores_gemma":[0.00071073725,0.002453888,0.19742686,0.00013088394,0.0002952429,0.0045935214,0.0014413885,0.44241744,0.009872881,0.33198327,0.008376283,0.0002975858],"about_ca_topic_score_codex":0.0011739715,"about_ca_topic_score_gemma":0.00079364626,"teacher_disagreement_score":0.013229624,"about_ca_system_score_codex":0.00048353142,"about_ca_system_score_gemma":0.0011929941,"threshold_uncertainty_score":0.06996578},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019750826,0.00010570326,0.9788801,0.0001112302,0.000010415249,0.000021046002,0.00014186346,0.0001277398,0.00085100846],"genre_scores_gemma":[0.38956937,0.00022228036,0.60560215,0.00011013093,0.00004869547,0.00016717492,0.0009849415,0.000074502685,0.0032207237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980545,0.0011340828,0.00009200638,0.00036781604,0.00024851557,0.00010306044],"domain_scores_gemma":[0.9927153,0.0046888026,0.00067699194,0.0012310217,0.00058004016,0.00010790917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038972301,0.0004207613,0.00080855034,0.0010172524,0.0007107683,0.0009368553,0.0013397526,0.00072090636,0.0017166056],"category_scores_gemma":[0.019381061,0.00030295955,0.00082400616,0.0014026593,0.0005639946,0.0014507164,0.0020713934,0.0011215012,0.00044241073],"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":[0.00012277711,0.000100664925,0.037306435,0.00016588469,0.00035220053,0.00039461596,0.0007607386,0.4253018,0.0026320214,0.19774756,0.005224004,0.32989144],"study_design_scores_gemma":[0.00001293057,0.000020006872,0.00607996,0.000017881785,0.000027009986,0.00015430042,0.000050906998,0.8808963,0.00074178993,0.10859282,0.0033794811,0.000026551803],"about_ca_topic_score_codex":0.0048905034,"about_ca_topic_score_gemma":0.009306741,"teacher_disagreement_score":0.0048905034,"about_ca_system_score_codex":0.00071047095,"about_ca_system_score_gemma":0.00061973854,"threshold_uncertainty_score":0.02061081},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001358932,0.00023615701,0.9978428,0.000092075425,0.00001288521,0.000012587996,0.000037553804,0.00011567099,0.0002912498],"genre_scores_gemma":[0.3849096,0.0027349226,0.60265905,0.00025824143,0.00031832504,0.000670221,0.0012886762,0.00049885525,0.0066620084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975228,0.0012236132,0.00014816721,0.00046337245,0.00052607595,0.00011602825],"domain_scores_gemma":[0.991867,0.006006025,0.00081861956,0.00047301318,0.00072465977,0.00011067648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004837073,0.0016004933,0.0021029836,0.001625145,0.00050746894,0.0015661347,0.001931247,0.0017802523,0.0015685307],"category_scores_gemma":[0.022981932,0.001181924,0.0019113027,0.0012047057,0.0014482044,0.0019034096,0.0021999308,0.002624163,0.00059753406],"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":[0.000038538758,0.000023706183,0.00060665695,0.0001772066,0.00013463077,0.0000637577,0.00005645926,0.9112887,0.0012510546,0.061105162,0.00081670046,0.024437591],"study_design_scores_gemma":[0.000003664408,0.000007237201,0.000092507005,0.000008008276,0.000006692059,0.0000080606715,0.0000031223171,0.9826299,0.00021938788,0.016467223,0.00054379215,0.000010392984],"about_ca_topic_score_codex":0.007445549,"about_ca_topic_score_gemma":0.0036216734,"teacher_disagreement_score":0.007445549,"about_ca_system_score_codex":0.0013010262,"about_ca_system_score_gemma":0.0014740124,"threshold_uncertainty_score":0.025581181},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004805874,0.00018767826,0.9938455,0.000036446956,0.000030332778,0.00031999277,0.00007064296,0.00021020611,0.00049335405],"genre_scores_gemma":[0.04960978,0.00021018782,0.9471398,0.000055330525,0.00002345198,0.002117591,0.00014321526,0.000062647014,0.00063802436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9571094,0.036834102,0.0008636744,0.0021057015,0.0026210865,0.00046602826],"domain_scores_gemma":[0.945466,0.046337564,0.0022080103,0.0031310632,0.0024278504,0.00042959733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031838845,0.002423375,0.0035056851,0.0028781374,0.0011238696,0.0018882235,0.0020084449,0.0018237706,0.007789251],"category_scores_gemma":[0.07341833,0.0014715658,0.0024327207,0.002331768,0.002541505,0.0018249551,0.0024529602,0.0019638108,0.0011567308],"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":[0.0028133846,0.0005050065,0.002173067,0.001103688,0.0005758694,0.00013868198,0.0006671015,0.36079347,0.008197654,0.22379126,0.0021715483,0.39706925],"study_design_scores_gemma":[0.00056711567,0.0015085223,0.0014573025,0.00015715887,0.00016622255,0.00008643784,0.00010451201,0.8120085,0.007792627,0.16714753,0.008823965,0.00018021034],"about_ca_topic_score_codex":0.0013677516,"about_ca_topic_score_gemma":0.0013900469,"teacher_disagreement_score":0.031838845,"about_ca_system_score_codex":0.0017602125,"about_ca_system_score_gemma":0.0024371871,"threshold_uncertainty_score":0.16838193},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056238547,0.00080516323,0.99193937,0.00021428074,0.0000450077,0.00007531694,0.0003668093,0.00038458704,0.00054552703],"genre_scores_gemma":[0.3035657,0.0046173926,0.66182053,0.0003627855,0.0004542777,0.0014946981,0.0045875125,0.00056089414,0.022536142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942046,0.0037065912,0.00027633042,0.0009974828,0.00054481154,0.00027018832],"domain_scores_gemma":[0.9781522,0.017962838,0.0015197387,0.0010980236,0.0010519325,0.0002152964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013979201,0.0017505243,0.003125135,0.003971136,0.0009857152,0.0025886565,0.0069114603,0.0030569446,0.0053712204],"category_scores_gemma":[0.03544921,0.0019473605,0.0032096468,0.0048812,0.0017008277,0.0046303775,0.0028410424,0.0038183662,0.002448859],"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":[0.00020032446,0.00009607449,0.004073522,0.0003513718,0.0004239699,0.000263717,0.00042208526,0.6985218,0.0007508963,0.23807575,0.0024651145,0.054355394],"study_design_scores_gemma":[0.000020137892,0.000022791468,0.00062816986,0.000025257272,0.00004712523,0.000060228867,0.000020387944,0.9466265,0.0000915212,0.050520677,0.0019041888,0.000033108714],"about_ca_topic_score_codex":0.018720562,"about_ca_topic_score_gemma":0.012691194,"teacher_disagreement_score":0.018720562,"about_ca_system_score_codex":0.0018838331,"about_ca_system_score_gemma":0.0010667716,"threshold_uncertainty_score":0.073929965},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.870739,0.00017240217,0.1257117,0.00033512124,0.000020738742,0.00011674598,0.0008014396,0.00011862585,0.0019842472],"genre_scores_gemma":[0.9920753,0.00003898554,0.006817724,0.000048850812,0.000022465836,0.000059813385,0.00072863686,0.000011078712,0.00019715099],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9583134,0.023129614,0.0028041187,0.009282004,0.004120007,0.0023507331],"domain_scores_gemma":[0.77761257,0.15617074,0.027042413,0.03005948,0.0060275793,0.003087295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029528985,0.00057979295,0.0025702554,0.002952467,0.002138777,0.0040367143,0.0028553242,0.002197185,0.004153456],"category_scores_gemma":[0.13132009,0.0008096915,0.002674523,0.004018764,0.0037669435,0.0048636496,0.0051360037,0.0023962131,0.00043862284],"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":[0.0009971599,0.0005109687,0.79346055,0.00014245993,0.0041522705,0.0012385453,0.0038552175,0.062228985,0.0045209625,0.062340826,0.0011177133,0.06543434],"study_design_scores_gemma":[0.00023394624,0.0013471713,0.38978618,0.00011409428,0.0008307707,0.00077852857,0.0029963057,0.41278931,0.0035915638,0.18415457,0.003105025,0.0002725464],"about_ca_topic_score_codex":0.0068612266,"about_ca_topic_score_gemma":0.0048248856,"teacher_disagreement_score":0.029528985,"about_ca_system_score_codex":0.0012117483,"about_ca_system_score_gemma":0.0015331432,"threshold_uncertainty_score":0.15616608},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0280689,0.0010184591,0.9627753,0.0017530195,0.00017718111,0.00020302273,0.002898701,0.0006746579,0.0024307854],"genre_scores_gemma":[0.7560765,0.0028159223,0.20728654,0.0007749217,0.0004397532,0.0019427503,0.0068113166,0.00027008026,0.023582118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948507,0.0029713598,0.00026769922,0.0009812115,0.00040895044,0.00052009796],"domain_scores_gemma":[0.9734267,0.019326143,0.0027639635,0.002170683,0.0016375192,0.00067501276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01614226,0.0012093505,0.0021188536,0.0030257946,0.001265587,0.0025746282,0.005772483,0.002720679,0.011409037],"category_scores_gemma":[0.034004148,0.001208506,0.002925583,0.0038511555,0.0025278954,0.004891267,0.003732871,0.0044968235,0.0016095212],"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":[0.00024383728,0.00011357625,0.013289324,0.00024076286,0.00035201502,0.00036781622,0.00065181096,0.4386239,0.00027507864,0.52042866,0.004364467,0.021048713],"study_design_scores_gemma":[0.00003817266,0.00005100444,0.0017104953,0.00004742431,0.00006727311,0.000073776966,0.000121669094,0.8335571,0.00007755014,0.16111492,0.0031039827,0.00003669003],"about_ca_topic_score_codex":0.030732451,"about_ca_topic_score_gemma":0.02448328,"teacher_disagreement_score":0.030732451,"about_ca_system_score_codex":0.0031233989,"about_ca_system_score_gemma":0.0026558316,"threshold_uncertainty_score":0.08536947},"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":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07750776,0.8323055,0.08108303,0.0026556728,0.0009851771,0.00080443977,0.0024836108,0.00041641152,0.0017583754],"genre_scores_gemma":[0.8090415,0.13472095,0.049820594,0.0012791009,0.0006150634,0.0017618395,0.0018297218,0.00017515972,0.0007561709],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.94172937,0.047822844,0.004271267,0.0032772562,0.002533634,0.00036556338],"domain_scores_gemma":[0.860341,0.123435356,0.0067859073,0.00657788,0.0024934956,0.00036637663],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07101934,0.0026468025,0.012639966,0.0061670714,0.00056466664,0.0040597096,0.0024700111,0.0031772116,0.0014108568],"category_scores_gemma":[0.16547294,0.0017671223,0.029813701,0.0050932295,0.0009517477,0.00211973,0.0013986363,0.0027665747,0.00021879376],"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":[0.0056631127,0.00006128326,0.023021081,0.03257786,0.8780232,0.00022319086,0.00010868432,0.019124055,0.00081548933,0.0017346246,0.0012133074,0.037434194],"study_design_scores_gemma":[0.0015316854,0.0006225885,0.012857922,0.0041269143,0.9552001,0.00019658207,0.00004397203,0.014428646,0.000930521,0.007452646,0.0025450033,0.00006355252],"about_ca_topic_score_codex":0.0041739945,"about_ca_topic_score_gemma":0.0038266515,"teacher_disagreement_score":0.92898065,"about_ca_system_score_codex":0.0021434522,"about_ca_system_score_gemma":0.0018898884,"threshold_uncertainty_score":0.37559062},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002501998,0.0009959047,0.004280298,0.98292834,0.009576744,0.000014560794,0.00014968238,0.00004730217,0.0017569549],"genre_scores_gemma":[0.0058128214,0.0006135451,0.005327963,0.95769584,0.02784677,0.00016593213,0.000058011883,0.00006643729,0.0024126777],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95959204,0.020647751,0.004059267,0.005106096,0.009282178,0.0013126897],"domain_scores_gemma":[0.800527,0.17112423,0.004259157,0.008762699,0.012707021,0.002619877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04688935,0.0013333592,0.0035182359,0.0021163165,0.004517772,0.0076829884,0.0066474704,0.07633656,0.0057989983],"category_scores_gemma":[0.2592338,0.0014817986,0.0025115267,0.0020276073,0.019197106,0.011694591,0.005963666,0.10093926,0.0061839577],"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":[0.0001100962,0.000034414712,0.00044548928,0.00008743523,0.00008959615,0.00029237947,0.00042103828,0.00038027248,0.0001208262,0.09651699,0.88303983,0.018461622],"study_design_scores_gemma":[0.00028741756,0.00003503998,0.0008799843,0.0005742124,0.00010725669,0.00042525714,0.0003103251,0.0028773309,0.000472173,0.5583808,0.43549362,0.0001566028],"about_ca_topic_score_codex":0.009825076,"about_ca_topic_score_gemma":0.008894987,"teacher_disagreement_score":0.07633656,"about_ca_system_score_codex":0.0045823893,"about_ca_system_score_gemma":0.0065303897,"threshold_uncertainty_score":0.24797755},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066466746,0.00024590752,0.99839383,0.000094479234,0.000030574032,0.000038261696,0.00006213147,0.00011792899,0.00035221418],"genre_scores_gemma":[0.044668436,0.001045045,0.94732195,0.00025481542,0.00020983464,0.0010856908,0.000713281,0.00032830253,0.0043726456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98990834,0.007898629,0.0002601237,0.00076914864,0.0010165948,0.00014714226],"domain_scores_gemma":[0.97803646,0.018176585,0.0008838809,0.0017059346,0.0009466615,0.00025042266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01801453,0.0009225699,0.001524599,0.0026393195,0.0007971745,0.0013552584,0.0032286213,0.0020175078,0.008640615],"category_scores_gemma":[0.054839782,0.00081684196,0.001657878,0.003148208,0.0016111925,0.0031699769,0.0024658614,0.0035611147,0.0032679804],"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":[0.0002780372,0.00015493788,0.0038699612,0.0004733093,0.00032415308,0.00039359066,0.00063719467,0.06446049,0.0023993072,0.5916117,0.008033994,0.3273634],"study_design_scores_gemma":[0.00010147886,0.00020704884,0.0018755374,0.00018259246,0.00009628142,0.00044802466,0.00010360517,0.60099196,0.0013691038,0.35955527,0.034964297,0.0001047358],"about_ca_topic_score_codex":0.0027095426,"about_ca_topic_score_gemma":0.0022566894,"teacher_disagreement_score":0.01801453,"about_ca_system_score_codex":0.0008344842,"about_ca_system_score_gemma":0.0021383995,"threshold_uncertainty_score":0.09527111},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035937384,0.006079321,0.9227557,0.0029843284,0.002832474,0.01684381,0.0031870822,0.0010131119,0.008366768],"genre_scores_gemma":[0.12759727,0.0017081208,0.8304509,0.0014710436,0.00050226704,0.03550055,0.0012788188,0.00017968194,0.0013113706],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8910901,0.07447766,0.009627154,0.004897049,0.019255778,0.0006522621],"domain_scores_gemma":[0.5749985,0.39517367,0.009345856,0.010786036,0.009051596,0.00064433966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12164785,0.0011045382,0.0036125237,0.0049142395,0.00093921873,0.0016926707,0.0027606138,0.0033312265,0.008104946],"category_scores_gemma":[0.48496112,0.0008404576,0.0020312732,0.0024105727,0.002546718,0.003394642,0.0015791083,0.0030315798,0.001412113],"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":[0.007198705,0.0006383404,0.0152993845,0.00412096,0.0015542686,0.000320239,0.0012372178,0.005556021,0.004644738,0.08156191,0.032358073,0.8455101],"study_design_scores_gemma":[0.014315156,0.032093767,0.049892236,0.006568203,0.0043995474,0.0042148153,0.0017697226,0.16301678,0.059690785,0.4601146,0.20303209,0.00089221174],"about_ca_topic_score_codex":0.0004509638,"about_ca_topic_score_gemma":0.000568337,"teacher_disagreement_score":0.12164785,"about_ca_system_score_codex":0.0012969848,"about_ca_system_score_gemma":0.0018181681,"threshold_uncertainty_score":0.643343},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033570163,0.00014604189,0.96462286,0.00024146993,0.000035576755,0.000102144215,0.0003046071,0.00038405514,0.0005931371],"genre_scores_gemma":[0.5165481,0.00059286464,0.4720175,0.00020193799,0.000103230755,0.00086544047,0.0026504118,0.00036070493,0.006659844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99457186,0.0026619937,0.00029922082,0.0013328532,0.00077515806,0.00035891123],"domain_scores_gemma":[0.9826539,0.012758782,0.0014970482,0.0014621537,0.0012970219,0.00033113177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012966053,0.0013455643,0.0019559602,0.0036192134,0.0006925394,0.0028282541,0.0033083353,0.0021459432,0.0031234971],"category_scores_gemma":[0.043038286,0.0010588897,0.003786639,0.0035826988,0.0020339352,0.0030525243,0.0025668938,0.0032771917,0.0013693979],"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":[0.0007128044,0.000367153,0.06348172,0.00017991966,0.0006442553,0.00043182305,0.0012206496,0.51251394,0.0032173407,0.22668765,0.004397529,0.18614525],"study_design_scores_gemma":[0.000023227625,0.000049788683,0.00468257,0.000030152032,0.00006135635,0.00008441537,0.0000521577,0.95380247,0.00035252876,0.03984534,0.0009659574,0.000050012208],"about_ca_topic_score_codex":0.018339464,"about_ca_topic_score_gemma":0.01091627,"teacher_disagreement_score":0.018339464,"about_ca_system_score_codex":0.0025395185,"about_ca_system_score_gemma":0.0019826933,"threshold_uncertainty_score":0.068571866},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014514036,0.00019900782,0.99772793,0.00012995659,0.000013871873,0.00004391729,0.000049458744,0.00012935467,0.0002550848],"genre_scores_gemma":[0.1989589,0.0011940509,0.7942812,0.0002828172,0.00030932992,0.0014239303,0.00089405547,0.00033169676,0.0023240559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97986376,0.016697356,0.00055471214,0.0012491503,0.0013637228,0.00027118006],"domain_scores_gemma":[0.88427615,0.104432456,0.0035367687,0.0053190384,0.0021251808,0.00031041214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034659397,0.0012302276,0.0020758554,0.002874557,0.00057400495,0.0021843049,0.003111133,0.0022191522,0.0035578453],"category_scores_gemma":[0.18256378,0.00092117564,0.0015777752,0.0029609983,0.002924338,0.003258335,0.003015454,0.0044211773,0.0011506124],"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":[0.00033136868,0.0001508968,0.014527977,0.0006199715,0.00047346743,0.00055055594,0.0005814442,0.20949507,0.0014769798,0.54013574,0.004109729,0.22754689],"study_design_scores_gemma":[0.000095291856,0.00012365622,0.0022061942,0.000114789575,0.000060094088,0.00033382737,0.00007046028,0.7116587,0.0009018035,0.27964783,0.0047215223,0.0000657923],"about_ca_topic_score_codex":0.0018989507,"about_ca_topic_score_gemma":0.0009041396,"teacher_disagreement_score":0.034659397,"about_ca_system_score_codex":0.0012712894,"about_ca_system_score_gemma":0.0018343587,"threshold_uncertainty_score":0.18329865},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59034806,0.0082770055,0.37939346,0.004156866,0.00090718694,0.0013459348,0.006033526,0.0011667134,0.008371275],"genre_scores_gemma":[0.9699616,0.00053839356,0.025839133,0.00047807003,0.00014743277,0.00024979957,0.002327337,0.00012762858,0.00033055374],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.77301955,0.18105166,0.010652871,0.01830621,0.014481785,0.002487926],"domain_scores_gemma":[0.20279759,0.6973918,0.034359314,0.053954873,0.010551155,0.0009452857],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25413132,0.0014432864,0.0024113778,0.00622725,0.001400211,0.006211539,0.0040511163,0.002668129,0.002084124],"category_scores_gemma":[0.64294213,0.0015015018,0.004614892,0.004559298,0.0044963197,0.004468872,0.0055476436,0.0035179532,0.00065379025],"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":[0.0041005085,0.00028744285,0.7245588,0.0015453675,0.020455675,0.000725424,0.0025277804,0.1316414,0.0010289499,0.019075321,0.0039535174,0.09009993],"study_design_scores_gemma":[0.0008685162,0.0012652803,0.4616958,0.0016760036,0.0067154295,0.0018848559,0.0021897075,0.42447954,0.004948399,0.08579624,0.007940248,0.000540072],"about_ca_topic_score_codex":0.012556161,"about_ca_topic_score_gemma":0.003599664,"teacher_disagreement_score":0.7458687,"about_ca_system_score_codex":0.0026317453,"about_ca_system_score_gemma":0.0013744994,"threshold_uncertainty_score":0.9197889},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07042867,0.0010483208,0.9202808,0.0019387704,0.00024137982,0.0002124876,0.0013550626,0.00038624217,0.0041081733],"genre_scores_gemma":[0.71688336,0.002690033,0.24641535,0.0007635241,0.0006002888,0.0010316283,0.0021836292,0.00023398698,0.029198272],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982705,0.0007886022,0.00007486181,0.0004942021,0.0002158912,0.00015594091],"domain_scores_gemma":[0.99555993,0.0027664606,0.0006491877,0.00044912205,0.00040892168,0.00016630438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069543743,0.0009921291,0.0012765822,0.0009620644,0.00069340155,0.0012701766,0.0038010315,0.0014873749,0.004552033],"category_scores_gemma":[0.011301248,0.0006438154,0.0015193969,0.001871359,0.0014687679,0.0016919642,0.0015882605,0.0024398281,0.0006801316],"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":[0.00044325297,0.00023241877,0.031112349,0.0003597492,0.00039109553,0.0012244508,0.0012672564,0.36612573,0.002775153,0.48480147,0.00829097,0.1029761],"study_design_scores_gemma":[0.0001039822,0.00014022859,0.009059799,0.00005382957,0.0001275709,0.00044146267,0.00010542034,0.8872358,0.000281296,0.09012706,0.012253448,0.000070147646],"about_ca_topic_score_codex":0.025800465,"about_ca_topic_score_gemma":0.025011359,"teacher_disagreement_score":0.025800465,"about_ca_system_score_codex":0.0015116582,"about_ca_system_score_gemma":0.0025493177,"threshold_uncertainty_score":0.051300585},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010837155,0.00016159454,0.98788875,0.000098076365,0.000014685753,0.00014148421,0.0001524297,0.00034083895,0.00036492513],"genre_scores_gemma":[0.37642026,0.0004537419,0.6188019,0.00013377864,0.00010908369,0.0017560177,0.0013497833,0.00023399036,0.00074138143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9682022,0.022940204,0.0012557332,0.003041117,0.0038738148,0.00068694865],"domain_scores_gemma":[0.6677719,0.29560632,0.017028516,0.013790352,0.00469502,0.0011078366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044207513,0.0013679535,0.002238854,0.0061985976,0.0008098848,0.0022439002,0.0031783716,0.00205385,0.0035196985],"category_scores_gemma":[0.25458714,0.0011413373,0.0028506117,0.0028684281,0.0022249965,0.0034841264,0.0026844444,0.004481759,0.0010699583],"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":[0.001134077,0.00062374264,0.07596186,0.00065557146,0.0015636755,0.00054048427,0.0012952856,0.30651504,0.0028798308,0.1886613,0.0032349208,0.41693422],"study_design_scores_gemma":[0.000091462956,0.00038858375,0.010660992,0.00013546876,0.00016467147,0.00035335886,0.00009481626,0.81443244,0.0019473922,0.17003578,0.0015542898,0.00014068451],"about_ca_topic_score_codex":0.0027126328,"about_ca_topic_score_gemma":0.0016145555,"teacher_disagreement_score":0.044207513,"about_ca_system_score_codex":0.0016253779,"about_ca_system_score_gemma":0.0022258584,"threshold_uncertainty_score":0.23379445},"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":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006541547,0.32586375,0.2400154,0.3830419,0.008070317,0.000115392606,0.00043051047,0.00016354826,0.03575764],"genre_scores_gemma":[0.40774626,0.23557849,0.22197933,0.07896009,0.031283252,0.0005377735,0.00044925587,0.00063505553,0.022830408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97016317,0.019536654,0.0019494386,0.0033898947,0.0046662623,0.00029457],"domain_scores_gemma":[0.68591803,0.29597723,0.003147449,0.0074127857,0.006115628,0.0014287817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030017395,0.001021382,0.0028509598,0.006168506,0.0021919883,0.008124366,0.001606115,0.0054158643,0.0030487245],"category_scores_gemma":[0.19511785,0.0013979166,0.0012472895,0.0066585597,0.021742253,0.014851092,0.0035171192,0.010779446,0.001218057],"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":[0.00013126951,0.000022786433,0.001917418,0.0006933278,0.0002576783,0.00031520863,0.00043776078,0.003372164,0.00020766912,0.864914,0.05654882,0.07118189],"study_design_scores_gemma":[0.000021538664,0.000015711506,0.00026462998,0.00041581725,0.000029869158,0.00023656349,0.00006181377,0.0022672706,0.00012102918,0.9630232,0.033509478,0.00003304909],"about_ca_topic_score_codex":0.0044102874,"about_ca_topic_score_gemma":0.0027498377,"teacher_disagreement_score":0.030017395,"about_ca_system_score_codex":0.0036333082,"about_ca_system_score_gemma":0.0035469702,"threshold_uncertainty_score":0.15874904},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042531057,0.0003283024,0.99427223,0.00026527123,0.000030031444,0.000087756256,0.00032194084,0.00030188618,0.00013953159],"genre_scores_gemma":[0.2540243,0.0012559848,0.73839533,0.0003039234,0.0002473367,0.0016343354,0.0019922045,0.00019117622,0.001955384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98842853,0.008978572,0.00041350635,0.0012433362,0.00069302897,0.00024303523],"domain_scores_gemma":[0.94114923,0.051841605,0.002567796,0.0026935332,0.0013020772,0.00044574583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023962513,0.0014728254,0.0022382853,0.00270794,0.00077942695,0.0022402264,0.0031182675,0.0019249977,0.0035056586],"category_scores_gemma":[0.05738476,0.0011650964,0.0031362488,0.0028007817,0.0012247534,0.0024079322,0.0023488286,0.00409585,0.0008112785],"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":[0.00075348234,0.00024690034,0.01470509,0.00051864097,0.0013627001,0.0003976416,0.000569066,0.6456427,0.0013762973,0.18321508,0.004701196,0.1465112],"study_design_scores_gemma":[0.00004150649,0.00006630598,0.00084341835,0.000039725794,0.000069314476,0.000049468414,0.000025742806,0.9188556,0.00023371897,0.07858583,0.0011568333,0.000032458796],"about_ca_topic_score_codex":0.0062547564,"about_ca_topic_score_gemma":0.0050642733,"teacher_disagreement_score":0.023962513,"about_ca_system_score_codex":0.001458477,"about_ca_system_score_gemma":0.0022316587,"threshold_uncertainty_score":0.1267274},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034911057,0.0000932135,0.9943539,0.000042450927,0.00004961024,0.00035149636,0.00013530935,0.00024495195,0.0012379901],"genre_scores_gemma":[0.16071187,0.0003615947,0.8290717,0.00026812826,0.00018574935,0.004095926,0.00067426666,0.00015870387,0.0044721174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9842461,0.011524067,0.0005181072,0.0015426258,0.0018250161,0.00034415582],"domain_scores_gemma":[0.9643092,0.02350575,0.0014126868,0.008132201,0.0021550548,0.00048512302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016971152,0.0009623395,0.0014195221,0.0022188681,0.0010820806,0.0015306014,0.0033227263,0.0014587451,0.0093469275],"category_scores_gemma":[0.05043092,0.00080881314,0.001408578,0.0023651416,0.001803103,0.0020778687,0.002764339,0.0018212142,0.0018437401],"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":[0.0009892181,0.00038334212,0.010574286,0.00047606707,0.00036472673,0.00028105266,0.000541248,0.098355055,0.003961148,0.37223396,0.0076050535,0.50423485],"study_design_scores_gemma":[0.0005421258,0.00074359396,0.0032148727,0.00017837016,0.00015452618,0.00049993023,0.000117092735,0.62610245,0.0034119978,0.33764315,0.027298871,0.000093065486],"about_ca_topic_score_codex":0.0014201609,"about_ca_topic_score_gemma":0.0016494642,"teacher_disagreement_score":0.016971152,"about_ca_system_score_codex":0.00075834047,"about_ca_system_score_gemma":0.001505613,"threshold_uncertainty_score":0.08975315},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023649387,0.001148673,0.9721619,0.00061950047,0.00008510276,0.00033329183,0.00009741626,0.00017284538,0.0017318183],"genre_scores_gemma":[0.26083773,0.0010575437,0.7335253,0.0006428755,0.00016288577,0.0018475031,0.00014662789,0.00013292993,0.0016466609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.77293503,0.20724216,0.0026839466,0.006903033,0.00909929,0.0011365509],"domain_scores_gemma":[0.45186576,0.5068075,0.015659198,0.019477613,0.005137843,0.001052048],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23012392,0.001400615,0.0030540046,0.0017472735,0.0010174424,0.0020366663,0.0022107689,0.0026019264,0.0042756763],"category_scores_gemma":[0.34385905,0.0013530505,0.0023283993,0.001724185,0.004067867,0.005172891,0.0024498142,0.0031966541,0.00040568554],"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":[0.0048783426,0.00080758496,0.013337305,0.0016638605,0.0011826176,0.00035008