{"meta":{"query_hash":"6a52b7c41d45","filters":{"topic":"Statistical Methods and Bayesian Inference"},"cohort_total":1222,"direct_labels_cover":16,"predictions_cover":1222,"exported":1222,"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/6a52b7c41d45","api":"https://metacan.xera.ac/api/v1/cohort?topic=Statistical+Methods+and+Bayesian+Inference"},"results":[{"id":"W10119467","doi":"","title":"Using DIC to compare selection models with non-ignorable missing responses","year":2010,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"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; Model selection; Computer science; Statistics; Econometrics; Bayesian probability; Data mining; Artificial intelligence; Mathematics; Machine learning","score_opus":0.17744324881677356,"score_gpt":0.4273438123093972,"score_spread":0.24990056349262366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W10119467","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":[0.03810188,0.0035913303,0.9457575,0.0015877548,0.0010978329,0.0013355218,0.0015757609,0.0006540869,0.006298264],"genre_scores_gemma":[0.4944378,0.0025081127,0.4904697,0.0012009544,0.0008367491,0.004113422,0.0035896061,0.00049189164,0.0023517204],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.70667565,0.25850135,0.007475479,0.010939559,0.014250608,0.0021573505],"domain_scores_gemma":[0.21773389,0.74167836,0.009465442,0.021771329,0.008323879,0.0010270762],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.24368714,0.0021816015,0.004527688,0.010202354,0.0032711676,0.005181863,0.0039238264,0.003196358,0.010253885],"category_scores_gemma":[0.5374525,0.0011948296,0.0050569074,0.009558437,0.00694317,0.004974168,0.006736886,0.0057984074,0.0010712474],"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":[0.0029283557,0.00044569172,0.06229705,0.0039125104,0.018457456,0.00087142363,0.002626139,0.21759287,0.00054926623,0.28817457,0.026539354,0.37560534],"study_design_scores_gemma":[0.0006624483,0.002408054,0.024007017,0.00082482427,0.0025046943,0.00086012867,0.0015869364,0.46909693,0.0014450351,0.46657297,0.02961616,0.0004148715],"about_ca_topic_score_codex":0.006469044,"about_ca_topic_score_gemma":0.0049858615,"teacher_disagreement_score":0.24368714,"about_ca_system_score_codex":0.0043493765,"about_ca_system_score_gemma":0.005238479,"threshold_uncertainty_score":0.9326684},"labels":[],"label_agreement":null},{"id":"W1139569558","doi":"10.3233/mas-2008-3302","title":"A comparison between the design-based and model-based approaches using longitudinal survey data","year":2008,"lang":"en","type":"article","venue":"Model Assisted Statistics and Applications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Saskatchewan; Saskatchewan Health Authority; Canadian Rural Health Research Society","funders":"","keywords":"Computer science; Longitudinal data; Data science; Statistics; Data mining; Mathematics","score_opus":0.6760744834399041,"score_gpt":0.4579861542244732,"score_spread":0.21808832921543087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1139569558","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":[0.0065287617,0.0063080667,0.9799978,0.002728286,0.00045636817,0.0005314597,0.0001957982,0.00012027926,0.0031331852],"genre_scores_gemma":[0.14549373,0.007450561,0.84137493,0.0017564753,0.00039570496,0.002036566,0.00029927888,0.00018247975,0.0010103553],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.5642285,0.40461117,0.0058556967,0.006104055,0.01844842,0.0007521567],"domain_scores_gemma":[0.5386324,0.40935668,0.010079624,0.027210418,0.013603091,0.0011177874],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22608103,0.0013375984,0.0027209518,0.0061957957,0.001451444,0.0052958755,0.0050160857,0.0033923956,0.0034739494],"category_scores_gemma":[0.39513296,0.0010168053,0.003411396,0.0074201217,0.0039888327,0.005666047,0.004197298,0.0027958627,0.00061769853],"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":[0.0007229471,0.0005478703,0.020233706,0.0074919034,0.0048813922,0.00022423588,0.004163318,0.027767146,0.001010957,0.51486343,0.0072782733,0.41081485],"study_design_scores_gemma":[0.00090150273,0.0029208185,0.022380758,0.003955889,0.002395546,0.0011828475,0.0032774508,0.13747893,0.0026162781,0.7735508,0.048790548,0.00054869516],"about_ca_topic_score_codex":0.0026007425,"about_ca_topic_score_gemma":0.00413355,"teacher_disagreement_score":0.22608103,"about_ca_system_score_codex":0.0036469481,"about_ca_system_score_gemma":0.004924288,"threshold_uncertainty_score":0.95437986},"labels":[],"label_agreement":null},{"id":"W1204530477","doi":"10.71781/15713","title":"Estimation simplifiée de la variance dans le cas de l’échantillonnage à deux phases","year":2011,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Statistical Methods and Bayesian Inference","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":"Humanities; Geography; Forestry; Library science; Cartography; Art; Computer science","score_opus":0.07116262572120588,"score_gpt":0.42149450734298377,"score_spread":0.35033188162177786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1204530477","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":[0.11504046,0.00024262397,0.8817808,0.00015127547,0.000074268384,0.0000859067,0.00022610402,0.0004140466,0.001984546],"genre_scores_gemma":[0.49329558,0.0002590828,0.4995989,0.00009800091,0.00005059948,0.00026908275,0.0006477147,0.00036685687,0.005414128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99724364,0.0005256054,0.00014243566,0.0007224255,0.001070886,0.00029508033],"domain_scores_gemma":[0.99213153,0.0040164483,0.0007594587,0.00123302,0.0016499072,0.00020965337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037768127,0.001062936,0.0011883785,0.0011887426,0.0006754771,0.0021115446,0.0011981569,0.0009766918,0.003475508],"category_scores_gemma":[0.011323765,0.0008212939,0.0018580059,0.0010271638,0.00091887446,0.0014395666,0.0012809407,0.0017787768,0.00059752015],"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":[0.001040603,0.00021925889,0.044705287,0.0005280115,0.00035828908,0.00041466093,0.0012371623,0.47981504,0.095907465,0.036937185,0.002076524,0.33676055],"study_design_scores_gemma":[0.000073923526,0.0007329557,0.04446039,0.0001139735,0.00023925063,0.00024988936,0.0006432607,0.8400203,0.071807474,0.0266828,0.014750674,0.00022505374],"about_ca_topic_score_codex":0.010949281,"about_ca_topic_score_gemma":0.01278495,"teacher_disagreement_score":0.010949281,"about_ca_system_score_codex":0.001258263,"about_ca_system_score_gemma":0.0020598734,"threshold_uncertainty_score":0.021771133},"labels":[],"label_agreement":null},{"id":"W133878601","doi":"10.1177/0008068320020509","title":"Analyzying Bivariate Ordinal Polytomous Data: A Marginal Multinomial Logistic Approach","year":2002,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Categorical variable; Ordinal data; Ordinal regression; Mathematics; Covariate; Bivariate analysis; Statistics; Contingency table; Econometrics; Polytomous Rasch model; Multinomial distribution; Marginal model; Bivariate data; Copula (linguistics); Ordered logit; Regression analysis; Item response theory; Psychometrics","score_opus":0.1810029678716092,"score_gpt":0.3733018601450405,"score_spread":0.19229889227343133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W133878601","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":[0.0101446165,0.00034250657,0.9878434,0.00034046447,0.000021239039,0.00006932947,0.00025971406,0.00015873958,0.00082005357],"genre_scores_gemma":[0.28097785,0.0011098924,0.71270275,0.00023102292,0.00015163935,0.00090603746,0.0011389905,0.00021143703,0.0025704368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890497,0.008324994,0.0004156769,0.0010382409,0.00093446753,0.00023687312],"domain_scores_gemma":[0.9801261,0.013516343,0.0020839805,0.0030237774,0.0009576437,0.00029218296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011310037,0.000729086,0.0016608957,0.003036312,0.00059234624,0.0021645299,0.0020559966,0.0007924043,0.0055764946],"category_scores_gemma":[0.056492474,0.000534088,0.0016115062,0.0046184524,0.001457539,0.0028805125,0.0030759503,0.002438944,0.0012851127],"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":[0.00044900202,0.00021147692,0.05389554,0.0007608553,0.0008345506,0.00093657913,0.0024517027,0.035691064,0.0023432828,0.46285775,0.0065024747,0.43306583],"study_design_scores_gemma":[0.000039929735,0.00013137344,0.013609995,0.00019521455,0.00016666626,0.0008371356,0.00056069,0.23161592,0.0006088857,0.73815525,0.0139999995,0.0000790547],"about_ca_topic_score_codex":0.0020032264,"about_ca_topic_score_gemma":0.0024373678,"teacher_disagreement_score":0.011310037,"about_ca_system_score_codex":0.00077899854,"about_ca_system_score_gemma":0.0012918155,"threshold_uncertainty_score":0.059813917},"labels":[],"label_agreement":null},{"id":"W135411103","doi":"10.1007/978-4-431-65955-6_21","title":"Using Several Data to Structure Efficient Estimation of Intraclass Correlation Coefficients","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"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 Regina","funders":"","keywords":"Intraclass correlation; Estimation; Correlation ratio; Correlation; Statistics; Computer science; Mathematics; Correlation coefficient; Engineering; Geometry","score_opus":0.18055447722759713,"score_gpt":0.3921472758310024,"score_spread":0.21159279860340527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135411103","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":[0.0019549425,0.00009491458,0.9967229,0.00008653675,0.000030918694,0.000022352158,0.00015363623,0.00028374497,0.0006500815],"genre_scores_gemma":[0.025662385,0.00017948826,0.9712336,0.00008440782,0.00006501899,0.00018005368,0.00076554564,0.00031510968,0.0015143276],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971718,0.0012000864,0.00019114086,0.0005757122,0.00073096633,0.00013028386],"domain_scores_gemma":[0.9846554,0.0084108,0.0007022265,0.00424905,0.0017843028,0.00019819064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058807484,0.0015196587,0.0018969607,0.0031838182,0.0012106973,0.0027538368,0.0029351441,0.0018486892,0.004997891],"category_scores_gemma":[0.035249986,0.0014863746,0.0021101728,0.0034751364,0.0015058983,0.0045778276,0.002858772,0.004865607,0.00303774],"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":[0.00015220827,0.00018912679,0.003678038,0.00032398806,0.00029970112,0.00019102376,0.0004465291,0.09806158,0.00913546,0.30305475,0.014040128,0.5704274],"study_design_scores_gemma":[0.0000371545,0.000036525136,0.0021069592,0.00006481436,0.000089128334,0.00017970763,0.000060911265,0.5192508,0.00569842,0.46105117,0.0113554625,0.0000688934],"about_ca_topic_score_codex":0.0036971818,"about_ca_topic_score_gemma":0.00675657,"teacher_disagreement_score":0.0058807484,"about_ca_system_score_codex":0.0010547045,"about_ca_system_score_gemma":0.001837611,"threshold_uncertainty_score":0.03110069},"labels":[],"label_agreement":null},{"id":"W1451217718","doi":"10.1016/j.jmva.2015.07.013","title":"On predictive density estimation for location families under integrated squared error loss","year":2015,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Simons Foundation","keywords":"Mathematics; Estimator; Mean squared error; Minimax; Statistics; Equivariant map; Bayes estimator; Minimax estimator; Scale parameter; Density estimation; Minimum-variance unbiased estimator; Applied mathematics; Mathematical optimization; Pure mathematics","score_opus":0.08742236911577866,"score_gpt":0.404138910699478,"score_spread":0.31671654158369933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1451217718","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":[0.0065388107,0.00050295395,0.99152815,0.00040908428,0.000026898124,0.00005176133,0.00012048984,0.0001329111,0.0006890301],"genre_scores_gemma":[0.44968593,0.008359004,0.5197642,0.0013560505,0.000934881,0.0012785725,0.0035958504,0.0008719685,0.014153541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919898,0.004952038,0.00033468366,0.0010725586,0.0011730846,0.00047779916],"domain_scores_gemma":[0.91113573,0.07784707,0.0028303254,0.0035282972,0.0036558502,0.0010026722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030601284,0.0026052648,0.0056522335,0.0038551455,0.0015164951,0.0034059,0.008104874,0.0042962474,0.0046455488],"category_scores_gemma":[0.10965847,0.0029929369,0.0033174437,0.005312621,0.0062909,0.010433269,0.008101543,0.0065695997,0.001014818],"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":[0.0001438124,0.000111404755,0.002740941,0.00026641757,0.00022379156,0.00017084107,0.0004093319,0.65895146,0.00035178458,0.29476142,0.0037094199,0.038159445],"study_design_scores_gemma":[0.0000121699195,0.000017623499,0.00021556602,0.000045312743,0.000027198808,0.000034527653,0.00003310374,0.90103304,0.000119273915,0.097908296,0.00053372903,0.000020082838],"about_ca_topic_score_codex":0.017226655,"about_ca_topic_score_gemma":0.008488397,"teacher_disagreement_score":0.030601284,"about_ca_system_score_codex":0.003656513,"about_ca_system_score_gemma":0.0035522543,"threshold_uncertainty_score":0.16183698},"labels":[],"label_agreement":null},{"id":"W1484712862","doi":"10.1002/0471667196.ess0731","title":"Small Area Estimation","year":2003,"lang":"en","type":"book","venue":"Encyclopedia of Statistical Sciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1169,"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":"Small area estimation; Mathematics; Statistics; Exponential family; Estimator; Linear model; Generalized linear mixed model; Generalized linear model; Bayes' theorem; Multivariate statistics; Bayesian probability","score_opus":0.08328228167444557,"score_gpt":0.35680046551216954,"score_spread":0.27351818383772397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484712862","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":[0.005951624,0.0013720344,0.9777256,0.00028301516,0.0001715726,0.00011348467,0.0005140712,0.0003888387,0.013479691],"genre_scores_gemma":[0.28380626,0.0032914712,0.66713,0.00043319588,0.0006130648,0.0010589203,0.003599404,0.0005512395,0.03951647],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99786854,0.00105015,0.000075664386,0.00049565424,0.00042723826,0.00008268777],"domain_scores_gemma":[0.98961604,0.0074320743,0.0004494514,0.0013100064,0.0010723064,0.00012010046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033995952,0.0008633145,0.0014258553,0.0022155917,0.0006318487,0.0015807765,0.00202789,0.00084669515,0.023181124],"category_scores_gemma":[0.027325926,0.00052300096,0.0013423777,0.0024633657,0.0008377467,0.0023504358,0.0017868816,0.0013586838,0.0043182764],"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":[0.00014555655,0.000069985676,0.009715043,0.0005417642,0.00043881856,0.00018932298,0.0003163339,0.09401207,0.0013763837,0.25386137,0.028805904,0.61052734],"study_design_scores_gemma":[0.00007077987,0.00010508591,0.01044271,0.0003628236,0.00022047454,0.0003575573,0.00028077187,0.4081918,0.002135121,0.4936541,0.084118456,0.000060257084],"about_ca_topic_score_codex":0.0035104356,"about_ca_topic_score_gemma":0.0034771005,"teacher_disagreement_score":0.023181124,"about_ca_system_score_codex":0.0005584674,"about_ca_system_score_gemma":0.0008834772,"threshold_uncertainty_score":0.07754856},"labels":[],"label_agreement":null},{"id":"W1493283065","doi":"10.1002/9780470057339.vam006","title":"Matric T‐Distribution","year":2006,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Mathematics; Distribution (mathematics); Random variable; Degrees of freedom (physics and chemistry); Statistics; Variance (accounting); Gaussian; Normal distribution; Sampling (signal processing); Variable (mathematics); Applied mathematics; Mathematical analysis; Physics","score_opus":0.01986856803409429,"score_gpt":0.29626791349999115,"score_spread":0.27639934546589684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493283065","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":[0.006702848,0.0022942033,0.91542315,0.0025754648,0.0011984849,0.00017620469,0.0031358502,0.0012422671,0.067251615],"genre_scores_gemma":[0.46717107,0.008517319,0.2916213,0.0034438355,0.004503797,0.0010278223,0.010566022,0.001998223,0.21115069],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969674,0.000791516,0.00015309568,0.0008071227,0.00096591463,0.00031506497],"domain_scores_gemma":[0.9947037,0.0018710756,0.00051439763,0.00091126957,0.0017286361,0.00027090363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001746037,0.001308129,0.0012279073,0.0025740615,0.0011929043,0.0038060613,0.0016449718,0.0015433186,0.07019492],"category_scores_gemma":[0.012873196,0.00039680777,0.0013892225,0.0025554164,0.002952947,0.0044523976,0.0023116858,0.0032994887,0.029788787],"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":[0.000050404662,0.000039963175,0.00065398245,0.00014050666,0.000035540153,0.0003272341,0.00012171069,0.009288296,0.00079807,0.8876753,0.036118515,0.06475049],"study_design_scores_gemma":[0.000024209821,0.00005431461,0.00061461056,0.00008650246,0.00001689963,0.000786727,0.000072545874,0.060687013,0.0010942427,0.81942815,0.1170859,0.00004885838],"about_ca_topic_score_codex":0.00199924,"about_ca_topic_score_gemma":0.0012027669,"teacher_disagreement_score":0.07019492,"about_ca_system_score_codex":0.0015691806,"about_ca_system_score_gemma":0.001417794,"threshold_uncertainty_score":0.23482543},"labels":[],"label_agreement":null},{"id":"W1503655999","doi":"10.1186/1471-2288-6-24","title":"Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables","year":2006,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; King Faisal Specialist Hospital and Research Centre","keywords":"Statistics; Sample size determination; Confidence interval; Mathematics; Variance (accounting); Coefficient of variation; Interval estimation; Transformation (genetics); Reliability (semiconductor); Binary number; Variable (mathematics); Random variable; Computer science","score_opus":0.42944468168766925,"score_gpt":0.5289955412166452,"score_spread":0.09955085952897591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503655999","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":[0.0013338195,0.00018952298,0.99771464,0.00014116048,0.0000255948,0.00012873515,0.000024204586,0.0000733059,0.00036896963],"genre_scores_gemma":[0.08156623,0.0005092877,0.9146209,0.00018132094,0.00012190843,0.002446897,0.00013632761,0.00009479021,0.0003223339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8535418,0.12672232,0.0027715676,0.007214248,0.00884768,0.0009023979],"domain_scores_gemma":[0.64732265,0.3166615,0.010870548,0.01481361,0.009513999,0.0008176246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09091135,0.0015509186,0.003148476,0.0036083094,0.00072434795,0.0026111421,0.0036841403,0.0033015984,0.003791737],"category_scores_gemma":[0.33842576,0.0012848508,0.0022207175,0.002850026,0.005020288,0.0032458848,0.003152885,0.004155358,0.0009473428],"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":[0.0009122289,0.00023694815,0.004050142,0.001172042,0.000521643,0.00022240629,0.0011991727,0.117399774,0.0025362514,0.6230724,0.002233216,0.24644387],"study_design_scores_gemma":[0.00046747824,0.00083000155,0.0029852996,0.00052135397,0.0002490287,0.00028560514,0.00013346477,0.3964697,0.0038510796,0.5866347,0.007429705,0.00014252671],"about_ca_topic_score_codex":0.00088626624,"about_ca_topic_score_gemma":0.00042721073,"teacher_disagreement_score":0.09091135,"about_ca_system_score_codex":0.0017703638,"about_ca_system_score_gemma":0.0034285663,"threshold_uncertainty_score":0.4807909},"labels":[],"label_agreement":null},{"id":"W1504690257","doi":"10.1023/a:1012544714667","title":"Dynamic Random Effects Models for Times Between Repeated Events","year":2001,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"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 Waterloo","funders":"","keywords":"Covariate; Context (archaeology); Proportional hazards model; Econometrics; Event (particle physics); Mathematics; Statistics; Random effects model; Variance (accounting); Hazard; Variance function; Regression analysis; Computer science","score_opus":0.0671459842602265,"score_gpt":0.3946858685831534,"score_spread":0.3275398843229269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504690257","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":[0.0073544206,0.0010510336,0.98817015,0.00081692176,0.00023807732,0.0002759347,0.000928584,0.00023853297,0.000926409],"genre_scores_gemma":[0.28837624,0.004738684,0.6633708,0.0014823732,0.0010128593,0.008654997,0.0047330316,0.0005729577,0.027058084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9684263,0.019829247,0.0011608104,0.0073631164,0.0020566045,0.001163932],"domain_scores_gemma":[0.8254622,0.14893427,0.007833435,0.014228756,0.0024026486,0.00113865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05640923,0.0031230738,0.0055027907,0.004389958,0.0018425058,0.0043876283,0.012505212,0.006620556,0.023729017],"category_scores_gemma":[0.15584524,0.0028718452,0.0070536025,0.0052412674,0.004489729,0.010490056,0.004477115,0.010755681,0.003598135],"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":[0.0007339991,0.0003091058,0.0049129035,0.00066640286,0.0016773982,0.00051436236,0.0009426029,0.07283368,0.0006803859,0.8494657,0.0047353525,0.0625282],"study_design_scores_gemma":[0.00029331684,0.00020711415,0.0019488926,0.00019895515,0.0009324453,0.0004246518,0.00014197464,0.15667018,0.00038262113,0.83098215,0.007699841,0.00011776848],"about_ca_topic_score_codex":0.0072129113,"about_ca_topic_score_gemma":0.006795224,"teacher_disagreement_score":0.05640923,"about_ca_system_score_codex":0.0034514533,"about_ca_system_score_gemma":0.0035655112,"threshold_uncertainty_score":0.29832405},"labels":[],"label_agreement":null},{"id":"W1508727806","doi":"","title":"Performances of different estimation methods for generalized linear mixed models.","year":2015,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"McMaster University","keywords":"Mathematics; Generalized linear mixed model; Estimation; Applied mathematics; Statistics; Econometrics; Engineering","score_opus":0.08373058950547209,"score_gpt":0.3711789848734143,"score_spread":0.2874483953679422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508727806","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":[0.014070373,0.0019623996,0.981725,0.00045643342,0.00010320738,0.0001437438,0.00014588426,0.0007898622,0.00060306845],"genre_scores_gemma":[0.10873143,0.0013840136,0.88704735,0.00028446753,0.00007406638,0.0006056129,0.0006826801,0.00036276097,0.0008276472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9795568,0.01682728,0.0007303301,0.0011912566,0.0014938228,0.00020045349],"domain_scores_gemma":[0.9444911,0.0490528,0.00173157,0.0019467211,0.0024656723,0.00031224356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03022855,0.0016465764,0.00150632,0.0021749102,0.00077466166,0.0018337572,0.00274105,0.0021807596,0.0025365248],"category_scores_gemma":[0.08450379,0.00092036446,0.0029755828,0.0019424444,0.001057221,0.0026692676,0.0019966853,0.0028619776,0.00102679],"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":[0.0012101927,0.0003453107,0.017932432,0.0021896139,0.0031930583,0.00035733235,0.0015871904,0.38023973,0.004116131,0.05378156,0.00473773,0.5303098],"study_design_scores_gemma":[0.000102557184,0.00019225072,0.00260911,0.00019938972,0.00022140188,0.00016976133,0.00018836188,0.9750826,0.00226606,0.015771296,0.0031012357,0.000096115415],"about_ca_topic_score_codex":0.007249567,"about_ca_topic_score_gemma":0.0075845416,"teacher_disagreement_score":0.03022855,"about_ca_system_score_codex":0.0012826322,"about_ca_system_score_gemma":0.0022248037,"threshold_uncertainty_score":0.1598658},"labels":[],"label_agreement":null},{"id":"W1511044732","doi":"","title":"Hot Deck Imputation for the Response Model","year":2005,"lang":"en","type":"article","venue":"Iowa State University Digital Repository (Iowa State University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"National Agricultural Statistics Service; Iowa State University","keywords":"Imputation (statistics); Monte Carlo method; Estimator; Statistics; Computer science; Missing data; Variance (accounting); Deck; Mathematics; Econometrics; Applied mathematics; Engineering","score_opus":0.03194455000218778,"score_gpt":0.26905078516531866,"score_spread":0.2371062351631309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511044732","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":[0.0023539895,0.0001927,0.98975766,0.00032889663,0.00020620135,0.00077705923,0.0028321515,0.00058977446,0.0029615844],"genre_scores_gemma":[0.06574716,0.00044448243,0.90648407,0.00063858595,0.0001839465,0.004843298,0.006844399,0.00064977864,0.014164271],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9122454,0.07231063,0.0031711317,0.0052216426,0.0055628135,0.0014884511],"domain_scores_gemma":[0.91605544,0.0431164,0.002725165,0.03000144,0.0074982117,0.0006034913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047636,0.0013931454,0.0045167985,0.0026328107,0.0022225217,0.0033247264,0.005579338,0.0028517204,0.044589855],"category_scores_gemma":[0.15011239,0.0017994479,0.0031099808,0.007722856,0.0014237089,0.0035086465,0.004283949,0.0068865917,0.015584262],"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":[0.001038692,0.00036413132,0.009235628,0.0012079199,0.0014150055,0.00044580916,0.0012841416,0.055678044,0.0006325497,0.36056322,0.10708434,0.46105054],"study_design_scores_gemma":[0.00036415076,0.00053388,0.0067388047,0.0007638364,0.0005292272,0.00089109084,0.00055739726,0.31209695,0.0023522947,0.56126034,0.11362703,0.00028500592],"about_ca_topic_score_codex":0.003293895,"about_ca_topic_score_gemma":0.004517154,"teacher_disagreement_score":0.047636,"about_ca_system_score_codex":0.0019008776,"about_ca_system_score_gemma":0.004492445,"threshold_uncertainty_score":0.25192624},"labels":[],"label_agreement":null},{"id":"W1521692154","doi":"10.1023/a:1012431008950","title":"Generalized Calibration Approach for Estimating Variance in Survey Sampling","year":2001,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":37,"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 Windsor","funders":"","keywords":"Estimator; Mathematics; Extremum estimator; Variance (accounting); Statistics; M-estimator; Empirical distribution function; Econometrics","score_opus":0.311805362925203,"score_gpt":0.446048395560018,"score_spread":0.13424303263481502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521692154","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":[0.0017975855,0.0002505993,0.99704057,0.00018465488,0.000032970118,0.00004084886,0.00007603936,0.00018310366,0.00039358737],"genre_scores_gemma":[0.19056836,0.0013290598,0.79951257,0.00066743337,0.00040747956,0.0011965964,0.0010972734,0.00040857692,0.0048126676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95524347,0.035065673,0.00106718,0.004386332,0.0032240434,0.0010132922],"domain_scores_gemma":[0.876137,0.09662451,0.0038891465,0.017628657,0.0049158027,0.000804772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04657888,0.0023345905,0.00463404,0.004291483,0.0018615475,0.003981249,0.00870742,0.005913745,0.007790714],"category_scores_gemma":[0.1772037,0.0032289359,0.004305001,0.007671052,0.005867535,0.0064229122,0.005684508,0.007446166,0.0016487711],"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":[0.00016756526,0.00010083717,0.0036433833,0.00034225325,0.00078456267,0.00018294339,0.00055329694,0.21067785,0.0005355738,0.6844355,0.0041444316,0.09443179],"study_design_scores_gemma":[0.00006622606,0.00006189588,0.0011070942,0.00007912105,0.00013030831,0.00013930532,0.00005791718,0.48878047,0.00036007594,0.5056264,0.0035174137,0.000073774885],"about_ca_topic_score_codex":0.008726889,"about_ca_topic_score_gemma":0.005867922,"teacher_disagreement_score":0.04657888,"about_ca_system_score_codex":0.0035658132,"about_ca_system_score_gemma":0.0038568873,"threshold_uncertainty_score":0.24633563},"labels":[],"label_agreement":null},{"id":"W1524513824","doi":"10.1002/0470867205.ch15","title":"Event History Analysis and Longitudinal Surveys","year":2003,"lang":"en","type":"other","venue":"","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Inference; Survival analysis; Observational study; Statistics; Event data; Duration (music); Econometrics; Variance (accounting); Computer science; Data science; History; Data mining; Mathematics; Artificial intelligence; Art","score_opus":0.0824464211909022,"score_gpt":0.3657364947425574,"score_spread":0.28329007355165525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524513824","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":[0.0037438327,0.020915074,0.92147887,0.0065313536,0.0006950842,0.00018514687,0.002741022,0.00075657543,0.042953108],"genre_scores_gemma":[0.25374165,0.08951032,0.517979,0.003961307,0.0048052534,0.0019759105,0.010618776,0.000717625,0.116690144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962709,0.0025516462,0.00016822437,0.0004163244,0.00050079136,0.00009210053],"domain_scores_gemma":[0.97860765,0.017242622,0.0012014823,0.0017411268,0.00097328774,0.000233861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00674042,0.00089426787,0.0011469607,0.0032136037,0.00051685254,0.0020662094,0.0012083667,0.0014121683,0.025814611],"category_scores_gemma":[0.032305192,0.0006067878,0.0008627521,0.0063822307,0.0012755552,0.0040111197,0.0012116359,0.0025618675,0.003915614],"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":[0.000026472553,0.00004623562,0.0027672285,0.0003633894,0.00009503957,0.000085646505,0.00026753623,0.0084525505,0.000096420845,0.80112606,0.034616776,0.1520566],"study_design_scores_gemma":[0.000012394027,0.000016602435,0.0024554774,0.00015145843,0.000025087293,0.00010030354,0.0000976793,0.017561287,0.00007244159,0.90956193,0.069922164,0.000023218843],"about_ca_topic_score_codex":0.0043432005,"about_ca_topic_score_gemma":0.0027392167,"teacher_disagreement_score":0.025814611,"about_ca_system_score_codex":0.0011815233,"about_ca_system_score_gemma":0.0015155532,"threshold_uncertainty_score":0.08635849},"labels":[],"label_agreement":null},{"id":"W1533143757","doi":"","title":"Estimateurs à noyau et théorie des valeurs extrêmes : comparaison de leur pouvoir prédictif dans l'analyse du coût des réclamations en assurance automobile","year":2014,"lang":"fr","type":"article","venue":"Archipelago (Université du Québec à Montréal)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.018462386382601028,"score_gpt":0.2606437426375234,"score_spread":0.24218135625492235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533143757","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":[0.07632933,0.0012285175,0.91482854,0.00026452407,0.000060117673,0.000125666,0.0006812469,0.0010778306,0.005404256],"genre_scores_gemma":[0.6625769,0.0018064512,0.32433408,0.00009056316,0.000066842076,0.000489094,0.0016114826,0.00050012127,0.008524463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997033,0.0007908642,0.00015960704,0.0008630777,0.00097074336,0.000182726],"domain_scores_gemma":[0.9876435,0.00950929,0.000702158,0.00074699795,0.0012988028,0.000099376935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004731945,0.0015122967,0.0010908741,0.0030043372,0.0006761666,0.003654045,0.0017663386,0.0015600711,0.0037192446],"category_scores_gemma":[0.021289676,0.00091835106,0.0024351405,0.0026049674,0.0009109792,0.0026556095,0.0014362336,0.002103275,0.00087513],"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":[0.00044330422,0.00012349723,0.05445316,0.0006037991,0.00043824213,0.00024730345,0.0017952239,0.6213416,0.007387921,0.030028373,0.0018425293,0.28129503],"study_design_scores_gemma":[0.00003753136,0.00021646392,0.029594896,0.00029607036,0.00015725197,0.00028269395,0.0009087911,0.9201744,0.010254513,0.02641062,0.011510359,0.00015638515],"about_ca_topic_score_codex":0.034268476,"about_ca_topic_score_gemma":0.020267121,"teacher_disagreement_score":0.034268476,"about_ca_system_score_codex":0.0019121278,"about_ca_system_score_gemma":0.0022205242,"threshold_uncertainty_score":0.068138},"labels":[],"label_agreement":null},{"id":"W1533780589","doi":"10.1007/978-3-319-30322-2_23","title":"Bias Study of the Naive Estimator in a Longitudinal Linear Mixed-Effects Model with Measurement Error and Misclassification in Covariates","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Estimator; Statistics; Observational error; Mixed model; Generalized linear mixed model; Econometrics; Generalized linear model; Linear model; Errors-in-variables models; Maximum likelihood; Computer science; Mathematics","score_opus":0.14542796141267134,"score_gpt":0.36328397792001915,"score_spread":0.21785601650734782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533780589","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":[0.021665405,0.0069801747,0.9652653,0.002401604,0.0006300913,0.00015436429,0.00017041866,0.00031791098,0.002414749],"genre_scores_gemma":[0.39074948,0.0047176243,0.58347917,0.0028362102,0.002457512,0.0009842267,0.0009232983,0.0009147312,0.012937654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9667481,0.026746508,0.001006776,0.0027073442,0.0022204667,0.0005708452],"domain_scores_gemma":[0.5590694,0.41575846,0.004224262,0.0105523905,0.009061074,0.0013344167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.095444724,0.0016357119,0.0034015619,0.0019934722,0.0011618763,0.0030247634,0.007573689,0.0041971845,0.0068880557],"category_scores_gemma":[0.3069325,0.0014473358,0.0031114998,0.0023435391,0.003935392,0.006376147,0.0038376332,0.0061424887,0.0007751816],"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":[0.0016087416,0.0003611338,0.018266054,0.002008439,0.0021107811,0.00066001934,0.0016939993,0.04761613,0.001795244,0.77656466,0.010759232,0.13655567],"study_design_scores_gemma":[0.0005365364,0.00046059096,0.0037499245,0.0005477115,0.0010554782,0.0007715117,0.000322618,0.4632932,0.0016867018,0.51966095,0.0077787098,0.0001360178],"about_ca_topic_score_codex":0.00586135,"about_ca_topic_score_gemma":0.0027558252,"teacher_disagreement_score":0.095444724,"about_ca_system_score_codex":0.0026237883,"about_ca_system_score_gemma":0.003348722,"threshold_uncertainty_score":0.504766},"labels":[],"label_agreement":null},{"id":"W1537823803","doi":"10.1023/a:1022478119885","title":"On the Positive Definiteness of the Information Matrix Under the Binary and Poisson Mixed Models","year":2002,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Positive definiteness; Applied mathematics; Poisson distribution; Generalized linear model; Generalized linear mixed model; Property (philosophy); Statistics; Variance (accounting); Positive-definite matrix","score_opus":0.14000370682179544,"score_gpt":0.35972916702858476,"score_spread":0.21972546020678932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537823803","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":[0.027726173,0.0021542942,0.956635,0.004696606,0.00023359664,0.0001218272,0.00064769655,0.00014846551,0.0076362416],"genre_scores_gemma":[0.5395867,0.013853152,0.41601455,0.0059278393,0.0048300177,0.0016002823,0.0025586293,0.00084059325,0.014788154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97381604,0.017104184,0.0014465001,0.0034503953,0.0032281198,0.00095479627],"domain_scores_gemma":[0.6443347,0.32281896,0.011563112,0.009099261,0.009383224,0.002800839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044442084,0.0028520527,0.0046763704,0.004164864,0.0021811759,0.0073927543,0.0064354124,0.0060395994,0.009487433],"category_scores_gemma":[0.19008844,0.0032099215,0.002894709,0.003990807,0.017702172,0.022813061,0.004708046,0.010469091,0.0016106325],"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":[0.0001178848,0.00007510427,0.0005812207,0.0002555537,0.000086268825,0.00020001503,0.00033419763,0.014417229,0.00057726057,0.97359157,0.0015650784,0.008198684],"study_design_scores_gemma":[0.000037281472,0.000049620463,0.0003022221,0.0000739696,0.00003364277,0.00017472832,0.000038516522,0.05880037,0.00019198957,0.9395751,0.000652378,0.00007020782],"about_ca_topic_score_codex":0.0032003683,"about_ca_topic_score_gemma":0.0026951663,"teacher_disagreement_score":0.044442084,"about_ca_system_score_codex":0.002716078,"about_ca_system_score_gemma":0.0039955657,"threshold_uncertainty_score":0.235035},"labels":[],"label_agreement":null},{"id":"W1550575192","doi":"","title":"Variance estimation with hot deck imputation simulation study of three methods","year":2006,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Statistics Canada","funders":"","keywords":"Statistics; Jackknife resampling; Estimator; Imputation (statistics); Econometrics; Population; Mathematics; Missing data; Point estimation; Demography","score_opus":0.09189468185055545,"score_gpt":0.447997944898127,"score_spread":0.35610326304757156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550575192","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":[0.14035887,0.0012891942,0.85079455,0.00072172075,0.00011856463,0.0008175883,0.00031781936,0.00038542683,0.0051963152],"genre_scores_gemma":[0.60466534,0.0005222818,0.3901989,0.0003601413,0.00006334861,0.0018772627,0.0004921675,0.00020808206,0.0016125398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8603869,0.12791227,0.0018047745,0.0030533525,0.0057717543,0.0010708866],"domain_scores_gemma":[0.45376688,0.50336283,0.0070088636,0.024132317,0.010576563,0.0011525176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09491668,0.00084625074,0.0023641787,0.002121617,0.0010754019,0.003123825,0.0030982606,0.0023124646,0.0030678136],"category_scores_gemma":[0.30844602,0.0012168597,0.0028532206,0.002558357,0.0023003719,0.0043967217,0.0031366602,0.0029354235,0.00037737718],"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":[0.0024580532,0.0005323911,0.024697753,0.0005667963,0.0014911419,0.00019139476,0.0012907269,0.6905968,0.0004100785,0.18583092,0.0024576406,0.08947623],"study_design_scores_gemma":[0.0003432963,0.0005205315,0.0038420218,0.00018501986,0.00020687676,0.00015270729,0.00022362184,0.9415529,0.0006925222,0.05044302,0.0017355087,0.00010195604],"about_ca_topic_score_codex":0.0046262434,"about_ca_topic_score_gemma":0.0033428115,"teacher_disagreement_score":0.09491668,"about_ca_system_score_codex":0.0024423152,"about_ca_system_score_gemma":0.0026765384,"threshold_uncertainty_score":0.5019734},"labels":[],"label_agreement":null},{"id":"W1552293040","doi":"10.1002/jwmg.748","title":"Hierarchical model analysis of the Atlantic Flyway Breeding Waterfowl Survey","year":2014,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"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. Fish and Wildlife Service","keywords":"Flyway; Waterfowl; Anas; Population; Branta; Population model; Geography; Multilevel model; Statistics; Population size; Breeding bird