{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":17,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":17,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"21f556fa8269","filters":{"venue":"Japanese Journal of Statistics and Data Science"}},"results":[{"id":"W3137829607","doi":"10.1007/s42081-021-00115-1","title":"Likelihood analysis and stochastic EM algorithm for left truncated right censored data and associated model selection from the Lehmann family of life distributions","year":2021,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Model selection; Weibull distribution; Expectation–maximization algorithm; Context (archaeology); Monte Carlo method; Parametric model; Parametric statistics; Inference; Statistics; Applied mathematics; Algorithm; Maximum likelihood; Computer science; Artificial intelligence","authors":[{"name":"Debanjan Mitra","is_ca":false},{"name":"Debasis Kundu","is_ca":false},{"name":"N. Balakrishnan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06169436170705538,"gpt":0.3539762867601199,"spread":0.2922819250530646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005781381,0.0008608872,0.001843924,0.00161524,0.000797072,0.001594683,0.002850575,0.001523788,0.003989242],"category_scores_gemma":[0.01685109,0.0008313741,0.002020399,0.002012178,0.000871675,0.001975383,0.002455028,0.002715343,0.001176993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037722,"about_ca_system_score_gemma":0.002896196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00454326,"about_ca_topic_score_gemma":0.004419663,"domain_scores_codex":[0.9978071,0.001309663,0.0001366182,0.0003704373,0.0002559992,0.0001201706],"domain_scores_gemma":[0.9931426,0.005601914,0.0002686629,0.0003577577,0.0005089534,0.000120132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002958337,0.0001444549,0.004533377,0.0004137828,0.0002949226,0.0004613385,0.0004694228,0.5697032,0.002319619,0.1962746,0.005657049,0.2194324],"study_design_scores_gemma":[0.00001980711,0.00002292928,0.0003870598,0.00002306477,0.00002511247,0.0001037339,0.00002806065,0.9586926,0.0004490078,0.03914734,0.001079489,0.00002179235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003849857,0.0001419632,0.9953693,0.0001127449,0.0000116111,0.0000263374,0.00006806457,0.0001552302,0.000264812],"genre_scores_gemma":[0.1440328,0.0008013558,0.8476337,0.0002128623,0.0001505819,0.0005991424,0.002073791,0.0003158243,0.004180096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005781381,"threshold_uncertainty_score":0.03057522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997114865","doi":"10.1007/s42081-019-00068-6","title":"Multivariate transformed Gaussian processes","year":2019,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"King Abdullah University of Science and Technology","keywords":"Multivariate statistics; Univariate; Autoregressive model; Gaussian process; Multivariate normal distribution; Multivariate analysis; Multivariate t-distribution; Gaussian; Computer science; Mathematics; Statistics","authors":[{"name":"Yuan Yan","is_ca":true},{"name":"Jaehong Jeong","is_ca":false},{"name":"Marc G. Genton","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02118051389719212,"gpt":0.2841538537514697,"spread":0.2629733398542776,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00167927,0.0008143939,0.0008377602,0.001412215,0.0004264918,0.001637046,0.001018178,0.001336015,0.009544179],"category_scores_gemma":[0.007886327,0.0003583984,0.001562611,0.002113409,0.001357021,0.001832564,0.00143182,0.002210118,0.002679632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009299186,"about_ca_system_score_gemma":0.001288975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00277052,"about_ca_topic_score_gemma":0.002146967,"domain_scores_codex":[0.9987327,0.0003415438,0.0000539365,0.0003672519,0.0003774803,0.0001270501],"domain_scores_gemma":[0.9976706,0.0006645883,0.0003760098,0.0005184839,0.0006471075,0.0001231497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001785319,0.00007974287,0.001935069,0.0001122909,0.00009990291,0.0003571576,0.0002580446,0.09235168,0.007142609,0.8235098,0.006287163,0.06768817],"study_design_scores_gemma":[0.00004477975,0.00007686816,0.004483789,0.00003572874,0.00007021314,0.0005567124,0.00009744123,0.5667039,0.003056254,0.4031943,0.02158319,0.00009673939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02819184,0.0004402452,0.962045,0.0004772855,0.0004410306,0.0000562711,0.0007379065,0.000537224,0.007073239],"genre_scores_gemma":[0.7374781,0.002279395,0.1736362,0.0005114725,0.000819567,0.0003314651,0.003094064,0.0005557061,0.08129405],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009544179,"threshold_uncertainty_score":0.03192842,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084811620","doi":"10.1007/s42081-020-00088-7","title":"Analysis