756,0.0013653033,0.13731883,0.0043583983,0.56590015,0.00186296,0.26697463],"study_design_scores_gemma":[0.0011458842,0.0066563464,0.012307004,0.0006058144,0.000746261,0.00030063753,0.00021635366,0.60624653,0.008261626,0.35565746,0.0076117213,0.00024440515],"about_ca_topic_score_codex":0.0010059385,"about_ca_topic_score_gemma":0.0009587809,"teacher_disagreement_score":0.23012392,"about_ca_system_score_codex":0.00221228,"about_ca_system_score_gemma":0.0038573365,"threshold_uncertainty_score":0.9493943},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19928873,0.00082640594,0.7973201,0.0009964899,0.000080790414,0.00006360415,0.00022476974,0.00026144908,0.00093758263],"genre_scores_gemma":[0.9305495,0.0006357677,0.06609192,0.0001957616,0.00013976848,0.00010947039,0.000421385,0.000044576766,0.0018118315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9916176,0.005401114,0.00034563546,0.0014905836,0.00070548034,0.00043960917],"domain_scores_gemma":[0.92156917,0.06915984,0.0042436607,0.003704959,0.00086492766,0.00045743288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020441346,0.00096628146,0.00213854,0.0014568418,0.00065371214,0.0022037432,0.0014254493,0.0018121888,0.0011638585],"category_scores_gemma":[0.09588454,0.0012750803,0.0015394746,0.001768324,0.002614935,0.0033895322,0.002423444,0.002931195,0.00030348453],"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":[0.00078578293,0.0002773242,0.107444085,0.00020532623,0.0011683025,0.0007216649,0.00076688855,0.69335264,0.0023164765,0.0958628,0.001113244,0.09598541],"study_design_scores_gemma":[0.000056121055,0.00012973169,0.011548661,0.000037048216,0.00009482751,0.00019169957,0.000085956606,0.9273471,0.000830379,0.058981974,0.0006514479,0.000044936634],"about_ca_topic_score_codex":0.006720464,"about_ca_topic_score_gemma":0.0048995367,"teacher_disagreement_score":0.020441346,"about_ca_system_score_codex":0.0009953338,"about_ca_system_score_gemma":0.0013190573,"threshold_uncertainty_score":0.10810542},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004738806,0.001002,0.0045132143,0.98204124,0.0039932593,0.000014786189,0.000093937495,0.000018697876,0.007848967],"genre_scores_gemma":[0.031744033,0.0011220244,0.0065947734,0.9085964,0.03641645,0.00020160741,0.00004328579,0.00007246821,0.015209043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96855617,0.020371178,0.0016217036,0.002242402,0.0059053404,0.0013032481],"domain_scores_gemma":[0.923179,0.06833892,0.0019034672,0.0022454606,0.0033346955,0.0009984642],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031160323,0.0008822229,0.0016688601,0.0015180846,0.004607288,0.007136688,0.0035555556,0.04800491,0.005704579],"category_scores_gemma":[0.13243854,0.0008459779,0.0017446763,0.001336301,0.0120141,0.0066221748,0.0032869831,0.040481616,0.0018944948],"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":[0.000083837745,0.000029793657,0.000610126,0.00007834543,0.00005409091,0.0010003503,0.0005154374,0.0006647464,0.00011596938,0.5415503,0.43772924,0.01756786],"study_design_scores_gemma":[0.00012362034,0.000026562848,0.0010234832,0.00026332884,0.00005640677,0.0009513924,0.00032695616,0.003374076,0.00032751713,0.7343933,0.2590572,0.00007625578],"about_ca_topic_score_codex":0.008334481,"about_ca_topic_score_gemma":0.010789666,"teacher_disagreement_score":0.9688397,"about_ca_system_score_codex":0.0063981498,"about_ca_system_score_gemma":0.004487053,"threshold_uncertainty_score":0.16479349},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012011669,0.00019148993,0.9976428,0.00009358873,0.0000098016235,0.00001546048,0.000023226865,0.00007482195,0.0007476909],"genre_scores_gemma":[0.21716009,0.001600809,0.7756848,0.00021868352,0.00017445117,0.0004330463,0.00038629043,0.00013639028,0.0042053717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994065,0.0036823289,0.00021675689,0.00059400196,0.0012160775,0.00022592435],"domain_scores_gemma":[0.9856005,0.011653428,0.00086156896,0.0009883378,0.00071189925,0.00018430005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008970702,0.00085542415,0.0014356743,0.0018028179,0.0008903175,0.0029034105,0.0023174216,0.0019381571,0.0036001923],"category_scores_gemma":[0.032760944,0.00092315645,0.0014362042,0.002583133,0.0019803038,0.0038330208,0.0025552122,0.0025489454,0.0010553785],"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":[0.000061146624,0.000031161617,0.00077419117,0.00010347026,0.000079181926,0.00010698071,0.00023404628,0.3641547,0.0005808968,0.56844896,0.001559564,0.06386572],"study_design_scores_gemma":[0.00000806248,0.000009791067,0.00009914567,0.000021145983,0.000009208757,0.000034041208,0.000012453496,0.82015276,0.00013949761,0.17816769,0.0013312557,0.000014911182],"about_ca_topic_score_codex":0.004727501,"about_ca_topic_score_gemma":0.003942859,"teacher_disagreement_score":0.008970702,"about_ca_system_score_codex":0.0013966392,"about_ca_system_score_gemma":0.0011732399,"threshold_uncertainty_score":0.047442198},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14170341,0.00012819545,0.8567999,0.000064684806,0.00002249013,0.00022597834,0.00024125111,0.00045615237,0.00035797633],"genre_scores_gemma":[0.6177124,0.00013426882,0.3789947,0.000110329136,0.00003812146,0.00086793257,0.0009919581,0.00009808797,0.0010521994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927256,0.0050138864,0.00036119594,0.0010075775,0.00067684404,0.00021494771],"domain_scores_gemma":[0.9812898,0.010101125,0.002918629,0.0043514324,0.0011128411,0.00022614982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021680957,0.0009813359,0.0011375871,0.000865764,0.0003646304,0.00082438905,0.0020879454,0.0012288364,0.000979675],"category_scores_gemma":[0.026059065,0.00074754306,0.0016618915,0.0008058517,0.0007231985,0.0012379439,0.001087446,0.00093786087,0.00034388123],"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":[0.0022036931,0.00081731286,0.10491146,0.00041918168,0.0017696199,0.00038431035,0.00061829895,0.5894134,0.020495608,0.028833695,0.0014857173,0.24864763],"study_design_scores_gemma":[0.0001595724,0.0007274557,0.018332606,0.000020529842,0.00031483322,0.00014297663,0.000023267441,0.9689113,0.0035373257,0.0064603128,0.0012772501,0.0000925113],"about_ca_topic_score_codex":0.0044313516,"about_ca_topic_score_gemma":0.005140896,"teacher_disagreement_score":0.021680957,"about_ca_system_score_codex":0.0007237852,"about_ca_system_score_gemma":0.0007898855,"threshold_uncertainty_score":0.11466122},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003173084,0.00015116167,0.99612,0.0001084924,0.000023008344,0.000013100435,0.000046387224,0.00012237756,0.00024229696],"genre_scores_gemma":[0.21006165,0.00064679096,0.785626,0.00029820146,0.00023453067,0.00025291284,0.00050410384,0.00031129585,0.002064428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99623543,0.0022647064,0.00021307527,0.00041428691,0.00074413855,0.00012833043],"domain_scores_gemma":[0.9773473,0.01706211,0.0013412745,0.0020169027,0.001920206,0.00031224082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075135,0.00066411274,0.0009275224,0.0016502977,0.00040880585,0.0014308617,0.0019606675,0.001076914,0.002727595],"category_scores_gemma":[0.054792773,0.00055808295,0.0011471743,0.0018318336,0.001965315,0.002307628,0.002360387,0.0022427805,0.00063284126],"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":[0.00047632496,0.000075765805,0.0111227855,0.00027802025,0.00033741823,0.00066143123,0.00038923253,0.37633494,0.006966377,0.334018,0.0032001555,0.26613957],"study_design_scores_gemma":[0.000041230527,0.000071710216,0.0022997735,0.000028646564,0.000027441623,0.00031068773,0.000038866143,0.8428694,0.0018403111,0.1491472,0.0032631292,0.000061650695],"about_ca_topic_score_codex":0.0017581267,"about_ca_topic_score_gemma":0.0011565556,"teacher_disagreement_score":0.0075135,"about_ca_system_score_codex":0.00075280236,"about_ca_system_score_gemma":0.0012891582,"threshold_uncertainty_score":0.039735615},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02417619,0.00032829779,0.9747439,0.00009639123,0.000018519086,0.000049730326,0.000041491803,0.000059930695,0.0004855063],"genre_scores_gemma":[0.34572098,0.0006110665,0.6512234,0.00015184053,0.00011701233,0.00046040263,0.00025357233,0.00006914211,0.0013926763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9709075,0.023043595,0.000663106,0.002620686,0.002436413,0.00032858748],"domain_scores_gemma":[0.89627826,0.08611974,0.0052502877,0.008732873,0.0033150213,0.000303798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027981713,0.00069607043,0.0011602101,0.0017366913,0.0004689524,0.0011730767,0.0017275442,0.0018143367,0.0009765634],"category_scores_gemma":[0.1456059,0.00070254493,0.0010665247,0.0019633519,0.0021981846,0.0018402376,0.0023469476,0.0014266879,0.00032883903],"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":[0.0002676986,0.00018313316,0.06935796,0.00074965745,0.00065835455,0.00026065108,0.0013350873,0.19975859,0.007136935,0.4050557,0.0018854252,0.3133508],"study_design_scores_gemma":[0.00011112975,0.00052255,0.026970118,0.00019612079,0.00018370769,0.0006547973,0.00016637635,0.6165845,0.0060275807,0.3424632,0.0059825554,0.00013735812],"about_ca_topic_score_codex":0.0014644001,"about_ca_topic_score_gemma":0.0012257653,"teacher_disagreement_score":0.027981713,"about_ca_system_score_codex":0.0011327378,"about_ca_system_score_gemma":0.00098592,"threshold_uncertainty_score":0.1479832},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019380853,0.0007930009,0.99506456,0.00013676762,0.0000914774,0.00070159533,0.00019767263,0.00014105673,0.0009358362],"genre_scores_gemma":[0.11769096,0.0018524621,0.86895937,0.0005024296,0.00038284008,0.007020691,0.0008725786,0.00012112804,0.0025974067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9562903,0.035980053,0.0011183971,0.0026102648,0.0037269578,0.0002739612],"domain_scores_gemma":[0.9321905,0.053037148,0.0036135893,0.009239301,0.0015726946,0.0003467792],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06139092,0.0016715084,0.002604903,0.00469418,0.0009053106,0.0016749814,0.0041488158,0.0020999347,0.007976822],"category_scores_gemma":[0.15437482,0.0010294424,0.0019118732,0.0039056763,0.0015913646,0.0019310482,0.002814091,0.0025962896,0.0012463197],"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":[0.0006126525,0.00061667647,0.01469282,0.0015943148,0.0028796277,0.000545123,0.0010662009,0.060509354,0.0021176867,0.49824557,0.006921822,0.41019812],"study_design_scores_gemma":[0.0004155812,0.0008244158,0.006626023,0.00052315695,0.000876352,0.0006957163,0.00027669396,0.29303467,0.0022660315,0.6669451,0.027399883,0.000116456904],"about_ca_topic_score_codex":0.0019201858,"about_ca_topic_score_gemma":0.001192401,"teacher_disagreement_score":0.93860906,"about_ca_system_score_codex":0.0010080328,"about_ca_system_score_gemma":0.0016267411,"threshold_uncertainty_score":0.32467008},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04482159,0.00060621166,0.949664,0.0008247919,0.000068421614,0.00058776425,0.00022879096,0.00033360554,0.002864873],"genre_scores_gemma":[0.25308827,0.00047190834,0.74284536,0.00027894566,0.00010744779,0.001294531,0.0004550765,0.00017838259,0.0012800797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9728968,0.019179024,0.0011387214,0.0015046769,0.0045570596,0.00072385056],"domain_scores_gemma":[0.8084407,0.17123294,0.0031397908,0.011019092,0.005197087,0.00097042526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027029822,0.0009034184,0.0026378653,0.0024967704,0.0011842325,0.0020853342,0.00380384,0.002009447,0.0035793057],"category_scores_gemma":[0.12689653,0.0009553426,0.0013333726,0.0025895406,0.0023267383,0.00473498,0.0033034505,0.002128351,0.00066019164],"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":[0.001398634,0.00053816003,0.008328858,0.00063016784,0.00030531498,0.00021704443,0.0005073322,0.41044626,0.0043772473,0.24798425,0.0055050147,0.3197617],"study_design_scores_gemma":[0.0003314741,0.00065823685,0.0023793285,0.00014315509,0.00011729126,0.00021707598,0.00016973843,0.77527463,0.0041312673,0.21340688,0.003082109,0.00008879906],"about_ca_topic_score_codex":0.0023110514,"about_ca_topic_score_gemma":0.0029102159,"teacher_disagreement_score":0.027029822,"about_ca_system_score_codex":0.0022042466,"about_ca_system_score_gemma":0.002881066,"threshold_uncertainty_score":0.1429491},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035372325,0.0007308031,0.99431807,0.00042336807,0.00002357297,0.000020553574,0.00007742817,0.00007843365,0.0007905993],"genre_scores_gemma":[0.29383713,0.003421119,0.69879144,0.00051251514,0.00033527525,0.00045062898,0.00061577145,0.0001357389,0.0019004549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9874368,0.0095326565,0.00043444743,0.0012758,0.0011177652,0.00020254817],"domain_scores_gemma":[0.9437458,0.05107689,0.0016663995,0.0019904412,0.0012830589,0.0002373547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020981327,0.0010194504,0.0020467348,0.0030080858,0.00079084735,0.0028977974,0.0027054998,0.0022360778,0.0032766825],"category_scores_gemma":[0.113670334,0.0011503405,0.0014922325,0.002729418,0.0031261365,0.004549678,0.0028977217,0.003031444,0.0005134866],"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":[0.000058869075,0.000035182984,0.0038210917,0.00041694872,0.0002830465,0.000117400494,0.0003427866,0.307572,0.0003785396,0.5634002,0.0020467327,0.121527106],"study_design_scores_gemma":[0.000017875485,0.000017350434,0.0007449843,0.00008602371,0.000033917022,0.00007510775,0.00003763126,0.37811333,0.0001725362,0.6184852,0.0021924567,0.00002355706],"about_ca_topic_score_codex":0.006387616,"about_ca_topic_score_gemma":0.0035417746,"teacher_disagreement_score":0.020981327,"about_ca_system_score_codex":0.0017983878,"about_ca_system_score_gemma":0.0016747047,"threshold_uncertainty_score":0.1109612},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33782125,0.000810365,0.6586856,0.00025183032,0.00002696828,0.000054766533,0.00018133997,0.00026801767,0.0018998875],"genre_scores_gemma":[0.9331339,0.00020862359,0.06585271,0.000040930376,0.000045909717,0.00004324016,0.00035996412,0.00007451491,0.00024010801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989673,0.0071769273,0.00033376555,0.0006332534,0.0019902885,0.00019273534],"domain_scores_gemma":[0.8470396,0.13340971,0.005392281,0.008373292,0.004826621,0.0009586098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023803407,0.00065053,0.00084922457,0.0026314638,0.0005827617,0.0014820392,0.001384057,0.0017819267,0.0010075753],"category_scores_gemma":[0.14980823,0.00034946424,0.00063654635,0.001393842,0.0019023846,0.0018023554,0.0019135325,0.000985274,0.00023069824],"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":[0.0033726846,0.0002951473,0.12679298,0.00053707813,0.001112405,0.0017403672,0.0016132501,0.5146663,0.026290897,0.07852723,0.0017481629,0.24330351],"study_design_scores_gemma":[0.000073362826,0.0004906393,0.03812529,0.00011771575,0.000075742086,0.0011624258,0.0002260523,0.90471786,0.0068087936,0.046865426,0.0012189526,0.00011772541],"about_ca_topic_score_codex":0.0007900542,"about_ca_topic_score_gemma":0.00043066853,"teacher_disagreement_score":0.023803407,"about_ca_system_score_codex":0.0004421251,"about_ca_system_score_gemma":0.00041899915,"threshold_uncertainty_score":0.12588596},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00076476263,0.00006476288,0.99501085,0.000038062622,0.000018346787,0.000039311253,0.0002995865,0.0035653352,0.00019903202],"genre_scores_gemma":[0.050249226,0.00019952723,0.94349474,0.00024514526,0.0000841179,0.00072442344,0.002334903,0.0016864879,0.0009813354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958312,0.0019211262,0.00019288952,0.00079211395,0.0011467352,0.000115939576],"domain_scores_gemma":[0.9844617,0.012085934,0.0008876227,0.0014727173,0.00082344515,0.00026856872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007884939,0.0017733173,0.0018495427,0.00213754,0.0009762411,0.0018926014,0.0041309423,0.0015146068,0.005437727],"category_scores_gemma":[0.03119027,0.0023013502,0.00206617,0.0019102006,0.0017482858,0.001563802,0.0025515421,0.0035346465,0.0023805788],"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":[0.0004471628,0.00015817695,0.008076112,0.0006948664,0.000863185,0.00022975203,0.00025066486,0.71091926,0.011777436,0.075103626,0.015663818,0.1758159],"study_design_scores_gemma":[0.000029480118,0.000016069416,0.0004512833,0.000015249691,0.00001984467,0.00004067899,0.000005855264,0.97176063,0.0019078458,0.02302229,0.0027037133,0.000027011925],"about_ca_topic_score_codex":0.008643864,"about_ca_topic_score_gemma":0.010466443,"teacher_disagreement_score":0.008643864,"about_ca_system_score_codex":0.0017841817,"about_ca_system_score_gemma":0.0028045266,"threshold_uncertainty_score":0.041700006},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044541913,0.00009765006,0.99431723,0.000100538455,0.0000096164595,0.000016816746,0.000039212733,0.00008777461,0.000877055],"genre_scores_gemma":[0.45897454,0.0012619082,0.533925,0.0003021108,0.00017695427,0.00038560052,0.00056701986,0.00024029956,0.004166581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99849594,0.000699468,0.00006072237,0.00021558501,0.00040285723,0.00012540973],"domain_scores_gemma":[0.9895745,0.008290662,0.00059781893,0.000710359,0.00068893546,0.00013774695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061754733,0.0007257585,0.0012544529,0.0014076078,0.0007060795,0.0016012714,0.001774654,0.0015099641,0.0027915903],"category_scores_gemma":[0.020685768,0.0007055586,0.0012988182,0.0012913371,0.0019301608,0.0032871908,0.0013956166,0.0017446954,0.00077826134],"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":[0.000026801965,0.000026085072,0.0011283667,0.000060200757,0.000046765792,0.00008716145,0.00008351689,0.6542814,0.00065900385,0.32677087,0.00052537536,0.016304392],"study_design_scores_gemma":[0.0000069539187,0.000009029395,0.0001627125,0.000008989939,0.0000066740367,0.000028420962,0.000005867705,0.90672743,0.00012104434,0.09247255,0.00043792618,0.000012434353],"about_ca_topic_score_codex":0.011311861,"about_ca_topic_score_gemma":0.008377015,"teacher_disagreement_score":0.011311861,"about_ca_system_score_codex":0.0013809011,"about_ca_system_score_gemma":0.0018873914,"threshold_uncertainty_score":0.03265941},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038962467,0.00015666393,0.9951474,0.00022859142,0.000019911715,0.000047382164,0.0000955621,0.00013958344,0.00026860164],"genre_scores_gemma":[0.31213355,0.0006160801,0.68222326,0.0004363982,0.00014531791,0.0005920693,0.0010064616,0.00016000512,0.0026869616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956038,0.0029774457,0.00018436066,0.00065671373,0.00041498363,0.00016273774],"domain_scores_gemma":[0.9898962,0.00786763,0.00095728686,0.00075925863,0.00040458475,0.00011500913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008061896,0.000975858,0.002046693,0.00091857027,0.00039904207,0.0011545203,0.0023129552,0.0013544544,0.0022588668],"category_scores_gemma":[0.025902864,0.00083046197,0.0014330543,0.0013317715,0.0011137399,0.0014808563,0.0016791687,0.0025542534,0.00062752556],"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":[0.00027484592,0.000122012505,0.0024622057,0.00016762152,0.00037819007,0.00006480017,0.00007252611,0.8295076,0.0010917555,0.04224913,0.0023385966,0.121270806],"study_design_scores_gemma":[0.00004217003,0.000055626184,0.00041941038,0.000013880721,0.000027115286,0.00001841229,0.000009143851,0.97176313,0.00039343696,0.026416486,0.0008228594,0.000018310027],"about_ca_topic_score_codex":0.0072971075,"about_ca_topic_score_gemma":0.005321205,"teacher_disagreement_score":0.008061896,"about_ca_system_score_codex":0.0010005626,"about_ca_system_score_gemma":0.0025386093,"threshold_uncertainty_score":0.042635918},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009491665,0.00086514204,0.9873777,0.0009478367,0.000061703,0.000034755074,0.00009477965,0.00018928823,0.0009371703],"genre_scores_gemma":[0.5485012,0.0027798975,0.4423819,0.0014259976,0.0008652512,0.0006359524,0.00071168697,0.00071058393,0.0019875767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9340234,0.055507235,0.0016600257,0.0047003413,0.0033383598,0.0007707088],"domain_scores_gemma":[0.48891136,0.4690452,0.011318057,0.027020892,0.0029229955,0.00078160333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.078842215,0.0020901822,0.0030970518,0.0030650017,0.0011131883,0.0039590066,0.0036562488,0.0027321891,0.0026930547],"category_scores_gemma":[0.29217395,0.0013056807,0.0058241026,0.0031580853,0.0075684725,0.007621415,0.0075273267,0.009687731,0.0005288329],"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":[0.00051538483,0.00012193296,0.010869273,0.00040713916,0.0016135193,0.00063042954,0.0016041779,0.3104081,0.0012158913,0.58178157,0.0018574639,0.08897511],"study_design_scores_gemma":[0.000048936246,0.00013269472,0.0017152701,0.00009557433,0.00018003455,0.00017403778,0.00008665207,0.33161652,0.00065583776,0.6634779,0.0017412977,0.00007529037],"about_ca_topic_score_codex":0.002841565,"about_ca_topic_score_gemma":0.0017283413,"teacher_disagreement_score":0.078842215,"about_ca_system_score_codex":0.0016779245,"about_ca_system_score_gemma":0.0017485155,"threshold_uncertainty_score":0.41696244},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00995326,0.0009749945,0.9817739,0.0015013677,0.00009116399,0.00035440302,0.0005139591,0.0001595766,0.0046774885],"genre_scores_gemma":[0.6044963,0.0038481671,0.37865868,0.0018494821,0.00038300402,0.0020445932,0.0007461538,0.00016601267,0.007807592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9778641,0.01797564,0.00058326946,0.0014313807,0.0015236569,0.00062192656],"domain_scores_gemma":[0.88967115,0.09840712,0.0035150829,0.0060389624,0.0020306658,0.0003369773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05130726,0.0017309841,0.0026237557,0.0023437198,0.0008356582,0.0025574616,0.0028037226,0.0026166616,0.0077895564],"category_scores_gemma":[0.13618082,0.0009691539,0.0035887994,0.0019503828,0.0021042689,0.003047564,0.0038995782,0.003552855,0.0005365019],"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":[0.00031451657,0.0000991887,0.0043127686,0.0006808716,0.0009865258,0.00091692177,0.0003375854,0.530206,0.00075075944,0.40373063,0.0037126092,0.05395164],"study_design_scores_gemma":[0.0000984291,0.00011208841,0.0022196968,0.00020112729,0.0005706421,0.0004264464,0.00009768491,0.49407977,0.0007374337,0.49500254,0.006349778,0.00010439363],"about_ca_topic_score_codex":0.010793947,"about_ca_topic_score_gemma":0.0055339234,"teacher_disagreement_score":0.05130726,"about_ca_system_score_codex":0.0026943446,"about_ca_system_score_gemma":0.0029094343,"threshold_uncertainty_score":0.27134198},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14165626,0.00038133905,0.8549374,0.00040270863,0.000040339914,0.000120580655,0.001197666,0.0009830137,0.00028083098],"genre_scores_gemma":[0.815556,0.00037244995,0.1798502,0.00010424613,0.00007020065,0.00040729617,0.0029236386,0.00013170282,0.0005843322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689984,0.001784209,0.00022255944,0.00055551605,0.00038341212,0.00015451288],"domain_scores_gemma":[0.95789135,0.03769693,0.001623915,0.001759199,0.00075587985,0.000272685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011994533,0.00059587054,0.00091007166,0.0014596184,0.0005085708,0.0010263851,0.0012011308,0.0006821098,0.0017177598],"category_scores_gemma":[0.026400443,0.000459042,0.0011872539,0.0011645477,0.0008354679,0.0011881392,0.00092605036,0.0017552761,0.00033027862],"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":[0.001509117,0.00046305012,0.1311574,0.0005405546,0.0021079937,0.0008604296,0.00089707016,0.5154299,0.012408639,0.053280994,0.003988133,0.2773568],"study_design_scores_gemma":[0.00003028051,0.00008552148,0.011297873,0.000015433283,0.00006831671,0.0000554836,0.00004813327,0.9637915,0.001389338,0.022735987,0.00045154482,0.000030524465],"about_ca_topic_score_codex":0.004344834,"about_ca_topic_score_gemma":0.0046792035,"teacher_disagreement_score":0.011994533,"about_ca_system_score_codex":0.00096877496,"about_ca_system_score_gemma":0.0010649033,"threshold_uncertainty_score":0.063433886},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064919414,0.0023200936,0.9817514,0.006866944,0.00013692385,0.00023872634,0.00019859022,0.00008062045,0.0019146998],"genre_scores_gemma":[0.35820037,0.0063202917,0.62404996,0.0030349428,0.0014743506,0.00317582,0.0005174663,0.0001704154,0.003056333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8460726,0.1374391,0.0033849534,0.0054933047,0.0069124,0.00069757126],"domain_scores_gemma":[0.2867206,0.67549753,0.01647968,0.015526005,0.004895105,0.00088110165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18210499,0.0019836305,0.004052334,0.0038411475,0.0016358044,0.0040099486,0.0028029254,0.0052810987,0.0036266374],"category_scores_gemma":[0.47472107,0.0014545012,0.0040458464,0.0041732555,0.014016003,0.00909113,0.0054521183,0.01104481,0.0003942333],"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":[0.00026225072,0.000057455916,0.003945402,0.00047616666,0.00042888874,0.0004203547,0.0005764536,0.041107614,0.00030951475,0.91173345,0.0014212321,0.039261173],"study_design_scores_gemma":[0.00007365564,0.00007638324,0.00049591664,0.00010857769,0.00005520073,0.00012731494,0.00004669098,0.0641145,0.00018149367,0.9334632,0.0012303999,0.00002677203],"about_ca_topic_score_codex":0.0017483715,"about_ca_topic_score_gemma":0.0008935788,"teacher_disagreement_score":0.18210499,"about_ca_system_score_codex":0.003456418,"about_ca_system_score_gemma":0.0043160506,"threshold_uncertainty_score":0.96307474},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012665499,0.012198468,0.89662135,0.07052465,0.0027913651,0.00010017386,0.00040702795,0.0001821844,0.015908314],"genre_scores_gemma":[0.13242283,0.022924542,0.75224507,0.0532546,0.016524637,0.0016263303,0.0008240458,0.0006801844,0.019497862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96530205,0.028545287,0.0009736831,0.0019083391,0.002799937,0.0004707084],"domain_scores_gemma":[0.9283647,0.06346703,0.0017213394,0.0037019628,0.002299727,0.00044521183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046050258,0.0020243735,0.0022768138,0.0025190555,0.0018333242,0.004048741,0.0075552273,0.008237761,0.011265139],"category_scores_gemma":[0.11078001,0.001646777,0.00400295,0.0041548703,0.009977148,0.010109519,0.004386797,0.013078809,0.0022629339],"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":[0.00000847862,0.000010691724,0.00014417992,0.00011844217,0.00005433756,0.000038922215,0.0001330219,0.0037263623,0.000029761213,0.9761297,0.008781871,0.010824205],"study_design_scores_gemma":[0.000007627553,0.000009878161,0.00011319099,0.00011901226,0.000013825261,0.000036319056,0.000025032046,0.012305317,0.00005221686,0.96810293,0.019196944,0.000017683082],"about_ca_topic_score_codex":0.0070597935,"about_ca_topic_score_gemma":0.0068199974,"teacher_disagreement_score":0.046050258,"about_ca_system_score_codex":0.004733661,"about_ca_system_score_gemma":0.002922677,"threshold_uncertainty_score":0.24353999},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.848523,0.002143183,0.14367777,0.00015867074,0.00009895462,0.0007847168,0.0018544362,0.00015999637,0.0025992943],"genre_scores_gemma":[0.9620737,0.000715746,0.03148891,0.00025269782,0.00004468761,0.0012765104,0.0018438828,0.000045333367,0.002258479],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933258,0.0036468673,0.00057792326,0.0014904682,0.000600502,0.00035831015],"domain_scores_gemma":[0.9791724,0.009921151,0.004856529,0.0044407235,0.0012629451,0.0003462221],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.022557491,0.0013430773,0.0013279945,0.001101237,0.0011306653,0.00094284496,0.003828148,0.0015722848,0.001359215],"category_scores_gemma":[0.02209078,0.0012330702,0.0024024325,0.0013518662,0.0009425486,0.001538158,0.0010286816,0.0011566322,0.00051887415],"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