survey; Wildlife; Ecology; Biology; Demography; Habitat; Mathematics","score_opus":0.06719019727895116,"score_gpt":0.3399574650261953,"score_spread":0.27276726774724414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552293040","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":[0.632552,0.00029705768,0.36041492,0.0004174502,0.00005660228,0.00025013427,0.0025665443,0.0011353054,0.0023099016],"genre_scores_gemma":[0.95088136,0.000077494245,0.04452105,0.0000802577,0.000024912992,0.0002592339,0.0022599197,0.0001252124,0.0017705729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941392,0.0041634184,0.0001808302,0.00073244335,0.0004056437,0.00037845722],"domain_scores_gemma":[0.9850032,0.01149103,0.0011825848,0.00078989053,0.0012072751,0.00032605554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009345479,0.0007279849,0.0007669046,0.0018968298,0.0006560367,0.0011196411,0.001489921,0.00049743865,0.004093686],"category_scores_gemma":[0.02005685,0.0005534236,0.0028224692,0.0011517873,0.0004417687,0.0011826499,0.00094715954,0.0012131232,0.00054850697],"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":[0.0005070909,0.00040996037,0.1501122,0.00014265462,0.0014448127,0.00029093813,0.00092680694,0.7464991,0.0012710206,0.028705599,0.0040136403,0.065676145],"study_design_scores_gemma":[0.000015673411,0.00007699407,0.014087964,0.000011288522,0.000059038124,0.00001266923,0.00007039035,0.98064476,0.00008802026,0.004533233,0.0003837592,0.000016190583],"about_ca_topic_score_codex":0.05967063,"about_ca_topic_score_gemma":0.06723797,"teacher_disagreement_score":0.05967063,"about_ca_system_score_codex":0.0024355727,"about_ca_system_score_gemma":0.001930926,"threshold_uncertainty_score":0.11864662},"labels":[],"label_agreement":null},{"id":"W1554200067","doi":"10.1016/j.csda.2015.04.010","title":"Modelling receiver operating characteristic curves using Gaussian mixtures","year":2015,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Receiver operating characteristic; Frequentist inference; Gaussian; Monte Carlo method; Parametric statistics; Mathematics; Curve fitting; Parametric equation; Mixture model; Statistics; Algorithm; Computer science; Applied mathematics; Bayesian probability; Bayesian inference; Physics","score_opus":0.29564307459163713,"score_gpt":0.437251055077163,"score_spread":0.14160798048552586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554200067","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":[0.003619336,0.00031199257,0.9950576,0.00019248307,0.000027207921,0.000033396682,0.00008708727,0.0003181682,0.00035274285],"genre_scores_gemma":[0.35174176,0.0020258254,0.63675535,0.00043786858,0.00028213352,0.0007329731,0.00086625014,0.00047854366,0.006679285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913458,0.005450405,0.00031885205,0.0012955918,0.0012049761,0.0003843747],"domain_scores_gemma":[0.9492814,0.04354048,0.002627166,0.002588233,0.0016674893,0.00029526107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015208277,0.0019609,0.0023681994,0.0034196572,0.0006559935,0.004492825,0.003814179,0.0045882305,0.0028352083],"category_scores_gemma":[0.071106635,0.0020808328,0.0032260288,0.0030559218,0.0020369396,0.00508818,0.0021660104,0.004694857,0.002150457],"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":[0.00031640838,0.00013085185,0.0035417543,0.00034278657,0.00034451205,0.00028824536,0.00062151987,0.7670328,0.0022710275,0.1350478,0.0026391908,0.08742313],"study_design_scores_gemma":[0.000023886618,0.00004184243,0.0005497735,0.000059528815,0.00005727752,0.00014352993,0.000024593674,0.9147723,0.00065600214,0.08206473,0.0015539051,0.00005261502],"about_ca_topic_score_codex":0.005253693,"about_ca_topic_score_gemma":0.0036880684,"teacher_disagreement_score":0.015208277,"about_ca_system_score_codex":0.0018619584,"about_ca_system_score_gemma":0.0016336465,"threshold_uncertainty_score":0.08043003},"labels":[],"label_agreement":null},{"id":"W15577552","doi":"10.1097/00002030-200411190-00020","title":"AN APPLICATION OF SMALL AREA ESTIMATION TECHNIQUES TO THE CANADIAN LABOUR FORCE SURVEY","year":2009,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"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":"Estimator; Estimation; Small area estimation; Smoothness; Sample (material); Reliability (semiconductor); Statistics; Econometrics; Stratified sampling; Mathematics; Economics; Demographic economics; Management","score_opus":0.0730070091035678,"score_gpt":0.38132284012712164,"score_spread":0.30831583102355387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15577552","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":[0.17098147,0.0031191208,0.80871755,0.0019959335,0.0002805667,0.00096722104,0.004170733,0.00081513374,0.008952305],"genre_scores_gemma":[0.5849662,0.0025072552,0.4040313,0.00021188623,0.00009688663,0.00058746367,0.0021802208,0.00012830118,0.005290553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971944,0.0018389759,0.000085109605,0.0003223941,0.00041660442,0.00014236779],"domain_scores_gemma":[0.99021965,0.006736196,0.00032518554,0.0005724944,0.0019502855,0.00019611714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063133202,0.0006456102,0.00081574905,0.0031557921,0.002054545,0.0009129529,0.001371897,0.0005165509,0.0027618061],"category_scores_gemma":[0.03929328,0.00040595344,0.00077410927,0.005540614,0.0006526069,0.0005175881,0.0008711938,0.0008512509,0.00027543795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040881088,0.00018686084,0.12047068,0.00047314473,0.0009356066,0.0005124953,0.0014768215,0.2005307,0.0011370329,0.07464518,0.018826285,0.5803965],"study_design_scores_gemma":[0.00015672311,0.00011028931,0.08350179,0.00012057612,0.00023882615,0.00017814797,0.0010391605,0.8626266,0.0006114135,0.02896555,0.022311814,0.00013921574],"about_ca_topic_score_codex":0.9587696,"about_ca_topic_score_gemma":0.9494043,"teacher_disagreement_score":0.04123038,"about_ca_system_score_codex":0.0080537135,"about_ca_system_score_gemma":0.015812373,"threshold_uncertainty_score":0.08294636},"labels":[],"label_agreement":null},{"id":"W1563896461","doi":"","title":"Almost Unbiased Estimation of the Poisson Regression Model","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian 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 Victoria","funders":"","keywords":"Poisson regression; Estimator; Poisson distribution; Monte Carlo method; Statistics; Econometrics; Mathematics; Regression analysis; Zero-inflated model; Regression; Bias of an estimator; Estimation; Minimum-variance unbiased estimator; Economics","score_opus":0.10909790843377697,"score_gpt":0.4309247577749133,"score_spread":0.3218268493411363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563896461","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":[0.006235762,0.00048370147,0.9910096,0.00052480947,0.000058557336,0.000016756201,0.00007508577,0.00014244563,0.0014532988],"genre_scores_gemma":[0.44279918,0.0035627163,0.5414671,0.0012555714,0.0007134619,0.0004267054,0.00089581794,0.0005464608,0.008333066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9909614,0.005773094,0.0002703455,0.00085765164,0.0017773879,0.00036003554],"domain_scores_gemma":[0.9629734,0.028918568,0.002306844,0.002613239,0.002910445,0.00027759283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013963935,0.0008475508,0.0016288457,0.0017534455,0.0005265815,0.0021484657,0.0024433774,0.0018710954,0.0028352207],"category_scores_gemma":[0.10918794,0.00090672687,0.000868268,0.0017027298,0.0015917699,0.0034369524,0.0023309472,0.0024543598,0.0011467438],"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":[0.000091509086,0.000048136164,0.0033818593,0.0002698844,0.00017356632,0.0002708526,0.0002442897,0.27363786,0.0023628273,0.6539618,0.0048113815,0.06074616],"study_design_scores_gemma":[0.000020401409,0.00001855873,0.0007855437,0.000067573106,0.000030568626,0.00015768259,0.000027919905,0.6900617,0.000906965,0.30561668,0.0022694895,0.000037012815],"about_ca_topic_score_codex":0.002322317,"about_ca_topic_score_gemma":0.0018544279,"teacher_disagreement_score":0.013963935,"about_ca_system_score_codex":0.0015318984,"about_ca_system_score_gemma":0.0018673287,"threshold_uncertainty_score":0.07384926},"labels":[],"label_agreement":null},{"id":"W1591713837","doi":"10.1111/j.1751-5823.2011.00166.x","title":"On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling","year":2012,"lang":"fr","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Mathematics; Humanities; Statistics; Philosophy","score_opus":0.23124814582460437,"score_gpt":0.4684831745484313,"score_spread":0.23723502872382693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591713837","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":[0.0048236214,0.000813482,0.9905167,0.00044815475,0.00010734568,0.00008912765,0.00008984571,0.00016413201,0.0029475223],"genre_scores_gemma":[0.20647246,0.0035999406,0.7793096,0.00095692696,0.0009435406,0.0014708767,0.00057460944,0.00044449576,0.006227692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97218364,0.023414645,0.00047967865,0.001027888,0.0023938473,0.000500248],"domain_scores_gemma":[0.9447376,0.044829536,0.0013312233,0.0056615123,0.0030246116,0.00041556676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021663956,0.00077869126,0.0018581086,0.0025931515,0.00084502395,0.0016121792,0.002161743,0.0015089622,0.00619893],"category_scores_gemma":[0.08654211,0.000622026,0.002154362,0.0033749007,0.002753716,0.0022148485,0.0031011426,0.0024470796,0.0012503612],"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":[0.0001492815,0.00003964116,0.0032886644,0.00044914504,0.00017381883,0.00034044153,0.00093543204,0.042972483,0.00096652907,0.738552,0.0047776215,0.20735495],"study_design_scores_gemma":[0.00009810553,0.00021210789,0.004159455,0.00048610658,0.000086589964,0.0004525603,0.000376998,0.19352116,0.0009779573,0.7382697,0.061288152,0.00007114048],"about_ca_topic_score_codex":0.0036676181,"about_ca_topic_score_gemma":0.003359775,"teacher_disagreement_score":0.021663956,"about_ca_system_score_codex":0.0011567445,"about_ca_system_score_gemma":0.001746303,"threshold_uncertainty_score":0.11457127},"labels":[],"label_agreement":null},{"id":"W1592532695","doi":"10.1002/cjs.11223","title":"An exchangeable Kendall's tau for clustered data","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Intraclass correlation; Statistics; Estimator; Mathematics; Nonparametric statistics; Statistic; Multivariate statistics; Asymptotic distribution; Econometrics; Reproducibility","score_opus":0.20559916796281788,"score_gpt":0.3793170714696874,"score_spread":0.17371790350686953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592532695","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":[0.05092323,0.00065662334,0.94506043,0.0004298058,0.00011845643,0.00010088062,0.00034189044,0.00022712669,0.0021414543],"genre_scores_gemma":[0.7045728,0.00051789725,0.29164532,0.00022438943,0.00027354207,0.00042194827,0.00064148125,0.00017104363,0.0015314738],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98074394,0.012379131,0.0008001804,0.0026689155,0.0029524823,0.0004553472],"domain_scores_gemma":[0.88349086,0.08260649,0.012010662,0.014190899,0.006439585,0.0012614722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025813915,0.0007177045,0.0017554924,0.0040122457,0.0012686973,0.0030508677,0.0022479852,0.0012401378,0.0033339302],"category_scores_gemma":[0.15604137,0.0005100554,0.0013927878,0.004952566,0.0027904515,0.0041820235,0.0019667784,0.002093715,0.00048448044],"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":[0.0005756681,0.00021108867,0.062440734,0.00072995,0.0013623659,0.0014662549,0.0021666433,0.08765259,0.0027152998,0.58925766,0.007367796,0.24405403],"study_design_scores_gemma":[0.00006198002,0.00052251917,0.034435116,0.0002631701,0.00027513737,0.000898777,0.0005594274,0.3974725,0.0015788882,0.55538726,0.008388259,0.00015699057],"about_ca_topic_score_codex":0.0024067173,"about_ca_topic_score_gemma":0.001955921,"teacher_disagreement_score":0.025813915,"about_ca_system_score_codex":0.0012255295,"about_ca_system_score_gemma":0.0022191585,"threshold_uncertainty_score":0.13651872},"labels":[],"label_agreement":null},{"id":"W1594103740","doi":"10.3233/mas-2012-0232","title":"The Monte Carlo simulation study to conduct comparison between multilevel modeling and standard regression techniques based on cross-sectional complex survey","year":2013,"lang":"en","type":"article","venue":"Model Assisted Statistics and Applications","topic":"Statistical Methods and Bayesian Inference","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":"University of Saskatchewan","funders":"","keywords":"Monte Carlo method; Multilevel model; Regression analysis; Statistics; Computer science; Econometrics; Mathematics","score_opus":0.3280278710753258,"score_gpt":0.4966158481464194,"score_spread":0.16858797707109358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594103740","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":[0.2825412,0.0008484494,0.7002821,0.0008656433,0.0003175665,0.0018721197,0.0010377907,0.0003648356,0.011870216],"genre_scores_gemma":[0.75562876,0.00034231925,0.23794681,0.0003026144,0.000071946524,0.0032188045,0.00064946816,0.00010953108,0.0017298012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9802043,0.01753849,0.00036116462,0.00078453013,0.00084417727,0.00026737363],"domain_scores_gemma":[0.7748209,0.20801672,0.0034856629,0.007846101,0.0049913772,0.0008391572],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026559073,0.0005608487,0.0012980322,0.0014607608,0.0009990551,0.0013140272,0.0015825101,0.0015934835,0.009805548],"category_scores_gemma":[0.12877147,0.00039994274,0.001523877,0.0018224986,0.0008507939,0.0017705281,0.0011883972,0.0016686496,0.0004374676],"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":[0.0023888308,0.0027988064,0.042587433,0.00073862635,0.0015047857,0.0003912488,0.00095644395,0.5082752,0.0007114734,0.3562459,0.0068775294,0.07652365],"study_design_scores_gemma":[0.00030835997,0.0008512164,0.0032083164,0.0001577399,0.00023330015,0.0001275453,0.00029257007,0.9579315,0.00054896873,0.033316452,0.0029889143,0.000035158617],"about_ca_topic_score_codex":0.005979378,"about_ca_topic_score_gemma":0.005455043,"teacher_disagreement_score":0.97344095,"about_ca_system_score_codex":0.001597999,"about_ca_system_score_gemma":0.0031904005,"threshold_uncertainty_score":0.14045948},"labels":[],"label_agreement":null},{"id":"W1594327042","doi":"10.1007/978-1-4939-2137-9_4","title":"Regression Models For Univariate Longitudinal Non-stationary Categorical Data","year":2014,"lang":"en","type":"book-chapter","venue":"Springer series in statistics","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Categorical variable; Multinomial logistic regression; Multinomial distribution; Econometrics; Statistics; Univariate; Mathematics; Logistic regression; Correlation; Multivariate statistics","score_opus":0.15415164337944395,"score_gpt":0.38941901057532596,"score_spread":0.235267367195882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594327042","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":[0.00093769963,0.007666599,0.9851505,0.0014845564,0.00030335478,0.000013163992,0.00050444383,0.00043713595,0.0035025093],"genre_scores_gemma":[0.11761912,0.05532162,0.7278723,0.002282802,0.004224465,0.0008964147,0.004966652,0.0014813415,0.08533525],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99819994,0.0010094611,0.000092191425,0.0003041289,0.00033541775,0.00005879956],"domain_scores_gemma":[0.99027294,0.008213463,0.00041985288,0.0005602134,0.00046717323,0.00006641285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051757386,0.0018362418,0.0019135944,0.0010825471,0.00029129646,0.0016140023,0.003290522,0.0021074163,0.009801297],"category_scores_gemma":[0.015230832,0.0012344556,0.001797978,0.0027617076,0.0011216091,0.002880829,0.0010570373,0.0044005746,0.0071334885],"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":[0.00003495685,0.000059596397,0.0010217974,0.00057041226,0.00023227342,0.00013794002,0.00022293525,0.07204957,0.0007012217,0.72936225,0.044356156,0.15125102],"study_design_scores_gemma":[0.00001164475,0.000027056398,0.00052503974,0.00013532845,0.00007296497,0.00013977024,0.000024374933,0.22661059,0.00018915656,0.7441111,0.028115397,0.000037568818],"about_ca_topic_score_codex":0.002934154,"about_ca_topic_score_gemma":0.003891548,"teacher_disagreement_score":0.009801297,"about_ca_system_score_codex":0.001005278,"about_ca_system_score_gemma":0.001133954,"threshold_uncertainty_score":0.032788634},"labels":[],"label_agreement":null},{"id":"W1594516908","doi":"10.1002/9781118445112.stat07350","title":"Matric t‐Distribution: Overview","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Distribution (mathematics); Mathematics; Degrees of freedom (physics and chemistry); Random variable; Statistics; Variance (accounting); Gaussian; Normal distribution; Sampling (signal processing); Variable (mathematics); Sample (material); Applied mathematics; Computer science; Mathematical analysis; Physics","score_opus":0.10800215999433352,"score_gpt":0.4074492268319808,"score_spread":0.2994470668376473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594516908","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":[0.0014799233,0.03535048,0.90861005,0.0039311587,0.0012935792,0.00010266191,0.0019488152,0.001136614,0.046146747],"genre_scores_gemma":[0.2284266,0.117258236,0.557645,0.004563596,0.014158022,0.0010420687,0.009077014,0.0028168533,0.06501264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969891,0.0011886748,0.00017226736,0.00059725024,0.00091461843,0.00013815655],"domain_scores_gemma":[0.993339,0.003801817,0.0004124573,0.00066554383,0.0015928745,0.00018827998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034242515,0.0010370318,0.0009283357,0.003410548,0.0007506952,0.0033016265,0.001771953,0.0014470546,0.026137443],"category_scores_gemma":[0.013888965,0.0004774946,0.0011576446,0.005176193,0.0021804473,0.0035060982,0.0018106514,0.002921624,0.015178617],"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":[0.000038419013,0.000037910013,0.00095284375,0.0005806774,0.000054947002,0.00019318837,0.00010660057,0.016154844,0.0004035564,0.7038562,0.06208154,0.21553928],"study_design_scores_gemma":[0.000013763168,0.00003825706,0.00086315605,0.00028850627,0.000021721167,0.00058838725,0.000044746066,0.04656854,0.00051720283,0.66820985,0.2828031,0.000042842123],"about_ca_topic_score_codex":0.0028882732,"about_ca_topic_score_gemma":0.0015204955,"teacher_disagreement_score":0.026137443,"about_ca_system_score_codex":0.0013900237,"about_ca_system_score_gemma":0.0018843282,"threshold_uncertainty_score":0.087438464},"labels":[],"label_agreement":null},{"id":"W1598685841","doi":"10.22329/amr.v13i2.3019","title":"The Effects Of Estimator Choice And Weighting Strategies On Confirmatory Factor Analysis With Stratified Samples","year":2010,"lang":"en","type":"article","venue":"Applied Multivariate Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Weighting; Estimator; LISREL; Mathematics; Econometrics; Maximum likelihood; Stratified sampling; Population; Confirmatory factor analysis; Estimation; Estimation theory; Restricted maximum likelihood; Standard error; Simple random sample; Structural equation modeling; Economics; Demography","score_opus":0.09080528179209234,"score_gpt":0.4388542782044845,"score_spread":0.3480489964123922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598685841","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","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":[0.08246453,0.003397831,0.8994291,0.0036382328,0.00047428336,0.0031706053,0.0002497923,0.000440608,0.0067349887],"genre_scores_gemma":[0.28905243,0.0013642081,0.7011659,0.0015342747,0.00013810507,0.005369041,0.00021119507,0.00034006566,0.000824742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.3015473,0.63425845,0.028775072,0.008746532,0.025320554,0.001352124],"domain_scores_gemma":[0.13682571,0.78912956,0.018061006,0.038958337,0.01625936,0.0007661145],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.54382616,0.0024255083,0.0022384143,0.0033222164,0.003282,0.004359883,0.003264942,0.0028064384,0.0038121373],"category_scores_gemma":[0.79567677,0.0022502162,0.0025669336,0.006454912,0.004306336,0.0074660406,0.0048649027,0.004140003,0.0010779127],"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":[0.0036198946,0.0010456622,0.083109945,0.0032642575,0.0039913035,0.00051832595,0.01900032,0.026525743,0.0046729306,0.13709262,0.010814675,0.7063443],"study_design_scores_gemma":[0.0042190924,0.0067024394,0.13750693,0.011922563,0.0055003283,0.0033805282,0.01011303,0.3187225,0.040613774,0.39721516,0.062324386,0.0017792652],"about_ca_topic_score_codex":0.0032023492,"about_ca_topic_score_gemma":0.003557051,"teacher_disagreement_score":0.54382616,"about_ca_system_score_codex":0.0026012643,"about_ca_system_score_gemma":0.0038854969,"threshold_uncertainty_score":0.56254363},"labels":[],"label_agreement":null},{"id":"W1598854239","doi":"","title":"Hypothesis testing in a generic nesting framework with general population distributions","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","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":"McMaster University","funders":"","keywords":"Nesting (process); Null hypothesis; Alternative hypothesis; Divergence (linguistics); Inference; Multinomial distribution; Statistical hypothesis testing; Null (SQL); Econometrics; Statistical inference; Mathematics; Population; Statistics; Inequality; Computer science; Data mining; Artificial intelligence","score_opus":0.18202977161934675,"score_gpt":0.3925656113281402,"score_spread":0.21053583970879347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598854239","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":[0.009202265,0.00019316094,0.9893964,0.00016990723,0.000015470105,0.000031677846,0.00007762779,0.00007231594,0.0008411913],"genre_scores_gemma":[0.33573845,0.0005867006,0.6598993,0.00040152125,0.00019843313,0.00036658216,0.00043995332,0.00010496596,0.0022640822],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97653234,0.016504634,0.0008791539,0.0029788755,0.002431488,0.000673527],"domain_scores_gemma":[0.9222874,0.061454117,0.005079316,0.007689246,0.0023331072,0.0011567317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029038116,0.001144705,0.0019904512,0.0019278912,0.0008880571,0.002787636,0.0035228378,0.0022388105,0.0027950464],"category_scores_gemma":[0.07756655,0.00088192115,0.0022274833,0.0019090554,0.0049193795,0.005915722,0.0047912295,0.003469782,0.0005562325],"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":[0.0001171758,0.00008716306,0.005142305,0.00025700018,0.00023431102,0.00066553795,0.00047360238,0.08720183,0.0018777096,0.86336774,0.0010335123,0.03954215],"study_design_scores_gemma":[0.000027251011,0.000072538365,0.00082412746,0.000041095213,0.000030902098,0.00027038364,0.000053265714,0.25962177,0.0002864568,0.73731583,0.0014370366,0.000019472132],"about_ca_topic_score_codex":0.0014002942,"about_ca_topic_score_gemma":0.0013261391,"teacher_disagreement_score":0.029038116,"about_ca_system_score_codex":0.0010470639,"about_ca_system_score_gemma":0.0014478485,"threshold_uncertainty_score":0.15357006},"labels":[],"label_agreement":null},{"id":"W1599223192","doi":"10.5539/gjhs.v8n1p133","title":"Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey","year":2015,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shiraz University; Shiraz University of Medical Sciences","keywords":"Imputation (statistics); Statistics; Multivariate statistics; Univariate; Linear regression; Non-response bias; Regression analysis; Multivariate normal distribution; Econometrics; Missing data; Mathematics; Medicine","score_opus":0.18553903841321345,"score_gpt":0.4727647081375717,"score_spread":0.28722566972435826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599223192","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":[0.027309414,0.002648981,0.9640407,0.0020267833,0.00034192027,0.0012841664,0.000633256,0.0004348859,0.0012798957],"genre_scores_gemma":[0.30345038,0.0019391613,0.6883821,0.0008301923,0.0003292794,0.0032691828,0.0008056878,0.000117147305,0.0008769323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.861649,0.12298073,0.0049087736,0.0042980895,0.005336468,0.00082693674],"domain_scores_gemma":[0.87992173,0.09016745,0.011221905,0.010405016,0.007730836,0.0005531243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09034928,0.0009390089,0.002350466,0.0026292282,0.0010571516,0.0012992009,0.002989608,0.0017203646,0.003291726],"category_scores_gemma":[0.20055275,0.0007642247,0.0026758641,0.0056484635,0.0008199258,0.0014453407,0.002039941,0.002231475,0.0005245341],"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":[0.000818665,0.0006018075,0.19106936,0.0046661724,0.0063284463,0.0007079465,0.0033711551,0.03564371,0.0011242307,0.025509818,0.012458134,0.71770054],"study_design_scores_gemma":[0.0010402872,0.00269331,0.1815756,0.0054933745,0.0044242768,0.0019438998,0.0022492234,0.60685766,0.007964799,0.13901362,0.04634419,0.00039979423],"about_ca_topic_score_codex":0.0026494397,"about_ca_topic_score_gemma":0.0024088337,"teacher_disagreement_score":0.09034928,"about_ca_system_score_codex":0.00090744713,"about_ca_system_score_gemma":0.003032169,"threshold_uncertainty_score":0.47781837},"labels":[],"label_agreement":null},{"id":"W1603573412","doi":"10.1186/1471-2288-6-57","title":"Dealing with missing data in a multi-question depression scale: a comparison of imputation methods","year":2006,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":655,"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; University of Calgary","funders":"Canadian Institutes of Health Research; Fondation pour la Recherche Médicale; Government of Canada; Heart and Stroke Foundation of Canada","keywords":"Missing data; Statistics; Imputation (statistics); Statistic; Cohen's kappa; Standard deviation; Population; Mathematics; Kappa; Regression; Psychology; Medicine","score_opus":0.7482629066797014,"score_gpt":0.6934678501720167,"score_spread":0.05479505650768468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603573412","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":[0.34502935,0.030079907,0.6126254,0.0030025928,0.00063850224,0.0028358956,0.0010456423,0.00074030104,0.0040024016],"genre_scores_gemma":[0.5303615,0.010369813,0.45400095,0.0005907669,0.000234408,0.0028849277,0.000835051,0.00020752223,0.00051500194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8324543,0.14747176,0.0063871746,0.003322291,0.009771772,0.00059273455],"domain_scores_gemma":[0.61256766,0.35605204,0.012087291,0.00840681,0.009949323,0.00093694194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15151525,0.0009509329,0.0025193107,0.0030990397,0.00081503316,0.0019672941,0.0028936493,0.002002324,0.0019154687],"category_scores_gemma":[0.23076567,0.00096250867,0.0048945826,0.0036382107,0.00093256356,0.0030547439,0.0022897818,0.0023656513,0.00048680985],"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":[0.016226828,0.0015093739,0.17330685,0.006692924,0.016453845,0.00023349887,0.0052095326,0.026597658,0.0010794655,0.0070784464,0.0044988324,0.74111277],"study_design_scores_gemma":[0.008248038,0.026328223,0.33523977,0.01619468,0.01460413,0.003256275,0.00627282,0.5130343,0.008188265,0.04595362,0.021049643,0.0016302638],"about_ca_topic_score_codex":0.0012796883,"about_ca_topic_score_gemma":0.001805761,"teacher_disagreement_score":0.15151525,"about_ca_system_score_codex":0.0012737147,"about_ca_system_score_gemma":0.0017104285,"threshold_uncertainty_score":0.8012988},"labels":[],"label_agreement":null},{"id":"W162150885","doi":"10.1007/978-1-4419-8342-8_8","title":"Longitudinal Mixed Models for Count Data","year":2011,"lang":"en","type":"book-chapter","venue":"Springer series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Count data; Longitudinal data; Mixed model; Statistics; Computer science; Mathematics; Data mining","score_opus":0.24952265821989295,"score_gpt":0.38211275749899143,"score_spread":0.13259009927909848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W162150885","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":[0.0003913936,0.011913007,0.98026216,0.0012367847,0.0005150258,0.00002212528,0.00056489045,0.0005464637,0.00454815],"genre_scores_gemma":[0.041626777,0.04001455,0.84922093,0.0024046572,0.0039638784,0.0011781041,0.005207963,0.0016763824,0.05470671],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976018,0.0014829298,0.00014508318,0.00033425173,0.00037651678,0.000059406066],"domain_scores_gemma":[0.9894175,0.008795554,0.00040367973,0.00078765093,0.00050314126,0.00009238052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00629291,0.0026025677,0.0023088558,0.0018397876,0.00042540193,0.0024610877,0.003789981,0.0030886745,0.01664622],"category_scores_gemma":[0.016369063,0.0018477495,0.0021911103,0.0034432982,0.001240523,0.003767561,0.0015709738,0.005152916,0.009273913],"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":[0.000028064022,0.0000443588,0.00065476866,0.00057428074,0.0002384873,0.00015776855,0.00024506336,0.019461036,0.00043022286,0.78081,0.056285236,0.14107066],"study_design_scores_gemma":[0.000011712136,0.000018828798,0.0003045612,0.00013973245,0.00006387999,0.00019408444,0.000022744684,0.060600486,0.00014645247,0.89303935,0.045421366,0.000036814443],"about_ca_topic_score_codex":0.0020658604,"about_ca_topic_score_gemma":0.0035679839,"teacher_disagreement_score":0.01664622,"about_ca_system_score_codex":0.0011284174,"about_ca_system_score_gemma":0.0011829169,"threshold_uncertainty_score":0.05568713},"labels":[],"label_agreement":null},{"id":"W1654792707","doi":"10.1002/9781118445112.stat05261","title":"Sensitivity Analysis: Introduction","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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","funders":"","keywords":"Sensitivity (control systems); Monte Carlo method; Interpretation (philosophy); Computer science; Econometrics; Statistics; Data mining; Mathematics; Engineering","score_opus":0.05371411958990592,"score_gpt":0.37448769006173294,"score_spread":0.32077357047182703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1654792707","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":[0.0052397144,0.04744856,0.77530307,0.023172455,0.0097477315,0.023350012,0.024230402,0.0019345417,0.08957343],"genre_scores_gemma":[0.23701392,0.029275386,0.57767624,0.021800345,0.0060984422,0.087037615,0.0077283033,0.0025420743,0.030827645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.7303919,0.22963205,0.012414889,0.0075642285,0.018369334,0.0016276455],"domain_scores_gemma":[0.6097601,0.33186474,0.010328166,0.019318726,0.027963372,0.0007649581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12771715,0.003723509,0.0047967397,0.0059201345,0.0011562802,0.006279211,0.0035578741,0.004144092,0.05546371],"category_scores_gemma":[0.3907893,0.0011125861,0.01053248,0.0060970476,0.002759161,0.0049439403,0.0037455808,0.0063661854,0.005894429],"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":[0.001963016,0.00027032752,0.0023128504,0.06786688,0.0086923335,0.00046828343,0.0011385111,0.06480691,0.0015812949,0.38354942,0.1889374,0.27841273],"study_design_scores_gemma":[0.0005009862,0.0008423034,0.0018970671,0.027335994,0.0028982416,0.00047061974,0.00040331608,0.033184383,0.0034332515,0.54898906,0.37970707,0.00033775214],"about_ca_topic_score_codex":0.0019386867,"about_ca_topic_score_gemma":0.0013745701,"teacher_disagreement_score":0.12771715,"about_ca_system_score_codex":0.0051012193,"about_ca_system_score_gemma":0.006351293,"threshold_uncertainty_score":0.6754409},"labels":[],"label_agreement":null},{"id":"W1660683093","doi":"10.1002/mpr.330","title":"Missing value imputation in longitudinal measures of alcohol consumption","year":2011,"lang":"en","type":"article","venue":"International Journal of Methods in Psychiatric Research","topic":"Statistical Methods and Bayesian Inference","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":"Centre for Addiction and Mental Health","funders":"National Institute on Alcohol Abuse and Alcoholism; Systembolaget; Stockholms Universitet; Syddansk Universitet; Medical Research Council; Sundhed og Sygdom, Det Frie Forskningsråd","keywords":"Imputation (statistics); Missing data; Statistics; Multivariate statistics; Alcohol consumption; Econometrics; Bayesian probability; Normality; Longitudinal study; Attrition; Multivariate normal distribution; Longitudinal data; Mathematics; Computer science; Medicine; Alcohol; Data mining","score_opus":0.5896188207123231,"score_gpt":0.6197999642988605,"score_spread":0.03018114358653745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1660683093","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":[0.02007253,0.0045767403,0.97075045,0.0013660475,0.00031709723,0.00034552516,0.0013426673,0.00033501058,0.00089395815],"genre_scores_gemma":[0.31844792,0.003954938,0.66895396,0.0007377524,0.00037515018,0.0027917784,0.0032651993,0.00016681243,0.0013065145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90558314,0.08558101,0.0030944955,0.0026796684,0.0025182075,0.00054338656],"domain_scores_gemma":[0.8245533,0.1466587,0.0112943,0.013373906,0.0035860962,0.00053370825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08804023,0.000848383,0.0026300927,0.0027397836,0.0012053215,0.0016615259,0.0029911802,0.0018806217,0.003061834],"category_scores_gemma":[0.23711391,0.0010625361,0.0021860008,0.006769745,0.0014025023,0.002382236,0.0023632431,0.0031591482,0.0006153865],"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":[0.0017630653,0.00053899834,0.12427703,0.0056101214,0.0069059785,0.0008162799,0.0032402447,0.09253546,0.0009901782,0.1738,0.025066683,0.564456],"study_design_scores_gemma":[0.0005716751,0.000986101,0.031870253,0.002238176,0.001419666,0.00078596076,0.0008173443,0.2834626,0.0025319965,0.65051466,0.024587307,0.00021425329],"about_ca_topic_score_codex":0.0027735718,"about_ca_topic_score_gemma":0.0029409302,"teacher_disagreement_score":0.08804023,"about_ca_system_score_codex":0.0010394249,"about_ca_system_score_gemma":0.0030602582,"threshold_uncertainty_score":0.4656068},"labels":[],"label_agreement":null},{"id":"W1666386643","doi":"10.1214/lnms/1215540964","title":"The Practical Implementation of Bayesian Model Selection","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes-monograph series","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":433,"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":"Selection (genetic algorithm); Computer science; Bayesian probability; Bayesian inference; Model selection; Artificial intelligence","score_opus":0.05280911275820354,"score_gpt":0.37702415102135883,"score_spread":0.3242150382631553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1666386643","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":[0.00044236612,0.00026191122,0.98930806,0.00087471836,0.0000592233,0.000033158463,0.00004776636,0.00042156377,0.008551289],"genre_scores_gemma":[0.026160013,0.0008603861,0.96544725,0.0003924949,0.00012245151,0.00022720866,0.00019923325,0.00023372144,0.0063572684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99457353,0.0035449488,0.00012852362,0.0003351254,0.0013172708,0.000100602636],"domain_scores_gemma":[0.99520314,0.003426852,0.00010871399,0.0006846087,0.0005196452,0.00005708199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074388143,0.00094202964,0.0008635951,0.001096693,0.00071310287,0.0024404167,0.0023767778,0.0019496611,0.014927636],"category_scores_gemma":[0.022786004,0.00070922315,0.0008118892,0.001693792,0.001063058,0.0026729375,0.0025074875,0.0029350163,0.007513294],"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":[0.0000307514,0.000055793727,0.0003614367,0.00019875263,0.000055721623,0.00018246805,0.0002543898,0.04337056,0.0009911014,0.5744089,0.02140414,0.35868597],"study_design_scores_gemma":[0.00002942507,0.000020167028,0.00018765577,0.00009427246,0.000016004635,0.00021426896,0.000059820384,0.17871524,0.0010749119,0.76302904,0.05652885,0.000030254638],"about_ca_topic_score_codex":0.0021788338,"about_ca_topic_score_gemma":0.0024736358,"teacher_disagreement_score":0.014927636,"about_ca_system_score_codex":0.0009792406,"about_ca_system_score_gemma":0.0016573375,"threshold_uncertainty_score":0.049937904},"labels":[],"label_agreement":null},{"id":"W168979206","doi":"10.22237/jmasm/1083369780","title":"A Comparison Of Methods For Longitudinal Analysis With Missing Data","year":2004,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Manitoba","funders":"","keywords":"Missing data; Mathematics; Statistics; Monotone polygon; Type I and type II errors; Longitudinal data; Econometrics; Data mining; Computer science","score_opus":0.3273429891458027,"score_gpt":0.5781698185418305,"score_spread":0.2508268293960278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W168979206","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":[0.006322654,0.010929141,0.97733605,0.00073975313,0.00081969495,0.0018401492,0.00049064367,0.0005000312,0.0010218688],"genre_scores_gemma":[0.041105956,0.007219891,0.938055,0.00042188822,0.0003343728,0.010594815,0.0005099765,0.0005067402,0.0012515356],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6761649,0.28457502,0.009266051,0.008480869,0.020696722,0.00081643724],"domain_scores_gemma":[0.3089191,0.64681554,0.01038507,0.018295737,0.014511582,0.0010728638],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23662595,0.0022856004,0.004412553,0.0053993277,0.0013576723,0.0035660064,0.004460918,0.0041022194,0.009161503],"category_scores_gemma":[0.5226621,0.0016919957,0.0065058316,0.004549989,0.0023140593,0.006721426,0.0041214703,0.0056113373,0.0016352462],"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":[0.010602363,0.0006200539,0.007634623,0.01072772,0.012355658,0.00015702682,0.0033430883,0.015312425,0.0013478622,0.15798168,0.009200322,0.7707172],"study_design_scores_gemma":[0.011247044,0.008866619,0.028373228,0.012723593,0.009689288,0.0022329083,0.0022586656,0.30807036,0.0051712743,0.5249548,0.08485147,0.0015606597],"about_ca_topic_score_codex":0.001875781,"about_ca_topic_score_gemma":0.0017187323,"teacher_disagreement_score":0.76337403,"about_ca_system_score_codex":0.0027730681,"about_ca_system_score_gemma":0.005213558,"threshold_uncertainty_score":0.9413761},"labels":[],"label_agreement":null},{"id":"W172874229","doi":"10.1023/a:1020750810409","title":"Marginal and hazard ratio specific random data generation: Applications to semi-parametric bootstrapping","year":2002,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Covariate; Censoring (clinical trials); Statistics; Bootstrapping (finance); Estimator; Event (particle physics); Mathematics; Parametric statistics; Computer science; Econometrics","score_opus":0.1842801634588534,"score_gpt":0.3748533784005708,"score_spread":0.19057321494171742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W172874229","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":[0.0016350019,0.000084315,0.9977083,0.00007934694,0.000021677852,0.000074171236,0.000041494546,0.00017723504,0.00017838561],"genre_scores_gemma":[0.075347066,0.00022394757,0.9223297,0.0001459267,0.00007629074,0.00076071225,0.00027450302,0.0002432217,0.000598582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9873345,0.009874912,0.00044386447,0.0008933286,0.001286214,0.0001671883],"domain_scores_gemma":[0.9145099,0.07210869,0.0021424904,0.0073208865,0.0032598646,0.00065815443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0337232,0.0009583867,0.0020837132,0.0022308421,0.00074426614,0.002018841,0.0035563917,0.0019225139,0.006001388],"category_scores_gemma":[0.17254777,0.0008627746,0.0017643598,0.002397245,0.0020424363,0.0024806182,0.0034737925,0.003550663,0.0011595748],"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":[0.00072827603,0.00021196247,0.0038069247,0.00045893705,0.0003004464,0.00037277295,0.0009840635,0.16774265,0.0013156211,0.38607806,0.006725982,0.4312744],"study_design_scores_gemma":[0.00011368271,0.000048375223,0.00058470847,0.00005708252,0.00004148812,0.00017800486,0.000038926824,0.57702774,0.0006855052,0.41892675,0.0022647383,0.00003304937],"about_ca_topic_score_codex":0.0008226093,"about_ca_topic_score_gemma":0.0009912773,"teacher_disagreement_score":0.0337232,"about_ca_system_score_codex":0.00092959614,"about_ca_system_score_gemma":0.0016363708,"threshold_uncertainty_score":0.17834747},"labels":[],"label_agreement":null},{"id":"W1748097147","doi":"10.1002/sim.6584","title":"Joint