of cyclic recurrent event data with multiple event types","year":2020,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Center for Advancing Translational Sciences","keywords":"Predictability; Estimator; Nonparametric statistics; Gaussian process; Event (particle physics); Mathematics; Computer science; Event data; Statistics; Algorithm; Applied mathematics; Gaussian","authors":[{"name":"Chien‐Lin Su","is_ca":true},{"name":"Feng‐Chang Lin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1266690097974926,"gpt":0.4154319901281967,"spread":0.2887629803307041,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01312776,0.0009556563,0.001720524,0.002242407,0.0005880206,0.001655018,0.002714416,0.001170008,0.002564534],"category_scores_gemma":[0.04222147,0.0005597958,0.001942404,0.002488683,0.001011421,0.002324602,0.001358581,0.001690394,0.000354076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006414304,"about_ca_system_score_gemma":0.001213289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002243965,"about_ca_topic_score_gemma":0.001678079,"domain_scores_codex":[0.9944596,0.002479909,0.0004486069,0.001523438,0.0007620648,0.0003263771],"domain_scores_gemma":[0.9445381,0.04532667,0.003577674,0.004248663,0.001818469,0.0004904188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002571488,0.0005968895,0.1008263,0.0009071461,0.002308039,0.002300065,0.0006252804,0.4111111,0.01116236,0.1364541,0.003891382,0.3272458],"study_design_scores_gemma":[0.0000284923,0.00010508,0.006257852,0.00002469828,0.0001898725,0.0002545466,0.00005595594,0.9617962,0.001197443,0.02928482,0.0007703381,0.00003474735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.100539,0.0004241997,0.897095,0.0002218299,0.00008342027,0.00009304419,0.0005716199,0.0003252029,0.0006467712],"genre_scores_gemma":[0.8882176,0.0004060702,0.1060679,0.0001189638,0.0003365281,0.0002853055,0.002868359,0.0001133166,0.001586014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01312776,"threshold_uncertainty_score":0.06942707,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390143601","doi":"10.1007/s42081-023-00231-0","title":"Stein-rule M-estimation in sparse partially linear models","year":2023,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Estimator; Interpretability; Mathematics; Linear model; Applied mathematics; Extremum estimator; Estimation; Linear regression; Computer science; Statistics; M-estimator; Artificial intelligence","authors":[{"name":"Enayetur Raheem","is_ca":false},{"name":"S. Ejaz Ahmed","is_ca":true},{"name":"Shuangzhe Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1671128408076237,"gpt":0.415598605836162,"spread":0.2484857650285383,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009517005,0.001676587,0.003185751,0.001610028,0.000793074,0.001790881,0.003277767,0.00250796,0.002275067],"category_scores_gemma":[0.05139199,0.001616596,0.001752163,0.001681594,0.002546012,0.003724137,0.003341355,0.00316818,0.0009315951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009403165,"about_ca_system_score_gemma":0.002544168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004398807,"about_ca_topic_score_gemma":0.004119821,"domain_scores_codex":[0.9956875,0.002534041,0.0002606558,0.0006734488,0.0006510814,0.0001932255],"domain_scores_gemma":[0.9722421,0.02248451,0.001134838,0.001849278,0.001827947,0.0004612383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003390688,0.0001270961,0.002839518,0.0006175799,0.0003244824,0.0003710524,0.0002548398,0.6319751,0.002781338,0.2504487,0.003853705,0.1060676],"study_design_scores_gemma":[0.00001769401,0.00004293464,0.0002295505,0.00002703174,0.00002186947,0.00006029091,0.00001416642,0.932206,0.0005304887,0.06632488,0.0005032431,0.00002191578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005612897,0.0003314358,0.9932926,0.0001566976,0.00002396196,0.00001971388,0.0000576198,0.00009040494,0.0004147355],"genre_scores_gemma":[0.3742275,0.00236637,0.6128448,0.0005401426,0.0003875042,0.0004718467,0.001369007,0.000310184,0.007482525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009517005,"threshold_uncertainty_score":0.05033129,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3077243495","doi":"10.1007/s42081-020-00083-y","title":"Variance estimation procedures in the presence of singly imputed survey data: a critical review","year":2020,"lang":"en","type":"review","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; University of Ottawa","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Variance (accounting); Statistics; Econometrics; Estimation; Survey data collection; Mathematics; Economics; Accounting","authors":[{"name":"David Haziza","is_ca":true},{"name":"Audrey‐Anne Vallée","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.296091431730889,"gpt":0.505309739435327,"spread":0.209218307704438,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03997349,0.001957489,0.005935465,0.006743788,0.0006000198,0.002891559,0.00528192,0.004813004,0.002250364],"category_scores_gemma":[0.09963667,0.00181794,0.002759481,0.008995136,0.003331784,0.005393147,0.00204531,0.005936256,0.001230112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002439218,"about_ca_system_score_gemma":0.007676492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005136842,"about_ca_topic_score_gemma":0.005207989,"domain_scores_codex":[0.9872108,0.007295998,0.001659914,0.001459009,0.002203681,0.0001706695],"domain_scores_gemma":[0.8365298,0.1489062,0.002762776,0.002596044,0.008814667,0.0003905711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001618684,0.00007949323,0.001144027,0.03755013,0.001306922,0.0002035956,0.0002255824,0.002450801,0.0002563536,0.03795271,0.0283415,0.8903272],"study_design_scores_gemma":[0.0002392847,0.0004167068,0.008293395,0.07669947,0.006065021,0.002913393,0.0005006967,0.01072357,0.001683482,0.1979917,0.6938694,0.0006039584],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008214651,0.9892579,0.008306324,0.001651517,0.0004556967,0.00001075455,0.000035928,0.00001714663,0.0001825523],"genre_scores_gemma":[0.002425549,0.9815366,0.01261189,0.001310805,0.001831528,0.00005549273,0.00006971654,0.00003696091,0.0001214674],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03997349,"threshold_uncertainty_score":0.2114026,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392144211","doi":"10.1007/s42081-023-00228-9","title":"Making statistical inferences about linkage errors","year":2024,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Linkage (software); Computer science; Statistics; Psychology; Mathematics; Biology; Genetics","authors":[{"name":"Abel Dasylva","is_ca":true},{"name":"Arthur Goussanou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3100787365167909,"gpt":0.507185537428243,"spread":0.1971068009114521,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1160861,0.001176644,0.002247401,0.007824725,0.002354998,0.006625164,0.003194965,0.002797255,0.004411228],"category_scores_gemma":[0.4991921,0.001201079,0.002830019,0.007107831,0.002967085,0.008849328,0.004830549,0.005485223,0.0008463338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273849,"about_ca_system_score_gemma":0.004162137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706165,"about_ca_topic_score_gemma":0.001117112,"domain_scores_codex":[0.89307,0.07688318,0.008461421,0.01036712,0.01007207,0.001146191],"domain_scores_gemma":[0.4444105,0.4950114,0.02076681,0.02627162,0.01213843,0.001401279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001546699,0.000419465,0.1025985,0.002222966,0.007272649,0.001911892,0.005849503,0.02633574,0.00236784,0.2131213,0.01615137,0.6202021],"study_design_scores_gemma":[0.0004403553,0.0003737732,0.01987703,0.0007690692,0.002940089,0.0007337618,0.002394732,0.1280473,0.005830704,0.8237171,0.01472856,0.0001476479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04704081,0.001310042,0.940572,0.003640662,0.000754496,0.0003160146,0.0006818345,0.0008767858,0.004807317],"genre_scores_gemma":[0.5111644,0.0009976127,0.4807773,0.001902394,0.001199399,0.0008738522,0.001338928,0.0003283396,0.001417858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1160861,"threshold_uncertainty_score":0.6139295,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3174702077","doi":"10.1007/s42081-021-00129-9","title":"The XGTDL family of survival distributions","year":2021,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of Limerick; Irish Research eLibrary","keywords":"Covariate; Logistic regression; Parametric model; Parametric statistics; Extension (predicate logic); Scale (ratio); Proportional hazards model; Mathematics; Survival function; Statistics; Function (biology); Econometrics; Log-logistic distribution; Scale parameter; Survival analysis; Applied mathematics; Computer science; Probability density function; Geography; Cumulative distribution function","authors":[{"name":"Gilbert