":[0.0012295353,0.0006012426,0.8727227,0.00062021863,0.006593853,0.0007729685,0.0008307963,0.06377356,0.008796987,0.0064980104,0.0015433304,0.036016807],"study_design_scores_gemma":[0.00043400034,0.0049371584,0.65332735,0.00022089963,0.0059683933,0.003780822,0.0005249847,0.3014463,0.012419199,0.0092652105,0.0073022097,0.0003734253],"about_ca_topic_score_codex":0.013183149,"about_ca_topic_score_gemma":0.017999189,"teacher_disagreement_score":0.9774425,"about_ca_system_score_codex":0.001394002,"about_ca_system_score_gemma":0.00074499566,"threshold_uncertainty_score":0.11929685},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01202916,0.00016194969,0.9872372,0.00010524408,0.00002084104,0.000013097481,0.000036445963,0.00018994207,0.00020603531],"genre_scores_gemma":[0.6856204,0.0007427763,0.3098576,0.00008619639,0.0001387794,0.00027847526,0.00037136016,0.0002207901,0.0026835378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998749,0.0006665654,0.000074054486,0.00024977143,0.00017413091,0.00008637674],"domain_scores_gemma":[0.99076754,0.007429264,0.00068149983,0.0006155409,0.00038757015,0.00011864063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005093143,0.00063558307,0.0012309462,0.0010620685,0.0006647697,0.0008571481,0.0014593189,0.0010856304,0.001203361],"category_scores_gemma":[0.020869317,0.0005852897,0.0014302792,0.0013534706,0.0010050007,0.0012386156,0.001150916,0.0014882202,0.00022631955],"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":[0.000055161217,0.000026272348,0.0030400464,0.00005734623,0.00016430384,0.0000870901,0.00017080433,0.8935501,0.0012347028,0.06302734,0.0005010786,0.038085803],"study_design_scores_gemma":[0.0000031295106,0.000008150906,0.00030719148,0.0000034691107,0.000008708469,0.000011745771,0.0000071396503,0.9854106,0.0001260763,0.013706351,0.00039911713,0.000008424591],"about_ca_topic_score_codex":0.019621627,"about_ca_topic_score_gemma":0.011591744,"teacher_disagreement_score":0.019621627,"about_ca_system_score_codex":0.000995607,"about_ca_system_score_gemma":0.0011364867,"threshold_uncertainty_score":0.039014876},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028356364,0.0011214461,0.96797323,0.0012126336,0.00011246302,0.00004579066,0.00044821645,0.00020114581,0.000528845],"genre_scores_gemma":[0.4228144,0.0031333563,0.56938595,0.00034454154,0.00033250343,0.00031947493,0.0016316384,0.00013308112,0.0019051605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98345387,0.012810506,0.0009391975,0.0010253771,0.0013816655,0.00038934944],"domain_scores_gemma":[0.9455994,0.041398488,0.005353086,0.005116519,0.002170179,0.00036238204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024073761,0.0006548704,0.00179402,0.0017262857,0.0010275489,0.0017081838,0.002427921,0.0017623195,0.0012637044],"category_scores_gemma":[0.09357958,0.0005905145,0.002745445,0.004457044,0.0010201577,0.001821555,0.0016301646,0.002136707,0.00042653954],"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":[0.00054559606,0.00028784608,0.099087834,0.00092216395,0.0020216417,0.001701634,0.0020895035,0.28982133,0.0012145007,0.1125241,0.0122614205,0.47752243],"study_design_scores_gemma":[0.00006887501,0.00015879406,0.01889443,0.00033285917,0.00038276173,0.0007558155,0.00044413225,0.75150716,0.0017831422,0.21909073,0.0064498996,0.00013135858],"about_ca_topic_score_codex":0.007862727,"about_ca_topic_score_gemma":0.011484496,"teacher_disagreement_score":0.024073761,"about_ca_system_score_codex":0.0009384933,"about_ca_system_score_gemma":0.0025589326,"threshold_uncertainty_score":0.12731576},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007731906,0.00020837817,0.9967849,0.00009802591,0.00011753802,0.0013902694,0.00015818204,0.00019403598,0.00027544744],"genre_scores_gemma":[0.011378428,0.00043518623,0.9700777,0.00014616182,0.00014269323,0.017000057,0.0002918948,0.000109863606,0.00041806058],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.78314096,0.18794239,0.0064657917,0.007009646,0.014557235,0.00088403054],"domain_scores_gemma":[0.68690795,0.25844914,0.015295107,0.028606255,0.009803916,0.00093757326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17913358,0.002686461,0.004383221,0.005507367,0.0016562743,0.002535238,0.004543866,0.003296327,0.01435541],"category_scores_gemma":[0.29562575,0.0015490281,0.004538279,0.00679115,0.0051636216,0.003410112,0.003962754,0.007412011,0.0030004692],"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":[0.0020883747,0.00069897145,0.00455964,0.004279817,0.0033231236,0.00029180886,0.0022996895,0.027440261,0.0026476835,0.5014015,0.014306312,0.4366628],"study_design_scores_gemma":[0.0019244986,0.0032892856,0.0075651268,0.0014701674,0.0008361932,0.00045955818,0.00044028834,0.18761423,0.004227742,0.7462733,0.045535106,0.00036445705],"about_ca_topic_score_codex":0.0009516398,"about_ca_topic_score_gemma":0.0010608849,"teacher_disagreement_score":0.17913358,"about_ca_system_score_codex":0.002048508,"about_ca_system_score_gemma":0.0045034112,"threshold_uncertainty_score":0.9473602},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010069256,0.00041444253,0.98867166,0.00022835747,0.000029248995,0.00005342539,0.000031799118,0.00011157842,0.00039022585],"genre_scores_gemma":[0.5642048,0.0009380872,0.4324379,0.00044709607,0.00016913628,0.0006176399,0.00021337521,0.00010323112,0.00086871686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97071975,0.022079537,0.001085047,0.0026002117,0.003056877,0.00045857785],"domain_scores_gemma":[0.79906416,0.17876738,0.00794858,0.010011618,0.0035875195,0.0006207637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057477567,0.00090536714,0.0017567748,0.0016716867,0.00067896524,0.0017766207,0.0024282536,0.0016414695,0.0007509034],"category_scores_gemma":[0.19168113,0.0003936433,0.00109526,0.0018019679,0.003395263,0.0021169218,0.002108607,0.0027556273,0.0002588735],"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":[0.0012561145,0.00022921892,0.03295802,0.0012121109,0.0009451369,0.002153131,0.0017087944,0.22492857,0.012947116,0.39452285,0.0034421394,0.32369685],"study_design_scores_gemma":[0.00024234984,0.0004224045,0.007852517,0.000139452,0.0002421433,0.0011429812,0.00021290491,0.7180271,0.007233636,0.26079682,0.0035918595,0.00009589258],"about_ca_topic_score_codex":0.0010404756,"about_ca_topic_score_gemma":0.0005687694,"teacher_disagreement_score":0.057477567,"about_ca_system_score_codex":0.0010708766,"about_ca_system_score_gemma":0.0017093739,"threshold_uncertainty_score":0.30397403},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026136676,0.15453142,0.20324746,0.0041419747,0.01139083,0.00030372659,0.0012345432,0.002223736,0.62031263],"genre_scores_gemma":[0.014802208,0.12686482,0.077205114,0.0025762976,0.00430569,0.0001682134,0.0011245735,0.00054879015,0.7724042],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967396,0.000025307692,0.000018592438,0.00006698256,0.0002000073,0.000015246017],"domain_scores_gemma":[0.9997943,0.00007906767,0.000011354175,0.000023252105,0.00008113198,0.00001093717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720952,0.0008522462,0.00063995493,0.0010509703,0.00030998184,0.0013041147,0.00072661287,0.0011823863,0.04948515],"category_scores_gemma":[0.000627915,0.00029882355,0.00036830374,0.0014630035,0.0003752359,0.0018205608,0.00076399965,0.0013399975,0.033999093],"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":[0.000045246794,0.00007440247,0.00020107318,0.00087441807,0.000018628616,0.00024883533,0.00018242007,0.0012416637,0.014443541,0.053236634,0.22998895,0.6994442],"study_design_scores_gemma":[0.0000044357053,0.000045086414,0.00047661434,0.00033337044,0.000010415506,0.0008309036,0.000044401386,0.00082777394,0.0022720094,0.009224641,0.98591226,0.000018003986],"about_ca_topic_score_codex":0.00037823222,"about_ca_topic_score_gemma":0.0007967751,"teacher_disagreement_score":0.04948515,"about_ca_system_score_codex":0.000316865,"about_ca_system_score_gemma":0.00042333052,"threshold_uncertainty_score":0.16554433},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06243064,0.012961829,0.85974854,0.0009815381,0.00031546934,0.00017306254,0.0012682619,0.0010398497,0.06108077],"genre_scores_gemma":[0.5963771,0.016792886,0.35674012,0.00035169534,0.0004964784,0.0003398084,0.0017819051,0.00045755642,0.026662478],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989849,0.00014360445,0.000032730135,0.0002134049,0.0005702336,0.000055125307],"domain_scores_gemma":[0.9983424,0.0011636432,0.00009231131,0.00018560319,0.00019593287,0.000020119876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083499844,0.00061486004,0.000753658,0.0026606976,0.00040007412,0.002406264,0.0009735196,0.0010060684,0.0069581163],"category_scores_gemma":[0.005173127,0.0004229941,0.00050371356,0.0025874462,0.0010223477,0.0045336564,0.0009758613,0.0008677329,0.0020822969],"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":[0.00008131061,0.00015451039,0.004784857,0.00066577987,0.000070214686,0.00013203478,0.00038238446,0.015012447,0.06667651,0.266493,0.0063866302,0.63916034],"study_design_scores_gemma":[0.000014765014,0.00026954254,0.027957646,0.00067704264,0.00014967118,0.0014793896,0.00069634797,0.19482552,0.21474892,0.51156706,0.04741122,0.00020285262],"about_ca_topic_score_codex":0.00049483054,"about_ca_topic_score_gemma":0.00033481856,"teacher_disagreement_score":0.0069581163,"about_ca_system_score_codex":0.0010033494,"about_ca_system_score_gemma":0.00030445243,"threshold_uncertainty_score":0.023277283},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083423115,0.00055913924,0.91226125,0.00096359575,0.00005587032,0.0012276219,0.0002849083,0.0002792235,0.00094529346],"genre_scores_gemma":[0.57956827,0.0002539705,0.416765,0.00045446336,0.000057330453,0.0014673286,0.00045402948,0.000050964798,0.00092862814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9456161,0.049178813,0.0011318723,0.002461856,0.001173246,0.00043808838],"domain_scores_gemma":[0.76370347,0.21139608,0.007328126,0.013244136,0.003039883,0.0012883514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10072523,0.0010674115,0.0035510662,0.0008901588,0.00065746345,0.001846691,0.0023366716,0.0030242414,0.0024366942],"category_scores_gemma":[0.19884,0.0010736428,0.0022610817,0.0014608477,0.0025073255,0.0030578964,0.002877367,0.004210171,0.00027588315],"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":[0.005763565,0.000826473,0.017990462,0.0006575059,0.0015816535,0.00030455552,0.00059788226,0.7364526,0.001545817,0.068392925,0.0021929021,0.16369365],"study_design_scores_gemma":[0.0011129673,0.0009430289,0.0027247255,0.00006344905,0.00018768341,0.00007584744,0.000046765526,0.9397118,0.0008503203,0.053274747,0.00094603706,0.00006260318],"about_ca_topic_score_codex":0.0034215557,"about_ca_topic_score_gemma":0.002846191,"teacher_disagreement_score":0.10072523,"about_ca_system_score_codex":0.0016602332,"about_ca_system_score_gemma":0.0036002113,"threshold_uncertainty_score":0.5326923},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049931325,0.000341336,0.9488328,0.00013800563,0.000017314826,0.00004016369,0.00016427411,0.00018090414,0.00035390526],"genre_scores_gemma":[0.75598246,0.0008757672,0.2377679,0.00013542936,0.00013633452,0.00029911887,0.0010366834,0.00016762958,0.0035985883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948768,0.003410663,0.0001898979,0.00077638956,0.00045618173,0.0002901222],"domain_scores_gemma":[0.95766115,0.033836763,0.0029154303,0.0035675252,0.0014029269,0.0006161306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012353541,0.00082226837,0.0014398174,0.001329408,0.000291591,0.0009894454,0.0025441453,0.00062820356,0.0030215252],"category_scores_gemma":[0.049131233,0.00044812675,0.0015413403,0.0010339677,0.0013695941,0.0019332747,0.002481756,0.0019414825,0.00037060928],"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":[0.0005658864,0.00019314978,0.032628365,0.00041819463,0.000764619,0.00074810284,0.00094203604,0.53068,0.0046422617,0.3130314,0.0017359558,0.11364999],"study_design_scores_gemma":[0.00001705242,0.00010417998,0.0050975247,0.000023495542,0.00006554689,0.0000646904,0.00007280575,0.9359766,0.00061843795,0.057055976,0.00087698014,0.000026734437],"about_ca_topic_score_codex":0.0043554436,"about_ca_topic_score_gemma":0.0029855783,"teacher_disagreement_score":0.012353541,"about_ca_system_score_codex":0.0007662409,"about_ca_system_score_gemma":0.0013795885,"threshold_uncertainty_score":0.06533253},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050721373,0.00008061746,0.9940339,0.000082628794,0.000013434885,0.000027107862,0.00008027161,0.00021582932,0.0003941546],"genre_scores_gemma":[0.30976617,0.0004456172,0.6858423,0.00017392554,0.0001493092,0.00032611677,0.0013008291,0.0003366747,0.0016590464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972606,0.0011666679,0.00017770054,0.0005611864,0.000689593,0.00014424937],"domain_scores_gemma":[0.9917754,0.004321502,0.0006617023,0.0011868915,0.0019331073,0.00012135085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059638666,0.0018893669,0.0011745498,0.002596738,0.00061301806,0.0021266686,0.0015947538,0.0015281258,0.0024009256],"category_scores_gemma":[0.02568358,0.00051449786,0.0014911075,0.0016245834,0.0013830385,0.0020607077,0.0019041718,0.0027846724,0.00075173285],"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":[0.00024493,0.00013345346,0.0049519404,0.00041923393,0.00022604373,0.00046038924,0.0005192725,0.42979863,0.016341986,0.121415205,0.00520843,0.4202804],"study_design_scores_gemma":[0.000013136469,0.000031413852,0.0013030935,0.000022911041,0.000018868877,0.00008237076,0.0000442136,0.93790716,0.0015539303,0.05722174,0.0017715513,0.000029481494],"about_ca_topic_score_codex":0.002049415,"about_ca_topic_score_gemma":0.0015261736,"teacher_disagreement_score":0.0059638666,"about_ca_system_score_codex":0.0007978164,"about_ca_system_score_gemma":0.0014680973,"threshold_uncertainty_score":0.031540275},"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":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014071533,0.43206528,0.3124733,0.12478214,0.109769516,0.00067038025,0.0016272919,0.000768682,0.016436277],"genre_scores_gemma":[0.039052445,0.45632648,0.3075564,0.032709632,0.10722009,0.0032731467,0.0011874042,0.0007281373,0.05194629],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9910721,0.005911333,0.0004966748,0.0007798961,0.0016179938,0.00012197556],"domain_scores_gemma":[0.9689362,0.02656927,0.001486767,0.0007857672,0.0018672991,0.00035461917],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019588761,0.0022631537,0.002108047,0.0027037452,0.0005800602,0.0018954455,0.0028241219,0.003900334,0.023777375],"category_scores_gemma":[0.042933688,0.0017149484,0.0015674178,0.004027794,0.0025669718,0.0025788292,0.000783435,0.006534391,0.0052718907],"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":[0.00027898184,0.00005882008,0.0004983381,0.002015534,0.00020470399,0.00016579189,0.00025138282,0.004281201,0.00030368424,0.04216756,0.78349745,0.16627659],"study_design_scores_gemma":[0.0005811632,0.00028127027,0.0022726764,0.002020522,0.0004321945,0.00086088927,0.00014474259,0.0125974445,0.0005421998,0.1762337,0.8038665,0.00016676253],"about_ca_topic_score_codex":0.0037123444,"about_ca_topic_score_gemma":0.005491706,"teacher_disagreement_score":0.98041123,"about_ca_system_score_codex":0.0017520724,"about_ca_system_score_gemma":0.0027847416,"threshold_uncertainty_score":0.10359645},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04530776,0.0003579785,0.9503715,0.00051594345,0.00009286765,0.0002642424,0.00033413022,0.0002752058,0.0024804971],"genre_scores_gemma":[0.6829349,0.00030651529,0.3120319,0.00043752842,0.00023221296,0.0011693122,0.0006504777,0.00014108937,0.0020961566],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97640806,0.01450481,0.0009812018,0.0035268813,0.0040489184,0.00053010235],"domain_scores_gemma":[0.9317642,0.051181313,0.004555812,0.008546568,0.003342207,0.00060976436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022831028,0.00080103107,0.0017781333,0.0026495466,0.0009807802,0.0019686532,0.0029306363,0.0017123038,0.0046750903],"category_scores_gemma":[0.10064078,0.0003529468,0.0021633809,0.0031334222,0.0033250973,0.0024369354,0.0027139517,0.0020841428,0.0005589597],"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":[0.0006117529,0.00030004882,0.06653275,0.0007081976,0.0021096237,0.0017618196,0.0014111565,0.096999764,0.0072690183,0.37371466,0.00737363,0.44120756],"study_design_scores_gemma":[0.000289835,0.0013623845,0.049002197,0.00021469659,0.0005543914,0.0016232638,0.00085902965,0.451157,0.0042462135,0.47763512,0.012738741,0.00031713612],"about_ca_topic_score_codex":0.001532366,"about_ca_topic_score_gemma":0.0011805029,"teacher_disagreement_score":0.022831028,"about_ca_system_score_codex":0.0010099058,"about_ca_system_score_gemma":0.0024355217,"threshold_uncertainty_score":0.12074345},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012409107,0.0003069904,0.98534334,0.0005569998,0.000025413568,0.000060146886,0.00009894883,0.00012482505,0.0010743381],"genre_scores_gemma":[0.7393029,0.0007007295,0.25630948,0.00041105642,0.00014594787,0.00047776895,0.0004462833,0.00014852005,0.0020573875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98683274,0.010353606,0.0004199131,0.0012096612,0.0008289103,0.0003552685],"domain_scores_gemma":[0.90872455,0.08163356,0.004388148,0.0033751328,0.0014323693,0.0004462804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027663471,0.0012206034,0.0019259232,0.0019382023,0.00054594595,0.002293819,0.0020762268,0.002129807,0.002532802],"category_scores_gemma":[0.1011735,0.000896838,0.002392283,0.0011219286,0.0025385139,0.002523183,0.0033047255,0.0027074716,0.00026659452],"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":[0.00019693068,0.00005769352,0.0043930407,0.00017037096,0.0003492586,0.00028810496,0.00019304361,0.77485865,0.00064990134,0.184696,0.0008989635,0.033248056],"study_design_scores_gemma":[0.000028890603,0.000060885326,0.00048207364,0.000027399494,0.000036938185,0.000042464613,0.000021351529,0.9076804,0.00030520948,0.090668105,0.00062312715,0.000023293376],"about_ca_topic_score_codex":0.003777699,"about_ca_topic_score_gemma":0.0016023054,"teacher_disagreement_score":0.027663471,"about_ca_system_score_codex":0.0015502253,"about_ca_system_score_gemma":0.0017192087,"threshold_uncertainty_score":0.14630014},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06081193,0.00067932927,0.9363242,0.00055479514,0.000045765362,0.00007747681,0.00036539667,0.00019712113,0.00094396423],"genre_scores_gemma":[0.840641,0.0020100346,0.14892091,0.00028510636,0.00018789415,0.00058337575,0.00092050014,0.00020721152,0.0062439586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99607337,0.0023097096,0.00017796035,0.00074084336,0.00034053827,0.00035750648],"domain_scores_gemma":[0.97461104,0.02122225,0.0022644612,0.00073215953,0.00076219364,0.00040782304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012716809,0.0016004286,0.0022435968,0.0015637253,0.0007312357,0.0024156428,0.0038960532,0.0021815982,0.0025039478],"category_scores_gemma":[0.025723755,0.0017333076,0.002750706,0.001831246,0.0018280273,0.0022140678,0.0026372406,0.0036369008,0.00047227647],"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":[0.00008429014,0.000062925305,0.014249074,0.00011510707,0.00042763748,0.0011031714,0.0004785309,0.8732118,0.0006414621,0.100881375,0.00071199686,0.008032566],"study_design_scores_gemma":[0.000016994805,0.000036324214,0.001389449,0.000019022755,0.0000846321,0.00013645798,0.000045141504,0.97481966,0.000107868516,0.02287662,0.00044298082,0.000024802732],"about_ca_topic_score_codex":0.016731668,"about_ca_topic_score_gemma":0.008573475,"teacher_disagreement_score":0.016731668,"about_ca_system_score_codex":0.0013654565,"about_ca_system_score_gemma":0.0018810888,"threshold_uncertainty_score":0.06725377},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068995816,0.00011251871,0.9988335,0.00003660278,0.000010875479,0.000014232184,0.000017863831,0.00014783707,0.00013659752],"genre_scores_gemma":[0.12007434,0.00084754475,0.8738099,0.00020953051,0.00016452826,0.0003625144,0.0004693733,0.00042489453,0.0036374538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971405,0.0014651724,0.00015812126,0.00044997988,0.0006762511,0.000110052584],"domain_scores_gemma":[0.98714477,0.0097957235,0.0010115367,0.0007825805,0.0011179761,0.00014748696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008344379,0.0015349495,0.0015555129,0.0024180214,0.0005806897,0.0011938432,0.0027823246,0.0021622097,0.0047428],"category_scores_gemma":[0.028294468,0.000835827,0.0021454704,0.0013182736,0.0014196816,0.0020425168,0.0023846307,0.00412194,0.0018368695],"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":[0.00017571286,0.000119806915,0.0023590438,0.00051716645,0.00029243267,0.00020725273,0.0004946116,0.53946656,0.005683872,0.17523985,0.0040964507,0.27134717],"study_design_scores_gemma":[0.000009161046,0.000027262751,0.00016951017,0.000026772794,0.000024626414,0.000058962956,0.000018804005,0.9735981,0.0007817046,0.023232834,0.0020284983,0.000023750323],"about_ca_topic_score_codex":0.0027203513,"about_ca_topic_score_gemma":0.002161752,"teacher_disagreement_score":0.008344379,"about_ca_system_score_codex":0.00086221704,"about_ca_system_score_gemma":0.0012231808,"threshold_uncertainty_score":0.04412985},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5037612,0.0028470375,0.48923308,0.00088777696,0.000038604445,0.00017398091,0.0007779916,0.00047573415,0.0018046083],"genre_scores_gemma":[0.9493434,0.00022513434,0.049647197,0.00007205161,0.000022158678,0.000075030075,0.00041136425,0.00002156806,0.00018192959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9767378,0.01758097,0.001168566,0.002157906,0.0017632666,0.00059142575],"domain_scores_gemma":[0.8819449,0.099048994,0.008612697,0.0073904004,0.0022017122,0.0008012428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048005126,0.0012288768,0.0024828967,0.0029084298,0.00030061923,0.002505785,0.0017155175,0.002095171,0.00069443154],"category_scores_gemma":[0.13240278,0.0006840494,0.0017756384,0.0019047371,0.0018179856,0.0017476287,0.0022345793,0.0013895266,0.0002804927],"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":[0.0060108593,0.00047272036,0.33918536,0.0007855422,0.004398337,0.00058262824,0.0005159581,0.4602985,0.005272199,0.012904477,0.0011627146,0.16841075],"study_design_scores_gemma":[0.00021645939,0.0010682015,0.055925168,0.0001719148,0.0011757027,0.00094681646,0.00010901059,0.90674525,0.005117924,0.027678173,0.00071228953,0.00013303386],"about_ca_topic_score_codex":0.0012863918,"about_ca_topic_score_gemma":0.0008904435,"teacher_disagreement_score":0.048005126,"about_ca_system_score_codex":0.0009859378,"about_ca_system_score_gemma":0.0015591757,"threshold_uncertainty_score":0.25387836},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015471072,0.00021136535,0.98370564,0.00011979202,0.000014060799,0.000025972968,0.00007008136,0.00014002304,0.00024186287],"genre_scores_gemma":[0.6111775,0.0012291266,0.383581,0.00016617627,0.00012994642,0.00033434058,0.00096999394,0.00024159823,0.002170216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973028,0.0013981153,0.00015474361,0.00062707573,0.00039760268,0.000119643555],"domain_scores_gemma":[0.9837571,0.013311113,0.0011319879,0.0008979,0.00072470336,0.00017713552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049329177,0.0014360587,0.0012827934,0.0015804492,0.0006122937,0.0011594269,0.0018450797,0.0013952524,0.0008689807],"category_scores_gemma":[0.031265177,0.0010209638,0.0015345666,0.0015182398,0.0013937199,0.0019074616,0.002121075,0.0033851198,0.00030374387],"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":[0.000053277363,0.0000496692,0.006446175,0.0001567971,0.00022716777,0.00018007692,0.00016406452,0.9028608,0.0023461746,0.029153882,0.00079132133,0.057570703],"study_design_scores_gemma":[0.000005424818,0.00001219494,0.0007019427,0.000012075392,0.000018331162,0.000023667488,0.000013812458,0.9803852,0.0004150443,0.01797143,0.00042499485,0.000015955728],"about_ca_topic_score_codex":0.008698386,"about_ca_topic_score_gemma":0.006947335,"teacher_disagreement_score":0.008698386,"about_ca_system_score_codex":0.0009935285,"about_ca_system_score_gemma":0.0013625135,"threshold_uncertainty_score":0.026088119},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004981031,0.0008765795,0.9934308,0.0001992627,0.000032519743,0.000106389234,0.00007047152,0.00009140572,0.00021149332],"genre_scores_gemma":[0.17465428,0.0024109199,0.8198148,0.0003180971,0.00018619494,0.00084537826,0.0006455371,0.00017128381,0.0009535604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96581453,0.028571311,0.0015638279,0.0020231348,0.0017230667,0.00030412746],"domain_scores_gemma":[0.79339904,0.18419173,0.0053957556,0.012543259,0.0040171435,0.0004530899],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08270143,0.0015715348,0.0026441365,0.003181543,0.0006804639,0.001802975,0.003568255,0.0016707042,0.002819076],"category_scores_gemma":[0.2271178,0.001293719,0.0021620905,0.0029837708,0.0015231712,0.003352803,0.0028157549,0.002459448,0.0007026879],"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":[0.00088016654,0.00038977058,0.056910127,0.0025145523,0.0035730917,0.0010965174,0.0012478994,0.2347986,0.0067388783,0.18861891,0.0043436317,0.49888787],"study_design_scores_gemma":[0.00030364367,0.0005013332,0.013193479,0.00043486108,0.00086584006,0.0006665727,0.00029093216,0.763073,0.0044963867,0.20542821,0.010605586,0.00014011814],"about_ca_topic_score_codex":0.0030738995,"about_ca_topic_score_gemma":0.0028042586,"teacher_disagreement_score":0.91729856,"about_ca_system_score_codex":0.0007662987,"about_ca_system_score_gemma":0.0025208786,"threshold_uncertainty_score":0.4373722},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004008038,0.000415501,0.9929039,0.00013536045,0.000048728973,0.00008417154,0.00021774674,0.0004880136,0.0016984977],"genre_scores_gemma":[0.30341622,0.0016422982,0.68595916,0.00028883037,0.00028492743,0.0009339738,0.002101329,0.0006933988,0.0046799406],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960919,0.0015387839,0.00019797524,0.0008796978,0.0010262306,0.00026552365],"domain_scores_gemma":[0.9924871,0.003099999,0.000554086,0.0014917383,0.0022233217,0.00014379472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055921124,0.0020477402,0.001852302,0.0035160417,0.0012370552,0.0027562184,0.0022617306,0.001736268,0.0052912706],"category_scores_gemma":[0.025937703,0.0006710306,0.0025697066,0.003555983,0.0017972325,0.0029864118,0.0024780876,0.0024898376,0.0017978366],"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":[0.00025976915,0.00015278374,0.005422572,0.00073317596,0.00051756017,0.00032461682,0.0005084983,0.24744338,0.009138626,0.09962268,0.014713706,0.62116265],"study_design_scores_gemma":[0.000020316793,0.000082748404,0.004208371,0.000096994234,0.00009963322,0.00039096095,0.00016643546,0.8920323,0.0031118477,0.08376118,0.015903786,0.00012540548],"about_ca_topic_score_codex":0.0047406843,"about_ca_topic_score_gemma":0.003461497,"teacher_disagreement_score":0.0055921124,"about_ca_system_score_codex":0.00092693884,"about_ca_system_score_gemma":0.0025484469,"threshold_uncertainty_score":0.029574275},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039963946,0.00033922805,0.9557871,0.00081851205,0.00011682459,0.00027346032,0.0011127432,0.00024346083,0.0013447352],"genre_scores_gemma":[0.67025685,0.00093441654,0.31530526,0.00059506827,0.00030938195,0.001727061,0.003127035,0.000106762636,0.007638012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97706693,0.017846894,0.00061629614,0.00249886,0.0013110819,0.0006600322],"domain_scores_gemma":[0.8867797,0.09087306,0.009358767,0.009752051,0.002422989,0.00081342994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04080389,0.001308372,0.0023634657,0.0013047613,0.0007157322,0.0024382097,0.003507576,0.0025062999,0.0052652415],"category_scores_gemma