estimation of multiple disease‐specific sensitivities and specificities via crossed random effects models for correlated reader‐based diagnostic data: application of data cloning","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random effects model; Computer science; Maximum likelihood; Bayesian probability; Maximization; Expectation–maximization algorithm; Statistics; Artificial intelligence; Marginal likelihood; Machine learning; Mathematics; Medicine; Mathematical optimization; Pathology","score_opus":0.19629008733233633,"score_gpt":0.402939802217801,"score_spread":0.20664971488546469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1748097147","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":[0.021836529,0.00020504947,0.97703326,0.00023735849,0.000022077105,0.00012115585,0.00017096975,0.00011202059,0.00026163645],"genre_scores_gemma":[0.4990153,0.0005949009,0.49533394,0.00039461444,0.00009516676,0.0011298645,0.0008168613,0.00013046578,0.0024888841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96702975,0.025848554,0.0011196956,0.0034392865,0.0018945694,0.00066809024],"domain_scores_gemma":[0.8228527,0.1555048,0.008732988,0.009005289,0.0031636718,0.0007405856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07077909,0.0019197544,0.0025810723,0.0022593034,0.000646727,0.0030540598,0.0047420077,0.00279212,0.0021830301],"category_scores_gemma":[0.114474736,0.0018135753,0.0044269385,0.0027945389,0.0035425425,0.0031986847,0.0038352513,0.0033020834,0.00041052574],"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":[0.0006885303,0.00021493714,0.01800047,0.00022538929,0.0010379334,0.00079685426,0.0008867321,0.73478234,0.0021084473,0.18589266,0.0007118891,0.05465382],"study_design_scores_gemma":[0.0000832067,0.0001543025,0.0021106286,0.000041983527,0.00022243433,0.0001753767,0.000034617966,0.92875427,0.0007999427,0.06669501,0.00084230606,0.00008590874],"about_ca_topic_score_codex":0.008606748,"about_ca_topic_score_gemma":0.004946235,"teacher_disagreement_score":0.07077909,"about_ca_system_score_codex":0.002532372,"about_ca_system_score_gemma":0.0022044175,"threshold_uncertainty_score":0.3743201},"labels":[],"label_agreement":null},{"id":"W17512885","doi":"10.1023/a:1015790929604","title":"A Random-Discretization Based Monte Carlo Sampling Method and its Applications","year":2002,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"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 Manitoba","funders":"","keywords":"Mathematics; Markov chain Monte Carlo; Slice sampling; Monte Carlo integration; Conjugate prior; Importance sampling; Rejection sampling; Applied mathematics; Posterior probability; Algorithm; Mathematical optimization; Metropolis–Hastings algorithm; Monte Carlo method; Hybrid Monte Carlo; Bayesian probability; Statistics","score_opus":0.24249053881518878,"score_gpt":0.41108403471550947,"score_spread":0.1685934959003207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W17512885","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":[0.0006434403,0.00019042735,0.99817693,0.00008340198,0.0000504221,0.000022938257,0.000016902322,0.00005664742,0.0007589178],"genre_scores_gemma":[0.049368545,0.0005901794,0.9467552,0.00015664303,0.00012354561,0.000274561,0.0001091694,0.00013661175,0.0024856343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975089,0.0015424012,0.00008621742,0.00020370788,0.00060088944,0.000057951467],"domain_scores_gemma":[0.99174786,0.0062429793,0.0002303535,0.0006367759,0.0009572723,0.0001848589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004650223,0.00056545757,0.0013966445,0.0013802391,0.00079486857,0.0012010026,0.0021928668,0.001640365,0.004691752],"category_scores_gemma":[0.015397766,0.00066330057,0.00090017775,0.002230545,0.001576314,0.0015135694,0.0014277467,0.0017038293,0.0009388819],"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":[0.00010381764,0.00010029117,0.00088845554,0.0002647292,0.00009870005,0.00015610608,0.0001901367,0.27950412,0.0017169738,0.582195,0.0050684363,0.12971324],"study_design_scores_gemma":[0.00002742195,0.000017836146,0.0001259729,0.000030675998,0.000015986267,0.00009398168,0.000010822266,0.9110896,0.0004255455,0.082269035,0.0058702156,0.000022854087],"about_ca_topic_score_codex":0.0031239158,"about_ca_topic_score_gemma":0.0028743919,"teacher_disagreement_score":0.004691752,"about_ca_system_score_codex":0.00083630625,"about_ca_system_score_gemma":0.0011516688,"threshold_uncertainty_score":0.024592996},"labels":[],"label_agreement":null},{"id":"W1753146223","doi":"10.1016/j.jclinepi.2015.08.015","title":"Sample size calculations for stepped wedge and cluster randomised trials: a unified approach","year":2015,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":241,"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":"Medical Research Council; National Institute for Health and Care Research","keywords":"Sample size determination; Cluster (spacecraft); Cluster randomised controlled trial; Wedge (geometry); Computer science; Cluster size; Statistics; Randomized controlled trial; Data mining; Mathematics; Medicine; Physics; Surgery","score_opus":0.733403766534936,"score_gpt":0.6107111935731112,"score_spread":0.12269257296182479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1753146223","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":[0.00023204551,0.00030071536,0.9982868,0.00030198216,0.000081751365,0.0003630375,0.000029974,0.00006972507,0.00033398176],"genre_scores_gemma":[0.0075771506,0.0004349418,0.9882328,0.0003507323,0.00010986914,0.0029393367,0.000040545925,0.00009030128,0.00022439395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.74267113,0.22112161,0.011821626,0.004926744,0.018592332,0.000866546],"domain_scores_gemma":[0.76022136,0.20412491,0.008694436,0.015443178,0.0107742185,0.00074190565],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.18937255,0.0029358629,0.005392577,0.007912553,0.0010599323,0.004856673,0.008418962,0.006706226,0.0072189304],"category_scores_gemma":[0.4407439,0.0024192722,0.0046755895,0.0064009335,0.0060309977,0.006915896,0.0063509797,0.0098830415,0.0018800219],"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":[0.0004208237,0.000118312724,0.0007287885,0.0024138512,0.0007067319,0.00033381264,0.0010147633,0.0648134,0.0009275713,0.7435537,0.0091644535,0.17580375],"study_design_scores_gemma":[0.0006493867,0.00048009848,0.0003653438,0.001435001,0.00034516474,0.00037496837,0.0001338698,0.20452008,0.0015175543,0.76854086,0.0215003,0.00013736934],"about_ca_topic_score_codex":0.00160979,"about_ca_topic_score_gemma":0.0012625922,"teacher_disagreement_score":0.81062746,"about_ca_system_score_codex":0.004178756,"about_ca_system_score_gemma":0.006922233,"threshold_uncertainty_score":0.999648},"labels":[],"label_agreement":null},{"id":"W1814634804","doi":"10.17269/cjph.106.4914","title":"Linking missing data to study outcomes using multiple imputations","year":2015,"lang":"en","type":"letter","venue":"Canadian Journal of Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Missing data; Computer science; Statistics; Mathematics; Machine learning","score_opus":0.5874388667483118,"score_gpt":0.5013832891295904,"score_spread":0.0860555776187214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1814634804","genre_codex":"commentary","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":[0.0006895973,0.0034159652,0.07197139,0.91345876,0.0072991853,0.00008993465,0.00030446672,0.00014539895,0.002625287],"genre_scores_gemma":[0.0515794,0.010038215,0.12723097,0.71324277,0.09016237,0.0012547038,0.00040995318,0.00037260778,0.0057089888],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8955685,0.0849205,0.0065770317,0.0036382177,0.00826605,0.0010297612],"domain_scores_gemma":[0.39114168,0.5719622,0.009157998,0.014078555,0.011118677,0.0025408869],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.116846725,0.0008886,0.003699376,0.0025841063,0.0023938275,0.005297639,0.0046329405,0.02157609,0.005216299],"category_scores_gemma":[0.5626385,0.001494727,0.0029463517,0.0039004986,0.0059865043,0.005816206,0.0033352217,0.03806833,0.0026503394],"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":[0.00049393723,0.00009880544,0.009851936,0.00097200164,0.001031213,0.0027516417,0.001272643,0.0034112285,0.0001562689,0.10983654,0.6380638,0.23206006],"study_design_scores_gemma":[0.0006356002,0.00009765481,0.0030750716,0.0019446184,0.0005472047,0.0026546381,0.00029883624,0.020462302,0.00030041044,0.8230517,0.14678177,0.00015019138],"about_ca_topic_score_codex":0.0102737015,"about_ca_topic_score_gemma":0.013608893,"teacher_disagreement_score":0.88315326,"about_ca_system_score_codex":0.0035858932,"about_ca_system_score_gemma":0.0071670087,"threshold_uncertainty_score":0.6179519},"labels":[],"label_agreement":null},{"id":"W1836995708","doi":"10.1111/j.1467-9892.2012.00781.x","title":"Estimation of regression and dynamic dependence paremeters for non‐stationary multinomial time series","year":2012,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Estimator; Mathematics; Statistics; Econometrics; Realization (probability); Multinomial logistic regression; Multinomial distribution","score_opus":0.021795709078350822,"score_gpt":0.3459423407276026,"score_spread":0.3241466316492518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836995708","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":[0.015129687,0.00017270917,0.983967,0.00012544458,0.000011354702,0.000031204814,0.00005746328,0.00010333955,0.00040178976],"genre_scores_gemma":[0.6362733,0.0016203141,0.35463852,0.00015842875,0.00013085944,0.00051247247,0.00091003143,0.00021688669,0.005539162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99562484,0.0026128863,0.00021201864,0.0009087316,0.0005033154,0.00013822509],"domain_scores_gemma":[0.964736,0.030570958,0.0022994278,0.0014423716,0.00078239385,0.00016889999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010714112,0.0009213428,0.0011996558,0.0015472037,0.00042320302,0.0011559773,0.0018606652,0.0012511511,0.0023674504],"category_scores_gemma":[0.06426913,0.00093376386,0.0013413018,0.0016580651,0.0016738501,0.0029396105,0.0019423595,0.0022386995,0.00039670052],"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":[0.000080160265,0.00008650018,0.0076569384,0.00019302816,0.00029030506,0.00019073692,0.00031000146,0.57245266,0.001987042,0.35142282,0.0006403524,0.06468946],"study_design_scores_gemma":[0.00000954745,0.00001828535,0.0010705331,0.000020096284,0.000017759145,0.00003574755,0.000018451696,0.9262577,0.0003071109,0.071752995,0.0004709611,0.000020751895],"about_ca_topic_score_codex":0.0058743455,"about_ca_topic_score_gemma":0.0049486104,"teacher_disagreement_score":0.010714112,"about_ca_system_score_codex":0.0011756422,"about_ca_system_score_gemma":0.0015243022,"threshold_uncertainty_score":0.05666232},"labels":[],"label_agreement":null},{"id":"W184316663","doi":"10.1007/bf03404950","title":"An Introduction to Multilevel Regression Models","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":147,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa; Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Health Canada","keywords":"Multilevel model; Hierarchical database model; Computer science; Regression analysis; Regression; Statistical model; Data mining; Statistics; Artificial intelligence; Machine learning; Econometrics; Mathematics","score_opus":0.2217328318533305,"score_gpt":0.4339817907005796,"score_spread":0.21224895884724906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W184316663","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":[0.00036174624,0.020294748,0.96329117,0.006099845,0.0011431844,0.000047962934,0.00067801937,0.00042207766,0.007661287],"genre_scores_gemma":[0.02221001,0.04429403,0.9018724,0.005343156,0.0067087607,0.00090941,0.0011659384,0.00070759637,0.016788697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959216,0.0024497036,0.0003684873,0.0003793766,0.00078104035,0.0000996595],"domain_scores_gemma":[0.98684233,0.011045212,0.00034686443,0.0008331004,0.00078455865,0.00014791074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054050414,0.0015737405,0.0020882564,0.0032258418,0.0008129291,0.002518072,0.0029157582,0.0032300856,0.018941294],"category_scores_gemma":[0.01941304,0.0017209502,0.0032995497,0.005779096,0.0023485927,0.0034590552,0.0023725203,0.008827837,0.0067844386],"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":[0.000011948074,0.000041235184,0.00034699196,0.00053035613,0.000100507714,0.000114686576,0.00020850006,0.0076699895,0.00034317345,0.86837894,0.04183096,0.080422804],"study_design_scores_gemma":[0.00001066029,0.000017551878,0.00038903923,0.00020403347,0.00003835178,0.00013821693,0.000028095956,0.016430076,0.00010065676,0.8646823,0.11791807,0.000043001015],"about_ca_topic_score_codex":0.00611361,"about_ca_topic_score_gemma":0.00848157,"teacher_disagreement_score":0.018941294,"about_ca_system_score_codex":0.0020036984,"about_ca_system_score_gemma":0.002618721,"threshold_uncertainty_score":0.06336492},"labels":[],"label_agreement":null},{"id":"W1845661996","doi":"10.6000/1929-6029.2015.04.03.7","title":"Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":465,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; U.S. President’s Emergency Plan for AIDS Relief; Centers for Disease Control and Prevention; Georgia State University","keywords":"Categorical variable; Missing data; Imputation (statistics); Computer science; Data mining; Multivariate statistics; Statistics; Econometrics; Mathematics; Machine learning","score_opus":0.4574323852140853,"score_gpt":0.598124388630447,"score_spread":0.14069200341636173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1845661996","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":[0.000455444,0.00042478228,0.99764484,0.00058207277,0.00009465556,0.000118528726,0.00015312551,0.00023053425,0.00029601407],"genre_scores_gemma":[0.01588058,0.0008454687,0.9807349,0.0004601938,0.00018612848,0.0009774745,0.00048757828,0.00015563502,0.000272068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8830714,0.10319077,0.004312978,0.0028399157,0.0060067456,0.0005781569],"domain_scores_gemma":[0.8534623,0.11462827,0.009551147,0.015827341,0.0055745654,0.0009564669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08471342,0.0012580785,0.0018463501,0.0038641454,0.001255518,0.0022958433,0.0042369333,0.0029396694,0.0061546452],"category_scores_gemma":[0.24677205,0.0016232072,0.00330751,0.00815137,0.002762179,0.0035261235,0.0037996487,0.0076816776,0.001948381],"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":[0.0002704508,0.00012536177,0.011071838,0.0030209436,0.001137503,0.000794199,0.0020298238,0.07060639,0.001732735,0.533648,0.045549855,0.33001298],"study_design_scores_gemma":[0.00017169664,0.00034877664,0.003682172,0.001663818,0.00031110697,0.0008867013,0.00031621195,0.34190813,0.0023484917,0.59224,0.055801608,0.00032140204],"about_ca_topic_score_codex":0.0028182743,"about_ca_topic_score_gemma":0.004388935,"teacher_disagreement_score":0.08471342,"about_ca_system_score_codex":0.0013162857,"about_ca_system_score_gemma":0.0071257423,"threshold_uncertainty_score":0.44801277},"labels":[],"label_agreement":null},{"id":"W1850438056","doi":"","title":"A pseudo-GEE approach to analyzing longitudinal surveys under imputation por missing responses","year":2011,"lang":"en","type":"article","venue":"Journal of Official Statistics","topic":"Statistical Methods and Bayesian 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 Waterloo; University of British Columbia","funders":"","keywords":"Missing data; Estimator; Statistics; Imputation (statistics); Mathematics; Generalized estimating equation; Gee; Marginal model; Econometrics; Estimating equations; Regression analysis","score_opus":0.19174119103394777,"score_gpt":0.39776172296818546,"score_spread":0.2060205319342377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1850438056","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":[0.0015887436,0.00011951415,0.99768734,0.000088464054,0.00002616846,0.000053816257,0.00008317303,0.00013842309,0.00021436955],"genre_scores_gemma":[0.08601264,0.0009094861,0.9080757,0.00043581016,0.000181566,0.0012500194,0.0008796515,0.00028335836,0.001971715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97362185,0.021463765,0.00069257536,0.0017499011,0.002071966,0.0003999134],"domain_scores_gemma":[0.95552593,0.030680416,0.0032415115,0.006493793,0.003660881,0.00039753344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028273918,0.0012600212,0.0022067828,0.0029554977,0.00080311805,0.0015840634,0.0040500523,0.0015001098,0.0038897605],"category_scores_gemma":[0.08862422,0.0010694106,0.0035805202,0.0037545895,0.0015605742,0.0033563457,0.0031927533,0.002865564,0.001266527],"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":[0.00027391769,0.00030086812,0.018320719,0.0009456524,0.0020539192,0.00062011817,0.0015929885,0.1460085,0.0020214263,0.42953435,0.006667356,0.39166006],"study_design_scores_gemma":[0.00007686452,0.0003333898,0.0053584683,0.0001440733,0.00023591013,0.00076587533,0.00022504124,0.48899606,0.0009466606,0.48595703,0.016764432,0.00019619078],"about_ca_topic_score_codex":0.0029435765,"about_ca_topic_score_gemma":0.0031970034,"teacher_disagreement_score":0.028273918,"about_ca_system_score_codex":0.0006743608,"about_ca_system_score_gemma":0.0024671988,"threshold_uncertainty_score":0.14952862},"labels":[],"label_agreement":null},{"id":"W1864744707","doi":"10.1146/annurev-statistics-022513-115617","title":"League Tables for Hospital Comparisons","year":2016,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Covariate; Logistic regression; Context (archaeology); Variation (astronomy); League table; Homogeneous; Multilevel model; League; Statistics; Econometrics; Medicine; Computer science; Geography; Mathematics; Economics","score_opus":0.038578099178544795,"score_gpt":0.38042640763881275,"score_spread":0.3418483084602679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1864744707","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014683454,0.032954607,0.9143484,0.008576669,0.0045033298,0.0005893618,0.0070212567,0.0020898057,0.028448228],"genre_scores_gemma":[0.058575567,0.023452118,0.8710751,0.0075090206,0.008615471,0.0064841723,0.010190494,0.0022690075,0.011829098],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.944469,0.04263588,0.0030130378,0.0031573924,0.0063187066,0.00040600938],"domain_scores_gemma":[0.6864269,0.27291796,0.012485992,0.016848018,0.010507574,0.00081360136],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04659254,0.0015956949,0.0025854488,0.0072439276,0.0008985954,0.0042646164,0.0039025277,0.0022939316,0.046021946],"category_scores_gemma":[0.28548628,0.00079203746,0.0022453354,0.010234652,0.0029943318,0.006541564,0.0023379403,0.005238347,0.007911329],"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":[0.00016167371,0.000056137647,0.0020632888,0.0017020683,0.00051100826,0.000091017995,0.00033159752,0.0047243116,0.00009118568,0.59855586,0.14529389,0.24641798],"study_design_scores_gemma":[0.00007140847,0.000095889016,0.0012026116,0.0011547224,0.00013040706,0.00015588537,0.00012413067,0.011372642,0.00016523781,0.81380665,0.17166664,0.000053918542],"about_ca_topic_score_codex":0.0020762961,"about_ca_topic_score_gemma":0.0017985967,"teacher_disagreement_score":0.95340747,"about_ca_system_score_codex":0.0025178266,"about_ca_system_score_gemma":0.0027519017,"threshold_uncertainty_score":0.24640787},"labels":[],"label_agreement":null},{"id":"W1866446821","doi":"10.1002/sta4.95","title":"The perils of quasi‐likelihood information criteria","year":2015,"lang":"en","type":"article","venue":"Stat","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":"Jewish General Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Correlation; Statistics; Econometrics; Model selection; Information Criteria; Mathematics; Sample (material); Matrix (chemical analysis); Computer science; Machine learning","score_opus":0.08157430578771294,"score_gpt":0.4012855105905634,"score_spread":0.3197112048028504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866446821","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":[0.00462451,0.0025770282,0.9793754,0.007506825,0.00022660218,0.00016649981,0.00013924415,0.00015355689,0.005230293],"genre_scores_gemma":[0.29009536,0.0025163414,0.695106,0.005220416,0.001225237,0.001993813,0.0003982461,0.0005193495,0.002925199],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7848261,0.18617886,0.005980306,0.004632721,0.017431881,0.00095018523],"domain_scores_gemma":[0.27702767,0.6727689,0.0111600645,0.024363713,0.013487247,0.0011924128],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20746565,0.001548896,0.0037855494,0.0038810053,0.0022504914,0.007839008,0.0055599688,0.004232309,0.0046423967],"category_scores_gemma":[0.5233025,0.0017439605,0.002028219,0.0040431935,0.013309722,0.011120152,0.006269668,0.011182881,0.0011057626],"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":[0.00010757251,0.000034731667,0.0012646939,0.00039847576,0.00018071738,0.00011353591,0.00062773516,0.009034082,0.00012603756,0.9472234,0.0023878717,0.03850103],"study_design_scores_gemma":[0.00007045246,0.000082814186,0.0005516135,0.00030242925,0.000028178418,0.00009920913,0.000108914304,0.057788186,0.00036188692,0.93508995,0.0054534595,0.00006292197],"about_ca_topic_score_codex":0.0031348222,"about_ca_topic_score_gemma":0.0020022925,"teacher_disagreement_score":0.20746565,"about_ca_system_score_codex":0.0035881056,"about_ca_system_score_gemma":0.0053245677,"threshold_uncertainty_score":0.97733593},"labels":[],"label_agreement":null},{"id":"W1870121983","doi":"10.1111/j.1467-842x.2011.00623.x","title":"SMALL AREA ESTIMATION USING SURVEY WEIGHTS WITH FUNCTIONAL MEASUREMENT ERROR IN THE COVARIATE","year":2011,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","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 Manitoba","funders":"","keywords":"Jackknife resampling; Covariate; Statistics; Estimator; Small area estimation; Mathematics; Mean squared error; Consistency (knowledge bases); Observational error; Linear regression; Regression analysis; Errors-in-variables models; Regression; Sample size determination; Econometrics","score_opus":0.44857872673497534,"score_gpt":0.3658354479033796,"score_spread":0.08274327883159577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1870121983","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":[0.009365068,0.00006550728,0.99022067,0.00004966147,0.00001053553,0.000032694137,0.000029871615,0.00007729516,0.00014873166],"genre_scores_gemma":[0.3354879,0.00030932878,0.66056675,0.00013707753,0.000053815038,0.0005440639,0.00044134032,0.00009850527,0.0023611006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9830634,0.013009364,0.00047640424,0.001928041,0.0012336263,0.00028911477],"domain_scores_gemma":[0.9473747,0.03734592,0.005158418,0.0071261395,0.0025817903,0.00041295015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02596147,0.0011403973,0.002076616,0.0014585117,0.00058787904,0.0011779419,0.0028013587,0.0013696705,0.0019290118],"category_scores_gemma":[0.10276052,0.0011497561,0.001327734,0.0030420332,0.0014748507,0.0030542123,0.0024765036,0.0018762791,0.0005046978],"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":[0.00027606817,0.0002420517,0.040412016,0.00033259395,0.0007664524,0.00021886453,0.0005816511,0.5938463,0.0013971513,0.15454686,0.0018513238,0.20552872],"study_design_scores_gemma":[0.000053510714,0.00015807324,0.004073708,0.00005375776,0.00006597742,0.00008179315,0.000064434615,0.90005356,0.0007418317,0.092475794,0.0021425718,0.0000349535],"about_ca_topic_score_codex":0.008257126,"about_ca_topic_score_gemma":0.008903306,"teacher_disagreement_score":0.02596147,"about_ca_system_score_codex":0.0008035815,"about_ca_system_score_gemma":0.0017726405,"threshold_uncertainty_score":0.13729906},"labels":[],"label_agreement":null},{"id":"W1885976307","doi":"10.1002/0470011815.b2a09013","title":"Errors in the Measurement of Covariates","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","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; Extrapolation; Statistics; Calibration; Regression; Observational error; Replication (statistics); Regression analysis; Econometrics; Computer science; Mathematics","score_opus":0.04403644792157694,"score_gpt":0.3379527177788926,"score_spread":0.2939162698573156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885976307","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":[0.019146098,0.009988913,0.8928071,0.011058095,0.0027320504,0.00046208376,0.0073555387,0.0011052777,0.055344816],"genre_scores_gemma":[0.5932493,0.017013978,0.3085514,0.004732692,0.0020036919,0.0017918205,0.008722798,0.00074563734,0.06318873],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9648599,0.018813673,0.0028890981,0.003698645,0.009154592,0.0005840999],"domain_scores_gemma":[0.9048623,0.058152154,0.009844552,0.020604802,0.006203702,0.0003325094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026010089,0.00094222714,0.0013031971,0.0025906614,0.0006635898,0.0035648625,0.0021016703,0.0016140292,0.015609234],"category_scores_gemma":[0.19140017,0.000562396,0.0008381032,0.0057071196,0.0017757837,0.0022113528,0.0025703178,0.002623116,0.0059675965],"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":[0.00022852709,0.000108714295,0.029081814,0.0011601867,0.0003445625,0.0004658419,0.00093716825,0.012987459,0.0012653202,0.4075924,0.06282561,0.48300245],"study_design_scores_gemma":[0.000092418384,0.00016168872,0.037082694,0.0024997266,0.00028865327,0.0011788312,0.00032299463,0.047149528,0.009496152,0.6657003,0.2358818,0.00014521465],"about_ca_topic_score_codex":0.005586351,"about_ca_topic_score_gemma":0.002882365,"teacher_disagreement_score":0.026010089,"about_ca_system_score_codex":0.0018585073,"about_ca_system_score_gemma":0.00202214,"threshold_uncertainty_score":0.13755608},"labels":[],"label_agreement":null},{"id":"W1889791741","doi":"10.3968/j.pam.1925252820120402.s0803","title":"Robust Inference for Incomplete Binary Longitudinal Data","year":2012,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Missing data; Likelihood function; Outlier; Estimator; Computer science; Parametric statistics; M-estimator; Binary data; Inference; Maximum likelihood; Longitudinal data; Statistics; Data mining; Mathematics; Binary number; Algorithm; Estimation theory; Artificial intelligence; Machine learning","score_opus":0.31822050751602976,"score_gpt":0.4394026099627387,"score_spread":0.12118210244670896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889791741","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":[0.002599617,0.00043959878,0.9964545,0.00015102811,0.000022104974,0.00001538691,0.000059382128,0.00010214006,0.00015622012],"genre_scores_gemma":[0.28235793,0.0023912024,0.71134055,0.0003399256,0.00038106096,0.00050304364,0.0008272719,0.00017936282,0.0016795922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9864893,0.010144174,0.00048447927,0.001364898,0.0012304507,0.00028664301],"domain_scores_gemma":[0.93048865,0.05899402,0.0043933513,0.0038137382,0.0019440404,0.00036622642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025587639,0.0010296415,0.0024738288,0.0023325079,0.0006365952,0.0018302961,0.0037104103,0.001871236,0.0015079675],"category_scores_gemma":[0.0993694,0.0010072067,0.0018567991,0.0024924336,0.0020850175,0.00252902,0.0022086115,0.0030204915,0.0004220186],"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":[0.00033237884,0.00007518229,0.002983293,0.0006738864,0.0007457265,0.00031083304,0.00026762552,0.56590074,0.0012302974,0.30521286,0.0021413502,0.12012575],"study_design_scores_gemma":[0.000050437415,0.00004165699,0.00045648767,0.000040431976,0.00004795664,0.00004979711,0.000017774104,0.84521025,0.00044568104,0.15256885,0.0010450686,0.00002554952],"about_ca_topic_score_codex":0.0028279864,"about_ca_topic_score_gemma":0.0018538823,"teacher_disagreement_score":0.025587639,"about_ca_system_score_codex":0.0013395827,"about_ca_system_score_gemma":0.0017439206,"threshold_uncertainty_score":0.13532197},"labels":[],"label_agreement":null},{"id":"W1892050824","doi":"10.1111/j.1467-9469.2008.00607.x","title":"On a Unified Generalized Quasi–likelihood Approach for Familial–Longitudinal Non‐Stationary Count Data","year":2008,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Count data; Covariate; Statistics; Correlation; Generalized estimating equation; Quasi-likelihood; Longitudinal data; Random effects model; Estimation; Regression; Data set; Regression analysis; Generalized method of moments; Econometrics; Panel data; Data mining; Computer science; Meta-analysis","score_opus":0.13891049529111144,"score_gpt":0.3769823844625512,"score_spread":0.2380718891714398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892050824","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":[0.003539256,0.00019513741,0.9954269,0.00033501606,0.000016658316,0.000054774137,0.000078343466,0.000061506624,0.00029231282],"genre_scores_gemma":[0.15099336,0.0007494387,0.84271276,0.0004325662,0.0001982371,0.0010098398,0.00079460366,0.00017824784,0.0029308752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98281646,0.015263877,0.00030525005,0.00071446225,0.00068727974,0.00021268448],"domain_scores_gemma":[0.94927216,0.044688616,0.0018157735,0.0021891498,0.0016605754,0.00037377633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030097986,0.0011028544,0.002285421,0.00199778,0.00072866265,0.0020660216,0.0042044935,0.0022697914,0.0049340297],"category_scores_gemma":[0.06462526,0.0015182674,0.0021877058,0.002819127,0.0025524076,0.002776034,0.002978245,0.0022542258,0.0009458195],"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":[0.0001202771,0.00011861932,0.0049718563,0.00040274241,0.0003914253,0.00073563226,0.00069893827,0.37139457,0.00078181655,0.5605202,0.0025083406,0.05735564],"study_design_scores_gemma":[0.00003374607,0.000046070323,0.0006718271,0.00004280856,0.000033261975,0.000093233844,0.000040545405,0.91551226,0.00012715888,0.081888065,0.0014783622,0.000032661806],"about_ca_topic_score_codex":0.008396712,"about_ca_topic_score_gemma":0.007389246,"teacher_disagreement_score":0.030097986,"about_ca_system_score_codex":0.0017783636,"about_ca_system_score_gemma":0.0028092929,"threshold_uncertainty_score":0.15917528},"labels":[],"label_agreement":null},{"id":"W1902203376","doi":"10.1002/cjs.11245","title":"Pseudo‐empirical Bayes estimation of small area means based on James–Stein estimation in linear regression models with functional measurement error","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Jackknife resampling; Estimator; Small area estimation; Statistics; Mathematics; Covariate; Mean squared error; Bayes' theorem; Observational error; James–Stein estimator; Econometrics; Efficient estimator; Minimum-variance unbiased estimator; Bayesian probability","score_opus":0.2248664731144845,"score_gpt":0.34959677328301814,"score_spread":0.12473030016853365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902203376","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":[0.015083771,0.0002944125,0.9837933,0.00017617064,0.00003034566,0.00004933179,0.000043680415,0.00008312004,0.00044590153],"genre_scores_gemma":[0.4553304,0.0012789285,0.53818005,0.00029179637,0.00023705179,0.00052389374,0.00049676956,0.0001695285,0.0034915993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99039906,0.0066124834,0.00037135833,0.0012540518,0.0010904341,0.00027266078],"domain_scores_gemma":[0.9330073,0.055310935,0.0038644548,0.002893071,0.0043438585,0.0005804186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021178406,0.0010598106,0.002236693,0.0025397458,0.00097332866,0.0019628194,0.00246052,0.0014578076,0.0030188633],"category_scores_gemma":[0.09719717,0.0009547914,0.0014960828,0.0023305614,0.0030833592,0.0030332345,0.0023692485,0.0024027124,0.00052292115],"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":[0.00022898655,0.00016277819,0.026727784,0.00053703383,0.00062158745,0.00034797424,0.0007525894,0.4679207,0.0018966398,0.34899214,0.0039245095,0.14788733],"study_design_scores_gemma":[0.000025253565,0.00006110363,0.0030917188,0.00008004378,0.00005244075,0.00007718847,0.00006721996,0.89424926,0.0005759644,0.10023273,0.0014420004,0.00004509145],"about_ca_topic_score_codex":0.009121115,"about_ca_topic_score_gemma":0.008502555,"teacher_disagreement_score":0.021178406,"about_ca_system_score_codex":0.0015128446,"about_ca_system_score_gemma":0.0030815438,"threshold_uncertainty_score":0.112003446},"labels":[],"label_agreement":null},{"id":"W1903799253","doi":"10.1002/widm.1094","title":"Bayesian treed response surface models","year":2013,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Acadia University","funders":"","keywords":"Computer science; Machine learning; Bayesian probability; Artificial intelligence; Model selection; Bayesian linear regression; Tree (set theory); Inference; Gaussian process; Bayesian inference; Data mining; Mathematics; Gaussian","score_opus":0.16156548228287826,"score_gpt":0.4164045060676965,"score_spread":0.25483902378481826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1903799253","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":[0.006359495,0.0014536913,0.982969,0.00093614485,0.00010398847,0.00007087347,0.0012488521,0.0004472145,0.0064107585],"genre_scores_gemma":[0.50743,0.0077119893,0.4391696,0.0013570874,0.00054918,0.0014446472,0.0044153496,0.0005840433,0.03733817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972204,0.0013817272,0.000113422866,0.00048766405,0.00062383234,0.00017293025],"domain_scores_gemma":[0.99522567,0.003394727,0.0004057684,0.00025568265,0.00059890386,0.00011920584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004411372,0.0010606373,0.0021374961,0.0014666268,0.00044723804,0.0024019354,0.0030667242,0.0028192098,0.009895732],"category_scores_gemma":[0.0161201,0.0008434252,0.0016215421,0.0022662329,0.0010017686,0.0021860383,0.001405913,0.0020819937,0.0030782276],"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":[0.00008333032,0.000046884164,0.0016733223,0.00026474704,0.00015605522,0.000094070754,0.00014532995,0.5349441,0.0005862385,0.40345362,0.008120726,0.050431557],"study_design_scores_gemma":[0.0000188992,0.000015936714,0.00031128636,0.00003555292,0.000024477891,0.00003491158,0.000014050506,0.8518364,0.00007658805,0.14246926,0.005141771,0.000020835942],"about_ca_topic_score_codex":0.0059319045,"about_ca_topic_score_gemma":0.004536224,"teacher_disagreement_score":0.009895732,"about_ca_system_score_codex":0.001197307,"about_ca_system_score_gemma":0.0012111628,"threshold_uncertainty_score":0.03310454},"labels":[],"label_agreement":null},{"id":"W1905468391","doi":"10.1080/10920277.2006.10597423","title":"Compound Poisson Model with Covariates","year":2006,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Poisson distribution; Actuarial science; Econometrics; Poisson regression; Count data; Aggregate data; Population; Random effects model; Aggregate (composite); Product (mathematics); Economics; Statistics; Demography; Mathematics; Medicine","score_opus":0.03018042691583615,"score_gpt":0.32047695827673295,"score_spread":0.2902965313608968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905468391","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":[0.085855044,0.0016690306,0.8892381,0.00371814,0.0005780086,0.0007727944,0.008140709,0.0009892986,0.009038903],"genre_scores_gemma":[0.8120987,0.003062959,0.12309561,0.0009138026,0.0010659208,0.0023009577,0.0063124155,0.00022090266,0.05092878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99475455,0.00222722,0.00029392223,0.0012482394,0.0008207577,0.0006552756],"domain_scores_gemma":[0.969453,0.023816152,0.0028941808,0.0013862577,0.001968238,0.0004822439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013866994,0.0016380606,0.0038471771,0.0026256035,0.0011285116,0.0037314754,0.005718468,0.004560971,0.017156435],"category_scores_gemma":[0.030662376,0.0014435332,0.0021580071,0.0041971146,0.0024142598,0.0033509107,0.001753752,0.0039582225,0.00312114],"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":[0.0005666007,0.0002608437,0.021380454,0.0004839968,0.0003670083,0.0018900322,0.0006297449,0.43888247,0.00079163077,0.49651375,0.009731325,0.028502133],"study_design_scores_gemma":[0.00017728441,0.00014467766,0.0034485278,0.00008278424,0.00014015072,0.0004404429,0.00011359518,0.85590035,0.00027673942,0.1343729,0.0048204437,0.00008216461],"about_ca_topic_score_codex":0.020397445,"about_ca_topic_score_gemma":0.0099661425,"teacher_disagreement_score":0.020397445,"about_ca_system_score_codex":0.0024288646,"about_ca_system_score_gemma":0.0024223377,"threshold_uncertainty_score":0.07333654},"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":"W1933202348","doi":"10.1111/j.1467-9868.2007.00613.x","title":"Statistical Classification with Missing Covariates","year":2007,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","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":"Carleton University","funders":"Carleton University","keywords":"Missing data; Covariate; Imputation (statistics); Parametric statistics; Bayes' theorem; Classifier (UML); Statistics; Mathematics; Computer science; Bayes classifier; Artificial intelligence; Econometrics; Bayesian probability","score_opus":0.12836077954428285,"score_gpt":0.40152608514070975,"score_spread":0.27316530559642693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933202348","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":[0.0033600498,0.0075709783,0.9792036,0.004488097,0.0006897731,0.00003610129,0.0003825256,0.00025513044,0.004013744],"genre_scores_gemma":[0.36774462,0.025085934,0.5725044,0.00410942,0.012456547,0.0007469734,0.0033650978,0.00065971084,0.013327313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.986051,0.007333703,0.00083358114,0.0018040726,0.0035460002,0.00043151237],"domain_scores_gemma":[0.9223209,0.058855694,0.0039238296,0.007885496,0.006218275,0.00079577556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02097544,0.0012205156,0.003431309,0.004589239,0.0012134474,0.0033550092,0.0031973377,0.002534447,0.006545935],"category_scores_gemma":[0.08427399,0.0009861139,0.0023648883,0.005885054,0.0038353172,0.0061579426,0.0023719922,0.006079631,0.0028486173],"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":[0.00009583486,0.00010421683,0.0033427523,0.0010588754,0.00037724088,0.00056193664,0.00029695831,0.06584448,0.0006878317,0.75501454,0.02149113,0.15112427],"study_design_scores_gemma":[0.000008112036,0.000028706916,0.0004963764,0.00015573268,0.000035140667,0.00013954102,0.00002431007,0.08110759,0.00018241892,0.9086581,0.009136021,0.00002792229],"about_ca_topic_score_codex":0.0015219447,"about_ca_topic_score_gemma":0.0007334271,"teacher_disagreement_score":0.02097544,"about_ca_system_score_codex":0.0021242767,"about_ca_system_score_gemma":0.0021692396,"threshold_uncertainty_score":0.110930085},"labels":[],"label_agreement":null},{"id":"W1940841315","doi":"10.1002/bimj.201100037","title":"Bias analysis and the simulation‐extrapolation method for survival data with covariate measurement error under parametric proportional odds models","year":2012,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"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","keywords":"Covariate; Estimator; Observational error; Extrapolation; Econometrics; Statistics; Nominal