MacKenzie","is_ca":false},{"name":"Milica Bucknall","is_ca":false},{"name":"Yasin Al-tawarah","is_ca":false},{"name":"Defen Peng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1540351162558155,"gpt":0.4233803617571187,"spread":0.2693452455013031,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007980003,0.0008972351,0.001070263,0.003240656,0.0008535055,0.002442752,0.002420461,0.001198482,0.009592331],"category_scores_gemma":[0.02157396,0.0005217997,0.001885235,0.002072752,0.0021841,0.002617975,0.002562228,0.003242528,0.002817943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919659,"about_ca_system_score_gemma":0.001061286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002591687,"about_ca_topic_score_gemma":0.001200317,"domain_scores_codex":[0.9961967,0.002001617,0.0001874961,0.0005196243,0.000825798,0.0002687377],"domain_scores_gemma":[0.9868233,0.008507608,0.001075108,0.001177763,0.001919846,0.0004963267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000061789,0.00004216654,0.003419264,0.0002408773,0.00006332296,0.0006786636,0.0004598468,0.05003604,0.0006625057,0.8855696,0.00743275,0.05133325],"study_design_scores_gemma":[0.00004428613,0.00007577681,0.001405047,0.00009459762,0.00002469256,0.000946463,0.0001214639,0.5299841,0.0003411194,0.4504246,0.01649066,0.00004713991],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01428196,0.0007067188,0.978009,0.0007643435,0.00008218351,0.00008881887,0.00070086,0.0004383863,0.004927777],"genre_scores_gemma":[0.6409958,0.004270721,0.3013653,0.00107467,0.0006704128,0.001827962,0.004763449,0.0005972842,0.04443431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009592331,"threshold_uncertainty_score":0.04220277,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403890161","doi":"10.1007/s42081-024-00276-9","title":"Methodological challenges in studying disease processes using observational cohort data","year":2024,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Observational study; Disease; Psychological intervention; Cohort; Appeal; Data science; Computer science; Cohort study; Longitudinal data; Psychology; Medicine; Management science; Risk analysis (engineering); Data mining; Engineering; Pathology","authors":[{"name":"Richard J. Cook","is_ca":true},{"name":"Jerald F. Lawless","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5360041320382929,"gpt":0.4693180519617581,"spread":0.06668608007653476,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3879016,0.001452861,0.003483739,0.005096621,0.003061876,0.006443632,0.00704852,0.002596346,0.001988367],"category_scores_gemma":[0.6272157,0.001695373,0.003152358,0.01041464,0.007032946,0.005813717,0.006946926,0.007438235,0.0007043305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003368501,"about_ca_system_score_gemma":0.01019674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01854607,"about_ca_topic_score_gemma":0.01118145,"domain_scores_codex":[0.5821406,0.3552319,0.02425035,0.0135113,0.02374414,0.00112164],"domain_scores_gemma":[0.3107996,0.6108367,0.01974443,0.03826619,0.0185287,0.001824394],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007050789,0.0005484238,0.1374259,0.01361931,0.01037035,0.001669326,0.009279819,0.03384575,0.001260628,0.4130048,0.03172941,0.3465412],"study_design_scores_gemma":[0.0003657312,0.0005062729,0.04101811,0.005943961,0.001862523,0.001472284,0.004579141,0.04953665,0.0008791971,0.8231148,0.0703796,0.0003417437],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01014854,0.01678277,0.9355274,0.02625042,0.00323566,0.0026067,0.002103735,0.0002914105,0.003053308],"genre_scores_gemma":[0.2189306,0.01631452,0.7235886,0.01623805,0.005964709,0.01422282,0.002313149,0.0004222303,0.002005272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6120984,"threshold_uncertainty_score":0.7548263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417222887","doi":"10.1007/s42081-025-00322-0","title":"On multivariate binary outcomes copulas-regression problem","year":2025,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé","keywords":"Multivariate statistics; Copula (linguistics); Covariate; Binary number; Binary data; Estimator; Marginal distribution; Logistic regression; Multivariate normal distribution","authors":[{"name":"Youssef Handi","is_ca":true},{"name":"Karim Oualkacha","is_ca":true},{"name":"Mhamed