":[0.09381355,0.0008677284,0.002703933,0.0024091478,0.0022048203,0.0022096143,0.0022451496,0.0038422514,0.0009263901],"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":[0.0010089551,0.00041585517,0.08667001,0.0004662614,0.0017434005,0.0007499723,0.0012219284,0.48855633,0.0012381485,0.30505836,0.004692489,0.108178295],"study_design_scores_gemma":[0.000109111075,0.0003745114,0.01368664,0.00011231223,0.00032428527,0.0001633817,0.0001439187,0.82125765,0.00064735295,0.15921226,0.0038863535,0.00008217408],"about_ca_topic_score_codex":0.008779897,"about_ca_topic_score_gemma":0.0073859245,"teacher_disagreement_score":0.04080389,"about_ca_system_score_codex":0.001220547,"about_ca_system_score_gemma":0.0019385508,"threshold_uncertainty_score":0.2157942},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2264554,0.00021580947,0.77258086,0.00008722992,0.0000063018774,0.000019849444,0.00014065548,0.00019261568,0.0003013476],"genre_scores_gemma":[0.90219516,0.0001723998,0.096498415,0.000027798798,0.00001146507,0.000052874686,0.0004193676,0.000016704646,0.000605825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959224,0.00016690137,0.000014039909,0.00014390721,0.0000584685,0.000024436022],"domain_scores_gemma":[0.9972882,0.0020795597,0.00030401663,0.000139271,0.00013981636,0.00004915698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009398951,0.00036093275,0.0004066039,0.00068171596,0.00016805463,0.00032402444,0.00067166466,0.00061507133,0.00024861807],"category_scores_gemma":[0.0053357575,0.00024086852,0.00041804338,0.00044325416,0.00043319212,0.00042810274,0.00041636828,0.0005719011,0.00007368551],"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":[0.00004119006,0.00001842843,0.007088186,0.000026430307,0.000030711984,0.000042295083,0.000029022503,0.96573937,0.0049935007,0.003028902,0.00009314877,0.018868852],"study_design_scores_gemma":[0.0000014001467,0.0000058303963,0.0011911007,0.000001032556,0.0000025256466,0.000007765677,0.0000029295998,0.99716574,0.0004928382,0.0010685539,0.000057292553,0.0000028898191],"about_ca_topic_score_codex":0.0062438175,"about_ca_topic_score_gemma":0.005172089,"teacher_disagreement_score":0.0062438175,"about_ca_system_score_codex":0.0006327436,"about_ca_system_score_gemma":0.00045715336,"threshold_uncertainty_score":0.012414992},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006090785,0.00029878964,0.9909513,0.00030055753,0.000055005446,0.00010432389,0.0009910858,0.00055113697,0.0006569383],"genre_scores_gemma":[0.315541,0.0013715941,0.65709823,0.00052806287,0.0005206888,0.0022134571,0.0081066685,0.00067799643,0.01394232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937447,0.0035022614,0.00041516795,0.0013390976,0.0006327957,0.0003659603],"domain_scores_gemma":[0.9461105,0.04588641,0.0025832409,0.0031961189,0.0017726404,0.0004510991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016549528,0.0017562897,0.0023682432,0.0021432491,0.0011520254,0.0029141258,0.005783281,0.0032857286,0.009519032],"category_scores_gemma":[0.040991,0.0014787038,0.0028390086,0.0021477637,0.0022055567,0.004528931,0.0031219933,0.0054877107,0.002275275],"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":[0.00027454155,0.00010821222,0.0041543925,0.00027656264,0.00021590586,0.0003739069,0.00044451072,0.68861204,0.0009748253,0.2626857,0.0033610617,0.038518377],"study_design_scores_gemma":[0.000016125685,0.00001570123,0.0003558321,0.000017729426,0.000014518189,0.000026848462,0.000013954432,0.93404233,0.00010228748,0.06450155,0.0008733273,0.00001983512],"about_ca_topic_score_codex":0.014127985,"about_ca_topic_score_gemma":0.015979623,"teacher_disagreement_score":0.016549528,"about_ca_system_score_codex":0.002686664,"about_ca_system_score_gemma":0.0019204371,"threshold_uncertainty_score":0.08752334},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009891537,0.00089675654,0.9867085,0.0008464042,0.00015148765,0.000104540886,0.00045733337,0.00018353971,0.0007598533],"genre_scores_gemma":[0.56477296,0.0025614318,0.41345677,0.0008916196,0.00070513145,0.0014298629,0.0015690965,0.000263693,0.014349471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98363197,0.012173344,0.00050497596,0.00181229,0.0013248537,0.00055248325],"domain_scores_gemma":[0.9533845,0.035079334,0.0027744141,0.006407847,0.0019225852,0.00043138574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03019103,0.0010994374,0.0015449978,0.0014155209,0.0007339844,0.0018078224,0.0037888577,0.0020495113,0.0037794835],"category_scores_gemma":[0.07475773,0.00081708573,0.002400198,0.0024881575,0.0016158336,0.002152013,0.0028651296,0.0039726775,0.00087248854],"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":[0.0008463222,0.000115618146,0.030017259,0.0007292877,0.0009923407,0.00111974,0.0006205238,0.18279478,0.0013053324,0.6141407,0.009221801,0.15809633],"study_design_scores_gemma":[0.00023488111,0.00028244042,0.0055171046,0.00016062352,0.0003885953,0.00043339323,0.00009776027,0.68550485,0.0017371551,0.29208073,0.013443485,0.000118909076],"about_ca_topic_score_codex":0.0044897506,"about_ca_topic_score_gemma":0.0033065074,"teacher_disagreement_score":0.03019103,"about_ca_system_score_codex":0.0013658616,"about_ca_system_score_gemma":0.0026937348,"threshold_uncertainty_score":0.15966737},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047661494,0.0017926106,0.98354083,0.0029473344,0.0002943347,0.0002649016,0.002858524,0.0013143573,0.00222104],"genre_scores_gemma":[0.3789049,0.005356451,0.5638422,0.0025410987,0.0016568803,0.004400568,0.008238115,0.000937676,0.034122057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99482286,0.0032458038,0.00018262112,0.00082108343,0.00056462386,0.00036303763],"domain_scores_gemma":[0.9905607,0.006820139,0.0007793844,0.0007851672,0.0008149267,0.0002396961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012945844,0.0015884821,0.002119367,0.0021877445,0.00069337717,0.0019350428,0.0058624255,0.0026888496,0.011021136],"category_scores_gemma":[0.02360611,0.00076969515,0.002330916,0.002459517,0.0011882999,0.0024528657,0.002057536,0.0036608654,0.003787251],"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":[0.00042212903,0.00019151422,0.015772212,0.000756049,0.00076045573,0.0009628374,0.00035133667,0.3577294,0.00091115327,0.36114794,0.062383197,0.19861181],"study_design_scores_gemma":[0.00010528181,0.00014950466,0.001554753,0.0001115593,0.00015853158,0.0004373821,0.00005450698,0.8518253,0.00026465167,0.1216297,0.023625461,0.00008332583],"about_ca_topic_score_codex":0.0127480915,"about_ca_topic_score_gemma":0.010153375,"teacher_disagreement_score":0.012945844,"about_ca_system_score_codex":0.0018646368,"about_ca_system_score_gemma":0.0037883807,"threshold_uncertainty_score":0.068464994},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043743093,0.0012931078,0.9533857,0.00053789746,0.00005372868,0.00012350957,0.00022310416,0.00019244816,0.0004473773],"genre_scores_gemma":[0.702539,0.0022526474,0.288271,0.00068748393,0.00031578317,0.0012576444,0.0015233542,0.00022659275,0.0029264498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98363435,0.012316728,0.0005903302,0.0017451368,0.0013085695,0.00040489755],"domain_scores_gemma":[0.7898096,0.1853511,0.012413981,0.008092602,0.0035390812,0.0007935402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059063997,0.0009317917,0.0021390936,0.002421211,0.0006495951,0.0015284503,0.0029379982,0.001746777,0.0026247208],"category_scores_gemma":[0.19987181,0.00077804155,0.0020672262,0.0020695806,0.0026525813,0.0026765068,0.0025930183,0.0024461881,0.00038115217],"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":[0.00070482306,0.0001554186,0.087259054,0.00132166,0.0016204417,0.0015195605,0.0018662243,0.40109572,0.0029177603,0.3403826,0.0029434124,0.1582133],"study_design_scores_gemma":[0.00009287699,0.00030549095,0.011700976,0.00024907658,0.00031188986,0.0004889158,0.00016122026,0.7939352,0.0011749471,0.18806674,0.0034383289,0.00007439071],"about_ca_topic_score_codex":0.003997906,"about_ca_topic_score_gemma":0.002157538,"teacher_disagreement_score":0.059063997,"about_ca_system_score_codex":0.0016971114,"about_ca_system_score_gemma":0.002881078,"threshold_uncertainty_score":0.31236404},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008310844,0.00067209906,0.9859542,0.00083109987,0.00041933966,0.00025155378,0.000457314,0.000816732,0.00228681],"genre_scores_gemma":[0.30785537,0.0006875091,0.68284667,0.0011558774,0.0008572124,0.0026438066,0.0012437096,0.00068964995,0.0020202354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9369326,0.048340544,0.0020190077,0.0060565616,0.0059239194,0.0007273132],"domain_scores_gemma":[0.7735054,0.20041308,0.0053762384,0.014712207,0.004671964,0.00132112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03408197,0.0014080764,0.002665689,0.0041299732,0.0016457253,0.0023940154,0.0027860538,0.0029911576,0.00869707],"category_scores_gemma":[0.22477494,0.0007367272,0.0019773082,0.0040165833,0.006535849,0.0035832687,0.0031593929,0.0056684,0.0018435972],"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":[0.0013288828,0.00045924835,0.032026533,0.0023140449,0.0019698807,0.0019322634,0.0011245669,0.07976096,0.0077175633,0.4794491,0.03274041,0.35917655],"study_design_scores_gemma":[0.00018969475,0.0005425796,0.0062619695,0.00032149907,0.00015581946,0.0010481656,0.00027128126,0.32749042,0.0031080875,0.6431319,0.017360179,0.00011839748],"about_ca_topic_score_codex":0.0010980091,"about_ca_topic_score_gemma":0.00063798425,"teacher_disagreement_score":0.03408197,"about_ca_system_score_codex":0.0011993102,"about_ca_system_score_gemma":0.0031302914,"threshold_uncertainty_score":0.1802448},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051487115,0.0001791859,0.9987343,0.00011159954,0.000015341577,0.00003225108,0.000075226235,0.000110691995,0.00022650139],"genre_scores_gemma":[0.064918034,0.0012881375,0.9274866,0.00031211317,0.00023905629,0.0010136152,0.0011125609,0.00039575298,0.0032341753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99301505,0.004481413,0.0003691266,0.00084269285,0.0011056236,0.00018606587],"domain_scores_gemma":[0.97056556,0.023564551,0.0013659461,0.002273363,0.0019210655,0.00030948102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014499989,0.0015820225,0.0021092794,0.0031039682,0.0011730046,0.0025709094,0.004133246,0.0027433855,0.006103503],"category_scores_gemma":[0.0510786,0.002168184,0.002140256,0.0036181237,0.0029175514,0.0038808656,0.0037974545,0.0040019066,0.0021637413],"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":[0.00012689008,0.000082596285,0.0013162843,0.00049287797,0.0003224046,0.00015603476,0.00030794225,0.54465157,0.0014324135,0.31068492,0.0041527348,0.1362733],"study_design_scores_gemma":[0.000017027469,0.000014931663,0.00021346613,0.000047675356,0.000017259757,0.000038769103,0.000021944461,0.8501712,0.00023917518,0.14648384,0.0027112477,0.00002342063],"about_ca_topic_score_codex":0.008621309,"about_ca_topic_score_gemma":0.00971938,"teacher_disagreement_score":0.014499989,"about_ca_system_score_codex":0.0020525516,"about_ca_system_score_gemma":0.0033079172,"threshold_uncertainty_score":0.07668418},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027023066,0.00060090324,0.9693086,0.0004453277,0.00008388329,0.00010968756,0.0009034638,0.00091648346,0.000608573],"genre_scores_gemma":[0.5885426,0.0014710982,0.39631593,0.0006524865,0.0001960258,0.001230117,0.004356386,0.00027201136,0.006963351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875355,0.00045761533,0.00008145168,0.0003863238,0.00018460435,0.00013643401],"domain_scores_gemma":[0.9944056,0.004665563,0.0003225532,0.00016303884,0.00034845146,0.00009486945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041880785,0.0010710709,0.0015234475,0.0012845993,0.0007972012,0.0011121167,0.0025878008,0.0019814963,0.002925114],"category_scores_gemma":[0.007907464,0.00095445575,0.0018014722,0.0011564316,0.0009841181,0.0016647079,0.0011626713,0.0030789184,0.00080616004],"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":[0.00042647388,0.0001081734,0.0075746826,0.00021796701,0.00020930899,0.0003211396,0.00026510106,0.9001154,0.0033828055,0.034623083,0.0019554244,0.050800577],"study_design_scores_gemma":[0.000015228885,0.000018026272,0.00034527038,0.000010951238,0.000019246325,0.000021224225,0.0000071775466,0.9902839,0.00040147686,0.008499736,0.0003639597,0.000013687859],"about_ca_topic_score_codex":0.023233986,"about_ca_topic_score_gemma":0.022713572,"teacher_disagreement_score":0.023233986,"about_ca_system_score_codex":0.0016140606,"about_ca_system_score_gemma":0.0020774752,"threshold_uncertainty_score":0.046197474},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010725933,0.00038444425,0.98554546,0.0013335088,0.000050913855,0.000118904056,0.0002836683,0.00012937647,0.0014278683],"genre_scores_gemma":[0.5601512,0.001323081,0.4288379,0.0010569094,0.0003842704,0.00295077,0.0006930647,0.000086176056,0.0045166374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9784799,0.017757274,0.00042969923,0.0015060195,0.0012124171,0.0006146962],"domain_scores_gemma":[0.96538836,0.02847579,0.002109552,0.0025645262,0.0010296109,0.00043202713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021028973,0.0012688176,0.0024056162,0.0019184165,0.0010043153,0.002469284,0.004590141,0.0019642864,0.0077110287],"category_scores_gemma":[0.048011426,0.0009892682,0.002891171,0.0030418742,0.0029229564,0.0032674335,0.004496209,0.004196228,0.0007710812],"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":[0.0001582443,0.00022362403,0.009382381,0.00031206498,0.00076277164,0.0004994969,0.0007408313,0.14255008,0.0005527394,0.8015219,0.0019935535,0.041302297],"study_design_scores_gemma":[0.000120722936,0.000114615716,0.0014725324,0.00005627641,0.00013714656,0.00015059674,0.00011890969,0.4217058,0.00015580097,0.57351416,0.0024013193,0.000052134554],"about_ca_topic_score_codex":0.0032962833,"about_ca_topic_score_gemma":0.0028721755,"teacher_disagreement_score":0.021028973,"about_ca_system_score_codex":0.0014866289,"about_ca_system_score_gemma":0.0021605953,"threshold_uncertainty_score":0.11121321},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026973365,0.0005245827,0.99533045,0.0002854484,0.00010774357,0.00004960264,0.00011643417,0.000576395,0.00031205997],"genre_scores_gemma":[0.057676382,0.0012766232,0.9364968,0.00035110035,0.0003914977,0.00038440395,0.0006722838,0.0005345524,0.002216329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99361897,0.0034544193,0.0002841776,0.0011065105,0.0013164133,0.00021958788],"domain_scores_gemma":[0.9863854,0.008355085,0.0011872909,0.0018617706,0.0018945687,0.00031595107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01060532,0.0015339705,0.0015250935,0.0026700993,0.0015845825,0.0022058003,0.0023347847,0.0019338506,0.002194144],"category_scores_gemma":[0.04110787,0.0009090218,0.0022967784,0.0040788506,0.0018478587,0.0021210506,0.0029392976,0.0031553379,0.0014734876],"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":[0.00039327145,0.00017143342,0.011500356,0.000745126,0.00056972896,0.0006201637,0.0007470593,0.28517857,0.007946454,0.12371816,0.015661137,0.5527486],"study_design_scores_gemma":[0.00003577062,0.00008909223,0.003308944,0.00007031931,0.000043696866,0.0004455757,0.000121579105,0.8625989,0.0036188546,0.116147466,0.01342567,0.000094153016],"about_ca_topic_score_codex":0.0069641885,"about_ca_topic_score_gemma":0.006264809,"teacher_disagreement_score":0.01060532,"about_ca_system_score_codex":0.0011484452,"about_ca_system_score_gemma":0.0027029277,"threshold_uncertainty_score":0.056086957},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028721516,0.00015612332,0.9957241,0.00044685634,0.000025318332,0.00011210694,0.00012135601,0.00011478715,0.00042725098],"genre_scores_gemma":[0.26384124,0.0009715458,0.7265736,0.0008821198,0.00023513022,0.0022614205,0.0012987038,0.00024588444,0.0036904712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98106223,0.016014384,0.00040690345,0.0013814671,0.0007997155,0.00033536725],"domain_scores_gemma":[0.9516759,0.04341742,0.0015937068,0.0018838642,0.0011213438,0.00030777033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024478914,0.0015463481,0.0029606223,0.0021617285,0.00093731016,0.0017930679,0.0031210422,0.0026490237,0.0063423705],"category_scores_gemma":[0.08025454,0.0010865554,0.0027364723,0.0023149466,0.0026259043,0.002202431,0.0029119346,0.004755678,0.001240929],"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":[0.00024454814,0.00012432973,0.0039205244,0.0003935475,0.00051947177,0.00032051103,0.000391613,0.49188542,0.0007450161,0.4060632,0.0048320848,0.09055979],"study_design_scores_gemma":[0.00007387529,0.00008731591,0.0005831279,0.0000726991,0.000059668822,0.000049138485,0.00003966693,0.7750694,0.0002973979,0.22130509,0.0023314185,0.000031133],"about_ca_topic_score_codex":0.006624273,"about_ca_topic_score_gemma":0.005393631,"teacher_disagreement_score":0.024478914,"about_ca_system_score_codex":0.0022407006,"about_ca_system_score_gemma":0.0038534852,"threshold_uncertainty_score":0.12945843},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027824527,0.0019190591,0.96481377,0.0004977855,0.00019813575,0.00015428517,0.00043697312,0.00026452425,0.003890914],"genre_scores_gemma":[0.7011808,0.002054798,0.2916994,0.00033305315,0.0005791403,0.0013258109,0.0010174302,0.0003022827,0.0015072635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9510617,0.031935133,0.0027541355,0.006823198,0.0066445856,0.0007811846],"domain_scores_gemma":[0.83491856,0.11873997,0.008910855,0.021868799,0.014668717,0.0008930989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03307843,0.0015957223,0.0025975124,0.0060987845,0.0009768631,0.0028152487,0.0021764268,0.001705919,0.0023326154],"category_scores_gemma":[0.20250626,0.00066684844,0.0023542147,0.0050748386,0.005808716,0.004071427,0.004820204,0.002935856,0.0007115514],"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":[0.00030273505,0.00007811065,0.036284287,0.0012584246,0.0019266708,0.00045326995,0.0032729127,0.13269722,0.0019444374,0.60153466,0.005954193,0.21429308],"study_design_scores_gemma":[0.00007446017,0.000410626,0.02367594,0.0004747744,0.00039553063,0.0007204052,0.00062053406,0.24393497,0.0016078637,0.71586907,0.011976716,0.0002391636],"about_ca_topic_score_codex":0.0024855223,"about_ca_topic_score_gemma":0.0011948603,"teacher_disagreement_score":0.03307843,"about_ca_system_score_codex":0.0017694413,"about_ca_system_score_gemma":0.0016257461,"threshold_uncertainty_score":0.17493755},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028601796,0.00044715308,0.99518776,0.00050703256,0.000039698487,0.000039636634,0.00007436031,0.0002956812,0.0005485394],"genre_scores_gemma":[0.12032953,0.0010282773,0.87349176,0.00078182505,0.0002649639,0.0003259099,0.0006771641,0.00045414723,0.0026463699],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97818255,0.017596439,0.000784139,0.0018063632,0.0013536771,0.0002767796],"domain_scores_gemma":[0.9046611,0.079115994,0.003193395,0.009541493,0.0031291808,0.00035879074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036899913,0.0017061301,0.0021334745,0.0020491173,0.00073749706,0.0017710937,0.0029647255,0.0018009971,0.0037171815],"category_scores_gemma":[0.15969056,0.0009426573,0.0016827536,0.0024336386,0.0014447055,0.003161203,0.003506853,0.0032491803,0.0018146503],"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":[0.00053134793,0.0003020585,0.01825139,0.00083867536,0.0011419023,0.000565177,0.0006546777,0.2779096,0.004840485,0.24235815,0.0121851945,0.4404213],"study_design_scores_gemma":[0.000064236534,0.00009149028,0.002298367,0.000110414454,0.00011971907,0.0002214167,0.000087035616,0.8551728,0.0018552234,0.13117547,0.008757059,0.000046774403],"about_ca_topic_score_codex":0.0038919046,"about_ca_topic_score_gemma":0.0047869775,"teacher_disagreement_score":0.036899913,"about_ca_system_score_codex":0.0010980655,"about_ca_system_score_gemma":0.0020403059,"threshold_uncertainty_score":0.1951477},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011801235,0.000120613906,0.9982724,0.0001317566,0.000013938977,0.000056755856,0.000026227068,0.000077393095,0.00012080316],"genre_scores_gemma":[0.09891485,0.00038126096,0.8977644,0.00036866576,0.00010752047,0.0011035682,0.00031439937,0.00012166272,0.00092359004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9741511,0.021559197,0.0007035606,0.0017309759,0.0015016514,0.00035355156],"domain_scores_gemma":[0.9128807,0.07713503,0.002371847,0.0049746046,0.0021512804,0.0004865688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043989383,0.0015041692,0.0028612756,0.0019422494,0.0010337741,0.0017067345,0.0047667995,0.0025433395,0.0032583878],"category_scores_gemma":[0.11021414,0.001398111,0.0028377846,0.0024673033,0.002584341,0.0028475812,0.005211998,0.0048451475,0.0006285048],"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":[0.0005013013,0.00025978466,0.0057220366,0.00068383984,0.0009234625,0.00040081318,0.00061834423,0.58259755,0.0013176255,0.21837217,0.0036640698,0.18493906],"study_design_scores_gemma":[0.00011715924,0.000091634465,0.0003645633,0.00005212393,0.00006111487,0.000054189357,0.00002521044,0.9066465,0.0005134361,0.09064367,0.0014091466,0.000021294121],"about_ca_topic_score_codex":0.0059300377,"about_ca_topic_score_gemma":0.0053090765,"teacher_disagreement_score":0.043989383,"about_ca_system_score_codex":0.0017770495,"about_ca_system_score_gemma":0.0042481436,"threshold_uncertainty_score":0.2326408},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8079574,0.00016127745,0.19006297,0.00027114447,0.000023281022,0.00009914188,0.0002666413,0.000080340935,0.0010778045],"genre_scores_gemma":[0.9776318,0.00007474425,0.021771373,0.000049394737,0.000022260914,0.000094414514,0.00016894957,0.000015102016,0.00017192005],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96500987,0.028774934,0.00088089734,0.0026389924,0.002031615,0.00066382077],"domain_scores_gemma":[0.6969743,0.27306226,0.0131745245,0.013951756,0.0020218678,0.00081533886],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.053736035,0.00055738364,0.00095525896,0.0012146472,0.0005006238,0.0011159988,0.001331171,0.0012385997,0.002298441],"category_scores_gemma":[0.19451135,0.00037644204,0.0013232146,0.001168424,0.002826067,0.0013351102,0.00149506,0.00096115476,0.000209516],"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":[0.0010314116,0.0001894094,0.8335101,0.00013600184,0.0015802991,0.00085915317,0.0020751266,0.04135787,0.0030428818,0.056553252,0.0005657898,0.059098803],"study_design_scores_gemma":[0.0003650682,0.0009964822,0.5249273,0.00011040708,0.0009678041,0.0011346015,0.0011317917,0.3851618,0.0033994967,0.08078898,0.00090279843,0.00011349581],"about_ca_topic_score_codex":0.002630449,"about_ca_topic_score_gemma":0.0018662454,"teacher_disagreement_score":0.94626397,"about_ca_system_score_codex":0.00049896707,"about_ca_system_score_gemma":0.0012552405,"threshold_uncertainty_score":0.28418672},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009528282,0.00052526186,0.99754375,0.00049988466,0.000024621097,0.000024136432,0.000069651054,0.000041672094,0.00031815903],"genre_scores_gemma":[0.1775364,0.004504351,0.8111874,0.0009282957,0.00073556544,0.0011136478,0.0005772371,0.00011673412,0.003300462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884802,0.008584227,0.0004123772,0.0010877217,0.0011969265,0.00023839562],"domain_scores_gemma":[0.97303444,0.02235659,0.0019211852,0.0012595992,0.0010425737,0.0003855057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021362651,0.0013767902,0.0023626292,0.0024557072,0.00078512065,0.002545438,0.004232982,0.0023755333,0.0030033574],"category_scores_gemma":[0.04550769,0.0013728746,0.0024779555,0.0030257658,0.0022453815,0.003255447,0.002608868,0.0040459405,0.0005877141],"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":[0.00010323799,0.000114686714,0.0039123073,0.00039685072,0.00059904554,0.00025468395,0.00038306348,0.20769985,0.0008686553,0.7001293,0.0024659464,0.083072476],"study_design_scores_gemma":[0.000046599045,0.000109015054,0.0012499217,0.00011590138,0.00016455572,0.0001926791,0.00004121256,0.5051579,0.0002210828,0.48836157,0.004279433,0.00006012064],"about_ca_topic_score_codex":0.006386275,"about_ca_topic_score_gemma":0.0066241818,"teacher_disagreement_score":0.021362651,"about_ca_system_score_codex":0.0016448348,"about_ca_system_score_gemma":0.0033491165,"threshold_uncertainty_score":0.1129778},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28935942,0.000759184,0.70347613,0.0018695467,0.00015964101,0.00018050233,0.00240666,0.0005439184,0.0012450445],"genre_scores_gemma":[0.9261507,0.00019974253,0.06935591,0.00034418967,0.00025330667,0.00029762686,0.0028473234,0.00006197917,0.0004892624],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96054596,0.021605218,0.0016982183,0.01093778,0.0038447983,0.0013680776],"domain_scores_gemma":[0.7755041,0.18732531,0.016276296,0.016124107,0.0027674688,0.0020026537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034932192,0.0009946731,0.0025911273,0.004301724,0.0011259789,0.0026724355,0.0039876318,0.0033397838,0.0032587333],"category_scores_gemma":[0.12739576,0.0006893947,0.0021929145,0.004272066,0.0038866708,0.0052758083,0.0034629875,0.0037936412,0.0005500569],"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":[0.0038195513,0.00090124266,0.57580745,0.00072781614,0.00472403,0.0018704982,0.00134923,0.1406353,0.007307055,0.09472311,0.0041889637,0.16394581],"study_design_scores_gemma":[0.00021047611,0.00056149554,0.04213127,0.00008354663,0.00029245202,0.0009160855,0.0005330856,0.7769377,0.0024296325,0.17360403,0.0022216984,0.00007852477],"about_ca_topic_score_codex":0.0015717085,"about_ca_topic_score_gemma":0.0009928826,"teacher_disagreement_score":0.034932192,"about_ca_system_score_codex":0.0010582372,"about_ca_system_score_gemma":0.0012064041,"threshold_uncertainty_score":0.18474132},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009003772,0.012609482,0.78046227,0.15085307,0.0060132667,0.00010980018,0.00048486138,0.00063662446,0.039826967],"genre_scores_gemma":[0.44038984,0.03454908,0.34800237,0.09120159,0.023296319,0.0008987708,0.00077355775,0.00054596504,0.06034245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99446,0.0030171594,0.00024639087,0.0007404113,0.0014026639,0.00013341372],"domain_scores_gemma":[0.9693728,0.023901014,0.0013786679,0.0031559044,0.0019265789,0.00026492242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008053748,0.00054225215,0.0005849389,0.0013885655,0.000823357,0.0015679791,0.0023690194,0.0047143716,0.004592403],"category_scores_gemma":[0.055944152,0.0004745789,0.00052158436,0.0017724085,0.0043516876,0.0039561023,0.0022992273,0.0065259547,0.0047431733],"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":[0.00018540406,0.000043229407,0.004396704,0.00044724863,0.000066603156,0.0015622486,0.00046960247,0.01776122,0.0010721817,0.4239838,0.13273431,0.41727751],"study_design_scores_gemma":[0.000053507065,0.000074226016,0.0034032152,0.00033095962,0.000021187041,0.0024611538,0.00011636272,0.070024066,0.00089944765,0.7503627,0.17218219,0.000070861555],"about_ca_topic_score_codex":0.0034017118,"about_ca_topic_score_gemma":0.0025424531,"teacher_disagreement_score":0.008053748,"about_ca_system_score_codex":0.0025799249,"about_ca_system_score_gemma":0.00080301735,"threshold_uncertainty_score":0.042592824},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.459279,0.22510868,0.30468726,0.0037187831,0.0007402468,0.001334337,0.0026894126,0.0005728473,0.0018694776],"genre_scores_gemma":[0.9513523,0.008885592,0.037343618,0.00047335232,0.0001689437,0.0005328386,0.00088547403,0.00005232115,0.00030556647],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9317317,0.06216912,0.0024130142,0.002272265,0.0011141571,0.00029970778],"domain_scores_gemma":[0.8471035,0.13968988,0.0055424217,0.0059808535,0.0011543694,0.0005290212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07066918,0.0018051459,0.005367813,0.0032752668,0.0005215744,0.002646362,0.0021777558,0