level; Parametric statistics; Computer science; Proportional hazards model; Errors-in-variables models; Mathematics; Confidence interval","score_opus":0.5724326153365282,"score_gpt":0.48660759469819453,"score_spread":0.0858250206383337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1940841315","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":[0.0045708255,0.000405084,0.99385357,0.00022659887,0.000045620625,0.000087572014,0.00004661972,0.00016624985,0.0005979641],"genre_scores_gemma":[0.33931848,0.0019218095,0.65295404,0.00054254115,0.0003235934,0.0013855482,0.0005888178,0.00025446355,0.0027106975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98542225,0.01119336,0.00047189376,0.0007472395,0.0018717858,0.00029350832],"domain_scores_gemma":[0.8989466,0.08949722,0.0034580713,0.004146382,0.0034505734,0.00050122256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028661761,0.0010629781,0.0015696655,0.0024534399,0.0007455557,0.0012852345,0.0024584457,0.0016967398,0.0038535728],"category_scores_gemma":[0.13289061,0.00054362795,0.0021528578,0.0019587034,0.0018910067,0.0021847996,0.003829448,0.0027525993,0.0005881403],"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":[0.00058654347,0.00015970247,0.018100968,0.0008838351,0.00046407658,0.00090275874,0.0008381052,0.38378212,0.0032042349,0.35700125,0.0033769505,0.23069938],"study_design_scores_gemma":[0.000073352465,0.00011641846,0.001288407,0.00016774157,0.00007596017,0.00034335686,0.000062436724,0.8619813,0.0014823322,0.13076702,0.003593752,0.000047958536],"about_ca_topic_score_codex":0.0019821085,"about_ca_topic_score_gemma":0.0011476136,"teacher_disagreement_score":0.028661761,"about_ca_system_score_codex":0.0011981245,"about_ca_system_score_gemma":0.0024316912,"threshold_uncertainty_score":0.15157968},"labels":[],"label_agreement":null},{"id":"W1947272822","doi":"10.1111/j.1467-9469.2008.00603.x","title":"Simplex Mixed‐Effects Models for Longitudinal Proportional Data","year":2008,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":62,"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; Alberta Cancer Foundation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Restricted maximum likelihood; Generalized linear mixed model; Laplace's method; Statistics; Quasi-likelihood; Outlier; Mixed model; Applied mathematics; Simplex; Inference; Likelihood function; Random effects model; Statistical inference; Count data; Maximum likelihood; Poisson distribution; Combinatorics; Computer science; Bayesian probability","score_opus":0.2331023566259542,"score_gpt":0.4111341075121906,"score_spread":0.1780317508862364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947272822","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":[0.0035240105,0.00013304969,0.9952636,0.00014353308,0.000033086075,0.00015107768,0.00025249115,0.00015298626,0.00034619344],"genre_scores_gemma":[0.13973904,0.0005188578,0.8501068,0.0003054239,0.00010618329,0.0033830344,0.0013075008,0.00015829415,0.0043748305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95865864,0.034513604,0.0010148959,0.002873741,0.0024258918,0.00051326427],"domain_scores_gemma":[0.9227424,0.06564342,0.0032388922,0.004991907,0.0028289778,0.0005543076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0505565,0.0020955359,0.0037340717,0.0031975235,0.0010992932,0.0028499963,0.007067017,0.002898124,0.009534978],"category_scores_gemma":[0.10359538,0.0018310855,0.0043881885,0.0032845049,0.002746453,0.0039558075,0.0040562195,0.0049046515,0.0016406017],"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":[0.0004163371,0.00016972997,0.00405519,0.00041899906,0.00092164194,0.00038932453,0.00088185776,0.31771067,0.0004524881,0.6180591,0.0026796178,0.05384507],"study_design_scores_gemma":[0.00011125472,0.00010107518,0.00052457466,0.00006339368,0.000109120956,0.00009308338,0.00007762684,0.73010874,0.00027132232,0.26579958,0.0026840644,0.000056110366],"about_ca_topic_score_codex":0.0061015165,"about_ca_topic_score_gemma":0.005778044,"teacher_disagreement_score":0.0505565,"about_ca_system_score_codex":0.0022773047,"about_ca_system_score_gemma":0.0021161241,"threshold_uncertainty_score":0.26737148},"labels":[],"label_agreement":null},{"id":"W1948911708","doi":"10.1111/sjos.12177","title":"Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation","year":2015,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse probability weighting; Missing data; Imputation (statistics); Mathematics; Estimator; Weighting; Statistics; Robustness (evolution); Covariate; Inverse probability; Probability distribution; Computer science; Posterior probability; Bayesian probability","score_opus":0.06789976975816031,"score_gpt":0.34902007953364034,"score_spread":0.28112030977548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948911708","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":[0.001572992,0.00016713557,0.99758124,0.00011462962,0.000043216995,0.000046938498,0.000024090452,0.00017549316,0.0002741367],"genre_scores_gemma":[0.07618452,0.0003894093,0.9210738,0.00021057662,0.00022825331,0.0003953932,0.0002696333,0.00026179582,0.0009865166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95447946,0.035020173,0.0020212734,0.0036985045,0.004014147,0.0007664945],"domain_scores_gemma":[0.8796293,0.09012176,0.004736133,0.017788991,0.0071228063,0.0006009918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05672647,0.0022864412,0.004420392,0.0044355,0.0011919076,0.0034401566,0.0048795235,0.003284127,0.0033606552],"category_scores_gemma":[0.18378915,0.0016553025,0.003197069,0.0066481973,0.0016350759,0.005330751,0.0068365377,0.0041335733,0.0013800011],"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":[0.00046987686,0.00032261643,0.010710937,0.0008900104,0.0018330303,0.0005801289,0.00095146894,0.25678906,0.004632364,0.1466047,0.0051135863,0.57110226],"study_design_scores_gemma":[0.00011711945,0.00014779792,0.001617586,0.00015142252,0.00023860738,0.00025199557,0.000103389386,0.87729305,0.0025361888,0.11010751,0.0073405257,0.000094798386],"about_ca_topic_score_codex":0.0026780204,"about_ca_topic_score_gemma":0.0021098233,"teacher_disagreement_score":0.05672647,"about_ca_system_score_codex":0.00083081063,"about_ca_system_score_gemma":0.002608631,"threshold_uncertainty_score":0.30000186},"labels":[],"label_agreement":null},{"id":"W1953290087","doi":"10.1002/sim.6733","title":"Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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; U.S. Department of Defense","keywords":"Test (biology); Computer science; Imperfect; Statistics; Diagnostic test; Medicine; Mathematics; Biology; Pediatrics","score_opus":0.07780357091141932,"score_gpt":0.3776192654494842,"score_spread":0.2998156945380649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953290087","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":[0.015027293,0.00056147715,0.9830125,0.00034691326,0.000029766796,0.00007251867,0.000105683226,0.0003249943,0.0005188617],"genre_scores_gemma":[0.34632105,0.00046160055,0.65125895,0.0004021792,0.00011722558,0.00031613684,0.00039877667,0.00007749364,0.0006466395],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98968047,0.004880838,0.00064625824,0.0021309976,0.0023498968,0.0003114558],"domain_scores_gemma":[0.9780733,0.014872531,0.0022869166,0.0020605866,0.0022558756,0.00045074357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016929168,0.0012126693,0.0027382257,0.0043888036,0.00059182453,0.0024727958,0.0029863664,0.0025740846,0.0010292253],"category_scores_gemma":[0.050360657,0.00090396724,0.0016270957,0.0022549082,0.0019020776,0.002854177,0.0025002297,0.002203613,0.0007058921],"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":[0.0013267999,0.0004426787,0.08417581,0.0009909883,0.0012533552,0.0010794076,0.00043017598,0.26203358,0.021615988,0.052537724,0.0039211325,0.5701924],"study_design_scores_gemma":[0.00013944757,0.0006314946,0.010511816,0.00016323717,0.00043863145,0.0015321806,0.000077628254,0.9307631,0.014250122,0.037290588,0.0040385835,0.00016308998],"about_ca_topic_score_codex":0.0018818796,"about_ca_topic_score_gemma":0.0015864085,"teacher_disagreement_score":0.016929168,"about_ca_system_score_codex":0.0012300783,"about_ca_system_score_gemma":0.002311122,"threshold_uncertainty_score":0.089531064},"labels":[],"label_agreement":null},{"id":"W1956056055","doi":"10.1002/bimj.201200195","title":"Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates","year":2013,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Covariate; Ordinal data; Marginal model; Statistics; Econometrics; Inference; Computer science; Framingham Heart Study; Ordinal regression; Parametric model; Mathematics; Parametric statistics; Data mining; Regression analysis; Artificial intelligence","score_opus":0.13739569565555101,"score_gpt":0.3993482810246013,"score_spread":0.2619525853690503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956056055","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":[0.011805042,0.00035339486,0.9870113,0.00018392217,0.000039151186,0.00008913383,0.00013174608,0.0001326289,0.0002537039],"genre_scores_gemma":[0.3731962,0.0007107471,0.62130857,0.00041135986,0.00018460289,0.0012206971,0.00080700737,0.00016172974,0.0019990937],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9745116,0.020439444,0.0007965253,0.0023127347,0.0016058591,0.0003338537],"domain_scores_gemma":[0.89083296,0.08593461,0.006494732,0.013088346,0.003132584,0.00051681424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049789526,0.001046654,0.0019911295,0.0023068169,0.00097346393,0.0014634284,0.0031956516,0.0012240116,0.003372786],"category_scores_gemma":[0.13157403,0.0006111314,0.0024307822,0.002283684,0.0025342496,0.0021058044,0.002582993,0.0026165147,0.000400357],"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":[0.001093798,0.00033039728,0.10086389,0.0011724782,0.0026283765,0.0007180594,0.0022965532,0.06040151,0.0028758775,0.4085614,0.005702259,0.41335544],"study_design_scores_gemma":[0.00014713909,0.00050550496,0.028301295,0.00026802588,0.0006488275,0.0006845152,0.0004301966,0.39715564,0.0033909448,0.55777293,0.010578889,0.00011601914],"about_ca_topic_score_codex":0.002605229,"about_ca_topic_score_gemma":0.002849712,"teacher_disagreement_score":0.049789526,"about_ca_system_score_codex":0.0011349476,"about_ca_system_score_gemma":0.00215081,"threshold_uncertainty_score":0.26331538},"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":"W1964670877","doi":"10.1016/j.csda.2008.02.034","title":"A random effects four-part model, with application to correlated medical costs","year":2008,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Virginia; Agency for Healthcare Research and Quality; Ryerson University","keywords":"Random effects model; Mathematics; Statistics; Laplace's method; Applied mathematics; Generalized estimating equation; Mixed model; Generalized linear model; Generalized linear mixed model; Multivariate statistics; Linear model; Laplace transform; Econometrics; Medicine; Mathematical analysis","score_opus":0.06068986729945356,"score_gpt":0.3676426477605897,"score_spread":0.3069527804611361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964670877","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":[0.03296161,0.0011747804,0.95742136,0.0023225965,0.00038870933,0.00045007298,0.0017109418,0.0006204471,0.0029494783],"genre_scores_gemma":[0.53634715,0.0030230046,0.40541494,0.0017688384,0.00081126014,0.0032256271,0.0029942908,0.0006656921,0.045749243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894126,0.0072412505,0.00035969884,0.001601176,0.0007815548,0.00060372194],"domain_scores_gemma":[0.94768935,0.044781156,0.0021747227,0.0025684119,0.0018640956,0.00092230324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023174688,0.0027767164,0.0070283585,0.002926355,0.0016092369,0.0048432327,0.007807669,0.0070821866,0.01729212],"category_scores_gemma":[0.052887205,0.0030063502,0.00681336,0.005142567,0.0032320542,0.0039651846,0.003216773,0.0057879076,0.0019813322],"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":[0.00091911457,0.00032737944,0.0043577515,0.00034607554,0.0013943514,0.0011239866,0.00039204897,0.7696336,0.00041814105,0.18792534,0.0045752823,0.028586933],"study_design_scores_gemma":[0.00031761758,0.00017205339,0.001651673,0.00006680596,0.0006767072,0.00029535603,0.000069555244,0.8900954,0.00013273249,0.104642004,0.001780585,0.00009952749],"about_ca_topic_score_codex":0.01687093,"about_ca_topic_score_gemma":0.015368155,"teacher_disagreement_score":0.023174688,"about_ca_system_score_codex":0.0033900507,"about_ca_system_score_gemma":0.0046082838,"threshold_uncertainty_score":0.12256092},"labels":[],"label_agreement":null},{"id":"W1966109447","doi":"10.1080/10618600.2000.10474871","title":"Note on “Obtaining the Maximum Likelihood Estimates in Incomplete<i>R</i>×<i>C</i>Contingency Tables Using a Poisson Generalized Linear Model”","year":2000,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"York University","funders":"","keywords":"Contingency table; Mathematics; Macro; Statistics; Poisson distribution; Standard error; Table (database); Generalized linear model; Missing data; Applied mathematics; Design matrix; Maximum likelihood; Algorithm; Linear model; Computer science; Data mining","score_opus":0.04626384178380755,"score_gpt":0.3564908719570994,"score_spread":0.31022703017329184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966109447","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":[0.0020411531,0.00017605122,0.9929202,0.001562858,0.0006468721,0.00010913177,0.0008314862,0.0006507448,0.0010614003],"genre_scores_gemma":[0.026479732,0.0005068189,0.96626216,0.0011882127,0.0009612735,0.00061586424,0.0014542965,0.00042056126,0.0021111434],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894265,0.008056194,0.00037284085,0.0006612005,0.0013610198,0.00012225394],"domain_scores_gemma":[0.92451817,0.06424333,0.0017451978,0.005356081,0.0037800623,0.00035720298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014212977,0.0007507864,0.0011583307,0.0016141259,0.0008186642,0.0010346984,0.0021789176,0.00068421435,0.021103302],"category_scores_gemma":[0.10742247,0.00070552895,0.0014455849,0.0022406837,0.001048779,0.0015515449,0.0012767311,0.0025840828,0.004168427],"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":[0.00036971635,0.0002441484,0.0064750714,0.0008679805,0.0004614496,0.0011898587,0.0006830842,0.059266962,0.006604829,0.22257927,0.32345173,0.37780583],"study_design_scores_gemma":[0.00027445814,0.00040884587,0.008993782,0.00058274704,0.00024799575,0.00089733314,0.00031248564,0.3403422,0.015789622,0.4775271,0.15422636,0.00039703987],"about_ca_topic_score_codex":0.0043095634,"about_ca_topic_score_gemma":0.007612876,"teacher_disagreement_score":0.021103302,"about_ca_system_score_codex":0.00063882757,"about_ca_system_score_gemma":0.00210496,"threshold_uncertainty_score":0.075166345},"labels":[],"label_agreement":null},{"id":"W1966441217","doi":"10.1006/jmva.2000.1905","title":"On Marginal Quasi-Likelihood Inference in Generalized Linear Mixed Models","year":2001,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":false,"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; Overdispersion; Applied mathematics; Generalized linear mixed model; Covariance; Moment (physics); Inference; Statistics; Quasi-likelihood; Random effects model; Covariance matrix; Computation; Econometrics; Algorithm; Computer science; Poisson distribution; Count data","score_opus":0.07918808592474905,"score_gpt":0.39587677139159694,"score_spread":0.3166886854668479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966441217","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":[0.0011836342,0.00046747233,0.9975241,0.000314793,0.000038225742,0.000020457235,0.000042304735,0.000068726855,0.00034031185],"genre_scores_gemma":[0.093717165,0.0031354872,0.89527905,0.0009804866,0.0009860634,0.0007384984,0.00080460386,0.0006937547,0.003664896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96919435,0.025581114,0.0009972107,0.0016221401,0.0020811683,0.0005240583],"domain_scores_gemma":[0.7666108,0.2203319,0.003103854,0.0051727127,0.003671107,0.0011096518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049716577,0.0029891974,0.005189543,0.0038474968,0.001894337,0.0048739417,0.008169746,0.0051477486,0.006196354],"category_scores_gemma":[0.16072634,0.0047216285,0.0044047358,0.004890153,0.009638708,0.010392169,0.0095908595,0.009486941,0.0011238545],"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":[0.00020950237,0.00011211052,0.0011604358,0.0005663598,0.0004614649,0.00022237953,0.00047898665,0.19373614,0.0005244085,0.7576568,0.0026904668,0.04218099],"study_design_scores_gemma":[0.00004201254,0.00003173748,0.00017808216,0.00007296477,0.000044801516,0.00004565527,0.000023871227,0.40059093,0.00013334557,0.5977284,0.0010637576,0.000044448545],"about_ca_topic_score_codex":0.008893226,"about_ca_topic_score_gemma":0.0077107074,"teacher_disagreement_score":0.049716577,"about_ca_system_score_codex":0.0032402927,"about_ca_system_score_gemma":0.0044300263,"threshold_uncertainty_score":0.26292956},"labels":[],"label_agreement":null},{"id":"W1966518060","doi":"10.1016/j.jmva.2013.11.006","title":"Consistency, bias and efficiency of the normal-distribution-based MLE: The role of auxiliary variables","year":2013,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":28,"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 British Columbia","funders":"","keywords":"Mathematics; Missing data; Consistency (knowledge bases); Statistics; Econometrics; Population; Distribution (mathematics); Variable (mathematics)","score_opus":0.028787341195829874,"score_gpt":0.308653370714076,"score_spread":0.27986602951824613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966518060","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":[0.017640797,0.0009270554,0.9783943,0.0011950991,0.000087300796,0.000060780647,0.00016001385,0.00023547109,0.001299172],"genre_scores_gemma":[0.48145363,0.001831833,0.50943404,0.00086954044,0.00071229925,0.0004565341,0.000946573,0.0011772997,0.0031181579],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97002965,0.022909284,0.0011444296,0.002819585,0.0025456618,0.00055136916],"domain_scores_gemma":[0.5642976,0.39591724,0.006185783,0.023931878,0.008450618,0.0012169378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06883743,0.001611276,0.0036056787,0.0032025685,0.0010330367,0.0056187892,0.0049465,0.0037353472,0.0032039252],"category_scores_gemma":[0.39588714,0.0018076453,0.001959426,0.0024692405,0.007594602,0.01094496,0.0055451295,0.0062530166,0.0009382148],"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":[0.0013766467,0.00022164948,0.014218977,0.00079990685,0.0009845461,0.00032242117,0.000684962,0.25281742,0.0035804564,0.6101873,0.004209129,0.11059656],"study_design_scores_gemma":[0.00017700155,0.00013644826,0.0023509564,0.00016610323,0.000168112,0.00031857577,0.000066373876,0.5299661,0.0025280637,0.46232137,0.0017066441,0.00009429285],"about_ca_topic_score_codex":0.0011818773,"about_ca_topic_score_gemma":0.000961736,"teacher_disagreement_score":0.06883743,"about_ca_system_score_codex":0.0011536827,"about_ca_system_score_gemma":0.0026106008,"threshold_uncertainty_score":0.36405146},"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":"W1967191198","doi":"10.1111/j.1467-9892.2005.00446.x","title":"On Parameter Estimation for Exponential Dispersion Arma Models","year":2005,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Statistical Methods and Bayesian 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":"York University; University of Waterloo; HEC Montréal","funders":"","keywords":"Autoregressive–moving-average model; Autoregressive model; Autocorrelation; Mathematics; Series (stratigraphy); Applied mathematics; Representation (politics); Residual; Exponential function; Estimation theory; Projection (relational algebra); STAR model; Moving average; Moving-average model; Autoregressive integrated moving average; Econometrics; Time series; Statistics; Algorithm; Mathematical analysis","score_opus":0.03869231548660052,"score_gpt":0.348935909033294,"score_spread":0.3102435935466935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967191198","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":[0.003769757,0.00027111295,0.99515617,0.00016552204,0.000018053945,0.000021672484,0.000036460126,0.00006177207,0.00049954525],"genre_scores_gemma":[0.50378907,0.0031999515,0.48472652,0.00042480225,0.00035741282,0.0008737666,0.0009915049,0.0002504212,0.005386558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99352646,0.0044497615,0.00029889093,0.0007375639,0.0007984407,0.00018895503],"domain_scores_gemma":[0.9644509,0.031453874,0.0016102049,0.001071263,0.0012730246,0.00014068774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01571254,0.0013613255,0.0021711004,0.0021597575,0.00062236784,0.001907407,0.0019108324,0.0020522147,0.0028232143],"category_scores_gemma":[0.06582466,0.0012521024,0.0015424218,0.002387676,0.002071416,0.0029060952,0.0026210633,0.0035648625,0.00077066757],"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":[0.000109375316,0.00004672259,0.001974023,0.0002199771,0.00019082271,0.00014602323,0.00021275494,0.74224335,0.0006425972,0.18786782,0.0014715749,0.06487492],"study_design_scores_gemma":[0.0000128261745,0.000015943142,0.00028894626,0.000049419017,0.000018284429,0.000023559693,0.000020183563,0.9027041,0.00018569523,0.09577688,0.0008838948,0.000020312107],"about_ca_topic_score_codex":0.0042865714,"about_ca_topic_score_gemma":0.0026362576,"teacher_disagreement_score":0.01571254,"about_ca_system_score_codex":0.0011214141,"about_ca_system_score_gemma":0.0012903289,"threshold_uncertainty_score":0.08309686},"labels":[],"label_agreement":null},{"id":"W1967488547","doi":"10.1007/s00184-006-0083-6","title":"Generalized linear mixed models with informative dropouts and missing covariates","year":2006,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"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 British Columbia","funders":"","keywords":"Covariate; Missing data; Generalized linear mixed model; Estimator; Mathematics; Generalized linear model; Statistics; Mixed model; Longitudinal data; Generalized estimating equation; Econometrics; Computer science; Data mining","score_opus":0.05670873068460871,"score_gpt":0.3348015824166259,"score_spread":0.2780928517320172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967488547","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":[0.004096823,0.0007206173,0.9928624,0.000893383,0.000116139134,0.00011221178,0.0004908516,0.00022857456,0.0004789691],"genre_scores_gemma":[0.15320383,0.002163066,0.82667094,0.0011232771,0.0007987858,0.0029120175,0.0020603188,0.00033164938,0.01073616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96011597,0.031911384,0.0013502993,0.0040696063,0.0016857428,0.0008670371],"domain_scores_gemma":[0.84879434,0.12771489,0.007438566,0.01231106,0.0028934109,0.0008478372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050628453,0.0037755675,0.0066174804,0.0030283502,0.0019773948,0.0047609783,0.011750545,0.006291816,0.009660915],"category_scores_gemma":[0.13701606,0.0043582995,0.0054581873,0.005296051,0.0051368056,0.008006485,0.005483829,0.009338352,0.0019570952],"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":[0.00067370856,0.00023550612,0.0030232966,0.0007524999,0.0013349929,0.0006942374,0.00081642834,0.084504545,0.0002697399,0.8439364,0.005654848,0.05810381],"study_design_scores_gemma":[0.00021198549,0.00008191645,0.00051286136,0.000134108,0.0003460705,0.00023467478,0.00008179163,0.24937156,0.00019060809,0.7451903,0.003583696,0.000060451075],"about_ca_topic_score_codex":0.00678916,"about_ca_topic_score_gemma":0.009105979,"teacher_disagreement_score":0.050628453,"about_ca_system_score_codex":0.0025127323,"about_ca_system_score_gemma":0.0046565686,"threshold_uncertainty_score":0.26775205},"labels":[],"label_agreement":null},{"id":"W1968026361","doi":"10.2307/3315903","title":"Empirical likelihood confidence intervals for the mean of a population containing many zero values","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Nonparametric statistics; Confidence interval; Mathematics; Population; Econometrics; Empirical likelihood; CDF-based nonparametric confidence interval; Context (archaeology); Parametric statistics; Sampling (signal processing); Survey sampling; Sample size determination; Demography; Computer science; Geography","score_opus":0.10641580573941598,"score_gpt":0.38314372231699545,"score_spread":0.27672791657757945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968026361","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":[0.10155228,0.0031350655,0.88402164,0.0010649787,0.00009595029,0.00007931099,0.0005697086,0.0003959973,0.00908508],"genre_scores_gemma":[0.87603223,0.00075605913,0.12100582,0.00022012377,0.00010701826,0.0001838924,0.0007259912,0.00006511856,0.000903736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9828404,0.01086454,0.00068320846,0.0014785795,0.0037443237,0.00038900194],"domain_scores_gemma":[0.6785256,0.29033232,0.010368414,0.008505132,0.011423327,0.0008451367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028797872,0.0005966304,0.0010579736,0.0038805779,0.0006439452,0.0031883218,0.0025745241,0.0019612117,0.0036333238],"category_scores_gemma":[0.31636688,0.00040543597,0.0008272495,0.002929533,0.0031238133,0.0029493985,0.0024986356,0.0021651622,0.00041106224],"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":[0.000667056,0.00012852674,0.039547507,0.00064673217,0.00047106252,0.00048806425,0.0014321536,0.16649841,0.0016293894,0.59019643,0.0063731326,0.19192162],"study_design_scores_gemma":[0.00014750435,0.00017915269,0.024943933,0.0007849381,0.00017161801,0.00083373394,0.0006926447,0.4903507,0.0029666764,0.47002974,0.008720736,0.0001785849],"about_ca_topic_score_codex":0.0054109404,"about_ca_topic_score_gemma":0.0025109726,"teacher_disagreement_score":0.028797872,"about_ca_system_score_codex":0.0014451535,"about_ca_system_score_gemma":0.0009416995,"threshold_uncertainty_score":0.15229958},"labels":[],"label_agreement":null},{"id":"W1968142332","doi":"10.1002/cjs.5550340302","title":"Conservative prior distributions for variance parameters in hierarchical models","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Variance (accounting); Hierarchical database model; Bayesian probability; Computer science; Econometrics; Bayesian hierarchical modeling; Conjugate prior; Mathematics; Statistics; Bayesian inference; Artificial intelligence; Data mining","score_opus":0.07441676994979826,"score_gpt":0.33038197528882646,"score_spread":0.2559652053390282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968142332","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":[0.0058119763,0.0002480356,0.9911623,0.00060608203,0.000042075284,0.000061438564,0.00014367401,0.00015328055,0.0017711358],"genre_scores_gemma":[0.40923253,0.0010881525,0.57862383,0.001261759,0.0003560197,0.0011352388,0.0011509276,0.0003698926,0.0067816074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9817131,0.012337118,0.00075310573,0.00199519,0.0025020803,0.0006994071],"domain_scores_gemma":[0.90325826,0.08505174,0.0028466373,0.0048936876,0.0033091777,0.0006405129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033546288,0.0014373774,0.0018292668,0.0025877736,0.001166333,0.0037402285,0.004050695,0.0035055685,0.0059652007],"category_scores_gemma":[0.14347604,0.0014610689,0.0020561148,0.002346989,0.003948393,0.0056606703,0.0028595414,0.0073150946,0.001460397],"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":[0.00008321925,0.000048257403,0.0017017731,0.0001656843,0.00008317618,0.0001830402,0.0005588612,0.0991407,0.00059097825,0.8685316,0.003687977,0.02522474],"study_design_scores_gemma":[0.00002943603,0.000024958028,0.0005661452,0.00013131047,0.000035891608,0.00007746402,0.000072767514,0.17011178,0.00029322683,0.825995,0.0026197843,0.00004224095],"about_ca_topic_score_codex":0.0046586706,"about_ca_topic_score_gemma":0.004886178,"teacher_disagreement_score":0.033546288,"about_ca_system_score_codex":0.002709156,"about_ca_system_score_gemma":0.001544041,"threshold_uncertainty_score":0.17741185},"labels":[],"label_agreement":null},{"id":"W1968282845","doi":"10.1016/s0895-7177(00)00117-5","title":"A comparison of some random effect models for parameter estimation in recurrent events","year":2000,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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 Waterloo","funders":"","keywords":"Mathematics; Statistics; Piecewise; Nonparametric statistics; Estimation theory; Mixing (physics); Multiplicative function; Applied mathematics; Mathematical analysis","score_opus":0.10142172702183037,"score_gpt":0.3857827368514351,"score_spread":0.2843610098296047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968282845","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":[0.008551606,0.0011664768,0.9890914,0.00032946793,0.00005534601,0.000058325837,0.00014849448,0.0001661081,0.00043277396],"genre_scores_gemma":[0.30146447,0.005207883,0.68617594,0.0005745759,0.00034039767,0.0010021359,0.0011917906,0.00051824993,0.003524563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.978963,0.017258832,0.00074286136,0.0016894632,0.0010265887,0.0003192354],"domain_scores_gemma":[0.8248121,0.16626833,0.0015830033,0.004839363,0.0020732577,0.00042382718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045400213,0.0015344416,0.002962823,0.002443253,0.0007736101,0.002788071,0.005636785,0.0038392397,0.0043443823],"category_scores_gemma":[0.12278255,0.001033496,0.004663792,0.0024889389,0.001554455,0.0049553984,0.0015519727,0.0028991355,0.0006385326],"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":[0.0018929645,0.00025573428,0.004944325,0.0013489422,0.0025839517,0.0003833165,0.0014372279,0.42553258,0.001336778,0.36530492,0.0030298075,0.19194944],"study_design_scores_gemma":[0.00021047308,0.00028069995,0.0020636488,0.00014268115,0.0007893038,0.00018548306,0.000100356665,0.85936606,0.00057059404,0.13399883,0.0021912763,0.00010065629],"about_ca_topic_score_codex":0.005744045,"about_ca_topic_score_gemma":0.0047601154,"teacher_disagreement_score":0.045400213,"about_ca_system_score_codex":0.001579397,"about_ca_system_score_gemma":0.0016809185,"threshold_uncertainty_score":0.24010217},"labels":[],"label_agreement":null},{"id":"W1968295273","doi":"10.1093/biomet/ast039","title":"Semiparametric estimation for the additive hazards model with left-truncated and right-censored data","year":2013,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":48,"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; Pfizer Canada; Medical Research Council; Health Canada; National Institute on Aging; National Institute of Allergy and Infectious Diseases; Pfizer","keywords":"Mathematics; Truncation (statistics); Estimator; Pairwise comparison; Statistics; Survival function; Estimating equations; Econometrics; Applied mathematics","score_opus":0.07803763665523032,"score_gpt":0.3663153265472528,"score_spread":0.28827768989202246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968295273","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":[0.0063473727,0.00006927147,0.9932547,0.00007159761,0.0000053396247,0.00001902656,0.000045019613,0.00004736426,0.00014043342],"genre_scores_gemma":[0.39453822,0.00062092865,0.60100836,0.00014936678,0.00007743003,0.000522502,0.0007237478,0.000095140975,0.0022643171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99248546,0.0056778747,0.00026987758,0.0005792031,0.00080437283,0.00018324784],"domain_scores_gemma":[0.96643674,0.028481407,0.0017660402,0.0022547494,0.0008539805,0.00020720877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01300851,0.00059408654,0.0015168836,0.0012159334,0.00034651533,0.0011810182,0.0025667655,0.0009728998,0.0021418282],"category_scores_gemma":[0.050817996,0.0005895231,0.0019082796,0.0011721043,0.0010262809,0.001731397,0.0024488731,0.0022151857,0.0004092603],"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":[0.00028459108,0.000231825,0.016406717,0.0006076423,0.00074959674,0.00051470165,0.00086365355,0.3485402,0.004380569,0.39594418,0.002301562,0.22917472],"study_design_scores_gemma":[0.000027036118,0.00011611462,0.0030150057,0.000046091238,0.0000793325,0.00023304693,0.00008755159,0.8267935,0.0010979357,0.1668935,0.0015657584,0.000045173292],"about_ca_topic_score_codex":0.0018728202,"about_ca_topic_score_gemma":0.0020616916,"teacher_disagreement_score":0.01300851,"about_ca_system_score_codex":0.0006465122,"about_ca_system_score_gemma":0.0018474259,"threshold_uncertainty_score":0.068796396},"labels":[],"label_agreement":null},{"id":"W1968677777","doi":"10.1002/sim.3829","title":"Bayesian adjustment for exposure misclassification in case–control studies","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of British Columbia; BC Cancer Agency; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bayesian probability; Statistics; Context (archaeology); Observational study; Econometrics; Computer science; Sample size determination; Observational error; Explanatory power; Mathematics","score_opus":0.08384288068993881,"score_gpt":0.43549343458039363,"score_spread":0.35165055389045485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968677777","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":[0.0063569667,0.0019016489,0.98958886,0.0008021235,0.0002471426,0.00017369479,0.00007848713,0.00021043625,0.00064066146],"genre_scores_gemma":[0.32166874,0.0032461474,0.66931444,0.0011136064,0.00064371416,0.0013551677,0.00047152702,0.00018641794,0.0020002197],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9150171,0.072323374,0.002651238,0.004008048,0.0053519467,0.0006483008],"domain_scores_gemma":[0.82649577,0.14639124,0.009556498,0.010872258,0.006124981,0.0005592096],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08449205,0.001313949,0.0030450413,0.0029100329,0.0011880265,0.002061464,0.004291045,0.0028866671,0.0020089545],"category_scores_gemma":[0.32934555,0.0011218229,0.002497229,0.0033326978,0.0019639034,0.002031281,0.0027232298,0.0037951118,0.0005253472],"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":[0.001421824,0.00027224462,0.030407105,0.0020982658,0.004146634,0.00094586884,0.0016946169,0.1903606,0.0026201096,0.24130638,0.01215325,0.51257306],"study_design_scores_gemma":[0.0005158264,0.00047874,0.016593028,0.00076148094,0.0012597474,0.0007212091,0.00018466826,0.49739167,0.00207427,0.46314314,0.016670994,0.0002051616],"about_ca_topic_score_codex":0.0060395338,"about_ca_topic_score_gemma":0.0047775414,"teacher_disagreement_score":0.915508,"about_ca_system_score_codex":0.0015850035,"about_ca_system_score_gemma":0.0026780677,"threshold_uncertainty_score":0.446842},"labels":[],"label_agreement":null},{"id":"W1968761139","doi":"10.1002/cjs.5550340408","title":"Multiple imputation methods for recurrent event data with missing event category","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; Amgen","keywords":"Imputation (statistics); Missing data; Covariate; Estimator; Event (particle physics); Statistics; Computer science; Event data; Econometrics; Mathematics; Data mining","score_opus":0.10036395083663105,"score_gpt":0.4141410349214595,"score_spread":0.3137770840848284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968761139","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":[0.002047486,0.0005244423,0.99660975,0.00021947848,0.00007060997,0.00006148389,0.00012847908,0.00015840858,0.00017989219],"genre_scores_gemma":[0.119204246,0.00087025104,0.8762151,0.00023563366,0.00024029489,0.0007563937,0.0009977097,0.00018459611,0.0012957912],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9617051,0.031955957,0.0014607796,0.0021167337,0.0023356897,0.00042583092],"domain_scores_gemma":[0.8445426,0.12826864,0.007629907,0.01242681,0.0064729424,0.0006589989],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.047099657,0.0009445669,0.0030856424,0.0036977825,0.0009548091,0.0021105253,0.006177947,0.0029451991,0.003404531],"category_scores_gemma":[0.1376779,0.0010014991,0.0027211986,0.006569273,0.0011369371,0.0023089184,0.002313665,0.004272042,0.0009452799],"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":[0.0006082444,0.0003419313,0.01562951,0.0013238231,0.0025042642,0.00088460546,0.00089970394,0.28943408,0.00084802107,0.24921587,0.012592718,0.42571726],"study_design_scores_gemma":[0.00014470836,0.000102562226,0.002267405,0.00022686322,0.00017608546,0.00026749153,0.00007183738,0.7365074,0.0005563806,0.2533848,0.006215217,0.00007922682],"about_ca_topic_score_codex":0.0027917868,"about_ca_topic_score_gemma":0.0030439007,"teacher_disagreement_score":0.95290035,"about_ca_system_score_codex":0.001081313,"about_ca_system_score_gemma":0.002063532,"threshold_uncertainty_score":0.24908984},"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":"W1969491622","doi":"10.1007/s11136-014-0824-3","title":"Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods","year":2014,"lang":"en","type":"article","venue":"Quality of Life Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"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 Manitoba; McGill University Health Centre; University of Calgary","funders":"Canadian Institutes of Health Research; University of Calgary","keywords":"Quality of Life Research; Missing data; Stroke (engine); Public health; Medicine; Population; Gerontology; Psychology; Statistics; Environmental health; Nursing; Mathematics","score_opus":0.7328720212795443,"score_gpt":0.6638590142253,"score_spread":0.06901300705424429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969491622","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":[0.69648993,0.0032442815,0.29434556,0.002261702,0.00018661763,0.0008591291,0.0010537861,0.00022905486,0.0013299715],"genre_scores_gemma":[0.9030546,0.00082371867,0.09285166,0.00045519666,0.0000746116,0.00086081965,0.00094016606,0.00007191543,0.0008673854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9122294,0.079218306,0.0018294589,0.003945877,0.0021673182,0.0006097065],"domain_scores_gemma":[0.5573882,0.4169371,0.006735032,0.012856822,0.0047539133,0.0013289531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16140354,0.00091679883,0.0023148188,0.0020562569,0.00082874147,0.0020274355,0.0040418725,0.0028787465,0.0033256316],"category_scores_gemma":[0.28378186,0.0007085395,0.004059164,0.0014120518,0.0014562524,0.0032329422,0.003039125,0.0033903632,0.00036350155],"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":[0.050351866,0.0042747245,0.3798368,0.0031839847,0.013949717,0.00030352993,0.0065807803,0.068305604,0.0015135998,0.021293286,0.0030493916,0.4473568],"study_design_scores_gemma":[0.007569173,0.01400518,0.25089967,0.0012367163,0.007322754,0.00084770826,0.0059247967,0.62405443,0.0039440133,0.07942228,0.0043270783,0.00044619723],"about_ca_topic_score_codex":0.0042833732,"about_ca_topic_score_gemma":0.0030089784,"teacher_disagreement_score":0.16140354,"about_ca_system_score_codex":0.0011667613,"about_ca_system_score_gemma":0.0031701159,"threshold_uncertainty_score":0.8535937},"labels":[],"label_agreement":null},{"id":"W1969750978","doi":"10.1002/cjs.11155","title":"Weighting in the regression analysis of survey data with a cross‐national application","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Economic and Social Research Council","keywords":"Weighting; Statistics; Econometrics; Logistic regression; Regression analysis; Mathematics; Survey data collection; European Social Survey; Regression; Variance (accounting); Survey