Mesfioui","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1058971062364061,"gpt":0.4482954307905468,"spread":0.3423983245541407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007416962,0.001971176,0.004421043,0.001865413,0.001095306,0.00306958,0.003445993,0.003130342,0.006816578],"category_scores_gemma":[0.03066492,0.001407536,0.002668483,0.003809335,0.002236483,0.004844909,0.003345431,0.00512637,0.0008329108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002101028,"about_ca_system_score_gemma":0.00280601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006816377,"about_ca_topic_score_gemma":0.003161254,"domain_scores_codex":[0.9949922,0.00293754,0.0001515779,0.001110975,0.0004129224,0.0003948095],"domain_scores_gemma":[0.9854335,0.0112046,0.001198832,0.0006626251,0.0009370025,0.0005635701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001183176,0.0002064588,0.003234467,0.000620774,0.0004176254,0.0007527135,0.0003437849,0.124727,0.0006299416,0.8258026,0.01277438,0.03037208],"study_design_scores_gemma":[0.00005124805,0.00004626662,0.001146197,0.00008664177,0.000135479,0.0002676104,0.00009786679,0.4713695,0.0001908823,0.5239857,0.00257982,0.00004275822],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02952906,0.002721354,0.9532036,0.00530651,0.0002616473,0.0001147286,0.0007039137,0.0002363295,0.007922863],"genre_scores_gemma":[0.7035004,0.009875648,0.2358633,0.002777876,0.003236967,0.0009959897,0.003144046,0.0007145373,0.0398913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007416962,"threshold_uncertainty_score":0.0392251,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413801157","doi":"10.1007/s42081-025-00314-0","title":"Applying non-negative matrix factorization with covariates to multivariate time series data as a vector autoregression model","year":2025,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Covariate; Vector autoregression; Multivariate statistics; Autoregressive model; Series (stratigraphy); Time series; Econometrics; Statistics; Mathematics; Computer science; Biology","authors":[{"name":"Kenichi Satoh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02502657164597192,"gpt":0.3298188312038354,"spread":0.3047922595578635,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003878393,0.001035671,0.00111479,0.001142142,0.0004893967,0.001096529,0.001132649,0.00101435,0.001825588],"category_scores_gemma":[0.009332703,0.0005630213,0.001769713,0.001603697,0.0008063291,0.001413374,0.0008114533,0.001689931,0.0004474434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007248016,"about_ca_system_score_gemma":0.001694978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037627,"about_ca_topic_score_gemma":0.01173481,"domain_scores_codex":[0.9981737,0.0009681741,0.00008172503,0.000444912,0.0002303386,0.0001011341],"domain_scores_gemma":[0.9963231,0.002508909,0.0004260932,0.0002932156,0.0003597257,0.00008882174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001113301,0.0001505374,0.008633998,0.0002500345,0.0003848094,0.0003188231,0.0002775938,0.7409821,0.003944644,0.09777294,0.00578502,0.1413882],"study_design_scores_gemma":[0.000006541439,0.00001707041,0.0006358802,0.00001027865,0.00001298317,0.00001728902,0.00000982273,0.981871,0.0001616335,0.01630794,0.0009377291,0.00001181438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006119713,0.0001748166,0.9929389,0.0002010449,0.00004111676,0.00002470335,0.0001344024,0.0001485968,0.0002167963],"genre_scores_gemma":[0.3694457,0.0009551675,0.6245132,0.0003050029,0.0003742736,0.0003794355,0.00120793,0.0001299102,0.002689417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01037627,"threshold_uncertainty_score":0.02063173,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408047762","doi":"10.1007/s42081-025-00298-x","title":"Application of machine learning methods in the imputation of heterogeneous co-missing data","year":2025,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Impact","funders":"Canadian Institutes of Health Research; McLaughlin Centre, University of Toronto","keywords":"Imputation (statistics); Missing data; Computer science; Machine learning; Artificial intelligence; Data mining","authors":[{"name":"Hon Yiu So","is_ca":false},{"name":"Jinhui Ma","is_ca":true},{"name":"Lauren