.001586323,0.0011497746],"category_scores_gemma":[0.11582621,0.0009472544,0.016135506,0.003284976,0.0006706419,0.0018648055,0.001930835,0.0030734085,0.00010671005],"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":[0.015443129,0.00038852714,0.08839224,0.012254732,0.68452597,0.0004517681,0.00025818858,0.1287424,0.0006840462,0.0034767913,0.0012736904,0.06410858],"study_design_scores_gemma":[0.0037752693,0.0018353191,0.028451772,0.0016099891,0.63288605,0.0003535014,0.00016289928,0.30464277,0.0011987516,0.022547206,0.0023709673,0.00016551127],"about_ca_topic_score_codex":0.004145153,"about_ca_topic_score_gemma":0.003633113,"teacher_disagreement_score":0.07066918,"about_ca_system_score_codex":0.0011643093,"about_ca_system_score_gemma":0.0011186694,"threshold_uncertainty_score":0.37373883},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013312476,0.00035149106,0.9843322,0.00066010945,0.00003308782,0.00006556377,0.00044048906,0.00020845524,0.0005961361],"genre_scores_gemma":[0.5622764,0.0015502451,0.42282572,0.0003807018,0.00024076531,0.0013151779,0.002688362,0.00016444027,0.008558186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945727,0.003652211,0.00023385487,0.00076095713,0.00051918137,0.00026117772],"domain_scores_gemma":[0.9715556,0.024607044,0.0017457661,0.001049545,0.0007284025,0.0003137254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014703992,0.00085968134,0.0019433256,0.0017966965,0.0009327684,0.002079545,0.003696627,0.0021719383,0.0034120283],"category_scores_gemma":[0.0384234,0.001530536,0.001795461,0.0028437776,0.0018279986,0.0034155257,0.0025422222,0.003428799,0.0007020122],"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":[0.00019974603,0.00012279817,0.005412086,0.00013082298,0.00020279348,0.00015772891,0.00038056794,0.6829641,0.0003151077,0.27491772,0.0018691738,0.033327334],"study_design_scores_gemma":[0.000018682436,0.0000126175855,0.0004961039,0.00001820054,0.000013780032,0.000015219452,0.000017220524,0.9372702,0.00004674372,0.061509725,0.0005664809,0.000014991957],"about_ca_topic_score_codex":0.018017571,"about_ca_topic_score_gemma":0.020944161,"teacher_disagreement_score":0.018017571,"about_ca_system_score_codex":0.0022414909,"about_ca_system_score_gemma":0.0021920362,"threshold_uncertainty_score":0.07776308},"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":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024798564,0.035697248,0.7465249,0.0094140805,0.00386579,0.0005363176,0.09311212,0.09019046,0.018179337],"genre_scores_gemma":[0.016070481,0.01658603,0.84717447,0.0021118405,0.0013667452,0.0023346927,0.055885345,0.0348026,0.02366787],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99166685,0.002906726,0.0012755658,0.0017386748,0.0021876025,0.00022459724],"domain_scores_gemma":[0.97561353,0.014735214,0.0018419686,0.0037783335,0.0034611216,0.00056973076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013563835,0.0045774197,0.0044920314,0.005849323,0.0012036505,0.0042614993,0.0036591252,0.0016352229,0.044098884],"category_scores_gemma":[0.027878389,0.004388541,0.003538382,0.009256378,0.0031898392,0.005216289,0.0029089607,0.006042711,0.04476775],"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":[0.00019716848,0.00007351674,0.0021280912,0.0025766473,0.00065602135,0.00028906093,0.0003520182,0.0045394967,0.0032942577,0.011918352,0.7581897,0.21578579],"study_design_scores_gemma":[0.00024416932,0.00017766458,0.013206986,0.0027451857,0.00063811836,0.0012668805,0.00035338348,0.019198457,0.0065634176,0.06915294,0.88609743,0.0003554388],"about_ca_topic_score_codex":0.019337583,"about_ca_topic_score_gemma":0.022225335,"teacher_disagreement_score":0.044098884,"about_ca_system_score_codex":0.0013164461,"about_ca_system_score_gemma":0.0040689376,"threshold_uncertainty_score":0.14752549},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035705266,0.003818476,0.0129025765,0.9731689,0.006263552,0.000029999028,0.00009429558,0.000050299415,0.0033148313],"genre_scores_gemma":[0.021860184,0.003557194,0.020198109,0.8965553,0.053455632,0.0005804881,0.00008170915,0.0001552415,0.0035561565],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.94025415,0.040527306,0.0035574692,0.0048769386,0.009931123,0.0008530454],"domain_scores_gemma":[0.6923528,0.28783056,0.0038371012,0.0059442543,0.008328337,0.0017069128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09886648,0.0009104442,0.0021551105,0.0015556412,0.0027715757,0.0053762617,0.0054508303,0.021851854,0.0050886604],"category_scores_gemma":[0.31021193,0.00085620285,0.001490204,0.0021090996,0.016880259,0.008237828,0.0030350552,0.03508369,0.0025608488],"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":[0.00023813061,0.00003653777,0.0006875636,0.00023723954,0.000081762446,0.0007548911,0.00045496438,0.0016159929,0.00023286721,0.36532518,0.56655407,0.063780874],"study_design_scores_gemma":[0.00018435251,0.00005682209,0.0006741644,0.00058885163,0.000041570776,0.00079763314,0.00015862063,0.006263719,0.00042849613,0.7640753,0.22665031,0.000080157435],"about_ca_topic_score_codex":0.0026495291,"about_ca_topic_score_gemma":0.0020432072,"teacher_disagreement_score":0.09886648,"about_ca_system_score_codex":0.0059902077,"about_ca_system_score_gemma":0.0035878993,"threshold_uncertainty_score":0.5228622},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021890702,0.00021438386,0.99547195,0.00028457507,0.00016048757,0.0002812264,0.00015736216,0.0007385343,0.0005023851],"genre_scores_gemma":[0.09420957,0.0004925285,0.8975764,0.0012570433,0.00044005277,0.003296678,0.00044921803,0.00071276416,0.0015657882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9206416,0.054092478,0.0038270706,0.008608708,0.011943512,0.00088669],"domain_scores_gemma":[0.74572074,0.19868909,0.01597804,0.029384064,0.00891711,0.0013108753],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.113365315,0.00291303,0.0033931357,0.004383327,0.00184736,0.002460783,0.004017709,0.004297835,0.0038273167],"category_scores_gemma":[0.27510902,0.001154219,0.003098969,0.004409674,0.006835463,0.0036410033,0.0035109096,0.008350466,0.001594306],"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":[0.003038778,0.00069026364,0.022074316,0.0020207125,0.0031988604,0.0016120146,0.0014714659,0.03024706,0.06039325,0.24942799,0.014513731,0.6113115],"study_design_scores_gemma":[0.0023468814,0.0035713424,0.023900814,0.0006610817,0.0021713562,0.005529199,0.0003164666,0.27796882,0.10096859,0.5364786,0.045013264,0.0010735705],"about_ca_topic_score_codex":0.0010924876,"about_ca_topic_score_gemma":0.0009641158,"teacher_disagreement_score":0.8866347,"about_ca_system_score_codex":0.0013209841,"about_ca_system_score_gemma":0.0047244057,"threshold_uncertainty_score":0.59954023},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014103004,0.0003377878,0.99771,0.0001156106,0.00003304216,0.00004164447,0.00006840282,0.00011720397,0.00016599066],"genre_scores_gemma":[0.12044284,0.0013397831,0.87498343,0.0003762461,0.0003722532,0.0006289272,0.0006064778,0.00015959852,0.0010904531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9873066,0.008795341,0.0006214153,0.0016302733,0.0013699179,0.00027649847],"domain_scores_gemma":[0.9528231,0.042034265,0.0015418194,0.0018616695,0.0013442987,0.00039484826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027625706,0.0015530074,0.0024621813,0.004421771,0.0011286223,0.0024476186,0.0030783962,0.0027380956,0.0029587848],"category_scores_gemma":[0.085397914,0.001180828,0.0025542316,0.0033010573,0.0030006804,0.0029486893,0.0019260278,0.0040801954,0.0005914335],"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":[0.000526598,0.00015577143,0.010913708,0.0008128818,0.0011676686,0.0011003116,0.0007020338,0.38662663,0.0033068187,0.35318834,0.004321879,0.23717733],"study_design_scores_gemma":[0.00010715962,0.00011085569,0.0013561873,0.00014437061,0.00022680867,0.00047685605,0.000058522844,0.62955725,0.0011767038,0.36208016,0.0046084076,0.00009674714],"about_ca_topic_score_codex":0.0060630627,"about_ca_topic_score_gemma":0.004458576,"teacher_disagreement_score":0.027625706,"about_ca_system_score_codex":0.0013516768,"about_ca_system_score_gemma":0.0026027942,"threshold_uncertainty_score":0.14610046},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23915204,0.0010508543,0.7541595,0.00067312625,0.00016230204,0.00044191326,0.0010230868,0.0006530768,0.002684055],"genre_scores_gemma":[0.8289327,0.00023178209,0.16726105,0.00023578669,0.00018185604,0.0006432368,0.0013339208,0.000102040016,0.00107765],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95292884,0.036855146,0.0021617468,0.003688806,0.0037500313,0.0006153873],"domain_scores_gemma":[0.7962258,0.17896603,0.008776958,0.011852645,0.0029056657,0.0012728886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04992566,0.0008976271,0.00135728,0.0030944864,0.00054066186,0.0015956288,0.0023671056,0.0012756056,0.0035350865],"category_scores_gemma":[0.16752584,0.00032881086,0.0016976998,0.0031631151,0.0026943625,0.0021227298,0.0024981736,0.0017650333,0.00048802528],"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":[0.0029302659,0.0004588358,0.5459445,0.0005806925,0.0033616717,0.0014284721,0.00096277514,0.06285005,0.0026980152,0.076320454,0.00376036,0.29870397],"study_design_scores_gemma":[0.0006116026,0.0035530757,0.16636798,0.00019951431,0.0008837417,0.0028319028,0.0006635431,0.7015063,0.0034958648,0.11251904,0.0071680667,0.00019939714],"about_ca_topic_score_codex":0.00080627564,"about_ca_topic_score_gemma":0.000666324,"teacher_disagreement_score":0.04992566,"about_ca_system_score_codex":0.00039272947,"about_ca_system_score_gemma":0.0010796402,"threshold_uncertainty_score":0.26403528},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020017238,0.00009393816,0.9788821,0.000326125,0.000034409895,0.00006747438,0.00013413354,0.00023519537,0.00020948338],"genre_scores_gemma":[0.44026724,0.0003566563,0.5514251,0.00038193597,0.0001512031,0.00092709553,0.0013805847,0.00027202597,0.004838201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941333,0.0045654825,0.00014695642,0.0006820377,0.00029821278,0.00017410994],"domain_scores_gemma":[0.9863485,0.010952091,0.00078239647,0.0008765066,0.0008241504,0.00021632768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010759522,0.0008867131,0.0011888456,0.00097238313,0.0007279311,0.0010627001,0.0024573198,0.0010253516,0.0042576184],"category_scores_gemma":[0.022186602,0.0007165761,0.0016402206,0.0012225565,0.0008530424,0.0010590652,0.0014091375,0.002244636,0.0005252485],"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":[0.00089799694,0.0004628348,0.05567714,0.0002426101,0.0014565077,0.0007513865,0.00064627273,0.51824915,0.00349554,0.10530095,0.007087683,0.30573198],"study_design_scores_gemma":[0.000048831826,0.000086642656,0.0027459015,0.000020242245,0.00008933845,0.0000946478,0.00002015366,0.98437405,0.00031084323,0.011082421,0.0010994452,0.000027538592],"about_ca_topic_score_codex":0.007236291,"about_ca_topic_score_gemma":0.008969991,"teacher_disagreement_score":0.010759522,"about_ca_system_score_codex":0.0007996868,"about_ca_system_score_gemma":0.00252242,"threshold_uncertainty_score":0.05690247},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00480937,0.00049746555,0.99414885,0.00014427827,0.0000333962,0.00004022696,0.00005705312,0.0001234679,0.0001459723],"genre_scores_gemma":[0.16872646,0.0011663125,0.8269398,0.00026376432,0.00018624925,0.00066785497,0.00036716368,0.0001696587,0.0015126385],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96405315,0.030369245,0.0010270892,0.002343925,0.0018710714,0.00033556367],"domain_scores_gemma":[0.87859124,0.100677356,0.0072873617,0.0102305785,0.0028120533,0.00040149904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053771093,0.0014369156,0.0020364192,0.0028398482,0.0006594741,0.0014921004,0.0040771156,0.0018832136,0.0022110739],"category_scores_gemma":[0.20178159,0.0009855457,0.0022995973,0.0043312614,0.0014149117,0.0029270619,0.0026358988,0.003221063,0.00043814207],"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":[0.0005339415,0.00029799147,0.035337757,0.0009630453,0.0042023314,0.0004925523,0.0010042717,0.20926648,0.0030207383,0.2846963,0.0029028652,0.4572817],"study_design_scores_gemma":[0.0001991676,0.00035876982,0.0068852417,0.00026438668,0.00078882487,0.00025093462,0.00010414325,0.7043119,0.002270215,0.276349,0.00813081,0.00008668431],"about_ca_topic_score_codex":0.004034955,"about_ca_topic_score_gemma":0.0033636156,"teacher_disagreement_score":0.053771093,"about_ca_system_score_codex":0.0009046329,"about_ca_system_score_gemma":0.0021940959,"threshold_uncertainty_score":0.28437215},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017194038,0.00003357221,0.99776936,0.00003511623,0.0000122469955,0.000020469779,0.00011595492,0.00023760209,0.00005634633],"genre_scores_gemma":[0.06383733,0.00020749804,0.93140596,0.00014349577,0.00008532091,0.0005413318,0.0011848963,0.0003220251,0.0022720932],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980781,0.0009453844,0.000085310545,0.0004996447,0.00030994782,0.00008159759],"domain_scores_gemma":[0.99554044,0.0028021543,0.00045500047,0.0006018397,0.00047390506,0.00012663085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005719653,0.00097301474,0.00095138326,0.0010083998,0.00055173953,0.00065464264,0.0022205706,0.0011709036,0.002592927],"category_scores_gemma":[0.016041802,0.0006460127,0.0015967678,0.0017060034,0.000902322,0.0011168226,0.0013609767,0.002173429,0.0008550676],"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":[0.00045632222,0.0002216378,0.015459362,0.0003962457,0.00090339227,0.00030617364,0.00044842192,0.40509978,0.02317941,0.10906582,0.0074721132,0.43699142],"study_design_scores_gemma":[0.000041153467,0.00009358187,0.0032886,0.000028694263,0.00006463158,0.00015454636,0.000019252882,0.9463273,0.0033714592,0.03851105,0.008050131,0.000049551825],"about_ca_topic_score_codex":0.0054750773,"about_ca_topic_score_gemma":0.008218212,"teacher_disagreement_score":0.005719653,"about_ca_system_score_codex":0.00082546833,"about_ca_system_score_gemma":0.0020455723,"threshold_uncertainty_score":0.030248761},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025346115,0.00029757022,0.9736462,0.00007495129,0.000007650529,0.000029054965,0.00010985755,0.00017279804,0.0003157241],"genre_scores_gemma":[0.5779247,0.0006279214,0.41857013,0.00011381242,0.00005569541,0.00029375224,0.0012546014,0.00007749448,0.001081856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973488,0.0018976886,0.00012445843,0.00033580422,0.00021351998,0.00007973661],"domain_scores_gemma":[0.985586,0.012388842,0.0009101693,0.0006738495,0.00037752226,0.00006358148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007213086,0.00054609036,0.0011530936,0.001080062,0.00030527293,0.00064324006,0.0013696984,0.00075845607,0.00086045946],"category_scores_gemma":[0.031248264,0.0006606401,0.00072823017,0.001418509,0.000575729,0.0014791567,0.0008515603,0.001020282,0.00029084523],"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":[0.00031547307,0.0001833222,0.07294422,0.0007518002,0.00075132784,0.0005290824,0.0005836174,0.52182746,0.00583804,0.040159814,0.002579667,0.3535361],"study_design_scores_gemma":[0.000043382683,0.00005859436,0.014097015,0.000053579148,0.000051746243,0.0001929207,0.00008395752,0.94297206,0.001554033,0.039406706,0.001449766,0.000036172758],"about_ca_topic_score_codex":0.002572375,"about_ca_topic_score_gemma":0.0025128652,"teacher_disagreement_score":0.007213086,"about_ca_system_score_codex":0.0003624275,"about_ca_system_score_gemma":0.00057682587,"threshold_uncertainty_score":0.038146913},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003772422,0.00034202097,0.99380046,0.00032121714,0.000036540034,0.00007532854,0.0003940848,0.00024949422,0.0010084752],"genre_scores_gemma":[0.251596,0.001961506,0.7299642,0.0007207799,0.0005425111,0.0017669058,0.0041153156,0.00046558664,0.008867165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894678,0.006956303,0.00046288458,0.0016093877,0.001143446,0.0003602096],"domain_scores_gemma":[0.9193925,0.07183773,0.002485779,0.003909721,0.0018439834,0.0005303078],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.030686181,0.0013421911,0.0035150757,0.0029942421,0.0012467116,0.0030529206,0.0057301815,0.0030239152,0.013315252],"category_scores_gemma":[0.11696364,0.0019319593,0.002958014,0.0039843055,0.0026919423,0.0045120325,0.002847125,0.0054912977,0.0018027913],"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":[0.00018738839,0.00012383306,0.0041676015,0.00038756986,0.00047796956,0.000355472,0.00041233507,0.41472402,0.00037735453,0.500677,0.0069679925,0.071141385],"study_design_scores_gemma":[0.000046671386,0.00001965545,0.0005084269,0.000054888747,0.00005726358,0.00007253508,0.000033230328,0.6635988,0.00010526048,0.33306673,0.002412016,0.000024538567],"about_ca_topic_score_codex":0.014693062,"about_ca_topic_score_gemma":0.014925827,"teacher_disagreement_score":0.9693138,"about_ca_system_score_codex":0.002863739,"about_ca_system_score_gemma":0.0025464213,"threshold_uncertainty_score":0.16228598},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068726577,0.00027421492,0.9921554,0.0002319678,0.00003230815,0.000034192224,0.000103546314,0.00010534538,0.00019036258],"genre_scores_gemma":[0.56565225,0.0021278677,0.42655554,0.00047646678,0.00033351194,0.00069665513,0.0013460784,0.00016204435,0.0026495738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907482,0.0062852087,0.00030941705,0.0010051382,0.0013579476,0.00029398318],"domain_scores_gemma":[0.9479516,0.045148842,0.003368993,0.001973353,0.0012266146,0.00033051637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017130652,0.0012787343,0.001655278,0.0018717074,0.0005226339,0.0016160819,0.003109805,0.0018088372,0.0017253056],"category_scores_gemma":[0.0686379,0.0006932638,0.0017398486,0.002205328,0.0022651355,0.002182517,0.0030083377,0.002647568,0.0003569422],"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":[0.00031031255,0.000087065295,0.0105875535,0.0005016649,0.00029949067,0.00063614483,0.00044423985,0.6468392,0.001680065,0.27113673,0.0020540836,0.065423466],"study_design_scores_gemma":[0.000023162847,0.00004473182,0.00077753235,0.000033346583,0.00004255549,0.000092924914,0.00003552404,0.8898184,0.0004550771,0.107935674,0.00071592,0.000025221783],"about_ca_topic_score_codex":0.0022245508,"about_ca_topic_score_gemma":0.0014530421,"teacher_disagreement_score":0.017130652,"about_ca_system_score_codex":0.0010520522,"about_ca_system_score_gemma":0.0014121553,"threshold_uncertainty_score":0.090596676},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014984632,0.000027052207,0.99817944,0.000044288783,0.000014149525,0.000043027638,0.000021261767,0.000098957855,0.00007333761],"genre_scores_gemma":[0.06344377,0.00007467639,0.93496877,0.00012893575,0.00009473195,0.00042793207,0.00020068242,0.000079821235,0.0005806875],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99180204,0.0059033334,0.0003003924,0.00093922287,0.00088760187,0.0001674749],"domain_scores_gemma":[0.96783286,0.025162742,0.0012745582,0.0033594342,0.0019860766,0.00038435252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016225904,0.0011849621,0.0016024779,0.0029107125,0.00088991807,0.0010305532,0.002585231,0.0013037273,0.004176118],"category_scores_gemma":[0.053664375,0.0006317228,0.0019836843,0.002084009,0.0018552735,0.0013585001,0.002266438,0.0031837788,0.0008455173],"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":[0.00075448654,0.00040310726,0.0066220956,0.0005088769,0.0010640352,0.00047401845,0.0003866605,0.17479728,0.015023221,0.16554666,0.0058559454,0.6285636],"study_design_scores_gemma":[0.00011692072,0.00020366788,0.0014998422,0.000035305333,0.000101492325,0.0001383473,0.00003063872,0.94360334,0.0058771754,0.045727838,0.002608227,0.000057162382],"about_ca_topic_score_codex":0.0019840724,"about_ca_topic_score_gemma":0.002427942,"teacher_disagreement_score":0.016225904,"about_ca_system_score_codex":0.00057867524,"about_ca_system_score_gemma":0.0023439461,"threshold_uncertainty_score":0.085811794},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014518003,0.00014481331,0.9978727,0.0001358549,0.000011777377,0.000021685042,0.000032552696,0.0002052759,0.00012352603],"genre_scores_gemma":[0.17064168,0.0009199878,0.8241707,0.0002440158,0.0002107212,0.0006120669,0.00042011956,0.0003418942,0.002438789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936706,0.0046555013,0.0001757922,0.0005575604,0.00079120195,0.00014937387],"domain_scores_gemma":[0.980604,0.016352901,0.0009783567,0.00084032095,0.0010676552,0.00015689922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009738403,0.0012341312,0.0017825468,0.0017136446,0.00054003036,0.0010476526,0.001609754,0.0019888813,0.002206551],"category_scores_gemma":[0.030108064,0.00076100806,0.0015535221,0.0023403773,0.0016783958,0.001067769,0.0019634173,0.0028208443,0.0010153136],"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":[0.00014667893,0.00012731126,0.0013766923,0.00023670308,0.00024491726,0.00027292376,0.00014174235,0.81234354,0.0040261922,0.06279094,0.0020301498,0.11626227],"study_design_scores_gemma":[0.000010523773,0.000029231227,0.0001803122,0.000006437853,0.000005725006,0.00004155292,0.000006544662,0.98209053,0.0003402949,0.01658575,0.0006882207,0.000014866496],"about_ca_topic_score_codex":0.0048207366,"about_ca_topic_score_gemma":0.0035258585,"teacher_disagreement_score":0.009738403,"about_ca_system_score_codex":0.00079401396,"about_ca_system_score_gemma":0.0016807345,"threshold_uncertainty_score":0.051502228},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061245905,0.00078443397,0.98902565,0.00070785434,0.000059098875,0.00008785081,0.0005108373,0.00021007672,0.0024895298],"genre_scores_gemma":[0.42196414,0.005431079,0.5248794,0.0011704885,0.0009148372,0.0025091865,0.0038074167,0.00059428933,0.038729172],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918276,0.004896339,0.0002876579,0.0012529464,0.0013001685,0.00043537543],"domain_scores_gemma":[0.9757564,0.019592892,0.001853082,0.0012025489,0.0012909822,0.00030409486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015949255,0.0018886323,0.0028798676,0.0032007042,0.0009549764,0.0031256508,0.004589008,0.0032098163,0.00886578],"category_scores_gemma":[0.04210811,0.001545334,0.0031556408,0.0032854134,0.0031228394,0.0048130886,0.0033423873,0.003859926,0.0016489837],"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":[0.0001001987,0.00007568951,0.0026061372,0.00017948585,0.00023939127,0.00013966473,0.00028776212,0.24369082,0.00042419138,0.714868,0.002840691,0.034548018],"study_design_scores_gemma":[0.000045258854,0.000037893307,0.00084524404,0.000045091696,0.000062199724,0.00006411768,0.00003243883,0.5293942,0.00013763152,0.46487275,0.0044178325,0.00004520614],"about_ca_topic_score_codex":0.010299329,"about_ca_topic_score_gemma":0.008470681,"teacher_disagreement_score":0.015949255,"about_ca_system_score_codex":0.0024222697,"about_ca_system_score_gemma":0.001732651,"threshold_uncertainty_score":0.08434868},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039704563,0.00036634284,0.9911963,0.0010934444,0.000072945346,0.000059963906,0.0006308768,0.00020174614,0.0024080428],"genre_scores_gemma":[0.29883006,0.0031806906,0.66246486,0.001568172,0.00079395797,0.0016920114,0.0026797669,0.00056195393,0.028228484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957183,0.002416452,0.00015895786,0.00085805124,0.0005339145,0.00031429925],"domain_scores_gemma":[0.98789006,0.009224398,0.000889089,0.00065503037,0.0010207825,0.00032057823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009026261,0.0015214491,0.002265918,0.002713585,0.0010842258,0.0027787294,0.0052704234,0.0032571526,0.011511947],"category_scores_gemma":[0.02306427,0.0013307319,0.0028676125,0.0032785377,0.003057177,0.0032287117,0.002416013,0.0035573295,0.0026873949],"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":[0.0000678577,0.00005054147,0.0032608262,0.00009408009,0.0001606963,0.00027453894,0.00027053765,0.29444787,0.00058218197,0.6656046,0.0049660197,0.030220225],"study_design_scores_gemma":[0.00005799424,0.000039499784,0.0008236286,0.00004323046,0.0000638496,0.00020374372,0.000033665216,0.6604746,0.000107795895,0.3314226,0.0066783596,0.000051040624],"about_ca_topic_score_codex":0.025218423,"about_ca_topic_score_gemma":0.019201063,"teacher_disagreement_score":0.025218423,"about_ca_system_score_codex":0.0026434876,"about_ca_system_score_gemma":0.0028199314,"threshold_uncertainty_score":0.0501433},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02401656,0.00038163198,0.971923,0.0003393674,0.00008421067,0.00018966058,0.0009073116,0.0009081777,0.001250085],"genre_scores_gemma":[0.4555386,0.00050955376,0.5358722,0.0006698552,0.00022670472,0.0010442868,0.0029581578,0.0002882531,0.002892388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9904417,0.0060057505,0.000384872,0.001703797,0.0012511753,0.00021268772],"domain_scores_gemma":[0.98516744,0.010097464,0.0017339091,0.0018614944,0.0009214248,0.00021831351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008491355,0.00084799435,0.0020229407,0.0016314415,0.0003506167,0.0011949503,0.003147915,0.0015614073,0.0038247672],"category_scores_gemma":[0.039067265,0.0002903806,0.0016398161,0.0018197162,0.0011085205,0.0009762015,0.0013318254,0.0012879868,0.001208803],"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":[0.001284602,0.0003515844,0.050626107,0.0009170591,0.0045568664,0.0016485071,0.00022928293,0.23324431,0.011944696,0.13126136,0.013862744,0.5500729],"study_design_scores_gemma":[0.00036285218,0.00078058743,0.021730822,0.00009490476,0.00064552436,0.0016687523,0.00007487179,0.8652938,0.0036586428,0.09608882,0.009411364,0.00018909125],"about_ca_topic_score_codex":0.0018248482,"about_ca_topic_score_gemma":0.0009838317,"teacher_disagreement_score":0.008491355,"about_ca_system_score_codex":0.00043415345,"about_ca_system_score_gemma":0.0015295729,"threshold_uncertainty_score":0.044907093},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063070744,0.00031423167,0.9349667,0.00029068912,0.000019423653,0.000044773762,0.00021744304,0.00017030066,0.00090567186],"genre_scores_gemma":[0.87791824,0.00097423646,0.11239872,0.00015968837,0.00006534619,0.00030053314,0.0011641693,0.00019610934,0.006823027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904305,0.00035035337,0.00003919523,0.0002258427,0.00020708113,0.00013442566],"domain_scores_gemma":[0.9962031,0.002444954,0.0005750564,0.00019892702,0.0003608003,0.00021717942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030631968,0.00070937193,0.0012433236,0.001496213,0.0006723506,0.0009836808,0.0014329582,0.0013669457,0.0020647931],"category_scores_gemma":[0.009610006,0.0007770901,0.0013791057,0.0011578833,0.0015305899,0.0015827051,0.0014931515,0.0015639687,0.0005414431],"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":[0.00010048306,0.000040006773,0.0044250726,0.000054809698,0.000058517475,0.00009883931,0.0001428952,0.905148,0.0034807816,0.0698904,0.000750536,0.0158097],"study_design_scores_gemma":[0.0000034123343,0.0000073936703,0.0006327937,0.0000043158084,0.0000047725243,0.000012740186,0.0000072468483,0.9912607,0.0001433275,0.007738786,0.00017332495,0.000011297066],"about_ca_topic_score_codex":0.01631824,"about_ca_topic_score_gemma":0.013858428,"teacher_disagreement_score":0.01631824,"about_ca_system_score_codex":0.0018815624,"about_ca_system_score_gemma":0.0017060831,"threshold_uncertainty_score":0.032446504},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022852898,0.00021362328,0.97499824,0.0004708833,0.000053468808,0.00015249666,0.00019535147,0.0005553864,0.0005075491],"genre_scores_gemma":[0.57896006,0.00046605046,0.41626385,0.000555115,0.00023950421,0.00065723393,0.0009146885,0.00012348566,0.0018199933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996192,0.0022209515,0.00021756941,0.00050013955,0.00070088176,0.00016841073],"domain_scores_gemma":[0.9657028,0.02710252,0.0018887157,0.0033351074,0.0014423522,0.00052859366