sampling; Politics; Economics; Sociology; Demography; Political science","score_opus":0.17853755928381393,"score_gpt":0.41755354751222495,"score_spread":0.23901598822841102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969750978","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":[0.0055337646,0.00038676875,0.993072,0.00018986294,0.000080757876,0.00017127808,0.000044212265,0.00008972237,0.00043170468],"genre_scores_gemma":[0.19952546,0.0010189705,0.7937716,0.00043711133,0.00037065294,0.0023173885,0.000355903,0.00015057389,0.0020523085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8070068,0.17031395,0.0052792314,0.0090178475,0.007497263,0.0008848939],"domain_scores_gemma":[0.6294908,0.30063555,0.014395394,0.045019217,0.009789391,0.00066970894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15187888,0.0016180367,0.0029711046,0.0046632495,0.0014071909,0.0030212898,0.0041029775,0.0034336466,0.004307851],"category_scores_gemma":[0.38864607,0.0016070466,0.0031803572,0.007313027,0.0047345487,0.0038794721,0.0046800496,0.0040795114,0.0009016963],"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":[0.000380148,0.00028859414,0.025249839,0.0011644287,0.0021786792,0.0004996615,0.0013010462,0.1185967,0.0021850953,0.54713815,0.0042216796,0.29679593],"study_design_scores_gemma":[0.0001475369,0.00042734234,0.0066957134,0.00040392682,0.000378439,0.000340765,0.00025888334,0.57428235,0.0024552331,0.40219003,0.012297024,0.0001228328],"about_ca_topic_score_codex":0.0033395563,"about_ca_topic_score_gemma":0.0023650918,"teacher_disagreement_score":0.15187888,"about_ca_system_score_codex":0.0018996774,"about_ca_system_score_gemma":0.0014109596,"threshold_uncertainty_score":0.8032219},"labels":[],"label_agreement":null},{"id":"W1970310067","doi":"10.1016/j.csda.2009.11.001","title":"Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Guelph; University of Waterloo","funders":"","keywords":"Missing data; Covariance; Longitudinal data; Drop out; Statistics; Mathematics; Analysis of covariance; Drop (telecommunication); Econometrics; Computer science; Data mining; Economics","score_opus":0.17255435794058965,"score_gpt":0.42876486821435345,"score_spread":0.2562105102737638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970310067","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":[0.041739684,0.0011821828,0.9489442,0.00083991804,0.0003361914,0.00042662205,0.0021269838,0.0014431274,0.0029610756],"genre_scores_gemma":[0.5170662,0.0008073189,0.47123137,0.00053469045,0.00043537552,0.0021459844,0.004124504,0.001123539,0.0025310353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9334862,0.04144626,0.007790068,0.0070771268,0.008718382,0.0014819334],"domain_scores_gemma":[0.37609762,0.51004434,0.029802544,0.062443797,0.017642511,0.0039692847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13592547,0.0024648998,0.0036525459,0.010691358,0.001992193,0.004293825,0.0064331326,0.003453957,0.009924886],"category_scores_gemma":[0.41349697,0.001506495,0.0053709582,0.00939788,0.0043444624,0.0067160926,0.004076035,0.0074664787,0.0015239473],"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":[0.0017489083,0.0006786513,0.1956074,0.0014303457,0.0047950465,0.0012121146,0.002761387,0.055797253,0.0013740056,0.34585997,0.026702633,0.36203235],"study_design_scores_gemma":[0.00026628884,0.0009141898,0.06364636,0.0010255743,0.0012649178,0.0018000341,0.001348969,0.43328118,0.0028240366,0.48303077,0.010187018,0.00041075228],"about_ca_topic_score_codex":0.0025197016,"about_ca_topic_score_gemma":0.002274825,"teacher_disagreement_score":0.13592547,"about_ca_system_score_codex":0.0019242421,"about_ca_system_score_gemma":0.005550575,"threshold_uncertainty_score":0.7188512},"labels":[],"label_agreement":null},{"id":"W1970646576","doi":"10.1214/14-aoas727","title":"Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":52,"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; Université de Montréal","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Breastfeeding; Confounding; Breastfeeding promotion; Context (archaeology); Estimation; Duration (music); Intervention (counseling); Random effects model; Breast feeding","score_opus":0.10728965630770694,"score_gpt":0.3946682202134613,"score_spread":0.2873785639057544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970646576","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":[0.055247303,0.0014484203,0.9405357,0.0013872463,0.00005388005,0.0003143915,0.00024634547,0.0002659401,0.00050081],"genre_scores_gemma":[0.5162977,0.0011521222,0.478884,0.0006305516,0.000113532136,0.0012053809,0.0005516768,0.000113124464,0.0010518954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9626595,0.034414463,0.00047608823,0.0015069982,0.0006737583,0.00026916285],"domain_scores_gemma":[0.8911803,0.100192904,0.0037737496,0.0032758557,0.001133097,0.0004440836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041447703,0.0010359362,0.0020013314,0.0016753769,0.0006879593,0.00124376,0.002661648,0.0020217032,0.0017955474],"category_scores_gemma":[0.10960792,0.00086697214,0.0031458372,0.001256725,0.0014030334,0.0012024117,0.0024896385,0.002330331,0.0001699108],"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":[0.0041182474,0.0009275773,0.04145105,0.0022958405,0.006426323,0.0007151958,0.001392825,0.5474221,0.0022881902,0.14923654,0.002807466,0.24091871],"study_design_scores_gemma":[0.0004477698,0.0005678952,0.0052289297,0.0001592131,0.00063130003,0.00010043245,0.000095250994,0.919386,0.0008523506,0.071371004,0.0011125376,0.000047226287],"about_ca_topic_score_codex":0.0045160996,"about_ca_topic_score_gemma":0.004033878,"teacher_disagreement_score":0.041447703,"about_ca_system_score_codex":0.0013825676,"about_ca_system_score_gemma":0.0020622548,"threshold_uncertainty_score":0.219199},"labels":[],"label_agreement":null},{"id":"W1970823887","doi":"10.1002/cjs.10074","title":"Variability explained by covariates in linear mixed‐effect models for longitudinal data","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Statistics; Mathematics; Covariance; Outcome (game theory); Analysis of covariance; Contrast (vision); Covariance matrix; Econometrics; Summary statistics; Computer science","score_opus":0.11230665331945251,"score_gpt":0.3588666776249898,"score_spread":0.2465600243055373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970823887","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":[0.015286881,0.00054159807,0.98320043,0.00026611803,0.00003985572,0.000046919973,0.0001495423,0.0002246277,0.00024409281],"genre_scores_gemma":[0.55266446,0.0015946063,0.44077873,0.00045531677,0.00031646702,0.0009347038,0.0010226052,0.00029787337,0.0019353278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9482096,0.041831985,0.0015577142,0.004446737,0.0031099324,0.0008440187],"domain_scores_gemma":[0.7042459,0.26787043,0.010989139,0.012404406,0.0037884281,0.0007016173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08539287,0.0016086027,0.0030965863,0.0036222052,0.0008796293,0.002896807,0.0034344643,0.002387358,0.001947428],"category_scores_gemma":[0.20448278,0.0012931721,0.0028835244,0.0042653577,0.0047695674,0.0046795188,0.0031916287,0.0031473674,0.00047079095],"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":[0.00069778034,0.00013664018,0.036679663,0.0006002023,0.0019502179,0.00086820626,0.0009198153,0.46925485,0.0014901079,0.40092465,0.002846204,0.083631575],"study_design_scores_gemma":[0.000048258313,0.00019704991,0.0052974788,0.000119004326,0.00019192914,0.00017194999,0.00008022235,0.68828076,0.0007483834,0.30335182,0.0014271956,0.00008592741],"about_ca_topic_score_codex":0.005418076,"about_ca_topic_score_gemma":0.0035238138,"teacher_disagreement_score":0.08539287,"about_ca_system_score_codex":0.0018991812,"about_ca_system_score_gemma":0.0023712497,"threshold_uncertainty_score":0.45160604},"labels":[],"label_agreement":null},{"id":"W1971203106","doi":"10.1111/1467-9868.00234","title":"Goodness of Fit of Generalized Linear Models to Sparse Data","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deviance (statistics); Statistic; Generalized linear model; Mathematics; Goodness of fit; Statistics; Poisson distribution; Negative binomial distribution; Ancillary statistic; Applied mathematics; Count data; F-test; Statistical hypothesis testing","score_opus":0.3421906082357111,"score_gpt":0.4363516888713699,"score_spread":0.09416108063565876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971203106","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":[0.042628936,0.00024460617,0.95536464,0.00033257334,0.00003089151,0.000047332323,0.00013706108,0.00025449772,0.0009595556],"genre_scores_gemma":[0.75876254,0.0005554643,0.23799945,0.0002524704,0.00012602103,0.00036953972,0.000840268,0.0002820985,0.00081203703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9827644,0.012868209,0.0005591335,0.0014147548,0.0020818685,0.00031156203],"domain_scores_gemma":[0.7704903,0.20449303,0.0072449464,0.01256585,0.004452284,0.00075366563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025323248,0.0007736657,0.0017422937,0.0031475856,0.00048131627,0.0016404846,0.001838429,0.0011452417,0.0026656522],"category_scores_gemma":[0.2190502,0.0005090529,0.0013557605,0.0020594168,0.0027601349,0.0023693047,0.0026573592,0.0023007975,0.0006925275],"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":[0.00023780974,0.000085803586,0.015744,0.00033639034,0.0004279659,0.00048660714,0.0006428099,0.7403028,0.0014480765,0.15018111,0.0026268545,0.087479755],"study_design_scores_gemma":[0.000024752426,0.00010803025,0.0037362806,0.00007434149,0.00002491962,0.00020449187,0.0001081421,0.78615737,0.0003117324,0.20832515,0.0008820103,0.000042790874],"about_ca_topic_score_codex":0.0017906739,"about_ca_topic_score_gemma":0.0013180884,"teacher_disagreement_score":0.025323248,"about_ca_system_score_codex":0.0007954301,"about_ca_system_score_gemma":0.0012150904,"threshold_uncertainty_score":0.13392371},"labels":[],"label_agreement":null},{"id":"W1971269112","doi":"10.1081/sac-200068364","title":"Bias in Penalized Quasi-Likelihood Estimation in Random Effects Logistic Regression Models When the Random Effects Are not Normally Distributed","year":2005,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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":"Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Random effects model; Logistic regression; Statistics; Econometrics; Multilevel model; Estimation; Inference; Mathematics; Regression analysis; Computer science; Meta-analysis; Medicine; Artificial intelligence; Economics","score_opus":0.1933648142924644,"score_gpt":0.46095065089595527,"score_spread":0.2675858366034909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971269112","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":[0.020372266,0.00043235463,0.97795093,0.00045046865,0.000039627084,0.000098652454,0.00004548587,0.00019887203,0.0004113037],"genre_scores_gemma":[0.5699469,0.000640557,0.4268783,0.00051140663,0.000094080955,0.0007685082,0.00028676412,0.00018260746,0.000690834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.82864785,0.16101077,0.0027239597,0.003220357,0.0036026973,0.0007943332],"domain_scores_gemma":[0.2579443,0.71788454,0.009696118,0.010763194,0.0032245088,0.00048734457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15653804,0.00085954653,0.0023829918,0.002325947,0.00078468234,0.0028553351,0.0035581852,0.0020524773,0.0018311483],"category_scores_gemma":[0.5310588,0.0015759208,0.0016999819,0.002545553,0.0039409082,0.0037445826,0.003417099,0.0031418665,0.00036982473],"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":[0.001503544,0.00021169888,0.043933973,0.0011083405,0.001987635,0.0008491699,0.001737718,0.579034,0.00074125023,0.23715226,0.0022836502,0.12945674],"study_design_scores_gemma":[0.00013546424,0.00011541511,0.0035446764,0.00021364179,0.00013514863,0.00017950154,0.000096766606,0.8480906,0.00056802755,0.14584325,0.0010167293,0.000060725364],"about_ca_topic_score_codex":0.006963657,"about_ca_topic_score_gemma":0.004728896,"teacher_disagreement_score":0.15653804,"about_ca_system_score_codex":0.0019778474,"about_ca_system_score_gemma":0.002315808,"threshold_uncertainty_score":0.82786214},"labels":[],"label_agreement":null},{"id":"W1971494092","doi":"10.1002/cjs.10024","title":"A longitudinal study of children's aggressive behaviours based on multivariate mixed models with incomplete data","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; York University; University of British Columbia","funders":"","keywords":"Covariate; Multivariate statistics; Missing data; Longitudinal data; Multivariate analysis; Statistics; Longitudinal study; Mixed model; Econometrics; Psychology; Computer science; Mathematics; Data mining","score_opus":0.16726254099478274,"score_gpt":0.3572842431181437,"score_spread":0.19002170212336097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971494092","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":[0.9466632,0.0008896045,0.050129138,0.0010384315,0.00005807185,0.0000613092,0.00073167065,0.0000464232,0.00038209907],"genre_scores_gemma":[0.9793109,0.0003020894,0.019168193,0.00006780266,0.00003534709,0.00013052914,0.00051744527,0.000016356033,0.0004512156],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98874426,0.00976502,0.00023061672,0.0005740501,0.0004106031,0.00027540256],"domain_scores_gemma":[0.9468095,0.041205637,0.0058844597,0.0033438366,0.0016604214,0.001096107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021238742,0.0006016877,0.0011449051,0.001317378,0.0010024107,0.001460461,0.0015009086,0.0009861365,0.0021689031],"category_scores_gemma":[0.052364714,0.00065226055,0.0016182662,0.0018011994,0.00081418315,0.0012224849,0.0013516813,0.0017447502,0.00018803269],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017619339,0.00041182456,0.90148205,0.00019009858,0.002330542,0.0011097213,0.00254255,0.03746116,0.000811716,0.014435172,0.0017640962,0.035699118],"study_design_scores_gemma":[0.00029536654,0.0016033809,0.46905982,0.00029678678,0.0016006706,0.0010972748,0.0025976368,0.49902207,0.0008730951,0.019239655,0.0040863683,0.00022777307],"about_ca_topic_score_codex":0.038233213,"about_ca_topic_score_gemma":0.033560943,"teacher_disagreement_score":0.038233213,"about_ca_system_score_codex":0.0014020436,"about_ca_system_score_gemma":0.0014054974,"threshold_uncertainty_score":0.11232257},"labels":[],"label_agreement":null},{"id":"W1971567137","doi":"10.1080/10543406.2011.557792","title":"Robust Small-Sample Inference for Fixed Effects in General Gaussian Linear Models","year":2012,"lang":"en","type":"article","venue":"Journal of Biopharmaceutical Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"University of Windsor","keywords":"Estimator; Sample size determination; Biometrics; Statistics; Mathematics; Covariance matrix; Covariance; Linear model; Computer science; Econometrics; Artificial intelligence","score_opus":0.21660311091138673,"score_gpt":0.44008312495510166,"score_spread":0.22348001404371493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971567137","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":[0.0017300999,0.00021114139,0.99739575,0.00010245469,0.00004222252,0.0000876874,0.00004903595,0.000204896,0.0001768291],"genre_scores_gemma":[0.12577082,0.00089260394,0.86929744,0.0004045992,0.00023698919,0.0015913205,0.000517988,0.00029409435,0.0009941363],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92470425,0.060132623,0.0021943967,0.0073689744,0.0047297506,0.000870016],"domain_scores_gemma":[0.6855916,0.28629953,0.0072763097,0.014769334,0.0054386845,0.0006246416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.108342476,0.0020877565,0.005712734,0.004034997,0.0015954267,0.0035395205,0.005627695,0.003211063,0.003523256],"category_scores_gemma":[0.38187018,0.0021779682,0.004868974,0.0037812807,0.0052221958,0.0046695173,0.0039514415,0.0057859463,0.0010668255],"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":[0.0006170784,0.0003257986,0.011460974,0.0016043251,0.0034307106,0.00071754656,0.0013419214,0.30709496,0.0025411132,0.35254776,0.006186711,0.3121312],"study_design_scores_gemma":[0.00019589272,0.00022292596,0.0024757802,0.00027714917,0.00039412896,0.00016208297,0.00012208184,0.67520064,0.0019344615,0.31520894,0.0036867121,0.00011916742],"about_ca_topic_score_codex":0.008754247,"about_ca_topic_score_gemma":0.008120441,"teacher_disagreement_score":0.108342476,"about_ca_system_score_codex":0.0027815215,"about_ca_system_score_gemma":0.004409209,"threshold_uncertainty_score":0.5729766},"labels":[],"label_agreement":null},{"id":"W1971907399","doi":"10.1198/jasa.2010.tm09534","title":"Pseudo–Empirical Likelihood Inference for Multiple Frame Surveys","year":2010,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Empirical likelihood; Inference; Point estimation; Statistics; Confidence interval; Likelihood function; Mathematics; Statistic; Population; Interval estimation; Confidence distribution; Econometrics; Statistical inference; Frame (networking); Expectation–maximization algorithm; Computer science; Estimation theory; Maximum likelihood; Artificial intelligence","score_opus":0.04316731173248054,"score_gpt":0.4051995834760186,"score_spread":0.362032271743538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971907399","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":[0.0017935128,0.00009730435,0.99749243,0.0000797659,0.000017382568,0.000022305827,0.000026859072,0.00005614357,0.000414236],"genre_scores_gemma":[0.18710609,0.0006100407,0.80878687,0.00024990315,0.00021529832,0.0007165423,0.00045136968,0.00011449445,0.0017493109],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9806136,0.01467819,0.0005663948,0.0016615043,0.0021910553,0.0002892741],"domain_scores_gemma":[0.90764594,0.07602982,0.0044732955,0.007939938,0.0034818498,0.00042911313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026282305,0.0010411955,0.0016255428,0.0025583399,0.00087020075,0.0025684554,0.0036042656,0.0017438991,0.004098814],"category_scores_gemma":[0.17317832,0.0011277599,0.0016957694,0.002765946,0.002712687,0.0058371737,0.003316964,0.0031907428,0.00085394515],"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":[0.00011790054,0.00005326097,0.0029705549,0.00019739599,0.00014975434,0.00020402008,0.0003657671,0.070716046,0.00044419878,0.83174103,0.0017714461,0.09126871],"study_design_scores_gemma":[0.00005232963,0.00006370711,0.0011975883,0.000077762255,0.00003735346,0.00017936005,0.000072591545,0.47311658,0.00058840134,0.5201866,0.004386427,0.000041410476],"about_ca_topic_score_codex":0.00217461,"about_ca_topic_score_gemma":0.0016107935,"teacher_disagreement_score":0.026282305,"about_ca_system_score_codex":0.0014828036,"about_ca_system_score_gemma":0.0017159245,"threshold_uncertainty_score":0.13899583},"labels":[],"label_agreement":null},{"id":"W1971921760","doi":"10.1016/s0378-3758(99)00195-0","title":"Noncanonical links in generalized linear models – when is the effort justified?","year":2000,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Generalized linear model; Deviance (statistics); Goodness of fit; Binomial regression; Statistics; Negative binomial distribution; Parametric statistics; Applied mathematics; Logit; Residual; Binomial distribution; Econometrics; Logistic regression; Algorithm; Poisson distribution","score_opus":0.09709194964356595,"score_gpt":0.3976390624505003,"score_spread":0.30054711280693436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971921760","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":[0.09732156,0.0032079718,0.8059905,0.042290907,0.0008739219,0.00010143774,0.00036319808,0.00046191114,0.04938859],"genre_scores_gemma":[0.8073238,0.0035964006,0.16999638,0.0037022438,0.0014587207,0.00043832936,0.00034534468,0.00051993167,0.012618854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879909,0.008454819,0.00051744824,0.0009722944,0.0016228299,0.0004417167],"domain_scores_gemma":[0.8336353,0.1414369,0.005121507,0.013311864,0.0048278077,0.0016666724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022327926,0.0010551738,0.001739429,0.0017552861,0.00202022,0.0053908844,0.0035239928,0.0046226806,0.012761794],"category_scores_gemma":[0.21036248,0.0013369884,0.00089442305,0.0028307862,0.008414692,0.030275004,0.0052140527,0.006670716,0.0016389013],"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":[0.00005134074,0.000021159567,0.0008077017,0.00006996719,0.000020274225,0.00007009622,0.00025410266,0.0037266754,0.000033154844,0.974241,0.0016246764,0.019079791],"study_design_scores_gemma":[0.000006805684,0.0000039644087,0.000071818315,0.000022850154,0.0000057635943,0.000021890883,0.00004590861,0.00923528,0.00002320716,0.9897602,0.0007969124,0.000005220563],"about_ca_topic_score_codex":0.0026786714,"about_ca_topic_score_gemma":0.005008661,"teacher_disagreement_score":0.022327926,"about_ca_system_score_codex":0.0013631223,"about_ca_system_score_gemma":0.002302175,"threshold_uncertainty_score":0.11808276},"labels":[],"label_agreement":null},{"id":"W1972689785","doi":"10.1016/j.jmva.2014.06.020","title":"Preserving relationships between variables with MIVQUE based imputation for missing survey data","year":2014,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Agence Nationale de la Recherche","keywords":"Imputation (statistics); Estimator; Missing data; Bivariate analysis; Statistics; Mathematics; Multivariate normal distribution; Econometrics; Multivariate statistics","score_opus":0.24445762045310396,"score_gpt":0.42778410545676054,"score_spread":0.1833264850036566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972689785","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":[0.004128225,0.00018873949,0.9947848,0.00022049138,0.00004363151,0.000035155055,0.00019416124,0.00015025913,0.000254668],"genre_scores_gemma":[0.1933504,0.00044099623,0.8006461,0.0005367013,0.0003224573,0.0005616667,0.0017385092,0.0002642952,0.0021389208],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9572343,0.034423232,0.0014971227,0.0035323622,0.0024120088,0.0009010285],"domain_scores_gemma":[0.8558182,0.10282172,0.004782951,0.031989247,0.0038423622,0.0007454579],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05104496,0.0009850425,0.003075532,0.0026812407,0.0018695865,0.0043101264,0.009292608,0.0031525928,0.0041121608],"category_scores_gemma":[0.17889026,0.002197272,0.004166889,0.0051002027,0.002497483,0.0053899125,0.005689105,0.0058102976,0.0010222524],"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":[0.0010375099,0.00043319006,0.024428636,0.0009160809,0.0025774324,0.00068170286,0.0014580933,0.16427167,0.0020288033,0.41789806,0.01059112,0.3736777],"study_design_scores_gemma":[0.000107184525,0.00018568619,0.0029419805,0.00013011177,0.00033231132,0.0003468853,0.00014153891,0.60249066,0.0012167888,0.38537487,0.0066535166,0.00007842177],"about_ca_topic_score_codex":0.0029924775,"about_ca_topic_score_gemma":0.003778058,"teacher_disagreement_score":0.94895506,"about_ca_system_score_codex":0.00096244545,"about_ca_system_score_gemma":0.003160931,"threshold_uncertainty_score":0.26995474},"labels":[],"label_agreement":null},{"id":"W1972837603","doi":"10.1002/sim.2367","title":"Comparison of variance estimation approaches in a two-state Markov model for longitudinal data with misclassification","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Jackknife resampling; Unobservable; Statistics; Variance (accounting); Markov chain; Covariance; Resampling; Computer science; Mathematics; Econometrics; Observable; Estimator","score_opus":0.31867627678948385,"score_gpt":0.4844635956960856,"score_spread":0.16578731890660175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972837603","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":[0.05929194,0.00095561513,0.93817604,0.00047297805,0.00004577094,0.00009179817,0.000045017176,0.00023813969,0.00068260095],"genre_scores_gemma":[0.5699182,0.0011403734,0.42645553,0.00020288331,0.000075786826,0.00047770838,0.00037998834,0.00023426091,0.0011151417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9726415,0.023215272,0.0006478215,0.0011191719,0.001928312,0.0004479427],"domain_scores_gemma":[0.774984,0.21252945,0.0033143421,0.0049543087,0.0036691462,0.0005487325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06467255,0.0010243694,0.0021058917,0.002646712,0.00065587007,0.0019748106,0.002437543,0.002582025,0.0009805878],"category_scores_gemma":[0.19063841,0.0009066836,0.0018334952,0.0018895359,0.0015498882,0.003890437,0.0024393396,0.0020765292,0.00024984826],"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":[0.0008979283,0.0001822482,0.015831957,0.00041574682,0.00095142255,0.00021607366,0.0014278213,0.70040303,0.0008753663,0.1390569,0.0010868587,0.13865466],"study_design_scores_gemma":[0.000058944777,0.00010135173,0.0025981402,0.00008017804,0.000087595756,0.000058140766,0.000093154704,0.946997,0.00038464414,0.04900829,0.00047720075,0.000055404867],"about_ca_topic_score_codex":0.006205496,"about_ca_topic_score_gemma":0.0046196496,"teacher_disagreement_score":0.06467255,"about_ca_system_score_codex":0.0019497455,"about_ca_system_score_gemma":0.002285758,"threshold_uncertainty_score":0.34202522},"labels":[],"label_agreement":null},{"id":"W1972870561","doi":"10.1016/j.csda.2013.10.027","title":"Inference for longitudinal data with nonignorable nonmonotone missing responses","year":2013,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Missing data; Inference; Monte Carlo method; Computer science; Computation; Generalized estimating equation; Longitudinal data; Statistical inference; Sampling (signal processing); Sample (material); Estimating equations; Statistics; Mathematics; Algorithm; Artificial intelligence; Data mining; Machine learning","score_opus":0.2026380183625323,"score_gpt":0.44893320202063297,"score_spread":0.24629518365810066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972870561","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":[0.007628332,0.0005624905,0.99009305,0.00078609696,0.0001214417,0.000065018125,0.00033280664,0.00018274495,0.00022794309],"genre_scores_gemma":[0.3401633,0.0022253662,0.64188635,0.001545772,0.0011071889,0.0019341029,0.003008707,0.00031561436,0.007813532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9597661,0.028699096,0.0022220183,0.0063623753,0.0020659172,0.00088446244],"domain_scores_gemma":[0.6650039,0.29385525,0.01011677,0.02630538,0.003371496,0.0013471079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09416519,0.0019691947,0.00527777,0.0027403126,0.0018783076,0.0036134715,0.0075631696,0.004510831,0.0085747205],"category_scores_gemma":[0.27001303,0.0036435868,0.0046259817,0.0037715505,0.0047200825,0.008581204,0.0043932167,0.007203473,0.0012521036],"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":[0.0014247992,0.00046891533,0.023067595,0.0015100978,0.002886951,0.0014952043,0.0011638374,0.09541581,0.0012153855,0.7037012,0.007921312,0.15972888],"study_design_scores_gemma":[0.00021584319,0.00011161143,0.0022712918,0.00017022248,0.00030943833,0.00040500407,0.00010209712,0.29598057,0.00054775784,0.6974136,0.0024198918,0.00005276879],"about_ca_topic_score_codex":0.005259765,"about_ca_topic_score_gemma":0.0058369027,"teacher_disagreement_score":0.09416519,"about_ca_system_score_codex":0.0019303969,"about_ca_system_score_gemma":0.005530872,"threshold_uncertainty_score":0.49799907},"labels":[],"label_agreement":null},{"id":"W1973228727","doi":"10.1016/j.clinthera.2012.01.023","title":"The Influence of Sparse Data Sampling on Population Pharmacokinetics: A Post Hoc Analysis of a Pharmacokinetic Study of Morphine in Healthy Volunteers","year":2012,"lang":"en","type":"article","venue":"Clinical Therapeutics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Boniface Hospital; University of Manitoba","funders":"","keywords":"Medicine; Population; Pharmacokinetics; NONMEM; Sampling (signal processing); Post-hoc analysis; Statistics; Internal medicine; Mathematics; Computer science","score_opus":0.3908157888608869,"score_gpt":0.552516115098791,"score_spread":0.16170032623790404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973228727","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":[0.994327,0.00007753831,0.005339475,0.000059008737,0.000019508667,0.000053736985,0.00004828653,0.000012257652,0.000063023894],"genre_scores_gemma":[0.99641067,0.000069648086,0.003070258,0.000049989692,0.000041009567,0.000062686,0.00013671891,0.000014185036,0.0001449658],"study_design_codex":"randomized_trial","study_design_gemma":"observational","domain_scores_codex":[0.99246526,0.0062194425,0.00018978822,0.00052308186,0.00044053918,0.00016184273],"domain_scores_gemma":[0.9006364,0.09053386,0.0020984712,0.0045159217,0.0016325025,0.0005829911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014920494,0.00058213883,0.0014131032,0.0002992946,0.00047797136,0.00053484325,0.00056772976,0.0007026306,0.0006862509],"category_scores_gemma":[0.040005453,0.00027824895,0.0016979049,0.0002529595,0.0014270175,0.00055937143,0.00050816545,0.0010915066,0.000061687766],"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":[0.31965125,0.013724614,0.25599757,0.001399531,0.012445731,0.0043654987,0.004137963,0.09956065,0.10388279,0.0028112275,0.002600371,0.17942275],"study_design_scores_gemma":[0.0066355634,0.1524668,0.50294363,0.000050106537,0.00928589,0.0018594827,0.0011622892,0.29625216,0.023350343,0.0043244106,0.001409797,0.00025957884],"about_ca_topic_score_codex":0.001757796,"about_ca_topic_score_gemma":0.0018725012,"teacher_disagreement_score":0.014920494,"about_ca_system_score_codex":0.00027586002,"about_ca_system_score_gemma":0.0009514958,"threshold_uncertainty_score":0.078908026},"labels":[],"label_agreement":null},{"id":"W1973688299","doi":"10.6000/1929-6029.2014.03.03.11","title":"A Simple Approach to Sample Size Calculation for Count Data in Matched Cohort Studies","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Research Resources; National Institutes of Health","keywords":"Overdispersion; Count data; Statistics; Poisson distribution; Sample size determination; Confounding; Poisson regression; Mathematics; Matching (statistics); Cohort; Zero-inflated model; Sample (material); Econometrics; Medicine; Population","score_opus":0.318769553307775,"score_gpt":0.5792475884654095,"score_spread":0.26047803515763446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973688299","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":[0.00085300853,0.00033627223,0.99433374,0.00044702465,0.00028470572,0.0026569895,0.00020877944,0.0002555025,0.0006240607],"genre_scores_gemma":[0.0148556875,0.00025601985,0.9740811,0.0004900823,0.00020617025,0.0091072135,0.00018174153,0.000100537254,0.0007214626],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90773165,0.06637136,0.0058870553,0.007170256,0.01225956,0.0005801279],"domain_scores_gemma":[0.9075662,0.07118847,0.003964925,0.011363682,0.0053412877,0.000575462],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10187251,0.0018540994,0.0031299074,0.00533323,0.0015572032,0.002720064,0.0053172214,0.005666463,0.009001076],"category_scores_gemma":[0.27258176,0.0016557178,0.0040492634,0.0052474816,0.0024937678,0.003103827,0.004188007,0.005820545,0.0019752204],"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":[0.00089285144,0.000354463,0.0074767484,0.0028363655,0.002134807,0.0006531035,0.0013847685,0.021387592,0.0039630514,0.28022283,0.023445208,0.6552482],"study_design_scores_gemma":[0.0029515314,0.0018546993,0.008875308,0.0019429799,0.0013373137,0.0023621756,0.0003483332,0.1739485,0.007631456,0.67855036,0.119765356,0.00043206225],"about_ca_topic_score_codex":0.0018608458,"about_ca_topic_score_gemma":0.002075243,"teacher_disagreement_score":0.8981275,"about_ca_system_score_codex":0.001736831,"about_ca_system_score_gemma":0.0029982077,"threshold_uncertainty_score":0.53875977},"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":"W1974232043","doi":"10.1002/cjs.10136","title":"A resampling approach to estimate variance components of multilevel models","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo; Wilfrid Laurier University","funders":"","keywords":"Resampling; Estimator; Variance (accounting); Statistics; Computer science; Multilevel model; Econometrics; Cluster (spacecraft); Variance components; Mathematics","score_opus":0.20334632679772197,"score_gpt":0.38216046706668344,"score_spread":0.17881414026896147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974232043","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":[0.0031380344,0.00013889358,0.9959628,0.0001379075,0.00004759374,0.00011208828,0.000046760826,0.00010397074,0.000312004],"genre_scores_gemma":[0.12644026,0.00028235544,0.87096745,0.00018362675,0.00014957863,0.001036393,0.00025398,0.00011117299,0.0005752124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95463145,0.0400603,0.00073540775,0.0013763488,0.00284482,0.00035170754],"domain_scores_gemma":[0.9212392,0.063715994,0.0032570618,0.0073029622,0.004059086,0.00042569393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037898146,0.0010641386,0.002204816,0.005024024,0.0012966595,0.0018348396,0.0032478385,0.0015513506,0.0036861503],"category_scores_gemma":[0.14074934,0.0010250049,0.0033319914,0.0034441461,0.001814557,0.0017765751,0.002624953,0.0030293323,0.00046605262],"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":[0.00023877405,0.00026569987,0.011029369,0.0006004749,0.002713176,0.00043556222,0.0016404027,0.26545638,0.0014817973,0.4863968,0.006703734,0.22303796],"study_design_scores_gemma":[0.00010880601,0.00017518307,0.0023453939,0.00016225598,0.00023745028,0.000113273134,0.00014389415,0.73108435,0.0007244308,0.25916055,0.0056660753,0.000078258],"about_ca_topic_score_codex":0.0121289985,"about_ca_topic_score_gemma":0.010792419,"teacher_disagreement_score":0.037898146,"about_ca_system_score_codex":0.0015453846,"about_ca_system_score_gemma":0.0019972136,"threshold_uncertainty_score":0.20042694},"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":"W1974895834","doi":"10.1016/j.pt.2003.11.008","title":"Bayesian statistics for parasitologists","year":2003,"lang":"en","type":"review","venue":"Trends in Parasitology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal General Hospital","funders":"","keywords":"Frequentist inference; Bayesian probability; Bayesian statistics; Statistical inference; Ivermectin; Bayesian inference; Identification (biology); Inference; Onchocerciasis; Statistics; Computer science; Econometrics; Biology; Mathematics; Artificial intelligence; Ecology","score_opus":0.20849026778811916,"score_gpt":0.5376510663029339,"score_spread":0.3291607985148147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974895834","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":[0.00019529334,0.93108994,0.055086426,0.006984446,0.0023085927,0.000033927445,0.00012819993,0.00014596892,0.0040272595],"genre_scores_gemma":[0.0051880344,0.9412954,0.03634171,0.0030392176,0.007867698,0.00014596927,0.00026065652,0.00008902159,0.0057722917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753916,0.0011318348,0.00022151259,0.00024225326,0.0008025019,0.000062762854],"domain_scores_gemma":[0.9894685,0.007682664,0.00046949703,0.0006643429,0.001542262,0.00017278816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004984679,0.001618844,0.0033610878,0.005382719,0.00059033587,0.0024451201,0.0022736401,0.003852129,0.0056753433],"category_scores_gemma":[0.018098013,0.00085979846,0.00083253626,0.007751498,0.004179828,0.0049953638,0.0017595775,0.0054837405,0.0034072546],"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":[0.000043500568,0.00007142762,0.00024493993,0.0037594312,0.00010957275,0.00009618753,0.00015466314,0.0033767042,0.0003172265,0.1211458,0.11092734,0.75975317],"study_design_scores_gemma":[0.000031052707,0.000038139573,0.00043749824,0.0018023567,0.000056791174,0.0004576411,0.000052824398,0.0028236532,0.00021479193,0.3194836,0.67455095,0.000050571845],"about_ca_topic_score_codex":0.004210135,"about_ca_topic_score_gemma":0.0036519868,"teacher_disagreement_score":0.0056753433,"about_ca_system_score_codex":0.002227389,"about_ca_system_score_gemma":0.0049920827,"threshold_uncertainty_score":0.026361823},"labels":[],"label_agreement":null},{"id":"W1975012984","doi":"10.1016/j.jclinepi.2014.09.025","title":"The special case of the 2 × 2 table: asymptotic unconditional McNemar test can be used to estimate sample size even for analysis based on GEE","year":2014,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; Institute for Clinical Evaluative Sciences; University Health Network; University of Toronto; SickKids Foundation; Hospital for Sick Children; Women's College Hospital","funders":"Canadian Institutes of Health Research","keywords":"McNemar's test; Mathematics; Sample size determination; Statistics; Generalized estimating equation","score_opus":0.2801607696838403,"score_gpt":0.5476459757565667,"score_spread":0.2674852060727264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975012984","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":[0.022052744,0.00061167363,0.9714537,0.0007294958,0.00033372434,0.00032796361,0.0020336097,0.00054770865,0.001909336],"genre_scores_gemma":[0.42070195,0.00068275013,0.56609005,0.0017290044,0.0006825639,0.0019338307,0.0034913062,0.00051647116,0.004172098],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9652121,0.023325805,0.002022868,0.0060392893,0.0025792343,0.00082073413],"domain_scores_gemma":[0.731442,0.23146908,0.0073756753,0.02650062,0.0025516914,0.0006610031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040748015,0.00089146005,0.004510116,0.0019247202,0.00087284023,0.0025406964,0.003426281,0.0036130408,0.01840095],"category_scores_gemma":[0.24170044,0.00087187224,0.0019490349,0.0030411368,0.0021010612,0.0049077524,0.0013568657,0.0034190172,0.0018599381],"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":[0.0043556294,0.0005369666,0.08306538,0.0025422208,0.004868177,0.0077172136,0.0014910592,0.056964673,0.0070627006,0.23497082,0.04423384,0.5521913],"study_design_scores_gemma":[0.0004937094,0.0012375839,0.02311475,0.00033831643,0.0008893709,0.010985028,0.00037173575,0.42961088,0.0037750872,0.5101022,0.01884751,0.0002338773],"about_ca_topic_score_codex":0.0018064299,"about_ca_topic_score_gemma":0.00206652,"teacher_disagreement_score":0.040748015,"about_ca_system_score_codex":0.00073576183,"about_ca_system_score_gemma":0.0013954213,"threshold_uncertainty_score":0.21549869},"labels":[],"label_agreement":null},{"id":"W1975073195","doi":"10.5539/mas.v4n6p2","title":"Confidence Intervals for Adjusted Proportions Using Logistic Regression","year":2010,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Logistic regression; Confidence interval; Statistics; Regression