E. Griffith","is_ca":true},{"name":"N. Balakrishnan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04797112399033372,"gpt":0.4199266282748576,"spread":0.3719555042845239,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02806741,0.001122197,0.003168865,0.003492759,0.001658027,0.002343907,0.004461805,0.002665721,0.001907675],"category_scores_gemma":[0.067368,0.001442299,0.003457924,0.004813095,0.00141265,0.00294932,0.003504144,0.004201591,0.0005077778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008753759,"about_ca_system_score_gemma":0.003015134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004172588,"about_ca_topic_score_gemma":0.003681507,"domain_scores_codex":[0.9856114,0.01066587,0.0006743175,0.001765849,0.0009780284,0.0003045335],"domain_scores_gemma":[0.9355209,0.0548122,0.00227017,0.004473477,0.002416698,0.0005065834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000525761,0.000412528,0.01880992,0.0006174088,0.001834611,0.0005299695,0.000630206,0.58246,0.001250693,0.09638571,0.003770595,0.2927726],"study_design_scores_gemma":[0.00003346376,0.0000305238,0.0009595833,0.00005758416,0.00009269107,0.0001195341,0.00003597066,0.9381368,0.0004254449,0.05906911,0.0009998013,0.00003960344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004628391,0.0003919858,0.9944518,0.0001742239,0.00004634793,0.00002385323,0.00005464789,0.00008871395,0.0001399292],"genre_scores_gemma":[0.2027976,0.0009734678,0.7934549,0.0002267532,0.0003120004,0.0002783393,0.0007610398,0.0001477992,0.001048047],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02806741,"threshold_uncertainty_score":0.1484364,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407248782","doi":"10.1007/s42081-025-00295-0","title":"Correction: Methodological challenges in studying disease processes using observational cohort data","year":2025,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Observational study; Data science; Cohort; Computer science; Medicine; Pathology","authors":[{"name":"Richard J. Cook","is_ca":true},{"name":"Jerald F. Lawless","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.8219778875377437,"gpt":0.6023865142488891,"spread":0.2195913732888546,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08594061,0.006316737,0.0102227,0.01079901,0.00619905,0.01080294,0.01155978,0.01480792,0.1270727],"category_scores_gemma":[0.6529557,0.004928711,0.006319258,0.01946896,0.00616988,0.006097487,0.005657888,0.01916047,0.03103858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00742147,"about_ca_system_score_gemma":0.01405953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02085302,"about_ca_topic_score_gemma":0.0326518,"domain_scores_codex":[0.8812363,0.04582284,0.03203709,0.01690448,0.01866683,0.005332519],"domain_scores_gemma":[0.4462572,0.3051906,0.03905353,0.078284,0.120819,0.01039569],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003635024,0.00003058065,0.002226264,0.003011239,0.001392693,0.000889709,0.0003791844,0.0003524476,0.0001815543,0.002244735,0.9729829,0.0159452],"study_design_scores_gemma":[0.003940329,0.0002130503,0.02468221,0.01141088,0.006403296,0.004485325,0.001598616,0.01898178,0.002655851,0.05048121,0.8742867,0.0008608411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"methods","genre_scores_codex":[0.001852403,0.003190356,0.02789537,0.06625561,0.8671321,0.0007229349,0.02560406,0.004328946,0.003018212],"genre_scores_gemma":[0.1755047,0.005067293,0.1810831,0.1851252,0.302066,0.009735281,0.01779124,0.01709152,0.1065357],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9140594,"threshold_uncertainty_score":0.4545028,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405708154","doi":"10.1007/s42081-024-00286-7","title":"Correction: Applications of Population Sampling to Insurance Ratemaking and Reserving","year":2024,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Sampling (signal processing); Population; Actuarial science; Computer science; Data science; Statistics; Econometrics; Business; Mathematics; Telecommunications; Medicine; Environmental health","authors":[{"name":"Sebastián Calcetero Vanegas","is_ca":true},{"name":"X. Sheldon Lin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1130398266135744,"gpt":0.3733645178651161,"spread":0.2603246912515417,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0096357,0.002456234,0.001856106,0.004789289,0.002278513,0.003641143,0.004414734,0.00394783,0.1075301],"category_scores_gemma":[0.2500822,0.001517481,0.001913301,0.006760363,0.002070958,0.002920273,0.001948022,0.006818179,0.0395989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004690888,"about_ca_system_score_gemma":0.007971941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05768643,"about_ca_topic_score_gemma":0.05240002,"domain_scores_codex":[0.9903699,0.003283813,0.00196265,0.001148285,0.002818809,0.0004165916],"domain_scores_gemma":[0.8499146,0.05057014,0.006264785,0.01545073,0.0755097,0.002290057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004474018,0.000004029659,0.0002729556,0.0001370906,0.00003217122,0.0001057394,0.00006332657,0.0003441833,0.00002992945,0.003453246,0.9880559,0.007456637],"study_design_scores_gemma":[0.0002002267,0.00002981208,0.004214939,0.0006134689,0.0001505145,0.000566003,0.0002425938,0.00886091,0.0008149573,0.02504924,0.9591091,0.0001482727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0008700567,0.001311256,0.02198491,0.0563839,0.8956437,0.0001313179,0.01462295,0.002604354,0.006447465],"genre_scores_gemma":[0.1042321,0.005638862,0.08790354,0.06110431,0.3544438,0.001256118,0.01524255,0.009704022,0.3604746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1075301,"threshold_uncertainty_score":0.359724,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2915585695","doi":"10.1007/s42081-019-00035-1","title":"Estimation