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008222791,0.0007943322,0.0013960793,0.0019121987,0.0009935441,0.0012672324,0.0017216499,0.0015761348,0.0020845206],"category_scores_gemma":[0.045251176,0.00044558034,0.0014916521,0.0017893958,0.0013541908,0.0019091853,0.0023249271,0.0017160419,0.00053590594],"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":[0.00093421625,0.0006147699,0.070500016,0.0010698948,0.00051674776,0.0028880248,0.0011864938,0.21510689,0.014375538,0.1475923,0.017479315,0.5277359],"study_design_scores_gemma":[0.000079412435,0.00017444507,0.010186321,0.000085245796,0.00006373658,0.00069622556,0.00017033175,0.82203925,0.003660168,0.15885766,0.0039214618,0.00006574299],"about_ca_topic_score_codex":0.0022427058,"about_ca_topic_score_gemma":0.0021415644,"teacher_disagreement_score":0.008222791,"about_ca_system_score_codex":0.00057435577,"about_ca_system_score_gemma":0.0015531909,"threshold_uncertainty_score":0.043486774},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03289405,0.0028778417,0.9495924,0.0095387725,0.00030243813,0.00013556489,0.00021603354,0.00024186236,0.0042011566],"genre_scores_gemma":[0.32104453,0.0029979076,0.66680217,0.0027099175,0.00061448367,0.0002228961,0.00024578552,0.00010794845,0.005254294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890147,0.009644338,0.00012616784,0.0003764554,0.00072077644,0.00011768764],"domain_scores_gemma":[0.9413279,0.050468687,0.0019880242,0.0026697775,0.003106684,0.0004389631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01466905,0.0006605888,0.0007585598,0.0023727336,0.00062849105,0.0012380081,0.0012049979,0.0013409819,0.004709385],"category_scores_gemma":[0.05048598,0.00027987084,0.0016836036,0.0029965618,0.0014606891,0.0012805229,0.0011296209,0.0026722231,0.00039310986],"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":[0.0004856414,0.00095112977,0.02752106,0.001090639,0.0010589372,0.0015055928,0.0014263772,0.1509533,0.004145835,0.42923173,0.017703934,0.36392578],"study_design_scores_gemma":[0.00017128143,0.0011836704,0.015566399,0.00031878403,0.00026600598,0.00045600493,0.0008584895,0.645095,0.0024299617,0.3115128,0.021975,0.00016651684],"about_ca_topic_score_codex":0.00534111,"about_ca_topic_score_gemma":0.007948661,"teacher_disagreement_score":0.01466905,"about_ca_system_score_codex":0.00076588104,"about_ca_system_score_gemma":0.0011276009,"threshold_uncertainty_score":0.07757825},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007861862,0.0002489391,0.98893565,0.00054581324,0.00005869237,0.0009702851,0.000092930066,0.00009952556,0.001186353],"genre_scores_gemma":[0.16654941,0.00032889933,0.8251684,0.0008496978,0.00009913981,0.0054382323,0.00019470496,0.00009241022,0.0012791759],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93648005,0.054578424,0.0018125081,0.0027816836,0.003950664,0.00039669866],"domain_scores_gemma":[0.7913603,0.19058575,0.0037784495,0.009747743,0.0038497995,0.0006779389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10249517,0.0010074908,0.0019568969,0.0014154852,0.00066551834,0.0014102665,0.003159375,0.0020243851,0.0056246375],"category_scores_gemma":[0.26561502,0.0009574093,0.0013988367,0.0013179573,0.0030090986,0.003259288,0.00291919,0.0031136791,0.00051862496],"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":[0.0021745379,0.00018248319,0.0043330085,0.00083601853,0.00037298087,0.00037941654,0.0009792721,0.09218037,0.0024245204,0.75025433,0.0042527397,0.14163035],"study_design_scores_gemma":[0.0010854965,0.0011758582,0.0014707806,0.00033306453,0.00018422378,0.00018899962,0.00014571064,0.54336,0.0028771535,0.4411244,0.008000573,0.00005374261],"about_ca_topic_score_codex":0.0010525495,"about_ca_topic_score_gemma":0.0010856837,"teacher_disagreement_score":0.10249517,"about_ca_system_score_codex":0.0013441669,"about_ca_system_score_gemma":0.002965126,"threshold_uncertainty_score":0.54205275},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016767883,0.00042918941,0.9968321,0.0004480474,0.00004315362,0.00006734014,0.00006393251,0.000092750364,0.00034676498],"genre_scores_gemma":[0.17048876,0.0019433519,0.8228903,0.00075493305,0.00039867786,0.0013495345,0.0003652117,0.00013245274,0.0016767686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9555868,0.038198214,0.00082377304,0.0021089434,0.0028399376,0.00044244571],"domain_scores_gemma":[0.87474424,0.11041274,0.0044134106,0.007802404,0.0020529998,0.0005741859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053954355,0.0012798213,0.0022341763,0.0034434882,0.0010174331,0.002755778,0.0040621874,0.0026436988,0.0026790486],"category_scores_gemma":[0.16357806,0.0010120064,0.002727503,0.004481295,0.0032592444,0.003554625,0.0037742874,0.0052133454,0.0006066247],"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":[0.00017728335,0.0001548802,0.00914659,0.0006102988,0.00078629825,0.00036040304,0.00067394564,0.2274628,0.0004449539,0.59683406,0.0033932033,0.15995526],"study_design_scores_gemma":[0.00007785734,0.00008693188,0.0012582937,0.00013296145,0.00012948984,0.00015300719,0.00008572316,0.4550846,0.00028734875,0.53811127,0.004544416,0.000048184385],"about_ca_topic_score_codex":0.0056825704,"about_ca_topic_score_gemma":0.004905194,"teacher_disagreement_score":0.053954355,"about_ca_system_score_codex":0.0016360219,"about_ca_system_score_gemma":0.0034559811,"threshold_uncertainty_score":0.28534126},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010177586,0.00028846104,0.9888164,0.00019214275,0.000021839433,0.000052521842,0.00006136738,0.00020252499,0.0001870681],"genre_scores_gemma":[0.3996717,0.0008156368,0.59581697,0.00028718525,0.00015231296,0.0007353214,0.0008468519,0.00020701348,0.0014669596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97826684,0.01887414,0.00043825226,0.0013561574,0.00073342613,0.00033119717],"domain_scores_gemma":[0.93099135,0.06348011,0.0023878159,0.0016463882,0.0011963806,0.0002979612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023062747,0.0010873926,0.0023646825,0.0023442227,0.0005590703,0.0011756389,0.0019824216,0.0013471551,0.0018823023],"category_scores_gemma":[0.07244237,0.001097688,0.0016281961,0.0025351658,0.0015629899,0.0013135499,0.0012243267,0.0023774249,0.00041725353],"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":[0.00035160943,0.00012523626,0.010817549,0.0002531669,0.0007007783,0.00011751423,0.00015430317,0.7927713,0.0008348379,0.042183973,0.001470617,0.15021914],"study_design_scores_gemma":[0.000044675264,0.000049813294,0.00076980545,0.000021455407,0.000026259651,0.000015082569,0.000012170806,0.9801168,0.00026767148,0.01819309,0.00046836914,0.000014857348],"about_ca_topic_score_codex":0.007863099,"about_ca_topic_score_gemma":0.0049353335,"teacher_disagreement_score":0.023062747,"about_ca_system_score_codex":0.0010628727,"about_ca_system_score_gemma":0.0017869729,"threshold_uncertainty_score":0.121968925},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1635689,0.000521951,0.8329288,0.0005732581,0.00008196868,0.00014963975,0.00080480415,0.0005780639,0.0007927093],"genre_scores_gemma":[0.8771804,0.00032457386,0.11824809,0.00010431924,0.00015524661,0.00027536353,0.0020589777,0.00006162076,0.0015913384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978351,0.0013253203,0.000089932284,0.0003864525,0.00022871599,0.0001345278],"domain_scores_gemma":[0.9944022,0.0033638345,0.00066920836,0.0008185171,0.0006064578,0.00013981706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037769254,0.001043645,0.00073361344,0.0012080353,0.000376453,0.0008763198,0.001085362,0.00085802906,0.00085055147],"category_scores_gemma":[0.0141623765,0.0002514072,0.0011386399,0.0012460977,0.00079895335,0.0007881322,0.0008064897,0.0014616597,0.0003199229],"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":[0.0007549248,0.00062533136,0.046253726,0.0002664467,0.00054538,0.0003804891,0.00025126545,0.5813417,0.0055842465,0.011247188,0.005163109,0.3475862],"study_design_scores_gemma":[0.000010621539,0.000058375477,0.010971363,0.000010943581,0.000019683293,0.000060046197,0.000021831343,0.98171633,0.0007564853,0.005836296,0.00051657355,0.000021453237],"about_ca_topic_score_codex":0.0061052856,"about_ca_topic_score_gemma":0.005869647,"teacher_disagreement_score":0.0061052856,"about_ca_system_score_codex":0.00046182066,"about_ca_system_score_gemma":0.0013153251,"threshold_uncertainty_score":0.01997453},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010920244,0.020693205,0.07940811,0.87812316,0.0079498,0.00034035265,0.00015875115,0.000113787,0.0121208485],"genre_scores_gemma":[0.054718133,0.009478843,0.10605257,0.79309934,0.027570194,0.0030342336,0.000079677295,0.0003832159,0.0055837915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7166954,0.23325342,0.008940051,0.012453657,0.026545156,0.0021123497],"domain_scores_gemma":[0.39510143,0.57901216,0.0050810324,0.007606667,0.01129714,0.0019014619],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.29414892,0.0027101624,0.004716397,0.0039546504,0.0045474544,0.009695626,0.01116788,0.02173778,0.007889035],"category_scores_gemma":[0.50217295,0.0019597823,0.0058521023,0.0045021055,0.038601305,0.026149875,0.007410225,0.058612116,0.0016409016],"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":[0.00014276188,0.000030188077,0.000367838,0.0004219327,0.0001464405,0.00013365362,0.00079964177,0.0014428879,0.00008656758,0.9189675,0.0556825,0.02177803],"study_design_scores_gemma":[0.00023428455,0.00012512229,0.00050211267,0.0013229639,0.00011006172,0.0001475754,0.0003048722,0.004119537,0.000273452,0.8861101,0.1066475,0.00010234607],"about_ca_topic_score_codex":0.0079862,"about_ca_topic_score_gemma":0.0044542183,"teacher_disagreement_score":0.7058511,"about_ca_system_score_codex":0.012279398,"about_ca_system_score_gemma":0.0082078725,"threshold_uncertainty_score":0.87044007},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012116418,0.00014890108,0.997982,0.00020037555,0.000025078625,0.000040797026,0.00006819633,0.00010147395,0.0002214472],"genre_scores_gemma":[0.1309112,0.0009493566,0.86359286,0.0006084811,0.0003854763,0.0011506517,0.00080735906,0.00016889727,0.0014257253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9750284,0.019578025,0.00081349426,0.0018061408,0.0023645062,0.00040944322],"domain_scores_gemma":[0.9056083,0.07214329,0.0048830626,0.013580748,0.0030379845,0.0007465491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039001994,0.0012859905,0.0027091186,0.003396241,0.0016378586,0.0024718249,0.0038870866,0.0020130246,0.00562932],"category_scores_gemma":[0.12385936,0.0011874535,0.0031435082,0.0037949153,0.0040347977,0.0034398297,0.0060636774,0.0050633242,0.0011628751],"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":[0.00020323868,0.0001705566,0.005669164,0.0005339191,0.000615886,0.00044198107,0.00038953486,0.10101574,0.0012979921,0.75118417,0.0047242744,0.13375343],"study_design_scores_gemma":[0.00008421266,0.00008403392,0.0011558406,0.00009372819,0.00007592874,0.00014101043,0.000043079064,0.44636235,0.00081580115,0.54693294,0.004162245,0.00004884706],"about_ca_topic_score_codex":0.0023478074,"about_ca_topic_score_gemma":0.0021462373,"teacher_disagreement_score":0.039001994,"about_ca_system_score_codex":0.0013294035,"about_ca_system_score_gemma":0.0032264707,"threshold_uncertainty_score":0.20626467},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03991153,0.00034777477,0.95734626,0.000715734,0.00008603868,0.00018470142,0.00056344643,0.00020159989,0.0006428627],"genre_scores_gemma":[0.6816673,0.0011134769,0.29891872,0.00081304135,0.00028339907,0.0018016879,0.0022784723,0.00012628475,0.012997533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98823476,0.0076265032,0.0003433632,0.0026579855,0.00069125067,0.0004460454],"domain_scores_gemma":[0.92957586,0.05614035,0.0061469804,0.0054260055,0.0022358615,0.00047486756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024430804,0.0018083827,0.0023957642,0.0016281883,0.00082541286,0.0020237532,0.0049881497,0.003337394,0.0040992224],"category_scores_gemma":[0.05412475,0.0011570922,0.00221378,0.002245394,0.0030968115,0.003252714,0.0024353955,0.003236249,0.00063154526],"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":[0.00039799768,0.0001575916,0.020015717,0.0003342665,0.0006914415,0.001074148,0.0009441611,0.5726761,0.0010063481,0.3461936,0.0022833657,0.054225206],"study_design_scores_gemma":[0.000073661606,0.00007103538,0.0023370583,0.000055188877,0.00011638003,0.00011787717,0.00007505022,0.8518657,0.0003002782,0.14361304,0.0013301447,0.000044537508],"about_ca_topic_score_codex":0.008589549,"about_ca_topic_score_gemma":0.008666018,"teacher_disagreement_score":0.024430804,"about_ca_system_score_codex":0.0018982965,"about_ca_system_score_gemma":0.0013191754,"threshold_uncertainty_score":0.12920398},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008873947,0.00030623493,0.9891823,0.00022302725,0.000053780535,0.00007131133,0.00029238788,0.00011830788,0.00087865646],"genre_scores_gemma":[0.58993316,0.0019102816,0.39228088,0.00046524836,0.00022071914,0.0010608834,0.0017767886,0.00023110857,0.012120864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99566144,0.0022087719,0.00017887086,0.0012187829,0.0004046115,0.00032751128],"domain_scores_gemma":[0.99441504,0.0035304744,0.000718903,0.00060769654,0.0005451083,0.00018285168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007563499,0.0012453751,0.0016755675,0.0018672327,0.0006870934,0.0020734398,0.0035619235,0.0017188396,0.0036947168],"category_scores_gemma":[0.019445669,0.0009149562,0.0018058496,0.0026603914,0.0013050155,0.0024042877,0.0020730866,0.0025409467,0.0009278025],"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":[0.00021157565,0.00013808107,0.011643539,0.000249082,0.00053450186,0.0007349611,0.00059631147,0.48817614,0.0018637935,0.40239242,0.0045350646,0.08892455],"study_design_scores_gemma":[0.00002756842,0.00007407267,0.0023789809,0.000039126207,0.00008654713,0.00016953284,0.00007301669,0.92004895,0.00029638814,0.072726205,0.0040344154,0.000045238838],"about_ca_topic_score_codex":0.009170684,"about_ca_topic_score_gemma":0.006428724,"teacher_disagreement_score":0.009170684,"about_ca_system_score_codex":0.001322081,"about_ca_system_score_gemma":0.0016685567,"threshold_uncertainty_score":0.04000008},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019706095,0.00047599713,0.97653526,0.0007997955,0.00014082502,0.00028319107,0.0006044908,0.00022044913,0.0012337723],"genre_scores_gemma":[0.55379903,0.0016085366,0.423435,0.0016874546,0.0005020926,0.0033362233,0.0025726426,0.0001921014,0.012866892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9714562,0.017655654,0.0015278492,0.004984582,0.0034090423,0.00096671184],"domain_scores_gemma":[0.91516876,0.06342523,0.007472039,0.009670938,0.0037828446,0.00048023276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045235794,0.0015821559,0.0031738398,0.0023346483,0.0014054767,0.0030091729,0.00916055,0.0039597116,0.0048774513],"category_scores_gemma":[0.1020584,0.00139126,0.0032159383,0.0045392527,0.0039905775,0.0037707337,0.003010442,0.0047712945,0.0015324156],"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":[0.0005976304,0.0002797451,0.036248732,0.00089260086,0.0005191964,0.001539912,0.0019329564,0.124570005,0.0025456585,0.73233944,0.0045164307,0.09401774],"study_design_scores_gemma":[0.00019906527,0.00028340545,0.00834527,0.00019675885,0.00032751667,0.00085858896,0.000408736,0.52226436,0.002538409,0.4557776,0.008631565,0.00016878522],"about_ca_topic_score_codex":0.004100048,"about_ca_topic_score_gemma":0.0029008882,"teacher_disagreement_score":0.045235794,"about_ca_system_score_codex":0.0019842263,"about_ca_system_score_gemma":0.0018321875,"threshold_uncertainty_score":0.2392326},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005315123,0.0011773907,0.9899541,0.00048993726,0.00011818719,0.0015558003,0.00014586003,0.00022361612,0.0010198673],"genre_scores_gemma":[0.30254093,0.0008163098,0.68330157,0.00068590284,0.00020871629,0.011359338,0.0003048774,0.00027126094,0.00051108195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7543753,0.21532272,0.007681117,0.010338598,0.011182815,0.0010994107],"domain_scores_gemma":[0.4141394,0.5383031,0.016270429,0.022683209,0.0077262186,0.0008776751],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23455039,0.0016613511,0.0051169153,0.004656639,0.00088635867,0.0027823735,0.0041379845,0.0034259455,0.0070136017],"category_scores_gemma":[0.57999974,0.0009887085,0.004401379,0.0046174307,0.0039393064,0.00412113,0.0036237473,0.004755383,0.00063382514],"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":[0.007876877,0.0004773653,0.011231936,0.011885379,0.008374763,0.0006498406,0.0013636508,0.16714254,0.0026997197,0.42307055,0.00912191,0.3561056],"study_design_scores_gemma":[0.00268686,0.0023957314,0.005019763,0.0021300365,0.001924961,0.00035414466,0.00018749785,0.5008851,0.0028948612,0.4727341,0.008639241,0.00014772067],"about_ca_topic_score_codex":0.000670507,"about_ca_topic_score_gemma":0.00038097272,"teacher_disagreement_score":0.23455039,"about_ca_system_score_codex":0.0021686049,"about_ca_system_score_gemma":0.003456155,"threshold_uncertainty_score":0.94393563},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006733257,0.00053275283,0.99063134,0.0007225887,0.000060850383,0.00008187189,0.00026677878,0.00008294907,0.0008875655],"genre_scores_gemma":[0.44704238,0.0029169668,0.53865665,0.001497768,0.000686882,0.0016921769,0.0013557259,0.00017465293,0.0059767794],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97810674,0.014779663,0.0008757951,0.0037188735,0.0019709882,0.0005480274],"domain_scores_gemma":[0.92019176,0.063857116,0.006626672,0.0068467734,0.0019054624,0.00057228777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035681505,0.0014906944,0.0023651156,0.0024870667,0.0008500877,0.0025006938,0.0040626912,0.0025485246,0.005013356],"category_scores_gemma":[0.10781631,0.0013039378,0.0031843297,0.0023699775,0.0036543715,0.0040289825,0.004725462,0.004460947,0.00081385265],"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":[0.00011647989,0.00008668625,0.006986135,0.00041020676,0.0006763231,0.00023406872,0.0005903306,0.09099512,0.00089627726,0.8404357,0.0017197728,0.056852955],"study_design_scores_gemma":[0.000046893398,0.00010586191,0.0020150817,0.00017385831,0.00026411406,0.00009247654,0.00006392389,0.24650584,0.000537167,0.74683607,0.003316673,0.00004206264],"about_ca_topic_score_codex":0.0030878931,"about_ca_topic_score_gemma":0.00352176,"teacher_disagreement_score":0.035681505,"about_ca_system_score_codex":0.0016825973,"about_ca_system_score_gemma":0.0026552193,"threshold_uncertainty_score":0.18870407},"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":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014626675,0.012449818,0.037897125,0.74135596,0.047545344,0.00050434674,0.0009556905,0.00045274285,0.15737632],"genre_scores_gemma":[0.033512503,0.0075913686,0.027665034,0.7398376,0.025245003,0.0016873345,0.00064074417,0.0009199185,0.16290052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.96798074,0.014261113,0.002079754,0.0042406353,0.009164896,0.0022728506],"domain_scores_gemma":[0.9449033,0.031051764,0.001798757,0.004552247,0.014162549,0.003531414],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.044605117,0.0011793537,0.0011175978,0.0020701715,0.0063584107,0.00966247,0.005355287,0.017522918,0.094185404],"category_scores_gemma":[0.1398096,0.0006335241,0.0021722647,0.0015576814,0.0065225638,0.011768401,0.006922734,0.016202431,0.024064671],"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":[0.00006386968,0.0000459587,0.0004927539,0.0002885108,0.000024592999,0.00025755956,0.0016616225,0.00043203362,0.00027230577,0.47233766,0.46885163,0.055271544],"study_design_scores_gemma":[0.0000104155315,0.000016983107,0.00016206208,0.0004975898,0.0000064286505,0.00017679026,0.00046152005,0.00015128321,0.00018727577,0.060585026,0.9377262,0.000018313956],"about_ca_topic_score_codex":0.003658894,"about_ca_topic_score_gemma":0.002773002,"teacher_disagreement_score":0.9058146,"about_ca_system_score_codex":0.00894617,"about_ca_system_score_gemma":0.008786319,"threshold_uncertainty_score":0.3150816},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062448997,0.00749641,0.007047375,0.001511265,0.0022174004,0.00005094263,0.0004665909,0.0010909708,0.9794947],"genre_scores_gemma":[0.007126268,0.0070413942,0.0037313967,0.0010492272,0.0006384249,0.000051465657,0.0007873063,0.0006076004,0.97896683],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951124,0.000066784785,0.000022392127,0.00009784929,0.00025398628,0.000047648613],"domain_scores_gemma":[0.9996803,0.0000805315,0.000016114043,0.000064923246,0.00010781672,0.00005039504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032895224,0.0008429347,0.0004036516,0.001745747,0.0012877482,0.0052210367,0.0012454867,0.0018132593,0.18377379],"category_scores_gemma":[0.0013256599,0.00030500678,0.00046825374,0.0011737719,0.00083786965,0.0050263624,0.0027517055,0.0019721882,0.0894068],"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":[0.00003175327,0.00002968929,0.00015516116,0.00033418887,0.0000052363544,0.00018491963,0.00089360896,0.00038477968,0.0010712513,0.22198665,0.4459604,0.3289623],"study_design_scores_gemma":[7.412502e-7,0.0000033821482,0.00004251007,0.000051765768,6.4545054e-7,0.00011548538,0.000047990205,0.000045516274,0.000083032006,0.0028571011,0.99674916,0.0000026616572],"about_ca_topic_score_codex":0.0020723843,"about_ca_topic_score_gemma":0.0036082475,"teacher_disagreement_score":0.18377379,"about_ca_system_score_codex":0.0013824847,"about_ca_system_score_gemma":0.00078082865,"threshold_uncertainty_score":0},"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":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005457138,0.20469466,0.0023749815,0.03214781,0.12985067,0.00031682153,0.0025440364,0.001838791,0.62568647],"genre_scores_gemma":[0.0015907583,0.057402175,0.0015417659,0.014114178,0.029201463,0.00012051065,0.00195759,0.0006183232,0.8934532],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984459,0.00018481803,0.00009574171,0.00025739096,0.00091788045,0.000098261946],"domain_scores_gemma":[0.9961959,0.0007539538,0.00025349288,0.00028298303,0.0018516149,0.0006619284],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008443016,0.0017924443,0.0015418461,0.0037756842,0.00095846574,0.0055984478,0.0017184616,0.0026787987,0.45542905],"category_scores_gemma":[0.0058328724,0.00064957404,0.0011484508,0.0033956924,0.0006562058,0.0036295955,0.0022383332,0.003243197,0.43047294],"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":[0.0000105272675,0.000017564975,0.000031559088,0.0001878204,0.0000034986385,0.000029549974,0.000016305246,0.000026841077,0.00010900441,0.00060298527,0.93842673,0.06053754],"study_design_scores_gemma":[0.0000030030403,0.00000583542,0.00007101536,0.0001766882,0.0000023377243,0.000105531806,0.000013979472,0.000011160372,0.000023130013,0.00027318884,0.9993106,0.0000035868545],"about_ca_topic_score_codex":0.0015815747,"about_ca_topic_score_gemma":0.0037322452,"teacher_disagreement_score":0.45542905,"about_ca_system_score_codex":0.0013865511,"about_ca_system_score_gemma":0.0017678493,"threshold_uncertainty_score":0.7767644},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017526039,0.00031516768,0.00043818,0.001438048,0.004595147,0.00029095312,0.012988307,0.002143137,0.9776159],"genre_scores_gemma":[0.0006150106,0.00018524987,0.00011712044,0.00059756666,0.0005703179,0.000060134083,0.003812653,0.0006258853,0.9934161],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926454,0.00007928417,0.00005071942,0.00011156203,0.0003929979,0.000100896665],"domain_scores_gemma":[0.99626416,0.0007008329,0.00016025465,0.00046145375,0.0016567119,0.00075660145],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009709098,0.0011511715,0.0012546806,0.0019059968,0.0016181313,0.00631353,0.0015014797,0.0025522509,0.9452601],"category_scores_gemma":[0.0059123607,0.0006494017,0.00063821167,0.0024993455,0.00050092547,0.0047799246,0.0018023659,0.0022345292,0.9249934],"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":[0.00001290796,0.000011438034,0.00002339899,0.000054698216,5.294383e-7,0.000008065417,0.0000062660724,0.000012714605,0.000047368114,0.00052660814,0.9878741,0.011421963],"study_design_scores_gemma":[0.000012019962,0.000012438518,0.00018304138,0.000057767884,7.673786e-7,0.00001594439,0.000019680116,0.00003020887,0.00005943476,0.00038600832,0.9992188,0.000003919422],"about_ca_topic_score_codex":0.0031836927,"about_ca_topic_score_gemma":0.004493864,"teacher_disagreement_score":0.054739892,"about_ca_system_score_codex":0.0011558596,"about_ca_system_score_gemma":0.0015663524,"threshold_uncertainty_score":0.07807982},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024383405,0.0003473843,0.00065373525,0.0019276267,0.006088006,0.00031950182,0.0120587135,0.0029538297,0.97540754],"genre_scores_gemma":[0.00074152515,0.00022758915,0.00018380757,0.0010751134,0.00094407686,0.00005722646,0.003773665,0.0007340323,0.99226296],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993912,0.0000649612,0.000048105387,0.00011034644,0.00029572737,0.00008970187],"domain_scores_gemma":[0.99600947,0.0006969402,0.00018221386,0.0006011232,0.0014996303,0.0010105665],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009514178,0.0012484002,0.0012419393,0.0019678674,0.0013932508,0.005839645,0.001522323,0.0027194654,0.9491602],"category_scores_gemma":[0.005574139,0.0006632591,0.000790503,0.0020464358,0.00050132093,0.0043995427,0.0018756747,0.0020103813,0.9238848],"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":[0.000019392186,0.000018777368,0.00003712413,0.00006970965,0.0000010130889,0.0000139624735,0.000006226179,0.000016030392,0.00009550359,0.0005040976,0.9771798,0.022038357],"study_design_scores_gemma":[0.000014487181,0.00001524517,0.00022861424,0.000051157764,0.000001013432,0.000026358004,0.000016405029,0.000038729722,0.00008364686,0.00038390045,0.99913627,0.000004282721],"about_ca_topic_score_codex":0.001888449,"about_ca_topic_score_gemma":0.0033631523,"teacher_disagreement_score":0.05083978,"about_ca_system_score_codex":0.00081828516,"about_ca_system_score_gemma":0.0013686795,"threshold_uncertainty_score":0.07251668},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017526039,0.00031516768,0.00043818,0.001438048,0.004595147,0.00029095312,0.012988307,0.002143137,0.9776159],"genre_scores_gemma":[0.0006150106,0.00018524987,0.00011712044,0.00059756666,0.0005703179,0.000060134083,0.003812653,0.0006258853,0.9934161],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926454,0.00007928417,0.00005071942,0.00011156203,0.0003929979,0.000100896665],"domain_scores_gemma":[0.99626416,0.0007008329,0.00016025465,0.00046145375,0.0016567119,0.00075660145],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009709098,0.0011511715,0.0012546806,0.0019059968,0.0016181313,0.00631353,0.0015014797,0.0025522509,0.9452601],"category_scores_gemma":[0.0059123607,0.0006494017,0.00063821167,0.0024993455,0.00050092547,0.0047799246,0.0018023659,0.0022345292,0.9249934],"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":[0.00001290796,0.000011438034,0.00002339899,0.000054698216,5.294383e-7,0.000008065417,0.0000062660724,0.000012714605,0.000047368114,0.00052660814,0.9878741,0.011421963],"study_design_scores_gemma":[0.000012019962,0.000012438518,0.00018304138,0.000057767884,7.673786e-7,0.00001594439,0.000019680116,0.00003020887,0.00005943476,0.00038600832,0.9992188,0.000003919422],"about_ca_topic_score_codex":0.0031836927,"about_ca_topic_score_gemma":0.004493864,"teacher_disagreement_score":0.054739892,"about_ca_system_score_codex":0.0011558596,"about_ca_system_score_gemma":0.0015663524,"threshold_uncertainty_score":0.07807982},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013360834,0.003282428,0.0002872598,0.029675994,0.019354094,0.00018981897,0.006496097,0.0006519999,0.93872637],"genre_scores_gemma":[0.0051542427,0.0011425649,0.0001243889,0.0021164715,0.0020179003,0.000044638316,0.0016576165,0.00012237439,0.98761976