analysis; Medicine; Demography; Mathematics","score_opus":0.23667284975590874,"score_gpt":0.4532104049455369,"score_spread":0.21653755518962814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975073195","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":[0.053590707,0.006775809,0.9246008,0.0009127017,0.0010456172,0.0008557933,0.0029174928,0.0016942051,0.007606932],"genre_scores_gemma":[0.5597427,0.0028093467,0.42239606,0.00068907614,0.00074929907,0.004961632,0.0050596027,0.001016258,0.002576049],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.87673944,0.089362174,0.006644606,0.010769257,0.014951326,0.0015331482],"domain_scores_gemma":[0.41689238,0.5274091,0.022825984,0.020995753,0.011123713,0.0007531093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.121290006,0.0017909902,0.0032945825,0.0074932054,0.0007256496,0.0034911563,0.0057448074,0.002583359,0.009179007],"category_scores_gemma":[0.5071614,0.00090479705,0.0052374653,0.0068102307,0.0032537098,0.005731469,0.0038382262,0.0061425585,0.0013198251],"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":[0.009181716,0.0006501101,0.087855354,0.0076715555,0.015176493,0.0021213803,0.0040197284,0.081307255,0.0030717521,0.26596206,0.02539447,0.4975881],"study_design_scores_gemma":[0.0016916959,0.0050870837,0.08823007,0.0076719373,0.0066021346,0.007068457,0.0037445005,0.41826704,0.013744585,0.36454788,0.08202351,0.0013211392],"about_ca_topic_score_codex":0.0020816915,"about_ca_topic_score_gemma":0.0005658215,"teacher_disagreement_score":0.121290006,"about_ca_system_score_codex":0.0011904314,"about_ca_system_score_gemma":0.00088649045,"threshold_uncertainty_score":0.6414505},"labels":[],"label_agreement":null},{"id":"W1976395947","doi":"10.3168/jds.s0022-0302(00)74978-2","title":"Strategies for Estimating the Parameters Needed for Different Test-Day Models","year":2000,"lang":"en","type":"review","venue":"Journal of Dairy Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":121,"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 Guelph","funders":"","keywords":"Test (biology); Econometrics; Statistics; Computer science; Biology; Mathematics; Ecology","score_opus":0.23595534682001354,"score_gpt":0.4514551026420646,"score_spread":0.21549975582205108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976395947","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":[0.0006484963,0.0070834938,0.9902597,0.00029236765,0.000058401136,0.000055320037,0.000078601784,0.00025017123,0.0012733805],"genre_scores_gemma":[0.01896229,0.01606796,0.96186817,0.00022294042,0.00015353934,0.00048911106,0.00039863682,0.00025422501,0.0015831722],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973316,0.001186733,0.0002995773,0.00047871724,0.00064144173,0.000061916086],"domain_scores_gemma":[0.99133366,0.0061232387,0.0004441662,0.0010074648,0.0009982043,0.000093124494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009329926,0.0020951082,0.0024574986,0.003466908,0.00044378455,0.0017870682,0.004475466,0.0024943503,0.0034797632],"category_scores_gemma":[0.025531441,0.0011257603,0.0017802278,0.0023638096,0.0012899148,0.0034387752,0.0016139946,0.0035269915,0.0031426507],"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":[0.00009367814,0.000094181036,0.0013693307,0.002092451,0.00032274518,0.00026084844,0.0002847503,0.045670874,0.0033560458,0.16636996,0.0054740924,0.77461106],"study_design_scores_gemma":[0.0001242091,0.00017243461,0.0031248971,0.0012424424,0.00051505736,0.0019919965,0.00024002219,0.27289817,0.015706113,0.5709782,0.13268243,0.00032399083],"about_ca_topic_score_codex":0.0024785213,"about_ca_topic_score_gemma":0.0024960735,"teacher_disagreement_score":0.009329926,"about_ca_system_score_codex":0.0015100468,"about_ca_system_score_gemma":0.0014883988,"threshold_uncertainty_score":0.049341977},"labels":[],"label_agreement":null},{"id":"W1976628155","doi":"10.4236/ojs.2013.34a001","title":"A Comparison of Statistical Methods for Analyzing Discrete Hierarchical Data: A Case Study of Family Data on Alcohol Abuse","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multilevel model; Hierarchical database model; Alcohol abuse; Computer science; Random effects model; Data mining; Psychology; Econometrics; Statistics; Meta-analysis; Medicine; Mathematics; Psychiatry; Machine learning","score_opus":0.42060277544744706,"score_gpt":0.585039938302551,"score_spread":0.1644371628551039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976628155","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":[0.086332224,0.0036699309,0.9016377,0.004181607,0.000097332886,0.0007886919,0.00033609688,0.00009242894,0.0028640172],"genre_scores_gemma":[0.28314766,0.0022390895,0.7119308,0.00046569464,0.00005604087,0.0013734587,0.00020203066,0.000100261685,0.0004849124],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.78973776,0.19877435,0.0026395693,0.0017422229,0.006645753,0.00046029044],"domain_scores_gemma":[0.37965283,0.59272623,0.0070817615,0.012460437,0.007250558,0.0008281858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1181361,0.00051725644,0.0010130014,0.003096055,0.0014248061,0.0021285668,0.0019433072,0.0017852436,0.0016491786],"category_scores_gemma":[0.3361675,0.00047284202,0.0022449445,0.00503059,0.0032168482,0.002421553,0.0021172906,0.0022273383,0.00018309438],"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":[0.0012647515,0.00059601123,0.11142517,0.002469118,0.0020370467,0.0020920308,0.021187732,0.03683814,0.0011936584,0.3948458,0.0061679464,0.41988263],"study_design_scores_gemma":[0.0008618092,0.0014924762,0.09493943,0.0032727607,0.0011465261,0.004154877,0.018136831,0.3983282,0.0027399901,0.45049635,0.024011549,0.0004191927],"about_ca_topic_score_codex":0.014243894,"about_ca_topic_score_gemma":0.024178313,"teacher_disagreement_score":0.1181361,"about_ca_system_score_codex":0.0027765355,"about_ca_system_score_gemma":0.0038729326,"threshold_uncertainty_score":0.6247709},"labels":[],"label_agreement":null},{"id":"W1977740308","doi":"10.1016/j.csda.2008.07.032","title":"On empirical Bayes penalized quasi-likelihood inference in GLMMs and in Bayesian disease mapping and ecological modeling","year":2008,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"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 British Columbia; Child and Family Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Ministry of Health, British Columbia; Michael Smith Health Research BC; Child and Family Research Institute","keywords":"Random effects model; Bayes' theorem; Statistics; Generalized linear mixed model; Bayesian probability; Bayesian inference; Context (archaeology); Prediction interval; Mathematics; Econometrics; Point estimation; Bayesian hierarchical modeling; Bayesian linear regression; Credible interval; Geography; Medicine","score_opus":0.16845305648082928,"score_gpt":0.42047765753257565,"score_spread":0.2520246010517464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977740308","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":[0.0020058805,0.00039731243,0.99634475,0.00064057356,0.000060063747,0.000022185173,0.000068008194,0.00008807464,0.00037311664],"genre_scores_gemma":[0.14095302,0.0018624581,0.84851664,0.0012678127,0.00086407125,0.000534628,0.0006277343,0.00064402464,0.004729586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9739328,0.021836916,0.0008276477,0.0014903549,0.0015190286,0.00039309982],"domain_scores_gemma":[0.7818659,0.20302802,0.0036638777,0.0073107895,0.0032097625,0.0009216382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043628186,0.00166793,0.0032125865,0.0022375176,0.0015465255,0.0033187792,0.0070399055,0.004918838,0.0056717503],"category_scores_gemma":[0.18111531,0.0031728651,0.002379698,0.0036411867,0.00787579,0.008710581,0.006154838,0.008213958,0.0009725384],"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":[0.00011562205,0.00007984913,0.001590068,0.0003145245,0.00021103112,0.00015678791,0.0004024637,0.2970609,0.00034001606,0.6502049,0.0036780776,0.045845773],"study_design_scores_gemma":[0.000021431264,0.000012005111,0.00021055907,0.000041440013,0.000019728892,0.00002962621,0.000016370808,0.44545907,0.00010445277,0.5530268,0.0010358216,0.000022725235],"about_ca_topic_score_codex":0.009070578,"about_ca_topic_score_gemma":0.00835943,"teacher_disagreement_score":0.043628186,"about_ca_system_score_codex":0.0023861113,"about_ca_system_score_gemma":0.003530479,"threshold_uncertainty_score":0.23073065},"labels":[],"label_agreement":null},{"id":"W1978344903","doi":"10.1111/2041-210x.12122","title":"Compound <scp>P</scp>oisson‐gamma vs. delta‐gamma to handle zero‐inflated continuous data under a variable sampling volume","year":2013,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":57,"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":"Sampling (signal processing); Covariate; Statistics; Variable (mathematics); Sample (material); Volume (thermodynamics); Biomass (ecology); Sample size determination; Mathematics; Econometrics; Computer science; Ecology; Biology; Physics; Detector","score_opus":0.0914059277897784,"score_gpt":0.3979834176320126,"score_spread":0.3065774898422342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978344903","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":[0.023138065,0.00064294774,0.9636918,0.0008572015,0.0005348099,0.00035277175,0.0016996255,0.003418547,0.0056643104],"genre_scores_gemma":[0.39681572,0.000853435,0.57953286,0.001384076,0.00084675726,0.0023266326,0.004512547,0.0024800838,0.011247874],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98567146,0.007902927,0.0012043143,0.002115099,0.0024357007,0.00067062024],"domain_scores_gemma":[0.93334466,0.048684906,0.0030473182,0.010080837,0.0042305137,0.0006116783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022417894,0.0014634904,0.0018186321,0.0016104017,0.0007207185,0.003276793,0.0034518237,0.0025396214,0.02180094],"category_scores_gemma":[0.06351949,0.00062976877,0.0028871614,0.00285261,0.001996155,0.0026512106,0.0029102613,0.0043389588,0.005327079],"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":[0.0015055608,0.00035431108,0.025457518,0.0016966093,0.0012662825,0.0024443083,0.00085088366,0.236047,0.009346787,0.2021487,0.058824297,0.4600578],"study_design_scores_gemma":[0.0001460168,0.00055844506,0.01346709,0.0003752061,0.00016587866,0.0012617405,0.00032651302,0.80585265,0.008018631,0.12024715,0.04938676,0.0001939307],"about_ca_topic_score_codex":0.0050560846,"about_ca_topic_score_gemma":0.0036904626,"teacher_disagreement_score":0.022417894,"about_ca_system_score_codex":0.0015942522,"about_ca_system_score_gemma":0.0021687474,"threshold_uncertainty_score":0.118558526},"labels":[],"label_agreement":null},{"id":"W1979394579","doi":"10.1002/sim.1974","title":"Influence analysis for linear mixed‐effects models","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"National Cancer Institute","keywords":"Linear regression; Leverage (statistics); Mathematics; Measure (data warehouse); Linear model; Regression analysis; Statistics; Generalization; Regression; Applied mathematics; Simple linear regression; Generalized linear model; Infinitesimal; Simple (philosophy); Econometrics; Computer science; Data mining","score_opus":0.06358645407001984,"score_gpt":0.4203984667152716,"score_spread":0.35681201264525175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979394579","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":[0.0021169442,0.00030821547,0.99657995,0.000116806135,0.000023527035,0.000048385507,0.00004264658,0.000111784095,0.0006517418],"genre_scores_gemma":[0.2386879,0.0018820256,0.75319886,0.00047092434,0.00044106654,0.0014657272,0.0005995459,0.000303342,0.0029505745],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9736076,0.020147476,0.00083697174,0.0020428337,0.0030416152,0.00032346515],"domain_scores_gemma":[0.8681608,0.12091748,0.0036173444,0.0036373704,0.0031179744,0.0005490935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026044691,0.0017035285,0.00226264,0.0042369496,0.0010043201,0.0018800757,0.0026832062,0.0015438999,0.0035825777],"category_scores_gemma":[0.116464615,0.0010005049,0.0033625867,0.0027896955,0.0023770502,0.002614364,0.0030956827,0.0027481748,0.0006117136],"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":[0.0001516616,0.00008239756,0.0055517456,0.00054532796,0.0010527471,0.00048864767,0.0006021416,0.12444476,0.00093583186,0.7319121,0.0020926488,0.13214009],"study_design_scores_gemma":[0.00003485787,0.00010106323,0.001154741,0.000086743814,0.00022740639,0.00017441073,0.00005437123,0.59796906,0.00074848015,0.3933662,0.0060407035,0.00004209862],"about_ca_topic_score_codex":0.0038449778,"about_ca_topic_score_gemma":0.003013823,"teacher_disagreement_score":0.026044691,"about_ca_system_score_codex":0.0017095389,"about_ca_system_score_gemma":0.0016049668,"threshold_uncertainty_score":0.13773918},"labels":[],"label_agreement":null},{"id":"W1979604055","doi":"10.1002/cjs.11237","title":"Generalized pseudo empirical likelihood inferences for complex surveys","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Empirical likelihood; Statistics; Mathematics; Estimator; Weighting; Statistic; Confidence interval; Calibration; Confidence distribution; Applied mathematics","score_opus":0.27405947241689294,"score_gpt":0.4136428448665517,"score_spread":0.13958337244965874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979604055","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":[0.01153323,0.00028623975,0.98622197,0.00024905516,0.000033162887,0.0000577493,0.00008626378,0.00011137479,0.0014209126],"genre_scores_gemma":[0.5584334,0.0009452608,0.4366556,0.00054325623,0.00019837056,0.00040253697,0.00044364136,0.00014669815,0.0022311497],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9798895,0.016513916,0.00046787687,0.0012184288,0.0016840901,0.00022614996],"domain_scores_gemma":[0.87323153,0.10771645,0.0061759017,0.008524476,0.0037337693,0.00061789795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022595072,0.0009837399,0.0015066089,0.0027701817,0.00057620904,0.0026339402,0.0027269025,0.0015825643,0.0053244545],"category_scores_gemma":[0.17173423,0.00060955563,0.001342294,0.0031217409,0.003337738,0.005461292,0.003341646,0.0023087822,0.0005923937],"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":[0.0001376376,0.00004729538,0.0036385674,0.00030517695,0.00027019883,0.00029374956,0.00028779375,0.2662076,0.000504511,0.6456744,0.0023138258,0.0803193],"study_design_scores_gemma":[0.00004369996,0.000049913968,0.0012232685,0.00006656335,0.00003190895,0.0001175235,0.00006272985,0.5278386,0.00043994217,0.46761206,0.002476599,0.000037252506],"about_ca_topic_score_codex":0.0018073492,"about_ca_topic_score_gemma":0.0012946585,"teacher_disagreement_score":0.022595072,"about_ca_system_score_codex":0.0013804894,"about_ca_system_score_gemma":0.0011596263,"threshold_uncertainty_score":0.11949563},"labels":[],"label_agreement":null},{"id":"W198053933","doi":"","title":"Reply to the rejoinder","year":2009,"lang":"en","type":"article","venue":"Canadian parliamentary review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Economics","score_opus":0.07674532991078745,"score_gpt":0.36845222914334586,"score_spread":0.29170689923255844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W198053933","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":[0.0001425148,0.0032854292,0.00010670464,0.96622187,0.027572518,0.000024104684,0.00019269601,0.00002448108,0.0024295663],"genre_scores_gemma":[0.0018201672,0.0011821388,0.00023756688,0.9762363,0.01295455,0.00005553092,0.000055555443,0.000051124607,0.007407149],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9622007,0.0053692395,0.0038213844,0.0044562365,0.01823847,0.005914021],"domain_scores_gemma":[0.90091103,0.043426186,0.0033072613,0.0033048983,0.0399619,0.009088717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032037504,0.0016577936,0.0045625605,0.004873662,0.014072775,0.011714421,0.010226937,0.106591694,0.011159316],"category_scores_gemma":[0.15875086,0.003049174,0.004125957,0.0062868623,0.01114434,0.005419073,0.0049181376,0.09178802,0.0070129074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024888766,0.0000045851752,0.000075918004,0.000068842026,0.000028395445,0.00006499695,0.00015363072,0.000029017001,0.00004114044,0.0040749065,0.9939136,0.0015199393],"study_design_scores_gemma":[0.00006865132,0.000009665101,0.001270799,0.00046696744,0.000101806574,0.00008147625,0.0004905711,0.000118131495,0.000126219,0.004772218,0.99237835,0.000115114584],"about_ca_topic_score_codex":0.5699586,"about_ca_topic_score_gemma":0.64890236,"teacher_disagreement_score":0.5699586,"about_ca_system_score_codex":0.0443213,"about_ca_system_score_gemma":0.095380835,"threshold_uncertainty_score":0.86514795},"labels":[],"label_agreement":null},{"id":"W1980708600","doi":"10.1007/s11136-013-0385-x","title":"Pitfalls in subgroup analysis based on growth mixture models: a commentary on van Leeuwen et al. (2012)","year":2013,"lang":"en","type":"letter","venue":"Quality of Life Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Safety Canada","funders":"","keywords":"Inference; Context (archaeology); Quality of Life Research; Set (abstract data type); Subgroup analysis; Psychology; Econometrics; Public health; Social psychology; Statistics; Computer science; Mathematics; Artificial intelligence; Medicine; History","score_opus":0.3543632523052332,"score_gpt":0.4982043516885939,"score_spread":0.1438410993833607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980708600","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":[0.00008878305,0.0054734335,0.00062405854,0.98408,0.009280979,0.000015512776,0.00010555863,0.000017205797,0.00031432795],"genre_scores_gemma":[0.002262018,0.0024034765,0.0015589246,0.9577271,0.03531892,0.00016007794,0.000037118974,0.00004455424,0.00048782642],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.87536347,0.082184464,0.01346486,0.008053723,0.019349476,0.0015840211],"domain_scores_gemma":[0.4067407,0.55304307,0.0067893104,0.0078174975,0.021992836,0.003616624],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18523048,0.0017854585,0.0059936587,0.003183653,0.005411205,0.0070003504,0.01258662,0.06406972,0.004366296],"category_scores_gemma":[0.46877873,0.001996765,0.007006854,0.0048934887,0.019989522,0.011415675,0.0061973087,0.09578102,0.0034218973],"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":[0.00024144095,0.00002646843,0.0005227135,0.00061990233,0.000327293,0.00021921106,0.00071969896,0.00021811543,0.000051863757,0.026563488,0.9296901,0.040799696],"study_design_scores_gemma":[0.001350689,0.00017672473,0.0019707175,0.00958859,0.0011424171,0.0010814857,0.0012032666,0.0023571514,0.00050055154,0.2936245,0.6866274,0.00037651474],"about_ca_topic_score_codex":0.020753859,"about_ca_topic_score_gemma":0.034209643,"teacher_disagreement_score":0.8147695,"about_ca_system_score_codex":0.010083078,"about_ca_system_score_gemma":0.015748031,"threshold_uncertainty_score":0.9796041},"labels":[],"label_agreement":null},{"id":"W1980747896","doi":"10.1002/sim.2326","title":"Checking stationarity of the incidence rate using prevalent cohort survival data","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":66,"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 Health Centre; Montreal Children's Hospital; McGill University","funders":"","keywords":"Incidence (geometry); Cohort; Kaplan–Meier estimator; Estimator; Statistics; Survival analysis; Medicine; Econometrics; Demography; Mathematics","score_opus":0.18573745560773047,"score_gpt":0.466115744944905,"score_spread":0.2803782893371745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980747896","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":[0.18322578,0.00022881286,0.8128597,0.00051882915,0.000040962976,0.00014640712,0.0011552843,0.00039917772,0.0014251145],"genre_scores_gemma":[0.8398974,0.00032442913,0.15671383,0.00015714618,0.00008368653,0.00023190529,0.0019486309,0.000093718576,0.0005493232],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9853233,0.008024211,0.0015596687,0.0022435659,0.002204993,0.00064437004],"domain_scores_gemma":[0.73935455,0.21373105,0.019176042,0.020578992,0.0062688743,0.0008904358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03801329,0.00043544025,0.0011402598,0.004190271,0.0010239327,0.0015923853,0.0016531392,0.0015699913,0.0022927292],"category_scores_gemma":[0.26166257,0.0005692971,0.0016671591,0.0034843828,0.001659684,0.0025745342,0.0017019105,0.0016149761,0.00041233108],"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":[0.0012309892,0.0002781589,0.5771235,0.0005374554,0.0010118014,0.0021196892,0.0041751517,0.07126428,0.006164528,0.18370718,0.0030945446,0.14929274],"study_design_scores_gemma":[0.00030898422,0.00076395605,0.23080435,0.0002664398,0.00053173315,0.002283364,0.0009092739,0.47678328,0.012169787,0.26641572,0.008563894,0.0001991863],"about_ca_topic_score_codex":0.009277405,"about_ca_topic_score_gemma":0.0055443617,"teacher_disagreement_score":0.03801329,"about_ca_system_score_codex":0.0009006706,"about_ca_system_score_gemma":0.002058968,"threshold_uncertainty_score":0.20103592},"labels":[],"label_agreement":null},{"id":"W1980825044","doi":"10.1111/j.1467-9868.2005.00511.x","title":"Calibrated Imputation in Surveys Under a Quasi-Model-Assisted Approach","year":2005,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Imputation (statistics); Missing data; Estimator; Statistics; Mathematics; Econometrics; Computer science","score_opus":0.14165795160630315,"score_gpt":0.3926318196771876,"score_spread":0.2509738680708844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980825044","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":[0.0041098464,0.00011952154,0.994796,0.00020937987,0.00002625144,0.00006331169,0.00004854702,0.00012966321,0.00049754686],"genre_scores_gemma":[0.27391818,0.0003665818,0.72242445,0.00037300258,0.00008835557,0.0006826036,0.00033934895,0.000087709785,0.0017197606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.933735,0.060871586,0.0007839172,0.0015807649,0.0026080336,0.00042065882],"domain_scores_gemma":[0.9403023,0.04011757,0.0043189884,0.011570823,0.0033670412,0.0003233801],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.035790198,0.0005256958,0.001789291,0.0015922496,0.00058745756,0.0017522199,0.0032477644,0.0015482795,0.0034551683],"category_scores_gemma":[0.09341718,0.0009915177,0.001463556,0.0026917795,0.0015349662,0.0024272315,0.0021636588,0.001535105,0.00076221506],"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":[0.00027908492,0.00018965462,0.008126302,0.00049635995,0.0007405332,0.00038148282,0.0007641263,0.4734871,0.0006270342,0.32823995,0.004774428,0.18189397],"study_design_scores_gemma":[0.00008196477,0.00012822344,0.0020388884,0.00009447636,0.000065627806,0.000120963705,0.00007339766,0.81283504,0.0005274486,0.179919,0.0040580127,0.000056937835],"about_ca_topic_score_codex":0.0031049896,"about_ca_topic_score_gemma":0.0025785817,"teacher_disagreement_score":0.9642098,"about_ca_system_score_codex":0.0014009065,"about_ca_system_score_gemma":0.0023197467,"threshold_uncertainty_score":0.1892789},"labels":[],"label_agreement":null},{"id":"W1980915258","doi":"10.6000/1929-6029.2014.03.02.4","title":"A Bayesian Shared Parameter Model for Analysing Longitudinal Skewed Responses with Nonignorable Dropout","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Skew; Skewness; Markov chain Monte Carlo; Deviance information criterion; Missing data; Deviance (statistics); Bayesian probability; Computer science; Random effects model; Statistics; Data set; Dropout (neural networks); Mixed model; Econometrics; Mathematics; Machine learning","score_opus":0.18773300399977777,"score_gpt":0.521479591404308,"score_spread":0.3337465874045302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980915258","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":[0.010165461,0.0003662144,0.9876336,0.00039815862,0.000051834995,0.00020350795,0.0004046162,0.00018230126,0.0005943663],"genre_scores_gemma":[0.37715015,0.0018234159,0.60351324,0.00089789723,0.00034752177,0.003755244,0.0026405747,0.00024331902,0.009628655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98389983,0.011284446,0.0005967405,0.0022033663,0.0014141245,0.00060147216],"domain_scores_gemma":[0.9664257,0.026304014,0.002120072,0.002557856,0.0020671308,0.00052527763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035179485,0.0016501271,0.00386046,0.0023523443,0.0010477528,0.0021991082,0.005737891,0.0038457743,0.005441587],"category_scores_gemma":[0.06307007,0.0014172541,0.00299036,0.003012786,0.0027099433,0.003844921,0.0030062487,0.004036676,0.0011050159],"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":[0.0013981871,0.00044857257,0.016596517,0.00088764477,0.0012286457,0.001061019,0.0017643037,0.40240172,0.0033890528,0.36295125,0.005629301,0.20224378],"study_design_scores_gemma":[0.00017797116,0.00033312643,0.003269154,0.0001646699,0.00031351778,0.00028972904,0.00014764292,0.83083624,0.00063498045,0.15977894,0.0039435537,0.000110428526],"about_ca_topic_score_codex":0.007513809,"about_ca_topic_score_gemma":0.0058881682,"teacher_disagreement_score":0.035179485,"about_ca_system_score_codex":0.0017966758,"about_ca_system_score_gemma":0.0038670923,"threshold_uncertainty_score":0.1860491},"labels":[],"label_agreement":null},{"id":"W1981758556","doi":"10.1002/env.870","title":"Spatial and mixture models for recurrent event processes","year":2007,"lang":"en","type":"article","venue":"Environmetrics","topic":"Statistical Methods and Bayesian 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":"University of Victoria; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Counting process; Parametric statistics; Point process; Population; Markov chain; Statistics; Econometrics; Mathematics; Overdispersion; Cox process; Event (particle physics); Computer science; Count data; Poisson distribution; Medicine","score_opus":0.06419161188884238,"score_gpt":0.3536018105407301,"score_spread":0.2894101986518877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981758556","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":[0.03901073,0.0009528918,0.95501846,0.0010408142,0.00010377229,0.00012501054,0.0007221634,0.0003789351,0.0026472423],"genre_scores_gemma":[0.7500694,0.002325005,0.21246193,0.00045683407,0.00054130645,0.0015149453,0.003180106,0.000341178,0.029109282],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940117,0.003386453,0.00026947283,0.0012082718,0.0006663585,0.00045780942],"domain_scores_gemma":[0.960131,0.03168505,0.0037956836,0.001850841,0.0017897065,0.0007476661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017595923,0.0017222163,0.0026788013,0.0040208586,0.001171236,0.003601491,0.005431372,0.0037827233,0.012608566],"category_scores_gemma":[0.04820321,0.0015473886,0.0038967875,0.0027862168,0.0034853993,0.0053064334,0.0032881612,0.00371513,0.0019099215],"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":[0.0001641027,0.000079166515,0.004208861,0.00012269165,0.00021521558,0.00023789602,0.0005021064,0.2847217,0.0003271785,0.6937589,0.0017526698,0.013909438],"study_design_scores_gemma":[0.00003202178,0.000029119818,0.00082469726,0.00003081596,0.00004724418,0.00006682497,0.000053723237,0.8113261,0.00008256136,0.18604843,0.0014296867,0.00002880868],"about_ca_topic_score_codex":0.013094494,"about_ca_topic_score_gemma":0.008296654,"teacher_disagreement_score":0.017595923,"about_ca_system_score_codex":0.0023672564,"about_ca_system_score_gemma":0.001108,"threshold_uncertainty_score":0.093057215},"labels":[],"label_agreement":null},{"id":"W1982062289","doi":"10.1081/sta-120021567","title":"A Threshold Dose-Response Model with Random Effects in Teratological Experiments","year":2003,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","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 Ottawa","funders":"National Cancer Institute; American Lebanese Syrian Associated Charities","keywords":"Negative binomial distribution; Dispersion (optics); Threshold model; Statistics; Mathematics; Litter; Random effects model; Correlation; Biology; Medicine; Poisson distribution; Physics; Internal medicine","score_opus":0.0831172227051938,"score_gpt":0.4626232393135303,"score_spread":0.3795060166083365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982062289","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":[0.04718248,0.00095014053,0.9437015,0.001149254,0.0001797939,0.0006933355,0.0012326898,0.00056530733,0.004345487],"genre_scores_gemma":[0.71577555,0.0022225506,0.24567936,0.0013533838,0.00030041925,0.004069585,0.0019357149,0.00032646547,0.028336966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98232067,0.010829305,0.00078412483,0.0030860412,0.0019591495,0.0010207524],"domain_scores_gemma":[0.9109251,0.07683015,0.004602781,0.0033340163,0.0034354106,0.0008726563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033352703,0.0023206496,0.005208488,0.002943283,0.0007103471,0.002826431,0.006664618,0.0056134053,0.009652569],"category_scores_gemma":[0.0636394,0.0014373898,0.0041641337,0.0026337537,0.003582016,0.00413581,0.0022758988,0.0050973175,0.0016667577],"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":[0.0010546106,0.00034329674,0.00481375,0.00064742705,0.0005806729,0.0006683676,0.00043971892,0.7177894,0.0024264061,0.25155845,0.0015855,0.018092345],"study_design_scores_gemma":[0.00020342482,0.00033196932,0.0011480012,0.00008094489,0.00024799345,0.00018811674,0.000051600844,0.89542925,0.0006373264,0.100057594,0.0015577158,0.00006604791],"about_ca_topic_score_codex":0.006262067,"about_ca_topic_score_gemma":0.0034180114,"teacher_disagreement_score":0.033352703,"about_ca_system_score_codex":0.0033863892,"about_ca_system_score_gemma":0.0017292151,"threshold_uncertainty_score":0.17638808},"labels":[],"label_agreement":null},{"id":"W1982580511","doi":"10.1016/j.csda.2010.06.003","title":"Estimation from aggregate data","year":2010,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","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":"HEC Montréal","funders":"","keywords":"Aggregate (composite); Data set; Estimation; Aggregate data; Reliability (semiconductor); Computer science; Statistics; Set (abstract data type); Stochastic process; Hazard; Econometrics; Process (computing); Algorithm; Data mining; Mathematics; Applied mathematics; Ecology; Engineering","score_opus":0.13021562141909196,"score_gpt":0.43285221441408644,"score_spread":0.30263659299499446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982580511","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":[0.003562698,0.00030073538,0.99446684,0.00020009771,0.00004200913,0.000023185936,0.00024365561,0.00019913484,0.00096167566],"genre_scores_gemma":[0.36279938,0.0022912207,0.622591,0.00042408716,0.00076444814,0.0004960382,0.0032326712,0.0004380127,0.006963252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939714,0.0030433086,0.00035255274,0.0013132757,0.0011085448,0.000210891],"domain_scores_gemma":[0.9799622,0.012531491,0.0012395272,0.0047746515,0.0012375371,0.00025459426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082943,0.001108849,0.0024162107,0.002801841,0.00066166057,0.0043462967,0.0015243632,0.0012362465,0.004230603],"category_scores_gemma":[0.038277373,0.0011502326,0.0018450868,0.0033907583,0.0011453883,0.0058538,0.00343279,0.0032090947,0.001841084],"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":[0.00028184112,0.00012288889,0.0071449745,0.0005294928,0.00073489174,0.00019300144,0.00047372875,0.20542224,0.0035961312,0.47227633,0.01078223,0.2984422],"study_design_scores_gemma":[0.000017323153,0.000048082307,0.0017050536,0.000075875716,0.000114443115,0.00011828421,0.00007862688,0.5415049,0.0019274447,0.44688347,0.0074970787,0.000029387975],"about_ca_topic_score_codex":0.0013820832,"about_ca_topic_score_gemma":0.0013718755,"teacher_disagreement_score":0.0082943,"about_ca_system_score_codex":0.0008010704,"about_ca_system_score_gemma":0.0013595988,"threshold_uncertainty_score":0.043864965},"labels":[],"label_agreement":null},{"id":"W1983751776","doi":"10.2307/3316014","title":"Extrapolation of subsampling distribution estimators: The i.i.d. and strong mixing cases","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd; Institut National de la Recherche Agronomique","keywords":"Estimator; Extrapolation; Mathematics; Mixing (physics); Statistics; Distribution (mathematics); Robustness (evolution); Jackknife resampling; Sampling distribution; Interpolation (computer graphics); Applied mathematics; Delta method; Mathematical analysis; Computer science; Physics","score_opus":0.08803902795191976,"score_gpt":0.34726503212277837,"score_spread":0.2592260041708586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983751776","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":[0.014751057,0.0004582418,0.98354083,0.00021609798,0.000024832063,0.000051060368,0.000029522347,0.00010583072,0.00082240073],"genre_scores_gemma":[0.52469844,0.0010811078,0.47165576,0.00039850042,0.00024342495,0.00035268042,0.00020486073,0.00011911885,0.0012460272],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99102575,0.0067488747,0.0003410348,0.0007053001,0.0009454255,0.00023349604],"domain_scores_gemma":[0.91612345,0.06890527,0.004892178,0.0072790463,0.002219009,0.00058104604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031538185,0.0010109287,0.0016896919,0.0016182269,0.00049214996,0.0010748903,0.0018679819,0.0021565917,0.001316885],"category_scores_gemma":[0.1106225,0.00081184885,0.0020215318,0.0010762699,0.002726743,0.002178361,0.0030732858,0.0024384384,0.00029507637],"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":[0.0007227362,0.00016181469,0.009514755,0.00046073264,0.0004146147,0.0010613682,0.0009922442,0.22822571,0.006370192,0.5960636,0.0021734992,0.1538388],"study_design_scores_gemma":[0.00007715367,0.00018464605,0.0023783266,0.000100962934,0.00008761629,0.00039376918,0.0000862905,0.7362878,0.0044520283,0.2529742,0.0029253345,0.000051941683],"about_ca_topic_score_codex":0.0011564008,"about_ca_topic_score_gemma":0.0005855291,"teacher_disagreement_score":0.031538185,"about_ca_system_score_codex":0.00075182994,"about_ca_system_score_gemma":0.0005619341,"threshold_uncertainty_score":0.16679186},"labels":[],"label_agreement":null},{"id":"W1983948884","doi":"10.1016/j.jmva.2010.09.003","title":"Robust inference for sparse cluster-correlated count data","year":2010,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","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":"Armand Frappier Museum; Institut National de la Recherche Scientifique","funders":"","keywords":"Mathematics; Estimator; Statistics; Inference; Count data; Statistical inference; Applied mathematics; Cluster (spacecraft); Correlation; Econometrics; Poisson distribution; Artificial intelligence; Computer science; Geometry","score_opus":0.14400240584370594,"score_gpt":0.41338238002649147,"score_spread":0.26937997418278553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983948884","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":[0.0031879942,0.0001695664,0.99600476,0.00017739723,0.000028067621,0.000024925203,0.00011855244,0.00015318989,0.00013566152],"genre_scores_gemma":[0.24382891,0.0012129729,0.7467351,0.00050070486,0.00063500804,0.0007237151,0.0032307655,0.00043643173,0.002696398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97838783,0.013160536,0.0012242631,0.0039960146,0.0026651763,0.00056617876],"domain_scores_gemma":[0.8197476,0.14685102,0.009861297,0.017248325,0.005288205,0.0010034962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030524395,0.001974824,0.005014465,0.004015111,0.0015826026,0.0042615742,0.009133919,0.0035878958,0.0031864932],"category_scores_gemma":[0.19322556,0.0031277149,0.003599007,0.006188786,0.0059355046,0.005066044,0.005445999,0.0053288126,0.0008714981],"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":[0.0005421074,0.00016407957,0.0044120583,0.0007956552,0.0017150206,0.00032863265,0.0004144338,0.4795687,0.0018404205,0.4054983,0.0054934775,0.099227175],"study_design_scores_gemma":[0.00005917936,0.000026290203,0.00060389464,0.000037983562,0.000084455496,0.00006744427,0.000024002811,0.7102866,0.0003999345,0.28753975,0.0008377458,0.000032612857],"about_ca_topic_score_codex":0.006310931,"about_ca_topic_score_gemma":0.0059462506,"teacher_disagreement_score":0.030524395,"about_ca_system_score_codex":0.00214769,"about_ca_system_score_gemma":0.0034493445,"threshold_uncertainty_score":0.1614303},"labels":[],"label_agreement":null},{"id":"W1984238728","doi":"10.1080/028275801300088288","title":"Saddlepoint Approximations for Statistical Inference of <i>PPP</i> Sample Estimates","year":2001,"lang":"en","type":"article","venue":"Scandinavian Journal of Forest Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; Natural Resources Canada","funders":"","keywords":"Estimator; Sample (material); Statistics; Inference; Series (stratigraphy); Econometrics; Statistical inference; Variance (accounting); Taylor series; Mathematics; Sampling (signal processing); Sample size determination; Sample mean and sample covariance; Computer science; Economics","score_opus":0.223215400254661,"score_gpt":0.5053381582638247,"score_spread":0.28212275800916364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984238728","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":[0.00084086193,0.00025541158,0.9978635,0.0002180384,0.00003306004,0.0000250409,0.000050821716,0.00008972076,0.0006235745],"genre_scores_gemma":[0.15110907,0.0025056005,0.83210206,0.000982113,0.00044073843,0.0017237237,0.0010556086,0.00083482265,0.0092462255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904405,0.0068595596,0.00044112548,0.00089021964,0.0010958778,0.00027269326],"domain_scores_gemma":[0.8988803,0.09286025,0.0020595016,0.003563871,0.0022511922,0.00038488314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032907855,0.0018439542,0.0033837848,0.0029239098,0.0012078823,0.003738804,0.0045737773,0.004010377,0.007840094],"category_scores_gemma":[0.15845098,0.003278731,0.002689892,0.003470016,0.004715611,0.0072495737,0.003514994,0.007016007,0.002036484],"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":[0.000073328825,0.000036152465,0.001133449,0.00030352027,0.0001891124,0.00017922978,0.0002447182,0.30180094,0.00030688214,0.661003,0.0046449206,0.030084781],"study_design_scores_gemma":[0.00002733055,0.000015600097,0.0001942922,0.00007400179,0.0000263415,0.000053176947,0.000020861225,0.5893236,0.00020422538,0.40816012,0.001880912,0.000019572166],"about_ca_topic_score_codex":0.0056912526,"about_ca_topic_score_gemma":0.003490904,"teacher_disagreement_score":0.032907855,"about_ca_system_score_codex":0.0026346971,"about_ca_system_score_gemma":0.0020675273,"threshold_uncertainty_score":0.17403549},"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":"W1985969598","doi":"10.1186/1471-2288-11-21","title":"Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study","year":2011,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"St. Joseph’s Healthcare Hamilton; Health Sciences Centre; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Statistics; Intraclass correlation; Estimator; Randomized controlled trial; Type I and type II errors; Context (archaeology); Confidence interval; Statistical power; Generalized estimating equation; Random effects model; Medicine; Gee; Sample size determination; Point estimation; Observational study; Standard error; Mathematics; Outcome (game theory); Meta-analysis; Surgery; Internal medicine","score_opus":0.8018225963297307,"score_gpt":0.692509018615857,"score_spread":0.10931357771387373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985969598","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":[0.12343599,0.026155496,0.826004,0.0031248217,0.00093748624,0.01404406,0.0009575925,0.00059273135,0.0047477786],"genre_scores_gemma":[0.5737268,0.0048158807,0.396107,0.0015063446,0.00019200006,0.02244628,0.00054069486,0.00013651415,0.000528538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6081162,0.3712999,0.009261519,0.0044129533,0.0056936042,0.0012158002],"domain_scores_gemma":[0.15077539,0.8148647,0.015319808,0.010932028,0.007109565,0.0009985319],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.28687578,0.0025965811,0.0059685498,0.0036812013,0.00096612866,0.0032709762,0.003578423,0.0054391176,0.005206075],"category_scores_gemma":[0.57494235,0.0015879574,0.012135228,0.0032780329,0.0022950112,0.004146039,0.0037547427,0.0059409286,0.00048961974],"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":[0.02800309,0.0018784112,0.024640433,0.017188247,0.026795045,0.00065871055,0.002187174,0.7089602,0.0005589207,0.06957082,0.0036707965,0.115888156],"study_design_scores_gemma":[0.01246688,0.0069275107,0.0038422728,0.0066674165,0.0072139315,0.00054503465,0.00037149116,0.87799287,0.00071509246,0.078347385,0.004616467,0.00029377214],"about_ca_topic_score_codex":0.003619016,"about_ca_topic_score_gemma":0.0022282805,"teacher_disagreement_score":0.7131242,"about_ca_system_score_codex":0.004929593,"about_ca_system_score_gemma":0.0055132494,"threshold_uncertainty_score":0.87940913},"labels":[],"label_agreement":null},{"id":"W1986211349","doi":"10.1016/j.csda.2012.03.011","title":"Bootstrap