strategy of multilevel model for ordinal longitudinal data","year":2019,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; Novartis Pharmaceuticals Corporation","keywords":"Estimator; Covariate; Random effects model; Ordinal regression; Inference; Ordinal data; Statistics; Mathematics; Econometrics; Data set; Multilevel model; Hierarchical database model; Likelihood function; Computer science; Maximum likelihood; Data mining; Artificial intelligence","authors":[{"name":"Shakhawat Hossain","is_ca":true},{"name":"Ian Hiebert","is_ca":true},{"name":"Saumen Mandal","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3367694729986219,"gpt":0.473330603572065,"spread":0.136561130573443,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01590674,0.001037947,0.002762702,0.002473421,0.001427002,0.002341299,0.005211195,0.001979689,0.01178103],"category_scores_gemma":[0.04581019,0.001091093,0.004167718,0.003512319,0.0009161487,0.002810506,0.003333309,0.003995518,0.001525182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125844,"about_ca_system_score_gemma":0.00432178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01157856,"about_ca_topic_score_gemma":0.008619625,"domain_scores_codex":[0.9865211,0.009490728,0.0006198303,0.001875175,0.0009256611,0.000567472],"domain_scores_gemma":[0.9775853,0.01728543,0.0009671731,0.002209955,0.001570212,0.0003818993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006401837,0.0003883503,0.06573342,0.0009897697,0.003046772,0.001346937,0.002388707,0.09945492,0.003661446,0.5435043,0.01435687,0.2644883],"study_design_scores_gemma":[0.0002080882,0.0003034614,0.009363349,0.0001832009,0.0009220777,0.0005025111,0.0005146348,0.7340562,0.001139621,0.2458048,0.006899661,0.0001024456],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008177146,0.0001506227,0.9901358,0.0003712387,0.00005083921,0.0001269595,0.0003760863,0.0001943677,0.0004169092],"genre_scores_gemma":[0.2458773,0.0005656903,0.7432489,0.0003987046,0.0002395667,0.002918723,0.002793282,0.0002469096,0.003710958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01590674,"threshold_uncertainty_score":0.08412385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401399991","doi":"10.1007/s42081-024-00260-3","title":"Applications of Population Sampling to Insurance Ratemaking and Reserving","year":2024,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sampling (signal processing); Weighting; Population; Credibility; Econometrics; Field (mathematics); Estimator; Data mining; Statistics; Economics; Mathematics","authors":[{"name":"Sebastián Calcetero Vanegas","is_ca":true},{"name":"X. Sheldon Lin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.128485849781474,"gpt":0.4582514481752856,"spread":0.3297655983938116,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01443897,0.0004050377,0.0008560516,0.001102369,0.0005228103,0.001231011,0.001818619,0.001117797,0.002827605],"category_scores_gemma":[0.04425039,0.0003593279,0.0006534321,0.0009678999,0.001918429,0.001753392,0.002699689,0.001968915,0.0002313906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011117,"about_ca_system_score_gemma":0.001132728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002228716,"about_ca_topic_score_gemma":0.001696976,"domain_scores_codex":[0.9929199,0.004864998,0.0002134421,0.0006292303,0.001140095,0.0002324427],"domain_scores_gemma":[0.9804529,0.0148242,0.001325908,0.001755446,0.001315767,0.0003257214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001309362,0.0001134419,0.00520764,0.00009681832,0.00007336463,0.0003030859,0.0004178738,0.5489096,0.001364024,0.349363,0.001258683,0.09276153],"study_design_scores_gemma":[0.0000138544,0.0000343972,0.0004039396,0.00002071685,0.000008143881,0.00004600378,0.00004861684,0.9143888,0.0006965745,0.08324529,0.001081431,0.00001234292],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01672178,0.00009355663,0.9813409,0.0002553654,0.00002831538,0.0000493643,0.0000202442,0.00006930712,0.001421218],"genre_scores_gemma":[0.6915467,0.0002369464,0.3058043,0.0001514382,0.0001075175,0.0001706418,0.00007751516,0.00006451993,0.001840463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01443897,"threshold_uncertainty_score":0.07636148,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400260432","doi":"10.1007/s42081-024-00261-2","title":"Bayesian