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980788,0.00012908463,0.00005969789,0.00024093516,0.0008273601,0.0006640111],"domain_scores_gemma":[0.9963445,0.00013420839,0.00011797797,0.00022626952,0.0018100613,0.0013669346],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012564989,0.00092061755,0.0007465571,0.0017908571,0.005646758,0.004831677,0.0013793383,0.0031898194,0.6208065],"category_scores_gemma":[0.0053103813,0.00033400877,0.0004786772,0.0018237014,0.00054688915,0.0017884593,0.0027777913,0.0027390306,0.33714876],"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":[0.000013574621,0.00000727985,0.00022394698,0.0000457537,0.0000020127134,0.000050161838,0.000071792005,0.000009524888,0.000096316835,0.0039928122,0.980796,0.014690927],"study_design_scores_gemma":[0.0000018718353,0.0000037322538,0.0005581759,0.000018182938,0.0000011873284,0.000016932867,0.00004779828,0.000006605747,0.000024770194,0.00012696163,0.99919146,0.0000023369084],"about_ca_topic_score_codex":0.08406211,"about_ca_topic_score_gemma":0.18004294,"teacher_disagreement_score":0.6208065,"about_ca_system_score_codex":0.006001126,"about_ca_system_score_gemma":0.011700258,"threshold_uncertainty_score":0.54087347},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008537243,0.0002219928,0.9905578,0.00010917935,0.00001283525,0.000024590729,0.00011658271,0.00017581653,0.00024401912],"genre_scores_gemma":[0.7480838,0.0016160496,0.24297276,0.00024666134,0.00011624565,0.00042674286,0.0011800482,0.00019110183,0.005166522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978326,0.0014086067,0.000075957425,0.00033017824,0.00019336773,0.00015923251],"domain_scores_gemma":[0.9911596,0.0070318123,0.00075060804,0.00050607184,0.00042606148,0.00012581384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067925276,0.0009039604,0.0012754599,0.0009610508,0.00031065373,0.0010607273,0.002496564,0.0011563199,0.0025315087],"category_scores_gemma":[0.017264364,0.0006800914,0.0015428079,0.0017259471,0.0010168614,0.0014997027,0.0017527204,0.0020491066,0.00048707717],"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":[0.000086186796,0.000050497816,0.0061031636,0.00011371115,0.00012728426,0.00011510696,0.000121450284,0.9113253,0.00067410996,0.052637517,0.00056850206,0.028077057],"study_design_scores_gemma":[0.000008821859,0.000015690905,0.0004515876,0.000007171251,0.000011670647,0.000013572587,0.000012380382,0.9867308,0.00011962829,0.012321549,0.00029891546,0.000008195936],"about_ca_topic_score_codex":0.008857338,"about_ca_topic_score_gemma":0.0056588696,"teacher_disagreement_score":0.008857338,"about_ca_system_score_codex":0.0009886477,"about_ca_system_score_gemma":0.0011388524,"threshold_uncertainty_score":0.035922766},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001679669,0.0014244483,0.00085324666,0.9591263,0.036749545,0.000023048065,0.00018221475,0.00011498092,0.0013581609],"genre_scores_gemma":[0.002409254,0.0015538018,0.001012232,0.92683333,0.05892582,0.00013230214,0.00007857653,0.00013323734,0.008921389],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9934587,0.0025167314,0.0009172427,0.0012224463,0.0014548321,0.0004301507],"domain_scores_gemma":[0.97587115,0.016714549,0.0011283982,0.000857738,0.0041919486,0.0012362422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009144612,0.0016325396,0.0018308825,0.0012425581,0.0028514618,0.0058894297,0.0023734525,0.037387,0.015317641],"category_scores_gemma":[0.06068168,0.00087980396,0.0016078822,0.00095031614,0.003906018,0.005554635,0.0021972405,0.05112468,0.0194923],"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":[0.000041702155,0.000016474587,0.00012434847,0.000027953241,0.000008489388,0.00017476175,0.000044382738,0.00004706691,0.00006961308,0.0017789307,0.9930789,0.004587413],"study_design_scores_gemma":[0.00014762547,0.00005684255,0.0006544641,0.0002976809,0.000020466285,0.00074888085,0.00021096741,0.0009106269,0.00030303063,0.010999783,0.9855592,0.000090391],"about_ca_topic_score_codex":0.003365829,"about_ca_topic_score_gemma":0.0034472554,"teacher_disagreement_score":0.037387,"about_ca_system_score_codex":0.0033839794,"about_ca_system_score_gemma":0.0024621743,"threshold_uncertainty_score":0.05124265},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20086937,0.0009763046,0.7938044,0.0009546353,0.000038491366,0.000078867306,0.00074783305,0.0003036689,0.0022264214],"genre_scores_gemma":[0.9564401,0.0007572173,0.036526367,0.00009299885,0.000047354657,0.0001238103,0.00056223676,0.00005018338,0.005399826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863005,0.0007005855,0.000060086564,0.00026362005,0.00016635397,0.00017937063],"domain_scores_gemma":[0.993926,0.004338134,0.00083300826,0.00040549962,0.00030357216,0.0001938466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035629203,0.00064052513,0.0008894226,0.00093740306,0.0004357614,0.00089994277,0.0015048627,0.0011142492,0.0023427913],"category_scores_gemma":[0.009796477,0.0005729743,0.0011137723,0.0011509708,0.0013214994,0.0012060198,0.001160555,0.0016524099,0.0004018172],"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":[0.00023724709,0.00011799662,0.03828225,0.00011011476,0.00017629916,0.00039627255,0.0005192545,0.8175548,0.0022050377,0.10606189,0.001441998,0.032896895],"study_design_scores_gemma":[0.000011941436,0.00003801236,0.00552886,0.000016655347,0.000029312112,0.0000775407,0.00005798659,0.96535134,0.0001787822,0.02806657,0.0006225584,0.000020439982],"about_ca_topic_score_codex":0.027515166,"about_ca_topic_score_gemma":0.023767203,"teacher_disagreement_score":0.027515166,"about_ca_system_score_codex":0.0013075611,"about_ca_system_score_gemma":0.0013524462,"threshold_uncertainty_score":0.05471003},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02374323,0.00035586595,0.9729778,0.00044391793,0.000065935616,0.00012422116,0.0011395278,0.00031636693,0.00083312165],"genre_scores_gemma":[0.6573569,0.0014778523,0.3106372,0.00049245375,0.00038631112,0.0016630858,0.006524736,0.00038184944,0.021079557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956898,0.0021596518,0.00023804806,0.0011248772,0.00043137794,0.00035622227],"domain_scores_gemma":[0.98145574,0.013469917,0.0020355557,0.0017489856,0.0009668096,0.00032307624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011312512,0.0012745224,0.0023842326,0.0014649887,0.0008080663,0.0023260931,0.0063677477,0.0025416478,0.0064451797],"category_scores_gemma":[0.025617553,0.0011816066,0.0021730764,0.0022884521,0.0021290733,0.004705734,0.0024894315,0.0040078163,0.0016267295],"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":[0.00042551933,0.00022314668,0.015668295,0.0004288731,0.00027481443,0.00067540223,0.0010685248,0.55786574,0.0017368764,0.37250218,0.00432537,0.0448052],"study_design_scores_gemma":[0.00003064361,0.00003571354,0.0015863681,0.00002988301,0.000026645017,0.000065790635,0.000066578345,0.9292278,0.00014509133,0.06703569,0.0017152262,0.000034531957],"about_ca_topic_score_codex":0.011305096,"about_ca_topic_score_gemma":0.011803472,"teacher_disagreement_score":0.011312512,"about_ca_system_score_codex":0.001876022,"about_ca_system_score_gemma":0.001144969,"threshold_uncertainty_score":0.05982703},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033978373,0.0002277674,0.9954182,0.00021002418,0.00003112711,0.000038448474,0.00008702865,0.00008603533,0.000503675],"genre_scores_gemma":[0.39750737,0.0016389362,0.59444875,0.00055756676,0.00028011974,0.0009722852,0.0007758926,0.00020096623,0.0036181256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907452,0.0065675974,0.00030578303,0.0008907713,0.0011948119,0.0002958591],"domain_scores_gemma":[0.957403,0.035251398,0.002578924,0.0030921276,0.0014824792,0.00019212782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016336266,0.0007958614,0.0016012364,0.0020161644,0.00045197297,0.0015220484,0.0031406092,0.0014771158,0.0034356688],"category_scores_gemma":[0.08751888,0.00051166426,0.0015233546,0.0019772085,0.0018661754,0.0019447966,0.002404925,0.0028441173,0.0005860916],"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":[0.00011585501,0.00011445154,0.013599004,0.00038709157,0.00044312174,0.00038282777,0.0003039726,0.12616105,0.0011227345,0.7339612,0.003524645,0.11988407],"study_design_scores_gemma":[0.00009238489,0.00010205353,0.003143842,0.00019826506,0.00017119215,0.0002575171,0.00010924632,0.4610184,0.0017862295,0.5265008,0.0065524797,0.00006743944],"about_ca_topic_score_codex":0.0013461591,"about_ca_topic_score_gemma":0.0008263848,"teacher_disagreement_score":0.016336266,"about_ca_system_score_codex":0.000890791,"about_ca_system_score_gemma":0.002136516,"threshold_uncertainty_score":0.08639544},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012233111,0.00053535885,0.0004956825,0.9886113,0.009103197,0.0000098087785,0.00009979073,0.00003192656,0.0009905859],"genre_scores_gemma":[0.0013565583,0.00021607411,0.00050943886,0.9855231,0.009806964,0.000050355364,0.000018732202,0.000039956198,0.0024788238],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95610106,0.019860148,0.0049729035,0.005094519,0.01129902,0.0026723184],"domain_scores_gemma":[0.8894839,0.08668533,0.005117637,0.0025655956,0.013421643,0.0027258373],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.039080996,0.0017626247,0.003136942,0.0016196558,0.008053214,0.009041569,0.0074955816,0.11408489,0.0062828837],"category_scores_gemma":[0.16218044,0.0018260647,0.0033032512,0.0025048319,0.009780564,0.009295427,0.004220549,0.106907696,0.0074017],"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":[0.000039071216,0.000018199631,0.00018630273,0.00004932432,0.000014231968,0.00024442925,0.00037864913,0.00008749721,0.00012210586,0.016750777,0.9793844,0.0027250429],"study_design_scores_gemma":[0.00011364764,0.000051114766,0.001417554,0.00044465857,0.00006310887,0.000365333,0.00080177205,0.0011031596,0.0006854242,0.044283833,0.9504524,0.00021802842],"about_ca_topic_score_codex":0.018095374,"about_ca_topic_score_gemma":0.02257223,"teacher_disagreement_score":0.960919,"about_ca_system_score_codex":0.008799582,"about_ca_system_score_gemma":0.011036958,"threshold_uncertainty_score":0.2066825},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033040075,0.00079878524,0.98976946,0.0004955255,0.00020709695,0.0026230193,0.00014979929,0.00022725508,0.0024250066],"genre_scores_gemma":[0.06851498,0.00043401125,0.9209807,0.0005549172,0.00011465143,0.008453726,0.00015524785,0.00012407031,0.00066773273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.804972,0.17122696,0.0062480634,0.0053887544,0.011491613,0.00067262084],"domain_scores_gemma":[0.61536014,0.35195485,0.011302585,0.013064806,0.007505186,0.00081240386],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20136,0.001758788,0.002495767,0.004688242,0.0009761875,0.0029280025,0.0041280086,0.004118886,0.0071842074],"category_scores_gemma":[0.50873476,0.0012486379,0.0025063476,0.0032848762,0.0039853277,0.00490332,0.003989505,0.0050927103,0.0010714097],"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":[0.0019625418,0.00034838627,0.009843474,0.002558639,0.0019712267,0.0005302017,0.0016556279,0.08036894,0.001929624,0.445245,0.008823556,0.44476274],"study_design_scores_gemma":[0.001794127,0.0021646577,0.0055939388,0.0027414248,0.0011786517,0.00070134003,0.00042630025,0.3669748,0.005655195,0.57218796,0.040330816,0.00025083183],"about_ca_topic_score_codex":0.0017477268,"about_ca_topic_score_gemma":0.0016213122,"teacher_disagreement_score":0.79864,"about_ca_system_score_codex":0.0023140293,"about_ca_system_score_gemma":0.003084044,"threshold_uncertainty_score":0.9848653},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3123257,0.0006136529,0.68136805,0.00095902616,0.000052012125,0.00010976639,0.0011801028,0.00030460296,0.0030870908],"genre_scores_gemma":[0.92520225,0.00034011315,0.0705997,0.00008659764,0.000038252074,0.0001708201,0.0005010095,0.000062693834,0.002998567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797183,0.0013613488,0.000060364247,0.0003719533,0.00010422768,0.00013026583],"domain_scores_gemma":[0.99578047,0.002694864,0.00078749156,0.0003539541,0.00024436493,0.00013874704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036830797,0.00037967888,0.000703327,0.0009453087,0.00049643,0.0011101146,0.001899503,0.0008492101,0.0019107369],"category_scores_gemma":[0.008285092,0.00038871833,0.0009168765,0.0012834808,0.0009291512,0.0009493587,0.001602352,0.0009172693,0.0003005785],"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":[0.00011952616,0.000070678994,0.035652794,0.00009592535,0.00018476117,0.00028418624,0.00048302792,0.88404477,0.0012371253,0.056413252,0.00124051,0.02017342],"study_design_scores_gemma":[0.0000130983,0.00002879556,0.005285436,0.0000142935105,0.000026088212,0.00006753815,0.000088817105,0.9792197,0.000116403906,0.014181893,0.00094257237,0.000015379148],"about_ca_topic_score_codex":0.034761447,"about_ca_topic_score_gemma":0.027276129,"teacher_disagreement_score":0.034761447,"about_ca_system_score_codex":0.0012086164,"about_ca_system_score_gemma":0.0009958124,"threshold_uncertainty_score":0.06911826},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00434355,0.0002805573,0.99484104,0.000118112104,0.000011943994,0.000019409272,0.000081180435,0.00015067302,0.00015350184],"genre_scores_gemma":[0.3459513,0.0018174526,0.6448539,0.00032861234,0.00024003489,0.00055213395,0.0015890648,0.00031881963,0.004348743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951776,0.003495026,0.00015024567,0.00063036644,0.00039155388,0.00015514127],"domain_scores_gemma":[0.97764254,0.019180413,0.0013443423,0.0009111399,0.00072680763,0.00019484913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009016325,0.0011681981,0.0020395867,0.001589433,0.00048247084,0.0013309701,0.0029515896,0.0015532144,0.0019072464],"category_scores_gemma":[0.029153975,0.0012917037,0.0020673699,0.0017712505,0.0014509597,0.001558878,0.0019209124,0.0024150042,0.00051483145],"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":[0.00012759089,0.000061830986,0.004641481,0.0003242695,0.00039021746,0.00019416882,0.0002480082,0.793685,0.0011967649,0.13585897,0.0017694563,0.061502244],"study_design_scores_gemma":[0.00001322087,0.00001536353,0.00041497956,0.0000203204,0.000024235222,0.00003278022,0.000013705992,0.9694045,0.00016959889,0.029022217,0.000853484,0.000015567952],"about_ca_topic_score_codex":0.008921003,"about_ca_topic_score_gemma":0.008599858,"teacher_disagreement_score":0.009016325,"about_ca_system_score_codex":0.0009459412,"about_ca_system_score_gemma":0.0012668377,"threshold_uncertainty_score":0.047683418},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022805491,0.00021680418,0.99687386,0.00026978782,0.000023306198,0.000016613434,0.0000122294705,0.00008997598,0.00021675746],"genre_scores_gemma":[0.13454758,0.0007028699,0.86138916,0.0005187781,0.0002143305,0.00033381113,0.00014880145,0.00025642806,0.0018882339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97828317,0.015675046,0.000965469,0.0020690577,0.002627442,0.0003797053],"domain_scores_gemma":[0.92669857,0.054308526,0.005907708,0.00822056,0.0041756867,0.00068902015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04212395,0.0011563054,0.0020012045,0.0026562358,0.0013086954,0.002000324,0.0038572208,0.0028092219,0.001989722],"category_scores_gemma":[0.11911459,0.0010549,0.0017500327,0.0028791297,0.0031979186,0.0037227096,0.0041209245,0.0053585237,0.00070546306],"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":[0.00029959725,0.00012770983,0.010770197,0.0006400118,0.00039584457,0.0008091109,0.0017524805,0.10374403,0.0051120515,0.4999655,0.0046358034,0.37174764],"study_design_scores_gemma":[0.00005726641,0.00011015843,0.0017983032,0.00017022633,0.000071686794,0.0005950737,0.00016532796,0.6473424,0.0036477565,0.33744135,0.008501659,0.00009880944],"about_ca_topic_score_codex":0.0017396518,"about_ca_topic_score_gemma":0.00132287,"teacher_disagreement_score":0.04212395,"about_ca_system_score_codex":0.0010409093,"about_ca_system_score_gemma":0.0024022576,"threshold_uncertainty_score":0.22277546},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020892091,0.0010201447,0.9666705,0.0021972503,0.00024326063,0.0006139007,0.0041067074,0.0005588057,0.0036973571],"genre_scores_gemma":[0.58910495,0.00391493,0.3653721,0.0012113606,0.0007657506,0.004254026,0.008134587,0.0003001761,0.02694206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916317,0.004955536,0.0004419459,0.0014810739,0.0009408593,0.0005488725],"domain_scores_gemma":[0.98480636,0.010659176,0.0017416464,0.0011081294,0.0014267832,0.00025802056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017219812,0.001480821,0.0021475335,0.003113343,0.00060946005,0.0025949946,0.0049031414,0.002283999,0.00644666],"category_scores_gemma":[0.032731067,0.0010584792,0.0028333676,0.00324553,0.0013049634,0.0028348227,0.0018815526,0.0037364387,0.0014704752],"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":[0.00022747034,0.00022607842,0.03092809,0.0004753259,0.00083587866,0.00065041386,0.0007577255,0.6039615,0.0008670833,0.29509306,0.010818818,0.055158645],"study_design_scores_gemma":[0.0000859888,0.00012488838,0.0037579432,0.000080712394,0.00019068574,0.0002537047,0.00009494241,0.9320024,0.00015218082,0.05457891,0.008624756,0.000052805673],"about_ca_topic_score_codex":0.03845186,"about_ca_topic_score_gemma":0.021812806,"teacher_disagreement_score":0.03845186,"about_ca_system_score_codex":0.0037091516,"about_ca_system_score_gemma":0.0034778342,"threshold_uncertainty_score":0.09106815},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018113745,0.00035901813,0.9722224,0.00029372206,0.00035853585,0.007483162,0.00022546777,0.00020292244,0.000741086],"genre_scores_gemma":[0.2412147,0.000570923,0.71710086,0.0006352885,0.00026625601,0.036966927,0.0004087977,0.00008111678,0.0027551444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92173123,0.0684993,0.001676187,0.004019628,0.0030076148,0.0010660414],"domain_scores_gemma":[0.9154654,0.061215367,0.006922266,0.011829795,0.0033490432,0.0012181224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09746495,0.0028697816,0.0041294023,0.0018837989,0.0010379114,0.0018034384,0.0037938228,0.0038973673,0.009622503],"category_scores_gemma":[0.11832572,0.0017837844,0.003741631,0.0014619025,0.003165078,0.0030782924,0.0028714163,0.0044623176,0.0010425742],"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":[0.020886399,0.0033112771,0.010061775,0.002054583,0.002008915,0.0005233463,0.0011856154,0.18397903,0.0052267695,0.5602733,0.004353266,0.2061357],"study_design_scores_gemma":[0.012061352,0.015624016,0.0031870278,0.00038764192,0.0010757322,0.00026032224,0.00016724666,0.60772127,0.0040097493,0.3446191,0.010574074,0.00031249467],"about_ca_topic_score_codex":0.00069833215,"about_ca_topic_score_gemma":0.00073045434,"teacher_disagreement_score":0.09746495,"about_ca_system_score_codex":0.0015893452,"about_ca_system_score_gemma":0.00408708,"threshold_uncertainty_score":0.5154501},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019415534,0.00021122769,0.99606735,0.00023923913,0.00006002137,0.000047798112,0.000296062,0.00018220117,0.0009545589],"genre_scores_gemma":[0.32887042,0.0015045458,0.6408979,0.00067864003,0.0007267249,0.0013089617,0.0019876556,0.00048372248,0.023541322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99293536,0.0034195324,0.00035106856,0.0020214396,0.00077952433,0.0004929631],"domain_scores_gemma":[0.9883936,0.0068306415,0.0016426991,0.0018079567,0.0009394794,0.0003857171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016285311,0.0018870861,0.001909038,0.0021845035,0.001444143,0.0027441748,0.012250064,0.0030303132,0.0073971576],"category_scores_gemma":[0.021632764,0.0022202844,0.004407426,0.0028293703,0.002517466,0.0043892954,0.0051197414,0.0038306983,0.002103507],"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":[0.0000463223,0.00006707698,0.0046778847,0.00017306136,0.00029134727,0.0005203825,0.00037427875,0.5310028,0.0006270668,0.43248308,0.0024636714,0.027273022],"study_design_scores_gemma":[0.000020197842,0.000048450718,0.00070677506,0.000028742394,0.0000645532,0.00020331898,0.000053251235,0.90406156,0.0001426287,0.09004256,0.0045832233,0.000044715118],"about_ca_topic_score_codex":0.019125355,"about_ca_topic_score_gemma":0.018636286,"teacher_disagreement_score":0.019125355,"about_ca_system_score_codex":0.002362109,"about_ca_system_score_gemma":0.0025786234,"threshold_uncertainty_score":0.08612603},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1364118,0.00017525344,0.8621792,0.00010163438,0.000026323227,0.00006199539,0.00018692283,0.00032414624,0.00053271477],"genre_scores_gemma":[0.7434804,0.00019721157,0.2545778,0.00008084375,0.000031205396,0.00012224691,0.00045756242,0.00004948057,0.0010033016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802303,0.0010659185,0.00011778068,0.00033380435,0.0003949038,0.00006456182],"domain_scores_gemma":[0.98747677,0.007620621,0.0021582926,0.0016958235,0.00094206986,0.00010642562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006128207,0.0005398994,0.00047431458,0.0005768594,0.0002705328,0.00047815777,0.00091651623,0.0005612778,0.0007470869],"category_scores_gemma":[0.026010191,0.0002680189,0.0004663955,0.0007302465,0.00049670425,0.00092733506,0.0012337974,0.000637845,0.00023865864],"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":[0.00030232334,0.00012230716,0.27046305,0.00017611466,0.00025743464,0.00025541056,0.00051339215,0.404962,0.01293133,0.019830965,0.0009771591,0.28920856],"study_design_scores_gemma":[0.000026319265,0.00019247168,0.06423917,0.00005262726,0.00006438993,0.00020876848,0.00016939768,0.90142345,0.011500295,0.01988214,0.002172857,0.00006820906],"about_ca_topic_score_codex":0.0056409244,"about_ca_topic_score_gemma":0.008078868,"teacher_disagreement_score":0.006128207,"about_ca_system_score_codex":0.00046453378,"about_ca_system_score_gemma":0.0007734969,"threshold_uncertainty_score":0.03240943},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014272438,0.026067223,0.0033779365,0.9208058,0.047346957,0.000090137204,0.00033238923,0.000066758585,0.0017702025],"genre_scores_gemma":[0.0064959503,0.040058326,0.00881226,0.79227084,0.14369369,0.0008185953,0.00037069523,0.00036055894,0.007119123],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9159339,0.053822532,0.008656525,0.004667321,0.015782034,0.0011377579],"domain_scores_gemma":[0.69088817,0.25195947,0.007472583,0.0070396047,0.03709041,0.005549832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16923697,0.0012950037,0.0036970887,0.0049654813,0.0031763872,0.0060500735,0.0063275243,0.020605505,0.02107277],"category_scores_gemma":[0.32696643,0.0016905417,0.0073785027,0.005962253,0.0050832797,0.010821225,0.005500122,0.03116126,0.0055978107],"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":[0.00022594682,0.000022544455,0.00024146418,0.0019634475,0.000381981,0.00014934046,0.00028466797,0.00039243174,0.00016346342,0.014517222,0.95108813,0.030569186],"study_design_scores_gemma":[0.00041745004,0.000088849905,0.0007231314,0.008694497,0.00051413284,0.00034449503,0.00014390137,0.0007101931,0.00047496308,0.06326788,0.92447823,0.00014216005],"about_ca_topic_score_codex":0.0056697386,"about_ca_topic_score_gemma":0.004311381,"teacher_disagreement_score":0.16923697,"about_ca_system_score_codex":0.0058856145,"about_ca_system_score_gemma":0.012328986,"threshold_uncertainty_score":0.8950214},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048643635,0.00028877336,0.9941755,0.00019836506,0.000019625111,0.000048475064,0.00009556824,0.0001080982,0.00020140279],"genre_scores_gemma":[0.20366442,0.0011021184,0.79200953,0.00021578625,0.00018700444,0.0007690902,0.00060288043,0.00010089057,0.0013482913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97947294,0.01605397,0.00073958276,0.0019571492,0.001485744,0.00029051417],"domain_scores_gemma":[0.8924818,0.09144476,0.005622754,0.008586921,0.001528555,0.0003352697],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031160885,0.0010001808,0.0022295974,0.0023602848,0.00061823067,0.0016389084,0.0032337322,0.0020223015,0.0033419],"category_scores_gemma":[0.13570856,0.001160989,0.0023194463,0.0032309853,0.0021689485,0.0024572741,0.003099439,0.0032593498,0.00048536883],"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":[0.0002246007,0.00013114137,0.015544789,0.00060360425,0.0013922667,0.00039773254,0.0006536939,0.17940319,0.0016713813,0.5937175,0.0019150351,0.20434497],"study_design_scores_gemma":[0.0000850668,0.0001630028,0.0034296915,0.00012674154,0.0002269229,0.0001694098,0.00008661092,0.51326543,0.00095948443,0.47760615,0.0038335172,0.00004801317],"about_ca_topic_score_codex":0.0036699208,"about_ca_topic_score_gemma":0.0033494572,"teacher_disagreement_score":0.9688391,"about_ca_system_score_codex":0.0011282907,"about_ca_system_score_gemma":0.0021317168,"threshold_uncertainty_score":0.16479653},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057405336,0.00015964452,0.99281377,0.000075940814,0.00001824698,0.000099204684,0.0002399679,0.00035950055,0.00049324974],"genre_scores_gemma":[0.24513692,0.00035131,0.7493825,0.00023950294,0.00007487465,0.0014392457,0.0010784881,0.0005222539,0.0017747998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.980272,0.015416599,0.00056960207,0.0014811413,0.0019468456,0.00031381022],"domain_scores_gemma":[0.92496616,0.061993897,0.0037286493,0.0067984,0.0021820904,0.00033082615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030177996,0.0012520046,0.0021365688,0.0024823651,0.00050072063,0.002113334,0.0036535522,0.0015751212,0.0059597692],"category_scores_gemma":[0.09387301,0.0007612801,0.0034225918,0.0017321167,0.0017495763,0.001984485,0.0036418163,0.0033879313,0.0006864481],"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":[0.0010546218,0.00026664618,0.013106212,0.0009162669,0.0015257729,0.0004917577,0.00057832163,0.5343896,0.0051603755,0.18368158,0.00469264,0.25413623],"study_design_scores_gemma":[0.0000621318,0.00033388115,0.0028454345,0.000076569624,0.00010861254,0.00022687067,0.00007212435,0.89058304,0.0025072116,0.098569445,0.0045601237,0.00005462381],"about_ca_topic_score_codex":0.0022044333,"about_ca_topic_score_gemma":0.0013070331,"teacher_disagreement_score":0.030177996,"about_ca_system_score_codex":0.0011707352,"about_ca_system_score_gemma":0.0016859255,"threshold_uncertainty_score":0.15959841},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021795903,0.00543423,0.13714531,0.8310286,0.010528653,0.00009521557,0.00044736534,0.00017844119,0.012962604],"genre_scores_gemma":[0.15298833,0.008485967,0.14776643,0.6078957,0.05067994,0.0010087225,0.00033666258,0.0005814601,0.030256849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98385066,0.009220156,0.00088842044,0.0024359857,0.003112834,0.0004918835],"domain_scores_gemma":[0.94502383,0.046040256,0.0010641599,0.0030360736,0.0041713086,0.00066437095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029992837,0.00091373565,0.0013682449,0.0011462561,0.002910437,0.005183407,0.0055936105,0.0123089105,0.0080570625],"category_scores_gemma":[0.08814289,0.00069786684,0.0022485533,0.0017490534,0.009975255,0.0124645,0.0029095032,0.019147513,0.002230506],"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":[0.00008439406,0.00003243287,0.00054888014,0.0001272993,0.00006864218,0.00022385894,0.00051745074,0.0026454153,0.00032165667,0.78577846,0.18690188,0.022749657],"study_design_scores_gemma":[0.00004245256,0.000029593817,0.0005162078,0.00019548654,0.000029063354,0.00016811864,0.00019653953,0.012426249,0.00095743395,0.8542523,0.13108389,0.00010259123],"about_ca_topic_score_codex":0.007168227,"about_ca_topic_score_gemma":0.0044403267,"teacher_disagreement_score":0.029992837,"about_ca_system_score_codex":0.0031406363,"about_ca_system_score_gemma":0.002457031,"threshold_uncertainty_score":0.15861917},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01586758,0.0025786837,0.9772536,0.0009853132,0.0005359329,0.000671781,0.00030066576,0.00023990766,0.001566549],"genre_scores_gemma":[0.41151962,0.0037768625,0.57256365