variance estimation with survey data when estimating model parameters","year":2012,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Sampling design; Statistics; Sampling (signal processing); Variance (accounting); Simple random sample; Stratified sampling; Mathematics; Inference; Population; Econometrics; Computer science; Artificial intelligence","score_opus":0.31892552233020255,"score_gpt":0.446430431089935,"score_spread":0.12750490875973247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986211349","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":[0.004219111,0.00015593132,0.99483055,0.00007755065,0.00004193246,0.000042678916,0.00006591839,0.00019242025,0.00037396635],"genre_scores_gemma":[0.2289351,0.0005810176,0.7665189,0.00025746992,0.00019830363,0.000771529,0.00097652315,0.00036797882,0.0013931681],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9702377,0.024857434,0.0007762599,0.0016540631,0.002141204,0.00033334427],"domain_scores_gemma":[0.8218907,0.15321968,0.0033339579,0.017834505,0.003203305,0.0005178285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041956853,0.0013092484,0.0022706005,0.0034129836,0.0012447258,0.002152989,0.0036580279,0.0025947369,0.0033229634],"category_scores_gemma":[0.28497943,0.0016544926,0.0022052,0.004868048,0.002446605,0.004262309,0.0026094054,0.0034177892,0.0011913202],"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":[0.00052048906,0.00029523097,0.024930473,0.0010114881,0.0013175528,0.0007266378,0.0010276565,0.22754751,0.002128648,0.3254334,0.012870158,0.40219077],"study_design_scores_gemma":[0.00005384664,0.000071081085,0.002732341,0.0001452781,0.00011270417,0.00027680126,0.00018183417,0.63614565,0.0018438236,0.3526801,0.005710415,0.000046134315],"about_ca_topic_score_codex":0.0036550297,"about_ca_topic_score_gemma":0.0051292176,"teacher_disagreement_score":0.041956853,"about_ca_system_score_codex":0.00088644825,"about_ca_system_score_gemma":0.001940059,"threshold_uncertainty_score":0.2218917},"labels":[],"label_agreement":null},{"id":"W1986245212","doi":"10.1007/s00038-012-0439-9","title":"Misclassification errors in prevalence estimation: Bayesian handling with care","year":2012,"lang":"en","type":"article","venue":"International Journal of Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Estimation; Public health; Medicine; Bayesian probability; Environmental health; Bayes' theorem; Bayes estimator; Statistics; Medical emergency; Mathematics; Nursing","score_opus":0.1393653872918277,"score_gpt":0.44040478345294487,"score_spread":0.3010393961611172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986245212","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":[0.009883144,0.0012371864,0.9851983,0.0025485463,0.00017806777,0.00012757439,0.00018244896,0.00013656523,0.0005082007],"genre_scores_gemma":[0.3609751,0.0017079803,0.62948704,0.0020211616,0.0010887107,0.0010693999,0.0006556924,0.00020260642,0.0027922792],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9076029,0.07326788,0.004329026,0.008568157,0.0050546397,0.0011774035],"domain_scores_gemma":[0.5232478,0.43823537,0.01102683,0.020592974,0.005690364,0.0012066818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11590056,0.0017292104,0.004827946,0.0033197685,0.002220932,0.005592364,0.008055519,0.007672084,0.0032684358],"category_scores_gemma":[0.48630384,0.002751797,0.0038180018,0.00477629,0.0046288283,0.0059913294,0.005094566,0.008972546,0.00037521802],"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":[0.001183376,0.0002914556,0.048259396,0.0011734503,0.002159197,0.0014466097,0.0030384087,0.20592913,0.00045661465,0.41932324,0.012254054,0.30448517],"study_design_scores_gemma":[0.00018013405,0.00011122811,0.004966134,0.00040706567,0.00059313484,0.0009365627,0.00022034207,0.4877983,0.0005629941,0.50057244,0.0035225258,0.00012905456],"about_ca_topic_score_codex":0.01578053,"about_ca_topic_score_gemma":0.011047028,"teacher_disagreement_score":0.11590056,"about_ca_system_score_codex":0.0027734966,"about_ca_system_score_gemma":0.0038614152,"threshold_uncertainty_score":0.61294806},"labels":[],"label_agreement":null},{"id":"W1987001026","doi":"10.1002/sim.3882","title":"The analysis of treatment effects for recurring episodic conditions","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"University of Waterloo; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Randomized controlled trial; Migraine; Disease; Neurology; Clinical trial; Asthma; Chronic Migraine; Intensive care medicine; Pediatrics; Psychiatry; Internal medicine","score_opus":0.04607669157443555,"score_gpt":0.44360969574707126,"score_spread":0.3975330041726357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987001026","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":[0.027111497,0.041418597,0.91191494,0.00930185,0.0012388179,0.0015798522,0.0017429496,0.00037604955,0.0053154933],"genre_scores_gemma":[0.47799855,0.032110225,0.47064823,0.004509346,0.0022053209,0.005933591,0.0016151115,0.00021644044,0.004763198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8973397,0.09249858,0.0024080523,0.0031471327,0.003998708,0.0006078227],"domain_scores_gemma":[0.6126915,0.37128028,0.008177493,0.005685147,0.001648465,0.0005171051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11314958,0.0014821845,0.005741997,0.0026305465,0.0006781087,0.0025521964,0.0029658657,0.0037973209,0.007597959],"category_scores_gemma":[0.2572396,0.00090502645,0.005633494,0.0023758973,0.00353116,0.00399574,0.0021450245,0.0061022514,0.00051342667],"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":[0.0034466882,0.00040819807,0.012154474,0.006961774,0.0104994625,0.00037233287,0.0006446549,0.10727567,0.00094983226,0.56431055,0.010209064,0.28276736],"study_design_scores_gemma":[0.0008819366,0.0013480837,0.0061136265,0.0010936637,0.0029828572,0.00017288941,0.00012883866,0.18776758,0.0009630801,0.78470963,0.013732251,0.00010557084],"about_ca_topic_score_codex":0.0029381278,"about_ca_topic_score_gemma":0.0018869891,"teacher_disagreement_score":0.11314958,"about_ca_system_score_codex":0.0023335721,"about_ca_system_score_gemma":0.0028314942,"threshold_uncertainty_score":0.59839934},"labels":[],"label_agreement":null},{"id":"W1989860334","doi":"10.1080/0266476022000030075","title":"An extension of the Dirichlet prior for the analysis of longitudinal multinomial data","year":2003,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multinomial distribution; Dirichlet distribution; Multinomial probit; Bayesian probability; Computer science; Econometrics; Prior probability; Longitudinal data; Statistics; Mathematics; Multinomial logistic regression; Data mining","score_opus":0.12119000676231616,"score_gpt":0.4159998562650252,"score_spread":0.29480984950270905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989860334","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":[0.002414765,0.00028655335,0.9956098,0.00043831355,0.000070545386,0.000069539296,0.00016480865,0.0001121036,0.0008334968],"genre_scores_gemma":[0.15121622,0.0019279325,0.837519,0.000864983,0.0007853402,0.001594862,0.0011435222,0.0003597609,0.0045883716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9838027,0.011592895,0.000666862,0.0019727459,0.001512398,0.0004523748],"domain_scores_gemma":[0.9265287,0.06278729,0.0021564951,0.0056273704,0.002086742,0.0008134706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03368767,0.0013513969,0.002563353,0.003884541,0.0025028107,0.0036646414,0.004714705,0.0030797208,0.008935577],"category_scores_gemma":[0.114489034,0.0015695586,0.0039657024,0.0046475073,0.004736805,0.008013373,0.004220423,0.007876952,0.0014732799],"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":[0.00010209878,0.00007535137,0.0029636086,0.00021002839,0.00023350501,0.00036499935,0.0009241937,0.048912045,0.0007009952,0.8914069,0.0032968796,0.050809287],"study_design_scores_gemma":[0.000040863084,0.000032712764,0.0008700656,0.00010812826,0.000054869895,0.00023392931,0.000108508204,0.16634142,0.00032886415,0.8256461,0.0061720894,0.00006242748],"about_ca_topic_score_codex":0.0060297404,"about_ca_topic_score_gemma":0.0065207323,"teacher_disagreement_score":0.03368767,"about_ca_system_score_codex":0.0023255697,"about_ca_system_score_gemma":0.0027374157,"threshold_uncertainty_score":0.17815953},"labels":[],"label_agreement":null},{"id":"W1990572896","doi":"10.1002/cjs.5550360208","title":"Inference for general parametric functions in box‐Cox‐type transformation models","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Power transform; Parametric statistics; Inference; Transformation (genetics); Applied mathematics; Normality; Mathematics; Parametric model; Function (biology); Algorithm; Computer science; Statistics; Artificial intelligence; Discrete mathematics","score_opus":0.13189170631643787,"score_gpt":0.3471842873732727,"score_spread":0.21529258105683485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990572896","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":[0.0029955418,0.00005065043,0.9964057,0.00006739258,0.000013700152,0.000026996539,0.000043299944,0.00007141413,0.00032523836],"genre_scores_gemma":[0.3192869,0.0007210663,0.6708658,0.00024487663,0.00010112037,0.0011153712,0.000612339,0.00022368522,0.0068288525],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917995,0.005689523,0.00026437137,0.000798605,0.0010857226,0.00036238623],"domain_scores_gemma":[0.93739486,0.055756215,0.0017733127,0.0031243106,0.0016774328,0.00027390048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035043556,0.0009491709,0.0021701688,0.0017245354,0.0006746289,0.0020069813,0.0027096185,0.001707092,0.007933887],"category_scores_gemma":[0.08759529,0.0009602312,0.0019714385,0.0016917612,0.00311358,0.002873472,0.0022373293,0.0034544459,0.00089702266],"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":[0.00019441627,0.00009955176,0.002516758,0.00019102338,0.00020570988,0.00021040304,0.00025532284,0.23603418,0.0009404123,0.7023131,0.0021597256,0.05487942],"study_design_scores_gemma":[0.00005140509,0.000047094738,0.00056514767,0.000033881333,0.0000325546,0.000054288903,0.0000357821,0.6135358,0.00063792226,0.3832619,0.0017161336,0.000028123754],"about_ca_topic_score_codex":0.0042224675,"about_ca_topic_score_gemma":0.002886806,"teacher_disagreement_score":0.035043556,"about_ca_system_score_codex":0.0014371591,"about_ca_system_score_gemma":0.0024942101,"threshold_uncertainty_score":0.18533021},"labels":[],"label_agreement":null},{"id":"W1990732953","doi":"10.1002/sim.2435","title":"Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"St. Michael's Hospital","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences; U.S. Public Health Service","keywords":"Covariate; Missing data; Categorical variable; Statistics; Parametric statistics; Estimating equations; Econometrics; Expectation–maximization algorithm; Outcome (game theory); Mathematics; Computer science; Maximum likelihood","score_opus":0.12276464088284904,"score_gpt":0.48109581167973214,"score_spread":0.3583311707968831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990732953","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":[0.0006501646,0.00033825496,0.9982374,0.00017388344,0.000040958428,0.000087513574,0.00010238353,0.00019302322,0.00017652637],"genre_scores_gemma":[0.042711336,0.0010966813,0.94989336,0.000473108,0.00029491735,0.0020263488,0.0011295342,0.00046037356,0.0019143516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9615839,0.033136345,0.0010872608,0.0017103618,0.0021720561,0.00031016808],"domain_scores_gemma":[0.8279058,0.1525535,0.006452598,0.00820517,0.0041029747,0.00077997555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046261713,0.0021570884,0.0033999325,0.003692439,0.0011249739,0.0032719541,0.007829505,0.003449407,0.0069108903],"category_scores_gemma":[0.15441099,0.00249916,0.003433299,0.004806914,0.0038659226,0.0059055625,0.0054306122,0.006201401,0.002726726],"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":[0.00069016346,0.00024927242,0.006473437,0.0022408452,0.0011123444,0.0009441327,0.0013813204,0.2539168,0.0011120671,0.4843242,0.0080442,0.23951131],"study_design_scores_gemma":[0.00014767365,0.000107648186,0.00070853956,0.00024353678,0.000070863236,0.00030075674,0.0001088219,0.58914447,0.0005021893,0.40015027,0.008423256,0.000091941154],"about_ca_topic_score_codex":0.0032051879,"about_ca_topic_score_gemma":0.0033566437,"teacher_disagreement_score":0.046261713,"about_ca_system_score_codex":0.0015772065,"about_ca_system_score_gemma":0.003973496,"threshold_uncertainty_score":0.24465823},"labels":[],"label_agreement":null},{"id":"W1990943797","doi":"10.1007/s40300-015-0059-2","title":"Inference in semi-parametric spline mixed models for longitudinal data","year":2015,"lang":"en","type":"article","venue":"METRON","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Estimator; Parametric statistics; Mixed model; Econometrics; Parametric model; Longitudinal data; Random effects model; Computer science; Statistics; Semiparametric model; Restricted maximum likelihood; Mathematics; Maximum likelihood; Data mining; Medicine","score_opus":0.48876850185544424,"score_gpt":0.4797835426863746,"score_spread":0.008984959169069662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990943797","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":[0.0016933123,0.00045845963,0.9970885,0.00032825576,0.000030116738,0.000022163422,0.000084454565,0.00013037055,0.0001643314],"genre_scores_gemma":[0.13333316,0.0026896852,0.85268134,0.00070571474,0.0007180367,0.0013263099,0.0013113442,0.000554968,0.0066793845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9653076,0.02875438,0.0012932087,0.0022745405,0.0018061597,0.00056405924],"domain_scores_gemma":[0.73732877,0.24485995,0.0054924563,0.0076401224,0.0034005065,0.0012781468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057934295,0.0026287131,0.0059916815,0.004849168,0.001886401,0.0043185675,0.008009433,0.0045243967,0.0061158757],"category_scores_gemma":[0.19770943,0.0042998963,0.005178951,0.0058742007,0.0063352855,0.007392772,0.006512431,0.00854404,0.0010752861],"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":[0.0003009292,0.00015755651,0.0026916827,0.00076621404,0.00081010885,0.00028701767,0.00064310984,0.16852315,0.00047130726,0.7695877,0.0029568442,0.05280449],"study_design_scores_gemma":[0.000057058787,0.000034566703,0.00023739671,0.00007094961,0.00006833538,0.00006132797,0.000036487854,0.41108453,0.00014761905,0.5869295,0.001238379,0.000033735454],"about_ca_topic_score_codex":0.010594459,"about_ca_topic_score_gemma":0.009696619,"teacher_disagreement_score":0.057934295,"about_ca_system_score_codex":0.003029452,"about_ca_system_score_gemma":0.0052722306,"threshold_uncertainty_score":0.3063895},"labels":[],"label_agreement":null},{"id":"W1991705432","doi":"10.1002/cjs.5550350110","title":"Marginalized transition random effect models for multivariate longitudinal binary data","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Marginal model; Random effects model; Covariate; Statistics; Binary data; Markov chain Monte Carlo; Logistic regression; Mathematics; Econometrics; Markov chain; Generalized linear mixed model; Binary number; Regression analysis; Computer science; Monte Carlo method","score_opus":0.1660987715882368,"score_gpt":0.3774538329787158,"score_spread":0.21135506139047902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991705432","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":[0.010784196,0.00063893537,0.98595005,0.0006132358,0.00008723111,0.00012489418,0.0006407227,0.00037047648,0.00079027814],"genre_scores_gemma":[0.42437935,0.0018147149,0.5550471,0.00060407165,0.0003812791,0.0024194736,0.0028252336,0.00037540586,0.012153268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9845916,0.011878626,0.00043058227,0.0016645733,0.00097157067,0.00046300542],"domain_scores_gemma":[0.9431395,0.046440434,0.0036119882,0.0036557384,0.0023907803,0.0007617015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02913236,0.0015667515,0.0026674555,0.0029138098,0.0007396247,0.0022414546,0.004672189,0.0021962498,0.009621086],"category_scores_gemma":[0.06335305,0.0011819691,0.0033854255,0.0030421028,0.0028232478,0.00364394,0.0027299633,0.004964656,0.0013089915],"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":[0.0003806892,0.00014528687,0.0056341924,0.000296513,0.00051036564,0.00041371092,0.0007591008,0.18772879,0.00047659772,0.7612311,0.0049965307,0.037427016],"study_design_scores_gemma":[0.000087074026,0.000092689465,0.0013099707,0.00008939961,0.00011571574,0.000107966785,0.000068818714,0.50927395,0.0001671073,0.4851015,0.0035289442,0.000056779747],"about_ca_topic_score_codex":0.009852592,"about_ca_topic_score_gemma":0.0072196894,"teacher_disagreement_score":0.02913236,"about_ca_system_score_codex":0.002293744,"about_ca_system_score_gemma":0.0019343264,"threshold_uncertainty_score":0.15406853},"labels":[],"label_agreement":null},{"id":"W1992410926","doi":"10.1002/cjs.10055","title":"Inferences in generalized linear longitudinal mixed models","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalized estimating equation; Covariate; Statistics; Generalized linear model; Generalization; Correlation; Generalized linear mixed model; Moment (physics); Contrast (vision); Econometrics; Mixed model; Binary data; Regression; Regression analysis; Estimating equations; Linear model; Binary number; Maximum likelihood; Mathematical analysis; Computer science","score_opus":0.10230481845633235,"score_gpt":0.34903062397481754,"score_spread":0.2467258055184852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992410926","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":[0.011205036,0.0009170502,0.98330784,0.0013720215,0.0001745968,0.00033432696,0.0006110689,0.00038854446,0.0016894584],"genre_scores_gemma":[0.373048,0.002085633,0.6150967,0.0015020367,0.00081674056,0.0024591675,0.0018341837,0.00018793489,0.0029696412],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89575034,0.09169016,0.002284104,0.005496775,0.0041101193,0.0006685368],"domain_scores_gemma":[0.7386909,0.237606,0.010451686,0.008840659,0.0037168544,0.0006938504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.084709816,0.0020601265,0.003620206,0.005479537,0.0014728752,0.004396033,0.0047344454,0.0022921436,0.00729729],"category_scores_gemma":[0.32931584,0.0019319932,0.0039510643,0.005736768,0.0036186806,0.005114229,0.004262475,0.0044970135,0.0009299017],"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":[0.0004970538,0.00018450842,0.01678392,0.0012568153,0.0033992718,0.000970164,0.0009042263,0.118430436,0.00036840956,0.7200966,0.006441853,0.13066678],"study_design_scores_gemma":[0.00017059149,0.0001197446,0.0020330227,0.00030177867,0.00038905613,0.00012227919,0.00016845456,0.29747683,0.00036397393,0.6953711,0.003417866,0.000065252825],"about_ca_topic_score_codex":0.0151395425,"about_ca_topic_score_gemma":0.012059158,"teacher_disagreement_score":0.084709816,"about_ca_system_score_codex":0.004083985,"about_ca_system_score_gemma":0.0037192819,"threshold_uncertainty_score":0.4479937},"labels":[],"label_agreement":null},{"id":"W1992676321","doi":"10.2307/3315997","title":"Nonlinear mixed‐effect models with nonignorably missing covariates","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Missing data; Gibbs sampling; Mixed model; Random effects model; Inference; Nonlinear system; Monte Carlo method; Statistics; Econometrics; Mathematics; Longitudinal data; Generalized linear mixed model; Computer science; Applied mathematics; Statistical physics; Artificial intelligence; Data mining; Physics","score_opus":0.041450224794941884,"score_gpt":0.30347278244959114,"score_spread":0.26202255765464927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992676321","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":[0.018873114,0.0010155237,0.97528744,0.0016014851,0.00018813877,0.00010641882,0.0008999659,0.0002252665,0.0018026803],"genre_scores_gemma":[0.56167144,0.0022106539,0.41033468,0.00070293236,0.00065414736,0.0009322435,0.0017823639,0.00019321057,0.021518355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929502,0.0050385613,0.00027080803,0.0008684579,0.00056919025,0.00030277186],"domain_scores_gemma":[0.9628166,0.031615753,0.0019967197,0.0018327159,0.0012610838,0.00047706362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016203275,0.0019271537,0.0026712602,0.0016036188,0.0009304996,0.002301957,0.004909555,0.0025439772,0.007564322],"category_scores_gemma":[0.036434885,0.001285682,0.0026208737,0.00242477,0.0020214722,0.0024213917,0.0024313778,0.0030656864,0.0011115697],"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":[0.00095039146,0.0002688425,0.00996129,0.00066336844,0.001118306,0.0015303874,0.0005604928,0.31748247,0.001049721,0.6106085,0.0047618537,0.05104434],"study_design_scores_gemma":[0.00013224571,0.00011293683,0.0013170622,0.00010751174,0.00026288416,0.0001661933,0.00007082682,0.7193959,0.00058407447,0.2728514,0.004947917,0.000051024228],"about_ca_topic_score_codex":0.010690601,"about_ca_topic_score_gemma":0.012167563,"teacher_disagreement_score":0.016203275,"about_ca_system_score_codex":0.0016280758,"about_ca_system_score_gemma":0.0013071545,"threshold_uncertainty_score":0.08569211},"labels":[],"label_agreement":null},{"id":"W1993000953","doi":"10.3758/s13428-013-0373-7","title":"The effective number of parameters in post hoc models","year":2013,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Statistical Methods and Bayesian Inference","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":"Post hoc; Simple (philosophy); Computer science; Set (abstract data type); Context (archaeology); Monte Carlo method; Statistics; Mathematics","score_opus":0.34766603907049415,"score_gpt":0.6206011347065685,"score_spread":0.2729350956360744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993000953","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":[0.0094525,0.00034289534,0.9869004,0.00094106526,0.00009326049,0.00007485328,0.00009547638,0.000096512966,0.0020030085],"genre_scores_gemma":[0.43971646,0.0014258495,0.54860663,0.00091615977,0.0006576637,0.0014582934,0.00039092425,0.00038573734,0.0064422037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96855766,0.023706263,0.0011785298,0.0031900853,0.0028069941,0.0005605111],"domain_scores_gemma":[0.5487536,0.41292843,0.005813951,0.027103357,0.0042458833,0.0011547595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04760501,0.0017663117,0.003351696,0.002260983,0.0017839032,0.0035075794,0.0052827923,0.004020055,0.0067927847],"category_scores_gemma":[0.28110036,0.0020679594,0.0019792458,0.0019319529,0.006748204,0.017529149,0.0034699782,0.007913653,0.00073761767],"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":[0.00027907858,0.00014450349,0.00301688,0.00035734204,0.00027059103,0.00018849001,0.00057114207,0.07919453,0.00061614584,0.8484026,0.0022613206,0.064697266],"study_design_scores_gemma":[0.00004423602,0.000060043083,0.00054101896,0.00006357782,0.00008673582,0.00010078865,0.00006502925,0.19227187,0.0003900416,0.8050723,0.0012790106,0.000025270345],"about_ca_topic_score_codex":0.0015569489,"about_ca_topic_score_gemma":0.0020056637,"teacher_disagreement_score":0.04760501,"about_ca_system_score_codex":0.0024667073,"about_ca_system_score_gemma":0.0035394197,"threshold_uncertainty_score":0.25176233},"labels":[],"label_agreement":null},{"id":"W1993310646","doi":"10.1002/sim.857","title":"Evaluation of an adjusted chi‐square statistic as applied to observational studies involving clustered binary data","year":2001,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":31,"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":"","keywords":"Statistics; Statistic; Chi-square test; Binary data; Test statistic; Observational study; Ancillary statistic; Mathematics; Binary number; Square (algebra); Statistical hypothesis testing; Pearson's chi-squared test; Econometrics; PRESS statistic; F-test; Arithmetic","score_opus":0.547003564024309,"score_gpt":0.5393908008446755,"score_spread":0.007612763179633553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993310646","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":[0.048268437,0.0017578952,0.94505745,0.0008789556,0.00045266506,0.0007934494,0.00053992314,0.00069703034,0.0015541771],"genre_scores_gemma":[0.38550118,0.00070781814,0.6103069,0.00041314863,0.00016817045,0.0016398357,0.0005207958,0.00020776577,0.0005343704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90084475,0.07757143,0.0056799953,0.003975238,0.011291064,0.0006374103],"domain_scores_gemma":[0.5997497,0.35081944,0.017707828,0.014880326,0.015455319,0.0013874111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0815065,0.0009860069,0.0024467164,0.0047630775,0.00075660617,0.0022756357,0.0027873416,0.0021493768,0.0028070936],"category_scores_gemma":[0.39923444,0.00055000605,0.0019850149,0.007082167,0.0023241893,0.002024284,0.0016572935,0.0020751196,0.00048304512],"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":[0.0043289405,0.0003543837,0.1745183,0.0045687635,0.007917035,0.0012901681,0.0024790878,0.0649214,0.011121664,0.09230794,0.008939078,0.6272533],"study_design_scores_gemma":[0.0015643094,0.0124865,0.21173696,0.0014865161,0.0036404436,0.0047674924,0.0018175306,0.5277156,0.029019326,0.15259199,0.052066,0.0011073292],"about_ca_topic_score_codex":0.002160296,"about_ca_topic_score_gemma":0.0016724501,"teacher_disagreement_score":0.0815065,"about_ca_system_score_codex":0.0011221718,"about_ca_system_score_gemma":0.0026595653,"threshold_uncertainty_score":0.43105268},"labels":[],"label_agreement":null},{"id":"W1993581462","doi":"10.1016/j.csda.2009.04.003","title":"Multivariate trees for mixed outcomes","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Categorical variable; Multivariate statistics; Multivariate analysis; Statistics; Tree (set theory); Outcome (game theory); Mathematics; Computer science; Mixed model; Econometrics; Combinatorics","score_opus":0.13898316285290196,"score_gpt":0.4511890468050191,"score_spread":0.3122058839521171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993581462","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":[0.0017739689,0.0005762239,0.9952295,0.00059137936,0.00007656667,0.000031339136,0.00031476602,0.00020261844,0.0012037699],"genre_scores_gemma":[0.14712305,0.0027914364,0.829211,0.0011183484,0.0013983676,0.001368316,0.0025689004,0.0008019022,0.013618603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99308187,0.004386329,0.0003511748,0.00091076735,0.0009984213,0.0002715319],"domain_scores_gemma":[0.9665891,0.025875608,0.0023660979,0.003318156,0.0011864951,0.00066467695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01058212,0.0013728348,0.0026316454,0.0028039746,0.0013324167,0.0031763616,0.003031565,0.0021925285,0.012378969],"category_scores_gemma":[0.043761138,0.001255708,0.0027872187,0.003808018,0.0025131414,0.0059442637,0.0037400937,0.006628724,0.0026391302],"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":[0.000031331652,0.000019274246,0.00034716335,0.00008300624,0.000056057917,0.00004418075,0.000118360855,0.008805442,0.00014358028,0.95598865,0.0035066002,0.03085645],"study_design_scores_gemma":[0.000010078975,0.0000074135073,0.00013567899,0.000023924522,0.000017991259,0.000037476875,0.0000090762915,0.05191645,0.0000426823,0.94508624,0.0027045568,0.000008356218],"about_ca_topic_score_codex":0.0020052218,"about_ca_topic_score_gemma":0.0024933761,"teacher_disagreement_score":0.012378969,"about_ca_system_score_codex":0.0015935184,"about_ca_system_score_gemma":0.0020767467,"threshold_uncertainty_score":0.05596429},"labels":[],"label_agreement":null},{"id":"W1993694231","doi":"10.2307/3316038","title":"Survival analysis with long‐term survivors and partially observed covariates","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Covariate; Statistics; Gibbs sampling; Term (time); Bayesian probability; Weibull distribution; Logistic regression; Accelerated failure time model; Survival analysis; Econometrics; Proportional hazards model; Mathematics; Monte Carlo method; Computer science","score_opus":0.08130488627961174,"score_gpt":0.3141121782399652,"score_spread":0.23280729196035344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993694231","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":[0.042490818,0.000330702,0.9559763,0.00028036835,0.00004149481,0.00004768514,0.00026955147,0.0003018197,0.00026125775],"genre_scores_gemma":[0.677141,0.00064824044,0.31550857,0.00012851028,0.00021110493,0.0004578766,0.0015743253,0.00012146576,0.0042088223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99711645,0.0018141665,0.00013160994,0.00036406843,0.000438809,0.00013492345],"domain_scores_gemma":[0.98461515,0.01130755,0.0011383927,0.001869955,0.00072882255,0.0003400954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008301257,0.00076219183,0.0014183356,0.0013563643,0.00061515404,0.0010578316,0.0018125941,0.0011642455,0.002885586],"category_scores_gemma":[0.024104705,0.0008380137,0.0017228476,0.0013393774,0.0010426546,0.0014833566,0.0018295707,0.0015774355,0.00044439183],"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":[0.0014930099,0.00024671626,0.045074627,0.00030559464,0.0015639284,0.0005825253,0.00041933838,0.69869286,0.0053893873,0.105726615,0.0030332075,0.1374722],"study_design_scores_gemma":[0.00006864962,0.00011501078,0.00597195,0.000028344097,0.0001284561,0.0001372383,0.00003303578,0.9253663,0.0009768377,0.06511278,0.0020116742,0.000049830047],"about_ca_topic_score_codex":0.0047959546,"about_ca_topic_score_gemma":0.004084336,"teacher_disagreement_score":0.008301257,"about_ca_system_score_codex":0.00073802087,"about_ca_system_score_gemma":0.0015078462,"threshold_uncertainty_score":0.0439018},"labels":[],"label_agreement":null},{"id":"W1994097230","doi":"10.1016/s0828-282x(09)70507-0","title":"Multiple imputation for missing cardiac magnetic resonance imaging data: Results from the Multi-Ethnic Study of Atherosclerosis (MESA)","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Medicine; Mesa; Imputation (statistics); Magnetic resonance imaging; Missing data; Ethnic group; Cardiac magnetic resonance imaging; Cardiac magnetic resonance; Cardiology; Internal medicine; Radiology; Statistics","score_opus":0.14988972025763722,"score_gpt":0.3760534788094305,"score_spread":0.2261637585517933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994097230","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":[0.9254143,0.005531706,0.055002354,0.003311939,0.00025413104,0.00016231042,0.0084604835,0.00028354395,0.0015791052],"genre_scores_gemma":[0.9702325,0.0007220117,0.021387521,0.00033071023,0.000108720255,0.0002281115,0.005911661,0.00011327058,0.00096549303],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9768835,0.018832155,0.0009326301,0.0018967751,0.0009871033,0.00046780563],"domain_scores_gemma":[0.9041167,0.06776478,0.0076704454,0.015148656,0.0041670767,0.0011323708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03128606,0.0013999202,0.0033160974,0.0009698531,0.0012167953,0.0017233496,0.0034019302,0.0017512384,0.003574593],"category_scores_gemma":[0.10489551,0.0010284911,0.004329637,0.0025512024,0.000562589,0.001496632,0.0017109555,0.0029886558,0.0006362934],"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":[0.013493904,0.0008980887,0.86796623,0.0007431506,0.027826423,0.00087505433,0.0012564304,0.011305157,0.0004954552,0.0028368125,0.012911159,0.059392154],"study_design_scores_gemma":[0.0027780128,0.0012393392,0.8809849,0.00047925816,0.021315655,0.0015223481,0.0014575013,0.06764668,0.0006412177,0.017956767,0.0037929332,0.00018540071],"about_ca_topic_score_codex":0.008897977,"about_ca_topic_score_gemma":0.0088893855,"teacher_disagreement_score":0.03128606,"about_ca_system_score_codex":0.00043131667,"about_ca_system_score_gemma":0.0012726627,"threshold_uncertainty_score":0.1654585},"labels":[],"label_agreement":null},{"id":"W1994102521","doi":"10.1080/01621459.2000.10473920","title":"Inference from Dual Frame Surveys","year":2000,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":91,"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":"Inference; Dual (grammatical number); Frame (networking); Computer science; Artificial intelligence; Statistics; Mathematics","score_opus":0.03189687030004611,"score_gpt":0.3681203927843366,"score_spread":0.3362235224842905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994102521","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":[0.036105722,0.00026143252,0.96169525,0.00020718524,0.000057586723,0.00007143454,0.00016970823,0.00009893784,0.0013327337],"genre_scores_gemma":[0.6820809,0.0005088849,0.31135106,0.00029948086,0.00022237895,0.00056327885,0.0009453535,0.000050275838,0.003978326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9847179,0.010834779,0.0003620859,0.0023385184,0.0012074988,0.0005391808],"domain_scores_gemma":[0.9691451,0.020585816,0.0034874117,0.004187922,0.0020668479,0.00052682677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019163214,0.00090753764,0.0019627353,0.0023504705,0.0007378335,0.0021558017,0.0020504254,0.0017408867,0.0053047147],"category_scores_gemma":[0.07385922,0.0010717823,0.0013203796,0.0018412197,0.0022290684,0.002762639,0.0028931566,0.0018140975,0.00055821176],"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":[0.0013249582,0.00024858548,0.030512076,0.00037551182,0.0006893645,0.0002538648,0.0008042714,0.13554747,0.0019184884,0.62081933,0.0046043284,0.20290181],"study_design_scores_gemma":[0.00024422994,0.00028291222,0.0073315753,0.00015481953,0.0001747174,0.00016639833,0.00020834256,0.58756375,0.0016662154,0.39556053,0.0065849777,0.000061485305],"about_ca_topic_score_codex":0.005414067,"about_ca_topic_score_gemma":0.0034684138,"teacher_disagreement_score":0.019163214,"about_ca_system_score_codex":0.0014265453,"about_ca_system_score_gemma":0.0011269507,"threshold_uncertainty_score":0.10134596},"labels":[],"label_agreement":null},{"id":"W1994207898","doi":"10.1002/cjs.5550340111","title":"Goodness-of-fit tests for linear regression models with missing response data","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Goodness of fit; Statistics; Estimator; Linear regression; Regression analysis; Missing data","score_opus":0.28188045762283037,"score_gpt":0.4070403763266192,"score_spread":0.12515991870378884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994207898","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":[0.015696358,0.0011505679,0.9794422,0.000777146,0.00024572288,0.00015666506,0.00032142302,0.00066245947,0.0015474627],"genre_scores_gemma":[0.46737394,0.0013766554,0.522687,0.0011472194,0.0005857448,0.0016374082,0.0022557634,0.00080732827,0.0021289173],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91796696,0.06883012,0.0026616566,0.0027455664,0.0069195097,0.00087623874],"domain_scores_gemma":[0.5298538,0.43269607,0.008874413,0.019012487,0.008298316,0.0012649421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06369908,0.0018918052,0.0027308771,0.0076525374,0.0012842189,0.002223663,0.005196916,0.0040291985,0.0057643224],"category_scores_gemma":[0.40421036,0.00101322,0.0048066857,0.0069163837,0.0040196655,0.0060208156,0.0036605156,0.0054389294,0.0018934924],"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":[0.0016934015,0.00076716556,0.06484486,0.0024494992,0.00561308,0.0013656595,0.0018705004,0.20810406,0.0024083082,0.3087489,0.02310878,0.37902576],"study_design_scores_gemma":[0.00036600675,0.0012011622,0.015307478,0.00055849296,0.0006417746,0.0012315606,0.0006461281,0.56247497,0.0033045702,0.40310696,0.010845547,0.00031533616],"about_ca_topic_score_codex":0.0015556375,"about_ca_topic_score_gemma":0.0014099979,"teacher_disagreement_score":0.06369908,"about_ca_system_score_codex":0.001413744,"about_ca_system_score_gemma":0.0019270583,"threshold_uncertainty_score":0.336877},"labels":[],"label_agreement":null},{"id":"W1994575264","doi":"10.1080/03610910701812436","title":"A Method for Simulating Multivariate Non Normal Distributions with Specified Standardized Cumulants and Intraclass Correlation Coefficients","year":2008,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia","funders":"","keywords":"Normality; Intraclass correlation; Multivariate statistics; Multivariate normal distribution; Statistics; Mathematics; Monte Carlo method; Cumulant; Normality test; Correlation; Biometrics; Pearson product-moment correlation coefficient; Applied mathematics; Econometrics; Computer science; Statistical hypothesis testing; Artificial intelligence; Psychometrics","score_opus":0.19158079494640812,"score_gpt":0.4883967357739464,"score_spread":0.29681594082753826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994575264","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":[0.00063572987,0.000016557344,0.9989064,0.000026612144,0.00001131781,0.00005531276,0.000024285111,0.00010097002,0.00022286433],"genre_scores_gemma":[0.025146894,0.00009562925,0.972828,0.000049198497,0.000026757383,0.00095590047,0.00012789837,0.00009334481,0.0006764123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995676,0.0030268412,0.00018077671,0.00031201294,0.0007019026,0.000102399936],"domain_scores_gemma":[0.97611326,0.019980274,0.0010061531,0.0014161097,0.0012547771,0.00022939128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0095322635,0.0010887572,0.0013316307,0.0024192918,0.0010570483,0.0011803674,0.0023515408,0.0017407177,0.0057242187],"category_scores_gemma":[0.038272873,0.0008278421,0.0017722322,0.0025000705,0.0014346548,0.0014195781,0.0019356958,0.0026887716,0.0009272308],"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":[0.00013403912,0.00013966017,0.0020845258,0.00022990501,0.00018237474,0.0002345267,0.000358839,0.6275869,0.0016747166,0.25980893,0.003552857,0.10401268],"study_design_scores_gemma":[0.000050803497,0.00004830734,0.00028642247,0.000042815605,0.000028705454,0.00015264112,0.000024966075,0.9346878,0.00068006816,0.061164625,0.0027961533,0.000036668785],"about_ca_topic_score_codex":0.0063746693,"about_ca_topic_score_gemma":0.0068028974,"teacher_disagreement_score":0.0095322635,"about_ca_system_score_codex":0.0009042606,"about_ca_system_score_gemma":0.002534862,"threshold_uncertainty_score":0.050412},"labels":[],"label_agreement":null},{"id":"W1995022809","doi":"10.1002/bimj.200710423","title":"Imputation Strategies for Missing Continuous Outcomes in Cluster Randomized Trials","year":2008,"lang":"en","type":"review","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":111,"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; Ottawa Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Imputation (statistics); Statistics; Cluster (spacecraft); Randomized controlled trial; Computer science; Mathematics; Econometrics; Data mining; Medicine; Internal