and minimax estimators of loss","year":2024,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Institut des Sciences Mathématiques, Université du Québec à Montréal","keywords":"Minimax; Bayesian probability; Estimator; Minimax estimator; Econometrics; Statistics; Computer science; Mathematics; Mathematical optimization; Minimum-variance unbiased estimator","authors":[{"name":"Christine Allard","is_ca":true},{"name":"Éric Marchand","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08655825488282168,"gpt":0.4197903353342878,"spread":0.3332320804514661,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02517161,0.001132007,0.002506135,0.003555914,0.0008951592,0.003952694,0.003359071,0.003311326,0.003822486],"category_scores_gemma":[0.1081668,0.00146516,0.001411375,0.002176439,0.004100034,0.009680917,0.004891692,0.004611629,0.0006330977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001909423,"about_ca_system_score_gemma":0.002692679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008016431,"about_ca_topic_score_gemma":0.000572387,"domain_scores_codex":[0.9907413,0.005469968,0.0005258202,0.001364781,0.001568202,0.0003300301],"domain_scores_gemma":[0.9271669,0.06226644,0.00297078,0.003750067,0.003066091,0.0007796169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001710301,0.00009844828,0.002149006,0.0002302328,0.0001360094,0.00006654003,0.0002529052,0.08991703,0.0005745349,0.8474997,0.003054912,0.05584968],"study_design_scores_gemma":[0.0000505187,0.00006065008,0.001166024,0.00008773398,0.00004915839,0.0001655345,0.00003588686,0.3141374,0.0005157838,0.6813534,0.002331928,0.000045901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01140639,0.0009195298,0.9850916,0.0008171856,0.00006647877,0.00002493006,0.0001172765,0.0001202067,0.001436293],"genre_scores_gemma":[0.3958747,0.003909386,0.5814008,0.0008386781,0.001377999,0.00097036,0.001343293,0.000623228,0.01366161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02517161,"threshold_uncertainty_score":0.1331218,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3042907151","doi":"10.1007/s42081-020-00084-x","title":"Empirical likelihood and estimating equations for survey data analysis","year":2020,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point estimation; Statistics; Computer science; Population; Bayesian probability; Mathematics; Econometrics; Statistical inference; Statistical hypothesis testing; Empirical likelihood; Confidence interval; Medicine","authors":[{"name":"Changbao Wu","is_ca":true},{"name":"Mary E. Thompson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3675017885248815,"gpt":0.4945654689663485,"spread":0.127063680441467,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0280734,0.001579392,0.003025637,0.003715354,0.0009501794,0.003374044,0.004248486,0.002576239,0.005333979],"category_scores_gemma":[0.1533513,0.001981157,0.002963656,0.005814987,0.003411818,0.006046788,0.003698252,0.006225415,0.001315672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002306668,"about_ca_system_score_gemma":0.00401641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007780139,"about_ca_topic_score_gemma":0.004198556,"domain_scores_codex":[0.9789216,0.0167121,0.001096956,0.001758593,0.001249056,0.0002618148],"domain_scores_gemma":[0.845294,0.1431673,0.002959582,0.005452703,0.002741411,0.0003849874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003458593,0.00004537866,0.002912691,0.0003676038,0.0002503913,0.0001614178,0.0004656396,0.0530818,0.0002092728,0.8790569,0.004302085,0.05911225],"study_design_scores_gemma":[0.00002034573,0.00001433265,0.0007942125,0.0000672892,0.00008205069,0.0001290386,0.00006092015,0.1940506,0.0001075711,0.7991706,0.005469924,0.00003307373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001511679,0.0008968629,0.9962627,0.0005006334,0.00003859311,0.00003188087,0.0002052637,0.0001094888,0.0004429852],"genre_scores_gemma":[0.1165109,0.006121821,0.8642263,0.0005607504,0.0007670231,0.00156942,0.002361622,0.0005120656,0.007370202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0280734,"threshold_uncertainty_score":0.1484681,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}