,0.0017700387,0.0009721334,0.004807749,0.000514159,0.00017671588,0.0038990055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8206215,0.15457126,0.0035356542,0.01124604,0.008837428,0.0011880832],"domain_scores_gemma":[0.6271744,0.31221503,0.02253755,0.031683184,0.005226981,0.0011629018],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14790088,0.0016547715,0.0030257283,0.0023667961,0.001572549,0.0026304515,0.00446147,0.00570014,0.007454223],"category_scores_gemma":[0.30768254,0.0012659144,0.0040587015,0.0035750126,0.0059838914,0.0030299544,0.0032608188,0.0049749115,0.00066292076],"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":[0.0023344913,0.00057967723,0.029977929,0.0013962343,0.002736639,0.0021630581,0.003401709,0.086740375,0.001391723,0.7132299,0.0053565158,0.1506917],"study_design_scores_gemma":[0.0013676446,0.0022953092,0.008833425,0.0007789238,0.0014148867,0.0018825303,0.00037715363,0.2646155,0.0015766313,0.69576657,0.020802917,0.00028840298],"about_ca_topic_score_codex":0.0029131449,"about_ca_topic_score_gemma":0.001513284,"teacher_disagreement_score":0.8520991,"about_ca_system_score_codex":0.0020323647,"about_ca_system_score_gemma":0.002216836,"threshold_uncertainty_score":0.78218395},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010024401,0.00008412665,0.98907965,0.000112022455,0.000022687771,0.00018448537,0.000046986985,0.00018481466,0.00026077518],"genre_scores_gemma":[0.33912644,0.000241003,0.6559166,0.00035501766,0.00007156817,0.0020085806,0.00029407084,0.00009009558,0.0018966987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9871265,0.009932895,0.00030568507,0.0012969532,0.00091959897,0.0004183243],"domain_scores_gemma":[0.9796769,0.01595146,0.0012278176,0.0015059434,0.0012067071,0.0004311811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018056696,0.0013780055,0.0025436298,0.0011888122,0.00065672415,0.0010707648,0.0022620503,0.0020347696,0.0035640122],"category_scores_gemma":[0.038513996,0.00084626523,0.0018415177,0.0011152528,0.001840941,0.0015154963,0.0026774246,0.002152402,0.000613678],"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":[0.0029852453,0.0005647951,0.005999312,0.00039250863,0.00066760584,0.00030918428,0.00039137888,0.6098343,0.009463528,0.11909948,0.001925787,0.24836688],"study_design_scores_gemma":[0.0003421468,0.00081113185,0.0009285608,0.00004082571,0.00014887721,0.00006797303,0.000017903974,0.9549157,0.002465971,0.03889,0.0013115096,0.000059328537],"about_ca_topic_score_codex":0.0020394274,"about_ca_topic_score_gemma":0.0015720295,"teacher_disagreement_score":0.018056696,"about_ca_system_score_codex":0.00089095737,"about_ca_system_score_gemma":0.002518907,"threshold_uncertainty_score":0.09549403},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016133986,0.0004713387,0.9806111,0.0009044677,0.000060289014,0.00026177044,0.00041582604,0.00013599277,0.001005291],"genre_scores_gemma":[0.5227661,0.0015228352,0.46532118,0.0007307383,0.00033379116,0.0037962662,0.0009622087,0.00009631622,0.0044704718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.974116,0.019996047,0.0006148399,0.0031215244,0.0014323711,0.00071919226],"domain_scores_gemma":[0.9464211,0.046451226,0.0027760027,0.0025873412,0.0013301508,0.00043421352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032608744,0.0016578978,0.0030284554,0.0019262488,0.0010822157,0.0021745586,0.0045080674,0.002351412,0.0071377824],"category_scores_gemma":[0.06475741,0.0012084282,0.0032620789,0.0027060388,0.0031842934,0.004141755,0.0035151877,0.0040180506,0.0007116843],"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":[0.00056427304,0.00038985445,0.014773486,0.00059469434,0.001742464,0.0005705207,0.0016247414,0.13261949,0.0011211416,0.74889445,0.0023242184,0.094780564],"study_design_scores_gemma":[0.00022946064,0.00030051376,0.0035120656,0.00011010041,0.0005288604,0.00018834801,0.0002156419,0.49436048,0.0003567427,0.49719334,0.002909602,0.00009488705],"about_ca_topic_score_codex":0.0073123113,"about_ca_topic_score_gemma":0.005409539,"teacher_disagreement_score":0.032608744,"about_ca_system_score_codex":0.0017992287,"about_ca_system_score_gemma":0.003127904,"threshold_uncertainty_score":0.17245358},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004995659,0.00009260219,0.9928686,0.00040960178,0.000023795139,0.000032231503,0.0003982993,0.0005186243,0.0006606324],"genre_scores_gemma":[0.30237466,0.00053269806,0.6873981,0.00068580126,0.00021043084,0.00053525466,0.0031784414,0.00075515197,0.0043294686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939073,0.0041524577,0.00021331635,0.0010430899,0.00048444362,0.00019948227],"domain_scores_gemma":[0.93580526,0.056719232,0.0016783567,0.004043532,0.0013057693,0.0004478175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012746082,0.001300719,0.0015935905,0.0029687595,0.0011005653,0.0031113385,0.0031894965,0.002109713,0.008652767],"category_scores_gemma":[0.07801226,0.0014214708,0.0027369342,0.0025085432,0.0021028437,0.0045769657,0.0036160822,0.0059205163,0.0015495945],"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":[0.00010995839,0.00006782595,0.006112116,0.00019755964,0.00022096804,0.0002897583,0.00034625988,0.7059689,0.0003879527,0.23636262,0.005051865,0.04488423],"study_design_scores_gemma":[0.00001728406,0.0000069027506,0.00029616687,0.00002398119,0.000016158723,0.000029384693,0.00002596885,0.80691224,0.0001135747,0.19128375,0.0012645193,0.000010048271],"about_ca_topic_score_codex":0.019562854,"about_ca_topic_score_gemma":0.025736846,"teacher_disagreement_score":0.019562854,"about_ca_system_score_codex":0.0020744246,"about_ca_system_score_gemma":0.0020507593,"threshold_uncertainty_score":0.0674085},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2033739,0.004299819,0.7875643,0.0010032177,0.0001180139,0.00021716287,0.0014856674,0.0004650217,0.001472818],"genre_scores_gemma":[0.91913724,0.00089802296,0.07716098,0.0001784884,0.00014454222,0.00025266592,0.0015774561,0.000050801893,0.000599827],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899597,0.0056240247,0.0010495238,0.0013239463,0.0016427401,0.00040011678],"domain_scores_gemma":[0.78435487,0.17845742,0.0174166,0.013224323,0.005725605,0.0008211469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035802837,0.00088909804,0.0012787015,0.004265214,0.00041734046,0.0014724048,0.0017166284,0.0019145428,0.0013604267],"category_scores_gemma":[0.17736366,0.00029185467,0.0013951549,0.0025852912,0.0016773719,0.0028309247,0.0018464448,0.0021809682,0.00030439848],"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":[0.0012176794,0.0002878073,0.34463266,0.0007965531,0.0021007883,0.00036895677,0.00093001116,0.3091381,0.003407884,0.048288688,0.0023280967,0.28650272],"study_design_scores_gemma":[0.000095548385,0.000873572,0.117408745,0.0003859508,0.0007033414,0.0008244688,0.00026748248,0.7951903,0.004324743,0.07636024,0.0033760273,0.00018952132],"about_ca_topic_score_codex":0.0014429793,"about_ca_topic_score_gemma":0.0011022467,"teacher_disagreement_score":0.035802837,"about_ca_system_score_codex":0.0010464047,"about_ca_system_score_gemma":0.0008461879,"threshold_uncertainty_score":0.18934578},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004136374,0.00011969395,0.9951238,0.00016875398,0.000028207807,0.00009977834,0.000072684226,0.00011536482,0.00013534866],"genre_scores_gemma":[0.19090782,0.0005024629,0.8037469,0.00049704144,0.00018461306,0.001585348,0.0006598691,0.00015887481,0.0017571329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9733368,0.02149316,0.0006917476,0.0026963833,0.0012610076,0.0005207864],"domain_scores_gemma":[0.89971954,0.08724231,0.0038879353,0.006614067,0.0020033913,0.000532782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038919773,0.0016742423,0.0030970913,0.0018439542,0.0006658216,0.0012814678,0.004479415,0.0027078176,0.0038256866],"category_scores_gemma":[0.11378364,0.0011586121,0.0033362776,0.0018083318,0.003700266,0.0021527256,0.0030118406,0.0039009505,0.0005762433],"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":[0.0008055767,0.00032871254,0.017035617,0.001048691,0.00183797,0.000972659,0.0008651268,0.3600193,0.0033072894,0.42045972,0.004445833,0.18887362],"study_design_scores_gemma":[0.00022210264,0.0003568478,0.002680577,0.000114063456,0.00027477904,0.00025865567,0.000058972917,0.833454,0.0012848916,0.15826711,0.0029649008,0.00006310673],"about_ca_topic_score_codex":0.004142902,"about_ca_topic_score_gemma":0.0033580002,"teacher_disagreement_score":0.038919773,"about_ca_system_score_codex":0.0012282892,"about_ca_system_score_gemma":0.003296205,"threshold_uncertainty_score":0.20582992},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003644667,0.00023519881,0.99550533,0.00012062316,0.000034174536,0.000056749657,0.00011226161,0.00012576027,0.00016524617],"genre_scores_gemma":[0.17746602,0.0009955672,0.8159903,0.00024656192,0.00021918691,0.0010335707,0.0011447924,0.00024912474,0.0026548724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9845733,0.010603623,0.000574769,0.002827936,0.0010008703,0.00041957502],"domain_scores_gemma":[0.9581799,0.034473263,0.0023626336,0.0030222274,0.0016604372,0.00030157983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024678461,0.0020989068,0.0026821168,0.002806078,0.001346953,0.0030448807,0.0041793673,0.0031183567,0.0028765614],"category_scores_gemma":[0.06980502,0.0016476233,0.0039607445,0.0033880554,0.002760993,0.0036174862,0.0027898797,0.0050341045,0.0009579358],"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":[0.00047372855,0.00017204195,0.01598892,0.0007128565,0.0013735467,0.00044595616,0.001645295,0.4479263,0.0023963465,0.29609495,0.0044138483,0.22835615],"study_design_scores_gemma":[0.00005256545,0.00008007901,0.0026195757,0.000112153204,0.0002009007,0.00013787727,0.00012992616,0.7997363,0.0007578172,0.19181633,0.0042666267,0.000089880574],"about_ca_topic_score_codex":0.011474308,"about_ca_topic_score_gemma":0.017179277,"teacher_disagreement_score":0.024678461,"about_ca_system_score_codex":0.0021261445,"about_ca_system_score_gemma":0.0029200187,"threshold_uncertainty_score":0.13051373},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027260765,0.00028286863,0.9708629,0.0003883477,0.000025976962,0.00010829269,0.00011019754,0.00013083014,0.0008298327],"genre_scores_gemma":[0.62209576,0.0005297691,0.37471297,0.0003062687,0.00008259682,0.00045973837,0.000397205,0.000054227556,0.0013615234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879183,0.008292498,0.00062298303,0.001650138,0.0011494623,0.00036665483],"domain_scores_gemma":[0.9352038,0.054952335,0.0046480545,0.0034478419,0.0014413401,0.00030662277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017744383,0.0006038374,0.0022257143,0.0012300911,0.00043852418,0.0017201419,0.0015475065,0.0017046105,0.0013749807],"category_scores_gemma":[0.0996581,0.0007566571,0.0012192781,0.0011798836,0.001600872,0.0017475653,0.0015223736,0.0023701682,0.00024201653],"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":[0.00050671387,0.00020343352,0.021643598,0.00029135324,0.00044954524,0.00021230236,0.00039210558,0.7045618,0.0018178,0.10491879,0.0012189738,0.16378354],"study_design_scores_gemma":[0.000069930764,0.0001299733,0.0028959245,0.000074022304,0.0000684505,0.000076019525,0.000040888553,0.9124233,0.0012667318,0.08180471,0.0011140152,0.00003606369],"about_ca_topic_score_codex":0.0049093408,"about_ca_topic_score_gemma":0.0025986333,"teacher_disagreement_score":0.017744383,"about_ca_system_score_codex":0.0015511039,"about_ca_system_score_gemma":0.0025309466,"threshold_uncertainty_score":0.09384239},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013530565,0.00019001606,0.9850172,0.00021280142,0.000021093696,0.00005179702,0.00008258508,0.00042019255,0.00047383283],"genre_scores_gemma":[0.59087366,0.0004795851,0.40238252,0.00056818523,0.00025934915,0.00044679895,0.00135148,0.00025416876,0.0033842095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972887,0.001404288,0.00013770814,0.0005733471,0.0004439979,0.00015197758],"domain_scores_gemma":[0.98549116,0.009922135,0.0011533522,0.0016869637,0.0014133553,0.0003331162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056079994,0.0010211845,0.0021461137,0.0008969966,0.0008254117,0.0011611638,0.0030128877,0.0015007656,0.001666868],"category_scores_gemma":[0.019246839,0.0006899807,0.0011797465,0.0007969198,0.001673025,0.0020146815,0.0022077323,0.0029537068,0.0007152893],"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":[0.00026275963,0.0002263182,0.004292351,0.00023871027,0.00018924783,0.0001503908,0.00029606136,0.78666973,0.002414001,0.037857786,0.0035328232,0.16386986],"study_design_scores_gemma":[0.000007319529,0.000016313841,0.00012994013,0.000006335781,0.000005411183,0.000014540416,0.0000053611116,0.9895191,0.00029924512,0.009823102,0.00016626809,0.0000070277324],"about_ca_topic_score_codex":0.0045894957,"about_ca_topic_score_gemma":0.005578134,"teacher_disagreement_score":0.0056079994,"about_ca_system_score_codex":0.0011122645,"about_ca_system_score_gemma":0.0023466742,"threshold_uncertainty_score":0.029658258},"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":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09217112,0.002664792,0.80589217,0.003017211,0.0023408262,0.084565304,0.0009117894,0.0010743362,0.007362401],"genre_scores_gemma":[0.38734573,0.0008744249,0.46314597,0.0018139302,0.00044886922,0.14248091,0.00032879206,0.00010482881,0.0034565248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90710366,0.08005129,0.0023002902,0.005646519,0.0037182607,0.0011801127],"domain_scores_gemma":[0.9147012,0.058441687,0.009072025,0.012541729,0.0030075973,0.0022358217],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09402713,0.0019033046,0.0037959537,0.0012541375,0.0007970447,0.0024361857,0.0023994562,0.004058626,0.007736764],"category_scores_gemma":[0.13463928,0.0011429214,0.0027336085,0.0010052191,0.0035615445,0.0026429147,0.0023206705,0.004716972,0.0015821817],"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":[0.20585123,0.0054227835,0.012444898,0.0077347974,0.004929935,0.0011994152,0.0013466438,0.09932005,0.012173538,0.29152858,0.010430361,0.3476177],"study_design_scores_gemma":[0.1265637,0.11007932,0.011239289,0.0015424796,0.0045231786,0.0009316569,0.00033580486,0.2845739,0.011444897,0.41427422,0.033928987,0.0005625545],"about_ca_topic_score_codex":0.00029526887,"about_ca_topic_score_gemma":0.00029756443,"teacher_disagreement_score":0.90597284,"about_ca_system_score_codex":0.0011584172,"about_ca_system_score_gemma":0.0030407696,"threshold_uncertainty_score":0.49726897},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024125418,0.0006505541,0.9927314,0.00047226693,0.0001388997,0.0019552913,0.00014883741,0.00021459257,0.0012757014],"genre_scores_gemma":[0.13096704,0.0009297514,0.8483151,0.000822159,0.00017256544,0.016842127,0.00036494515,0.00031913133,0.0012671641],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90192926,0.08545875,0.004192848,0.0031639969,0.004756873,0.0004982147],"domain_scores_gemma":[0.76081747,0.21498723,0.0069779414,0.012284873,0.0044438606,0.00048868574],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12633142,0.0014262212,0.0025327406,0.0026680937,0.00057092815,0.0016990853,0.0027081924,0.0022318657,0.00956528],"category_scores_gemma":[0.36083362,0.00097107876,0.0027985398,0.0024203358,0.0019940254,0.002913505,0.002618829,0.0036154238,0.0013222635],"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":[0.004379711,0.00035365,0.008624284,0.0058370377,0.0029220376,0.000579403,0.0010079049,0.076499015,0.0021332516,0.3858406,0.013560298,0.4982628],"study_design_scores_gemma":[0.0030789159,0.0032980812,0.004636233,0.0027073938,0.0015752768,0.00095029536,0.00030089726,0.43971607,0.0072581936,0.5018491,0.034472637,0.00015681607],"about_ca_topic_score_codex":0.00044808336,"about_ca_topic_score_gemma":0.0003539378,"teacher_disagreement_score":0.87366855,"about_ca_system_score_codex":0.0012062553,"about_ca_system_score_gemma":0.0026609492,"threshold_uncertainty_score":0.6681124},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003505833,0.000085990236,0.9959655,0.000102659724,0.000011688534,0.000014052001,0.000017980377,0.000077094235,0.00021924207],"genre_scores_gemma":[0.18451889,0.0005706948,0.81197006,0.00020929647,0.00012609301,0.00031084457,0.00016646167,0.00015332726,0.0019742926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907637,0.007209725,0.0002135397,0.0004980877,0.0011034583,0.00021141347],"domain_scores_gemma":[0.98349804,0.011154464,0.001284778,0.0025286225,0.0013244264,0.00020959748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013868767,0.0009594474,0.0013373793,0.0014678157,0.00048692472,0.00093573204,0.0022260414,0.0014640166,0.0015169386],"category_scores_gemma":[0.03138488,0.0006853881,0.0020578708,0.0018057507,0.0015699795,0.0015841856,0.002556818,0.0025494448,0.00044049328],"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":[0.00024212933,0.00012885417,0.0047868057,0.00028927674,0.000414705,0.00021958667,0.00043636787,0.4595779,0.0054635503,0.3458694,0.0020491353,0.18052235],"study_design_scores_gemma":[0.00005180873,0.00012133781,0.00084252254,0.00003910034,0.00006523318,0.00010077925,0.00002683194,0.89424384,0.0013354501,0.09922886,0.0039122445,0.000031959033],"about_ca_topic_score_codex":0.0018451104,"about_ca_topic_score_gemma":0.0018134157,"teacher_disagreement_score":0.013868767,"about_ca_system_score_codex":0.0006308543,"about_ca_system_score_gemma":0.00221554,"threshold_uncertainty_score":0.0733459},"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":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006641867,0.005372209,0.9842794,0.0013129994,0.000106104904,0.00049962796,0.0007407313,0.00036867356,0.0006784393],"genre_scores_gemma":[0.26557526,0.0045842705,0.72103345,0.0015316311,0.00033957575,0.0029465032,0.0018905906,0.00022945837,0.001869224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9657267,0.03018133,0.0009352937,0.0017237975,0.0011230195,0.0003099332],"domain_scores_gemma":[0.917594,0.07458638,0.0022435216,0.0038707303,0.0013156636,0.0003896857],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05983189,0.0021371667,0.005927497,0.0036500562,0.00083940953,0.003149943,0.0037588654,0.0027699473,0.0039523235],"category_scores_gemma":[0.104192294,0.0023928792,0.009597107,0.0033609935,0.0011926771,0.002799203,0.0025732168,0.003989405,0.0005698275],"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":[0.0023493017,0.00018672473,0.00923986,0.003264964,0.020775203,0.00040219192,0.00026218966,0.7549501,0.0012498235,0.06841055,0.0073102512,0.1315988],"study_design_scores_gemma":[0.0008268158,0.00032232353,0.0020872746,0.00043780616,0.0065088817,0.0001553817,0.000032085594,0.82609314,0.0005547372,0.15868388,0.0042003286,0.00009744937],"about_ca_topic_score_codex":0.0071674967,"about_ca_topic_score_gemma":0.010755717,"teacher_disagreement_score":0.9401681,"about_ca_system_score_codex":0.0018033235,"about_ca_system_score_gemma":0.0050283396,"threshold_uncertainty_score":0.31642503},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014630473,0.00015636532,0.9843179,0.00018141222,0.000025706177,0.000046095778,0.00017527424,0.00019850873,0.00026832064],"genre_scores_gemma":[0.51825833,0.00088543457,0.47381917,0.00023994627,0.00013353105,0.00058764254,0.0014462472,0.00017706834,0.004452614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99618965,0.0027148938,0.00013287929,0.00050008827,0.00031795507,0.00014464783],"domain_scores_gemma":[0.9862264,0.011507704,0.0007292919,0.00082520046,0.0005585423,0.00015290681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007964259,0.0008557323,0.0011166895,0.0012230729,0.00038383182,0.0010372482,0.002189768,0.0010808598,0.003200312],"category_scores_gemma":[0.025412742,0.00057572953,0.0023346595,0.0017899078,0.0010728253,0.0011158261,0.0018815598,0.0026129882,0.00053197955],"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":[0.00025399495,0.00013542586,0.02052473,0.00030117057,0.00039749072,0.00035744772,0.00029347226,0.7926345,0.001373428,0.067843646,0.0018645108,0.11402014],"study_design_scores_gemma":[0.000025758862,0.00006351354,0.0019164967,0.000028570903,0.000035542347,0.00004544323,0.00003622494,0.9650437,0.000225328,0.03148841,0.0010697737,0.000021290407],"about_ca_topic_score_codex":0.01186986,"about_ca_topic_score_gemma":0.014348471,"teacher_disagreement_score":0.01186986,"about_ca_system_score_codex":0.0006216597,"about_ca_system_score_gemma":0.0019542957,"threshold_uncertainty_score":0.042119503},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010501465,0.0006746759,0.98684186,0.0009584763,0.000092452145,0.00012044186,0.00024570958,0.00017709343,0.00038768494],"genre_scores_gemma":[0.19896956,0.0026845033,0.78920466,0.0011774676,0.00041410734,0.0015758821,0.0016962627,0.0002688231,0.0040086964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9779181,0.018000504,0.0008095234,0.001659385,0.0012043432,0.00040812502],"domain_scores_gemma":[0.92985946,0.054278407,0.005131891,0.00781091,0.0026300661,0.00028919033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040986177,0.00092964753,0.0026324687,0.0015017277,0.0004486311,0.0017662874,0.0039838743,0.0019077328,0.003189718],"category_scores_gemma":[0.16419347,0.0010352392,0.0026838174,0.0038589123,0.0012198008,0.0039674374,0.0017749597,0.004087736,0.0007800611],"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":[0.00034804954,0.00024145824,0.05994097,0.0009477393,0.002437728,0.00036079288,0.0012199893,0.10597974,0.0011519267,0.418256,0.010501665,0.39861396],"study_design_scores_gemma":[0.0001582732,0.00020473507,0.013129927,0.0004304786,0.00066049566,0.0002108586,0.00022343364,0.46032476,0.00066039397,0.51028574,0.013593029,0.00011782746],"about_ca_topic_score_codex":0.010654737,"about_ca_topic_score_gemma":0.010454282,"teacher_disagreement_score":0.040986177,"about_ca_system_score_codex":0.0010129971,"about_ca_system_score_gemma":0.0020246636,"threshold_uncertainty_score":0.21675825},"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":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003082643,0.00018871333,0.99521905,0.00008359493,0.00008755387,0.0009112579,0.0000617241,0.00011748169,0.0002480027],"genre_scores_gemma":[0.09593517,0.0003224868,0.8972825,0.00025871876,0.00012657883,0.0052865795,0.00023019438,0.00005066953,0.0005070694],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9093697,0.08136708,0.0018451861,0.003778033,0.0031044283,0.0005356778],"domain_scores_gemma":[0.908688,0.073856644,0.004354904,0.008631731,0.0037181445,0.000750561],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.071007565,0.0018609787,0.003089705,0.002230316,0.0010518308,0.0016920253,0.003513663,0.003075228,0.005642441],"category_scores_gemma":[0.110437095,0.001484227,0.003648172,0.0024803,0.0028629384,0.0019893327,0.0025900714,0.0036026083,0.00086217106],"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":[0.0043899375,0.0006705941,0.018497625,0.002336939,0.002505304,0.0007288108,0.0017816946,0.23759085,0.0066153654,0.41962335,0.0048629637,0.30039662],"study_design_scores_gemma":[0.0020566697,0.0034727508,0.003724911,0.00042154104,0.0008561203,0.0003823833,0.00019209311,0.729134,0.0048661893,0.23826584,0.016441828,0.00018569257],"about_ca_topic_score_codex":0.0015327886,"about_ca_topic_score_gemma":0.0013641417,"teacher_disagreement_score":0.92899245,"about_ca_system_score_codex":0.0013538697,"about_ca_system_score_gemma":0.0036146047,"threshold_uncertainty_score":0.3755284},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007869527,0.00007070656,0.99871945,0.000029638195,0.000009937431,0.000010207485,0.000018416715,0.00006937469,0.0002852838],"genre_scores_gemma":[0.15116322,0.0005271516,0.8433575,0.00014654131,0.00007207944,0.00029173738,0.00030489912,0.00025443785,0.0038823807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937683,0.00023976428,0.00002856656,0.000104547355,0.00021085318,0.000039480205],"domain_scores_gemma":[0.99859303,0.0008852161,0.0001266164,0.00007815563,0.00026344566,0.00005343711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015686465,0.0008887076,0.0009991855,0.00077060924,0.000569069,0.0006278349,0.0011033262,0.0011484917,0.0034741473],"category_scores_gemma":[0.004222335,0.0005041343,0.00084947026,0.0008959788,0.00076379324,0.00090686814,0.0012677654,0.0017234997,0.0009169211],"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":[0.00007160172,0.000050198927,0.000730051,0.00019370223,0.000079865844,0.00011951257,0.00016530888,0.8526449,0.006252943,0.05546784,0.0021458056,0.08207818],"study_design_scores_gemma":[0.0000023895177,0.000006249915,0.00005146409,0.000005309913,0.0000031213995,0.000008725443,0.000004257863,0.99525625,0.00020299427,0.003735253,0.0007181415,0.0000058373626],"about_ca_topic_score_codex":0.008709659,"about_ca_topic_score_gemma":0.0069934004,"teacher_disagreement_score":0.008709659,"about_ca_system_score_codex":0.00065305707,"about_ca_system_score_gemma":0.0016882762,"threshold_uncertainty_score":0.01731795},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010989213,0.0003035772,0.9822484,0.00042498854,0.00016156372,0.0005536315,0.000955886,0.0013520006,0.0030106555],"genre_scores_gemma":[0.3187682,0.00043015665,0.6663259,0.00042623482,0.00037386172,0.0026245709,0.0022160092,0.0008003413,0.008034676],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9647772,0.027337095,0.0010742925,0.0033697719,0.0026453072,0.0007963013],"domain_scores_gemma":[0.8552484,0.12868266,0.0039688433,0.006024834,0.0052848184,0.000790377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032778673,0.0018629247,0.0037155892,0.002012579,0.00056832563,0.0018387624,0.0033356987,0.0016182908,0.028952451],"category_scores_gemma":[0.16387607,0.0007603579,0.0025433179,0.0024730347,0.0018222106,0.002865493,0.0021076389,0.0031237036,0.004116076],"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":[0.0042158924,0.00047076665,0.027089745,0.0015553104,0.001737616,0.0016532462,0.0003721419,0.23297103,0.0025947257,0.17825064,0.023335544,0.5257533],"study_design_scores_gemma":[0.00055397546,0.0010196103,0.0044614263,0.00014626709,0.00024902713,0.0006144459,0.00007570095,0.87165016,0.0028575112,0.10745752,0.010831403,0.0000829847],"about_ca_topic_score_codex":0.0022296093,"about_ca_topic_score_gemma":0.0012311395,"teacher_disagreement_score":0.032778673,"about_ca_system_score_codex":0.0011919212,"about_ca_system_score_gemma":0.002980694,"threshold_uncertainty_score":0.17335224},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010997224,0.00025374765,0.9866939,0.00054415443,0.000021458252,0.000089658795,0.00014817141,0.00015095282,0.0011007743],"genre_scores_gemma":[0.56416684,0.0009715704,0.42706957,0.00079937075,0.00008780412,0.001054344,0.0007976428,0.00015383467,0.004898981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971969,0.001973655,0.0000987827,0.00034538907,0.00022563613,0.00015957376],"domain_scores_gemma":[0.9811921,0.016842967,0.0008381481,0.00057475135,0.0003953538,0.00015663619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008367406,0.0009139956,0.0020934888,0.0013553604,0.00046915872,0.0015576184,0.0019318893,0.0022949304,0.004548123],"category_scores_gemma":[0.033219714,0.0010052305,0.0017508144,0.0015040446,0.0016060668,0.0016865588,0.0018273349,0.002653866,0.000640411],"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":[0.00011794997,0.00005999023,0.0018708175,0.00009510159,0.00009638748,0.00005853208,0.00007757984,0.9051754,0.00027213886,0.06289456,0.0009449177,0.02833665],"study_design_scores_gemma":[0.000028713788,0.000027433087,0.00028545724,0.000026850988,0.000016925487,0.000014706605,0.00001889758,0.95154923,0.00021637388,0.047236085,0.000568045,0.000011238724],"about_ca_topic_score_codex":0.0067399316,"about_ca_topic_score_gemma":0.006131396,"teacher_disagreement_score":0.008367406,"about_ca_system_score_codex":0.0018331743,"about_ca_system_score_gemma":0.0025579834,"threshold_uncertainty_score":0.04425162},"labels":[],"label_agreement":null}]}