medicine","score_opus":0.3376661282180065,"score_gpt":0.5276863964240724,"score_spread":0.19002026820606593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995022809","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086727587,0.003032379,0.9891817,0.00077343255,0.00039395283,0.0041064434,0.00036841596,0.0006776036,0.0005988357],"genre_scores_gemma":[0.032902375,0.0021699304,0.92637897,0.0013970478,0.00037379912,0.03463571,0.0007347406,0.00036256918,0.0010448851],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.54166585,0.41848218,0.019128134,0.009035428,0.010692017,0.0009963361],"domain_scores_gemma":[0.39849588,0.53981686,0.022052014,0.027903846,0.010707814,0.001023567],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.33241326,0.0040044184,0.0099163335,0.0062730312,0.0014751182,0.005063759,0.011140136,0.007295633,0.01000186],"category_scores_gemma":[0.5394313,0.0035249926,0.008900386,0.009082239,0.0029617513,0.004680274,0.0051459074,0.008959776,0.0025214003],"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":[0.0048823,0.00041812225,0.0051705367,0.019933697,0.018412512,0.0008546259,0.002678445,0.15987805,0.0005914009,0.29441023,0.03385608,0.45891392],"study_design_scores_gemma":[0.004586288,0.0014718459,0.0012048997,0.005033343,0.004338502,0.00060395704,0.00023264608,0.39061046,0.0014293046,0.56526434,0.024880392,0.00034401708],"about_ca_topic_score_codex":0.0017669904,"about_ca_topic_score_gemma":0.0016892522,"teacher_disagreement_score":0.66758674,"about_ca_system_score_codex":0.002994521,"about_ca_system_score_gemma":0.0071995114,"threshold_uncertainty_score":0.82325333},"labels":[],"label_agreement":null},{"id":"W1995404757","doi":"10.1111/j.1745-3984.2009.01067.x","title":"The Reliability of Difference Scores in Populations and Samples","year":2009,"lang":"en","type":"article","venue":"Journal of Educational Measurement","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Carleton University","funders":"","keywords":"Reliability (semiconductor); Statistics; Population; Variance (accounting); Mathematics; Sample (material); Sample size determination; Standard deviation; Econometrics; Demography; Physics","score_opus":0.21340945573782985,"score_gpt":0.4190955226912946,"score_spread":0.20568606695346472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995404757","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":[0.6710046,0.0009633605,0.32280442,0.0002749214,0.00010194098,0.00022649554,0.00037897332,0.00031676955,0.0039285864],"genre_scores_gemma":[0.97423077,0.00016421691,0.024887014,0.000046897963,0.00002778729,0.00012690171,0.000295719,0.000035023866,0.00018573622],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.924015,0.048473705,0.0039123273,0.009264891,0.013586608,0.0007474637],"domain_scores_gemma":[0.6228648,0.31581652,0.013355953,0.031854067,0.015314822,0.0007937996],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0680208,0.00048486408,0.001222443,0.0037408113,0.00060080114,0.0026392029,0.0015535627,0.0015514067,0.0010804309],"category_scores_gemma":[0.45201245,0.000879377,0.00088222674,0.00214699,0.0054341904,0.003381425,0.0037874542,0.0018719798,0.00037874133],"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":[0.0018015358,0.00026394645,0.5846543,0.00095047505,0.002116921,0.0005371086,0.011804019,0.07848589,0.0062718256,0.102919236,0.0015985462,0.20859617],"study_design_scores_gemma":[0.0002631333,0.001351576,0.46519735,0.00058207836,0.0007287806,0.0024124174,0.0033281266,0.2423487,0.010449614,0.2657985,0.00711423,0.00042540175],"about_ca_topic_score_codex":0.001755256,"about_ca_topic_score_gemma":0.00080820173,"teacher_disagreement_score":0.9319792,"about_ca_system_score_codex":0.0011191112,"about_ca_system_score_gemma":0.0008857125,"threshold_uncertainty_score":0.3597327},"labels":[],"label_agreement":null},{"id":"W1995494985","doi":"10.1016/j.jmva.2012.02.015","title":"Small area estimation using survey weights under a nested error linear regression model with structural measurement error","year":2012,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Small area estimation; Estimator; Statistics; Mathematics; Covariate; Mean squared error; Linear regression; Regression analysis; Errors-in-variables models; Parametric statistics; Linear model; Observational error; Consistency (knowledge bases)","score_opus":0.3374660851919453,"score_gpt":0.42551196900462535,"score_spread":0.08804588381268003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995494985","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":[0.037921574,0.000097653996,0.9613453,0.00009120671,0.000025595402,0.000047973168,0.00007818319,0.00009536765,0.00029716754],"genre_scores_gemma":[0.5490428,0.00032178574,0.4461483,0.00011207031,0.000092273076,0.00048268575,0.0006087555,0.00009009285,0.0031011272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9808271,0.01376617,0.0007111432,0.003012251,0.0012016518,0.00048170766],"domain_scores_gemma":[0.9082764,0.073893584,0.0052349223,0.008405519,0.0033396827,0.0008500477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023212327,0.0011683766,0.0033811117,0.0018858031,0.00088926667,0.0019470813,0.004310501,0.0019597467,0.0029387414],"category_scores_gemma":[0.10570826,0.0018751125,0.0020834536,0.004109207,0.0018586427,0.0044576367,0.0033105987,0.0021027806,0.0005613284],"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":[0.0005956175,0.000588203,0.0665271,0.000443555,0.0019180197,0.00043838116,0.0011868336,0.5394951,0.0015004494,0.22212133,0.002606495,0.1625789],"study_design_scores_gemma":[0.00006652072,0.00014445004,0.003930574,0.00003555155,0.00015100125,0.00007842654,0.0001329763,0.8873657,0.00035425124,0.10689541,0.0008154192,0.000029665676],"about_ca_topic_score_codex":0.012154502,"about_ca_topic_score_gemma":0.017156217,"teacher_disagreement_score":0.023212327,"about_ca_system_score_codex":0.0009622795,"about_ca_system_score_gemma":0.0020332655,"threshold_uncertainty_score":0.12276},"labels":[],"label_agreement":null},{"id":"W1996208018","doi":"10.1371/journal.pone.0084601","title":"Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Statistical Methods and Bayesian Inference","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":"Université du Québec à Montréal; Montreal Heart Institute; McGill University","funders":"","keywords":"Statistics; Quasi-likelihood; Mathematics; Generalized linear mixed model; Linear regression; Generalized linear model; Binary data; Random effects model; Binary number; Negative binomial distribution; Poisson distribution; Medicine","score_opus":0.25873989004252035,"score_gpt":0.3621224319664293,"score_spread":0.10338254192390894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996208018","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":[0.026951132,0.0018354992,0.9668276,0.0021566572,0.00010922748,0.00024220653,0.0001406193,0.00020212407,0.0015349719],"genre_scores_gemma":[0.41989157,0.0013445407,0.5748458,0.0014729085,0.00020570874,0.0008680714,0.00044015908,0.00029173453,0.00063954695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8689023,0.115580015,0.003099709,0.005030602,0.0066551506,0.00073216885],"domain_scores_gemma":[0.56397307,0.38944277,0.02025181,0.017652199,0.0077413395,0.00093872723],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17318939,0.0010781777,0.0025441574,0.0028429031,0.00083840155,0.0037198777,0.0044398373,0.0026620368,0.0042186677],"category_scores_gemma":[0.509619,0.0009842203,0.002685992,0.0030826838,0.005473641,0.005767379,0.003731036,0.0032945604,0.00061965524],"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":[0.0014567745,0.0002781744,0.056557737,0.003257481,0.004230697,0.00057364965,0.0020330637,0.21783315,0.0015420435,0.46588567,0.0037118017,0.24263972],"study_design_scores_gemma":[0.00023590516,0.00043333916,0.0066543217,0.00075272896,0.00041876984,0.00025649983,0.00023061816,0.5026871,0.0016413438,0.4831029,0.0034866917,0.00009971825],"about_ca_topic_score_codex":0.0030412509,"about_ca_topic_score_gemma":0.0018415668,"teacher_disagreement_score":0.8268106,"about_ca_system_score_codex":0.0030773322,"about_ca_system_score_gemma":0.0033791282,"threshold_uncertainty_score":0.91592395},"labels":[],"label_agreement":null},{"id":"W1996635052","doi":"10.1016/j.annepidem.2004.08.007","title":"Regression Models for Clustered Binary Responses: Implications of Ignoring the Intracluster Correlation in an Analysis of Perinatal Mortality in Twin Gestations","year":2005,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Covariate; Logistic regression; Marginal model; Statistics; Generalized estimating equation; Regression analysis; Gee; Medicine; Regression; Twin study; Cluster (spacecraft); Econometrics; Mathematics; Computer science","score_opus":0.3993506667331309,"score_gpt":0.5358785617087105,"score_spread":0.1365278949755796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996635052","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":[0.120935015,0.001157429,0.87227184,0.0038279393,0.00030013052,0.00013708972,0.0002667833,0.00020124075,0.00090243097],"genre_scores_gemma":[0.73414844,0.0013543743,0.25617886,0.0016476408,0.0009614771,0.0006479451,0.00057323236,0.00023719828,0.004250795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8816705,0.10322078,0.0025915673,0.008138638,0.0028969753,0.0014816085],"domain_scores_gemma":[0.3806917,0.57624555,0.014650182,0.021646198,0.005224742,0.0015416649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15865955,0.001786641,0.0051807174,0.002259615,0.0022944708,0.0037032973,0.009133423,0.004784425,0.0026282219],"category_scores_gemma":[0.45581636,0.0025008675,0.0045228675,0.00424226,0.0057594627,0.00542925,0.00436068,0.00935616,0.00043201662],"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":[0.0017774717,0.00051627954,0.084177785,0.0005566253,0.0066729197,0.0021892183,0.0032881366,0.38796556,0.0008801801,0.43045646,0.0055622277,0.07595717],"study_design_scores_gemma":[0.0002752397,0.00017255051,0.009007145,0.00011763186,0.0008604194,0.0003182883,0.00021817232,0.73222446,0.00037985362,0.2552754,0.0010463896,0.00010455073],"about_ca_topic_score_codex":0.022177946,"about_ca_topic_score_gemma":0.019989574,"teacher_disagreement_score":0.15865955,"about_ca_system_score_codex":0.0022010712,"about_ca_system_score_gemma":0.004313555,"threshold_uncertainty_score":0.8390819},"labels":[],"label_agreement":null},{"id":"W1997654822","doi":"10.1016/s0047-259x(03)00020-4","title":"Factor models for multivariate count data","year":2003,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multivariate statistics; Mathematics; Count data; Poisson distribution; Exponential family; Factor analysis; Statistics; Monte Carlo method; Factor (programming language); Class (philosophy); Multivariate analysis; Applied mathematics; Algorithm; Computer science; Artificial intelligence","score_opus":0.21137058246004176,"score_gpt":0.4385373629608787,"score_spread":0.22716678050083694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997654822","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":[0.0019997975,0.0005577122,0.9965084,0.00024853248,0.000058534843,0.000031259187,0.00014043317,0.00018023537,0.0002750831],"genre_scores_gemma":[0.1909398,0.0049013025,0.79139656,0.00046788514,0.0009882643,0.0014495533,0.0022689688,0.00052337925,0.0070641953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98342055,0.011284249,0.0009886832,0.0022110792,0.0014063041,0.00068917876],"domain_scores_gemma":[0.8867336,0.092146836,0.006239294,0.010325917,0.0035180948,0.0010363227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026064571,0.0029495726,0.004306518,0.0037051067,0.0014955464,0.0045828153,0.005699702,0.003988524,0.008618127],"category_scores_gemma":[0.11584673,0.0022440006,0.0051753605,0.006359043,0.004624182,0.008367751,0.0033948196,0.0061651547,0.0022661295],"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":[0.00015532975,0.00007531527,0.0025142576,0.00036441121,0.00053269515,0.00016787817,0.00047123746,0.068221256,0.0005215178,0.8547095,0.004641615,0.06762501],"study_design_scores_gemma":[0.000037949765,0.000036016223,0.00047036613,0.00005616897,0.00009301892,0.00012752503,0.000042071748,0.18471514,0.00010725594,0.8116855,0.002585354,0.000043614054],"about_ca_topic_score_codex":0.0076449914,"about_ca_topic_score_gemma":0.0071176165,"teacher_disagreement_score":0.026064571,"about_ca_system_score_codex":0.002008901,"about_ca_system_score_gemma":0.0031643135,"threshold_uncertainty_score":0.13784426},"labels":[],"label_agreement":null},{"id":"W1997756525","doi":"10.1080/03610910701208973","title":"Mixture Distributions Based Methods of Calibration for the Empirical Log-Likelihood Ratio","year":2007,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Calibration; Empirical likelihood; Mathematics; Statistics; Square (algebra); Sample size determination; Confidence interval; Chi-square test; Confidence region; Distribution (mathematics); Mean squared error; Applied mathematics; Mathematical analysis","score_opus":0.3511093688112289,"score_gpt":0.561626822494866,"score_spread":0.21051745368363706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997756525","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":[0.0013839747,0.0002552061,0.9973546,0.00010745741,0.000026753012,0.00002567635,0.00002302904,0.00019175217,0.0006314671],"genre_scores_gemma":[0.18679924,0.0010176805,0.806313,0.00035231482,0.00029017808,0.0005578876,0.00041640943,0.00058900483,0.0036642423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.983214,0.010351422,0.0005055861,0.001987362,0.0035474622,0.00039409686],"domain_scores_gemma":[0.9141896,0.06914472,0.003350045,0.006887843,0.0059965327,0.00043128512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020492172,0.0016067451,0.0015203565,0.005185244,0.00088090455,0.0029880335,0.0042796833,0.0029332207,0.007612585],"category_scores_gemma":[0.14936393,0.0011691923,0.0017986585,0.0036464185,0.0042750104,0.005914323,0.004462498,0.0064141816,0.001909747],"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":[0.00018948114,0.00006651864,0.0051113386,0.00030507855,0.00023445766,0.00019487624,0.0006311215,0.34153327,0.002219446,0.43056923,0.004877281,0.21406788],"study_design_scores_gemma":[0.000030800144,0.000042140862,0.0017298536,0.000118338445,0.000032431224,0.00026904067,0.00006779739,0.83043194,0.001912803,0.15912132,0.0061570015,0.00008654102],"about_ca_topic_score_codex":0.0039767306,"about_ca_topic_score_gemma":0.0022546079,"teacher_disagreement_score":0.020492172,"about_ca_system_score_codex":0.0027885495,"about_ca_system_score_gemma":0.0014874855,"threshold_uncertainty_score":0.10837424},"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":"W1997947326","doi":"10.1186/1471-2288-11-18","title":"Imputation strategies for missing binary outcomes in cluster randomized trials","year":2011,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","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":false,"ca_institutions":"Hamilton Health Sciences; Population Health Research Institute; McMaster University; St. Joseph’s Healthcare Hamilton","funders":"McMaster University; Canadian Institutes of Health Research; Government of Ontario; Saint Paul University; University of Ottawa","keywords":"Imputation (statistics); Missing data; Randomized controlled trial; MEDLINE; Computer science; Statistics; Medicine; Psychology; Mathematics; Internal medicine; Biology","score_opus":0.8477093270696267,"score_gpt":0.6595906078465309,"score_spread":0.18811871922309575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997947326","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":[0.004928428,0.005549259,0.98209023,0.0015985143,0.00031257136,0.0033384757,0.00045665615,0.00056999986,0.0011558805],"genre_scores_gemma":[0.16799119,0.0035038802,0.8024204,0.0021119728,0.00041117758,0.021713335,0.00076519547,0.00027055945,0.0008123486],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.60672903,0.36740318,0.010918138,0.0061069676,0.007863196,0.0009794472],"domain_scores_gemma":[0.4687729,0.4734731,0.026659422,0.020308606,0.009244702,0.0015413805],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2615066,0.0023461864,0.00649474,0.0048017823,0.0012893111,0.003023366,0.006940058,0.003964817,0.005994429],"category_scores_gemma":[0.3933521,0.0018931095,0.007384889,0.005441517,0.002242103,0.0031842585,0.0038117426,0.005440093,0.00091602164],"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":[0.005907368,0.00054489437,0.019940045,0.015061434,0.026704239,0.00091472117,0.002711134,0.31307465,0.0006104902,0.1951934,0.024763372,0.3945742],"study_design_scores_gemma":[0.005865855,0.0020472754,0.0033227785,0.004478756,0.0068592895,0.00075048726,0.00025256607,0.5851321,0.0012985994,0.37449577,0.015196946,0.00029950243],"about_ca_topic_score_codex":0.0017524827,"about_ca_topic_score_gemma":0.0014031617,"teacher_disagreement_score":0.73849344,"about_ca_system_score_codex":0.0026066564,"about_ca_system_score_gemma":0.006718695,"threshold_uncertainty_score":0.9106939},"labels":[],"label_agreement":null},{"id":"W1998357780","doi":"10.1080/03610920902948236","title":"Robust Estimation of State Occupancy Probabilities for Interval-Censored Multistate Data: An Application Involving Spondylitis in Psoriatic Arthritis","year":2009,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Waterloo","funders":"","keywords":"Psoriatic arthritis; Covariate; Nonparametric statistics; Medicine; Statistics; Spondylitis; Econometrics; Categorical variable; Demography; Arthritis; Ankylosing spondylitis; Mathematics; Surgery; Internal medicine; Sociology","score_opus":0.12521337178473663,"score_gpt":0.46225060309493265,"score_spread":0.33703723131019603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998357780","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":[0.040461052,0.00042604192,0.95763195,0.00047432914,0.000041262436,0.00006912794,0.00034256128,0.00021083467,0.00034288553],"genre_scores_gemma":[0.66173685,0.0005764314,0.333266,0.0001854579,0.00012203139,0.0003187789,0.0013335356,0.00009918755,0.002361842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956731,0.0031524154,0.00013822733,0.000514761,0.00034167117,0.00017964773],"domain_scores_gemma":[0.96206343,0.032262377,0.0020611736,0.002126177,0.0011400143,0.0003468728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012136342,0.0005169464,0.0014273342,0.0010824766,0.0005485775,0.0012439922,0.0021629608,0.0012597872,0.002023755],"category_scores_gemma":[0.04435614,0.0005657112,0.0017923394,0.0015668857,0.0010072187,0.0010590315,0.0021268558,0.002192945,0.00024148708],"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":[0.00024019224,0.00017646076,0.02534515,0.0001937645,0.0004927691,0.00027266683,0.0002862642,0.83302766,0.0010324306,0.063697405,0.0021887142,0.07304647],"study_design_scores_gemma":[0.000016898784,0.000033422886,0.0029552064,0.000017671022,0.00002276508,0.000027410922,0.000032136897,0.9787719,0.00023928817,0.017441556,0.00042211544,0.000019541561],"about_ca_topic_score_codex":0.018163662,"about_ca_topic_score_gemma":0.01331524,"teacher_disagreement_score":0.018163662,"about_ca_system_score_codex":0.0014768858,"about_ca_system_score_gemma":0.0014654694,"threshold_uncertainty_score":0.06418389},"labels":[],"label_agreement":null},{"id":"W1999084857","doi":"10.2135/cropsci2006.04.0271","title":"Improved Experimental Design and Analysis for Long‐Term Experiments","year":2006,"lang":"en","type":"article","venue":"Crop Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":83,"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":"University of Kansas; Kansas State University","keywords":"Term (time); Design of experiments; Statistics; Covariate; Random effects model; Econometrics; Confounding; Computer science; Mathematics; Meta-analysis","score_opus":0.09841206126947039,"score_gpt":0.4192032385739805,"score_spread":0.3207911773045101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999084857","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":[0.0030867134,0.00019893107,0.9944701,0.00011625035,0.00021744535,0.00059826684,0.00019201037,0.00052684825,0.0005934699],"genre_scores_gemma":[0.016998244,0.00026071965,0.97481275,0.00022070108,0.000106253465,0.006282685,0.00026757273,0.00019675892,0.000854221],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95909506,0.026678933,0.0026812337,0.004397508,0.006570499,0.00057680276],"domain_scores_gemma":[0.9084963,0.048979443,0.008414433,0.023272151,0.009837229,0.0010004343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039823566,0.0014472386,0.0031538983,0.0013186667,0.0011855859,0.0016609012,0.0038522552,0.002170531,0.0068174624],"category_scores_gemma":[0.058930192,0.0013231605,0.0017509894,0.001785484,0.0016131365,0.0030159887,0.002291771,0.005643792,0.0019353338],"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":[0.0056569404,0.0024775334,0.006307353,0.0057296166,0.00065863854,0.00052948407,0.0016787387,0.038226143,0.19632469,0.15103416,0.014968296,0.5764085],"study_design_scores_gemma":[0.0035114984,0.015199777,0.03280968,0.0015065711,0.0014681675,0.0014720117,0.00035676724,0.24831045,0.15796362,0.27379838,0.26237515,0.0012279593],"about_ca_topic_score_codex":0.00054532214,"about_ca_topic_score_gemma":0.0010780514,"teacher_disagreement_score":0.039823566,"about_ca_system_score_codex":0.0016599535,"about_ca_system_score_gemma":0.002514058,"threshold_uncertainty_score":0.21060961},"labels":[],"label_agreement":null},{"id":"W1999351221","doi":"10.1139/l07-049","title":"Hierarchical Bayes methods for systems with spatially varying condition states","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Bayes' theorem; Context (archaeology); Bayesian inference; Statistical model; Data mining; Conditional independence; Inference; Spatial analysis; Statistical inference; Bayesian probability; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.029113670275458896,"score_gpt":0.3347031105932653,"score_spread":0.3055894403178064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999351221","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":[0.0020767592,0.00049102184,0.99604297,0.00018994813,0.00002944864,0.000049677077,0.00009902236,0.0001427473,0.0008783213],"genre_scores_gemma":[0.18759786,0.0017831249,0.7999835,0.0004039295,0.00035957783,0.0008402191,0.0008155543,0.00023796242,0.007978242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953721,0.002760888,0.00018429964,0.00065065926,0.0008213456,0.00021070777],"domain_scores_gemma":[0.97742134,0.019504402,0.0011280889,0.00080738787,0.0009691495,0.00016956848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010359085,0.0012300238,0.0020134805,0.0021923732,0.0011032504,0.0019459178,0.0032202608,0.0018514162,0.006035533],"category_scores_gemma":[0.033837464,0.001374669,0.0018125423,0.002010164,0.0022835254,0.0029783952,0.0020763217,0.0032530213,0.0010147758],"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":[0.000067982095,0.000049349714,0.001286937,0.00022836929,0.00015673363,0.00013408887,0.00035141723,0.54831535,0.0004188835,0.3753126,0.0025957616,0.071082495],"study_design_scores_gemma":[0.000017155417,0.00001064006,0.0001847876,0.000028967765,0.000020417883,0.000021309721,0.00002132857,0.795112,0.00010272519,0.20299353,0.0014708476,0.00001624879],"about_ca_topic_score_codex":0.024714597,"about_ca_topic_score_gemma":0.028180104,"teacher_disagreement_score":0.024714597,"about_ca_system_score_codex":0.0028624649,"about_ca_system_score_gemma":0.0032804655,"threshold_uncertainty_score":0.054784715},"labels":[],"label_agreement":null},{"id":"W1999469479","doi":"10.6000/1929-6029.2014.03.04.4","title":"Estimating the Population Standard Deviation with Confidence Interval: A Simulation Study under Skewed and Symmetric Conditions","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidence interval; Estimator; Statistics; Standard deviation; Coverage probability; Range (aeronautics); Interval estimation; Interval (graph theory); Mathematics; Sample size determination; Population; CDF-based nonparametric confidence interval; Robust confidence intervals; Population mean; Medicine; Engineering","score_opus":0.13182437283567777,"score_gpt":0.537956396631617,"score_spread":0.4061320237959392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999469479","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":[0.7404907,0.0016634833,0.25236806,0.00070287817,0.00008129983,0.00064301805,0.00050813804,0.0001694691,0.0033730415],"genre_scores_gemma":[0.9043243,0.0006025663,0.093836054,0.00006574839,0.000025286885,0.0003877977,0.00042094622,0.00003209076,0.00030516458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9854917,0.011521119,0.0004178229,0.00072949124,0.0014689096,0.00037086155],"domain_scores_gemma":[0.7591379,0.21982972,0.006459918,0.0067085233,0.0069410997,0.0009228518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033236828,0.0008196743,0.0012257419,0.0024514964,0.00070177735,0.0013019355,0.001718325,0.0018069973,0.0012662477],"category_scores_gemma":[0.111676455,0.00042732622,0.0013269678,0.002660065,0.0015070488,0.0023942522,0.0017408321,0.0019900647,0.00014958059],"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":[0.0011551948,0.0006182522,0.03607649,0.00045187282,0.00044412076,0.00065375405,0.00070993236,0.8856727,0.001068297,0.03455438,0.0014457249,0.037149336],"study_design_scores_gemma":[0.00023940744,0.0005504864,0.004438,0.0000958012,0.00012036508,0.00028135796,0.0003027317,0.98401546,0.0010595548,0.008294292,0.0005444465,0.000058046604],"about_ca_topic_score_codex":0.007055008,"about_ca_topic_score_gemma":0.0034427093,"teacher_disagreement_score":0.033236828,"about_ca_system_score_codex":0.0013152901,"about_ca_system_score_gemma":0.001452454,"threshold_uncertainty_score":0.17577529},"labels":[],"label_agreement":null},{"id":"W2000045933","doi":"10.1214/11-ejs594","title":"A Metropolis-Hastings based method for sampling from the G-Wishart distribution in Gaussian graphical models","year":2011,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wishart distribution; Mathematics; Metropolis–Hastings algorithm; Gaussian; Deviance (statistics); Graphical model; Conjugate prior; Sampling (signal processing); Statistics; Applied mathematics; Algorithm; Prior probability; Computer science; Markov chain Monte Carlo; Bayesian probability; Multivariate statistics","score_opus":0.10172348705551902,"score_gpt":0.3771730557496278,"score_spread":0.27544956869410875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000045933","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":[0.0014825341,0.00007791394,0.9979717,0.000040077866,0.000016540449,0.00003706417,0.00001693531,0.0001779085,0.00017937193],"genre_scores_gemma":[0.0729252,0.00031621868,0.9239768,0.00017305555,0.00009030824,0.00041835615,0.000205168,0.00026770364,0.0016271444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972747,0.0015593244,0.00011070386,0.00034524014,0.00061553984,0.00009452736],"domain_scores_gemma":[0.9941672,0.004212309,0.0002691146,0.0007366181,0.0004803827,0.00013442281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005253581,0.0010290581,0.0012271461,0.0013417457,0.0009770915,0.0011311086,0.003037786,0.0014431861,0.0026071563],"category_scores_gemma":[0.015226692,0.00090777886,0.0014181877,0.0017887511,0.0025745437,0.0024097038,0.0014266452,0.0031526978,0.0009446794],"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":[0.00033581152,0.00018832406,0.0027253393,0.00037437747,0.00031281696,0.00037972585,0.00048375104,0.31826413,0.008812643,0.47116196,0.0050041643,0.19195691],"study_design_scores_gemma":[0.00006655653,0.00007946372,0.0003810336,0.000027914535,0.000038761445,0.00013173395,0.000019328827,0.88376826,0.0035547381,0.10884183,0.0030237336,0.00006668176],"about_ca_topic_score_codex":0.003875855,"about_ca_topic_score_gemma":0.0050833425,"teacher_disagreement_score":0.005253581,"about_ca_system_score_codex":0.0011096346,"about_ca_system_score_gemma":0.0014177536,"threshold_uncertainty_score":0.02778393},"labels":[],"label_agreement":null},{"id":"W2000439279","doi":"10.1198/004017008000000064","title":"Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion","year":2008,"lang":"en","type":"article","venue":"Technometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; University of Michigan; Prevent Cancer Foundation; National Science Foundation","keywords":"Gibbs sampling; Multivariate statistics; Markov chain Monte Carlo; Inference; Ordinal data; Computer science; Bayesian inference; Bayesian probability; Ordinal regression; Statistics; Econometrics; Artificial intelligence; Mathematics; Data mining; Machine learning","score_opus":0.3730878298029854,"score_gpt":0.45860637012176325,"score_spread":0.08551854031877787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000439279","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":[0.0016207854,0.00024352285,0.9974269,0.00015404797,0.000014597332,0.00002168075,0.000052472697,0.000084686355,0.0003813424],"genre_scores_gemma":[0.13435633,0.0017881403,0.8598797,0.00030523541,0.00032520786,0.00057830557,0.00067478843,0.00023745568,0.0018548687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9850498,0.011651382,0.00043164726,0.0010767861,0.0015713,0.00021910228],"domain_scores_gemma":[0.93987554,0.054699823,0.0019106093,0.0023560748,0.0008802504,0.00027771603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01797278,0.0014261208,0.0024290576,0.0038162938,0.00096081186,0.0022499636,0.0023784519,0.0018305392,0.003991694],"category_scores_gemma":[0.07992531,0.0010723717,0.0019347729,0.0039587356,0.00283576,0.0051472285,0.0034069181,0.005355866,0.0010277776],"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":[0.00008953839,0.000099533936,0.0032727288,0.000310706,0.00036944367,0.0001874352,0.00039506494,0.2073532,0.0010502022,0.63414663,0.0025638654,0.15016165],"study_design_scores_gemma":[0.000028436276,0.000022197511,0.0006035276,0.00006402355,0.00003639262,0.00007929133,0.000035567016,0.4389868,0.00038816567,0.5572196,0.0024913934,0.00004451676],"about_ca_topic_score_codex":0.0021017252,"about_ca_topic_score_gemma":0.0023700283,"teacher_disagreement_score":0.01797278,"about_ca_system_score_codex":0.0013405605,"about_ca_system_score_gemma":0.0015473695,"threshold_uncertainty_score":0.095050275},"labels":[],"label_agreement":null},{"id":"W2000767414","doi":"10.1007/s10985-007-9065-x","title":"Generalized linear mixed models: a review and some extensions","year":2007,"lang":"en","type":"review","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":335,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Simon Fraser University","funders":"","keywords":"Computational statistics; Computer science; Generalized linear mixed model; Variety (cybernetics); Software; Generalized linear model; Statistical model; Data science; Programming language; Machine learning; Artificial intelligence","score_opus":0.35833242761253187,"score_gpt":0.4960117588798883,"score_spread":0.13767933126735643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000767414","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":[0.00031835932,0.9527865,0.043067567,0.0011248117,0.00036266388,0.000029103825,0.00015971545,0.00007788149,0.0020733548],"genre_scores_gemma":[0.0034242638,0.9558631,0.037413172,0.000599812,0.0012296768,0.00010409234,0.00017949901,0.00003950722,0.0011467211],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985599,0.00060062617,0.00016256847,0.00024441638,0.00039376764,0.000038686194],"domain_scores_gemma":[0.9942216,0.004680425,0.0002930085,0.00019928406,0.000545231,0.000060456037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044795256,0.0021091066,0.0035566925,0.0035510662,0.00041618868,0.0022242046,0.0031511246,0.0026869348,0.0035560287],"category_scores_gemma":[0.00781688,0.0010471811,0.0014798971,0.0077802786,0.0016554247,0.003041773,0.0013292922,0.0029907508,0.0023717675],"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":[0.0000637904,0.00012519905,0.0005188882,0.010441237,0.00030744128,0.00021555311,0.00017316546,0.0060851295,0.00046414015,0.07661703,0.032189183,0.8727993],"study_design_scores_gemma":[0.000066761444,0.00016462259,0.0018649764,0.0052185673,0.0006133352,0.0024615328,0.0001868912,0.011886174,0.00088135665,0.19825672,0.77820385,0.0001951339],"about_ca_topic_score_codex":0.0035773097,"about_ca_topic_score_gemma":0.004252957,"teacher_disagreement_score":0.0044795256,"about_ca_system_score_codex":0.001262244,"about_ca_system_score_gemma":0.0022861646,"threshold_uncertainty_score":0.023690283},"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":"W2002364333","doi":"10.2307/3315990","title":"Effects of omitting a covariate in poisson models when the data are balanced","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Poisson distribution; Multiplicative function; Poisson regression; Statistics; Mathematics; Standard error; Econometrics; Logistic regression; Generalized linear model; Linear model; Medicine","score_opus":0.09146784852705028,"score_gpt":0.3254330490759632,"score_spread":0.23396520054891293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002364333","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":[0.39199468,0.008840749,0.58148503,0.008321149,0.001062217,0.00038461472,0.0011518691,0.0006715561,0.0060882024],"genre_scores_gemma":[0.9319545,0.001977223,0.061082397,0.0014657875,0.0004119036,0.00035810636,0.00058136653,0.00017035643,0.0019982771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.87904274,0.09663035,0.005655216,0.00867461,0.0075045223,0.0024925547],"domain_scores_gemma":[0.34366435,0.5913451,0.027260756,0.03173634,0.004645727,0.0013477799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11472344,0.0013874626,0.0029735153,0.0018611196,0.00096596754,0.0025364633,0.0023218896,0.0033613879,0.003086267],"category_scores_gemma":[0.41708887,0.0012778589,0.0026766008,0.0027967629,0.004579671,0.004214697,0.0040019937,0.0045886487,0.0005254453],"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":[0.017911332,0.0013236853,0.17503214,0.0026970454,0.010838059,0.0034680637,0.007262209,0.11997737,0.0076492955,0.2710855,0.010489691,0.3722656],"study_design_scores_gemma":[0.001756801,0.0051272796,0.12102292,0.001301853,0.012820486,0.0017994669,0.0021320283,0.20350902,0.014651934,0.6125121,0.022685152,0.0006810305],"about_ca_topic_score_codex":0.005371012,"about_ca_topic_score_gemma":0.0037444315,"teacher_disagreement_score":0.11472344,"about_ca_system_score_codex":0.0015290959,"about_ca_system_score_gemma":0.0017526211,"threshold_uncertainty_score":0.6067228},"labels":[],"label_agreement":null},{"id":"W2002858652","doi":"10.1002/cjs.5550360302","title":"Bayesian analysis of elapsed times in continuous‐time Markov chains","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":344,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Prior probability; Frequentist inference; Bayesian probability; Posterior probability; Markov chain; Computer science; Mathematics; Bayesian inference; Econometrics; Statistics; Markov chain Monte Carlo","score_opus":0.028155158437390637,"score_gpt":0.2897788057720888,"score_spread":0.2616236473346982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002858652","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":[0.03413958,0.0005173124,0.9622414,0.00062114344,0.000052871786,0.000036095542,0.00015751531,0.00017029079,0.0020638376],"genre_scores_gemma":[0.84099716,0.0015797006,0.1486236,0.00030014926,0.00031947278,0.00029354575,0.00084618735,0.0002717971,0.006768307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9925432,0.003759699,0.00031513305,0.0014018147,0.0014617897,0.0005183332],"domain_scores_gemma":[0.9087135,0.07739622,0.0048709437,0.0032744925,0.004424882,0.001320039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019277222,0.0009270533,0.001994537,0.003269486,0.00090958015,0.0035326988,0.0033838027,0.0026155119,0.0065425397],"category_scores_gemma":[0.10082611,0.0015024521,0.00186065,0.0026753957,0.0036962952,0.0060558165,0.0021859726,0.0039780983,0.00063447666],"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":[0.00017878182,0.000049737,0.002399437,0.00011922153,0.00012722742,0.00014394212,0.00029520443,0.40152442,0.00067445496,0.57743555,0.0010157186,0.016036227],"study_design_scores_gemma":[0.000019290492,0.000024883862,0.00084403297,0.00005035368,0.000030518542,0.00003291308,0.000026789912,0.7984609,0.00027772802,0.19927616,0.0009179694,0.000038425274],"about_ca_topic_score_codex":0.009706189,"about_ca_topic_score_gemma":0.0047517396,"teacher_disagreement_score":0.019277222,"about_ca_system_score_codex":0.0034333644,"about_ca_system_score_gemma":0.0018536979,"threshold_uncertainty_score":0.10194898},"labels":[],"label_agreement":null},{"id":"W2003580170","doi":"10.1080/03610910008813655","title":"Hierarchical modeling with gaussian processes","year":2000,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"Gaussian process; Hierarchical database model; Gaussian; Gaussian network model; Computer science; Statistical physics; Process (computing); Population; Algorithm; Mathematics; Data mining; Physics","score_opus":0.20542729417698535,"score_gpt":0.477960928859824,"score_spread":0.27253363468283864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003580170","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":[0.0062040826,0.00018753798,0.9908785,0.00026402567,0.000040628973,0.00004005015,0.00021107653,0.00016909602,0.0020050157],"genre_scores_gemma":[0.5330649,0.0014927143,0.44933125,0.000699581,0.00031284886,0.00061453297,0.0013993783,0.00024951386,0.012835321],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996759,0.0014689971,0.00014397499,0.00060587586,0.000653176,0.00036893322],"domain_scores_gemma":[0.9931657,0.004176383,0.0007970873,0.0008476062,0.0007650236,0.0002482969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048543597,0.0009778272,0.0016096515,0.0013626246,0.00076426397,0.0024941082,0.0035745064,0.0019871187,0.0054843226],"category_scores_gemma":[0.016404362,0.0008611989,0.0021262963,0.002392854,0.0016504499,0.003507666,0.0023773569,0.0026878,0.0012536353],"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":[0.000060918268,0.00005534903,0.0015202779,0.00008612903,0.00012283772,0.00013600153,0.00032061437,0.47581854,0.0007033345,0.4987344,0.0025022049,0.01993933],"study_design_scores_gemma":[0.000017382168,0.000019656532,0.00025829044,0.000014092585,0.000026189955,0.000030057217,0.000019488616,0.86319095,0.000114713665,0.13489687,0.0013961372,0.000016186024],"about_ca_topic_score_codex":0.01635143,"about_ca_topic_score_gemma":0.016515268,"teacher_disagreement_score":0.01635143,"about_ca_system_score_codex":0.0017104344,"about_ca_system_score_gemma":0.0019436042,"threshold_uncertainty_score":0.032512486},"labels":[],"label_agreement":null},{"id":"W2004536301","doi":"10.1002/sim.3875","title":"On Bayesian shared component disease mapping and ecological regression with errors in covariates","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Child and Family Research Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Child and Family Research Institute","keywords":"Multivariate statistics; Covariate; Bayesian probability; Context (archaeology); Computer science; Markov chain Monte Carlo; Bayesian inference; Prior probability; Bayesian hierarchical modeling; Autoregressive model; Econometrics; Statistics; Machine learning; Artificial intelligence; Geography; Mathematics","score_opus":0.046227544003509174,"score_gpt":0.3725131808067419,"score_spread":0.32628563680323275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004536301","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":[0.0028034358,0.0016648264,0.9927492,0.0008553748,0.00004033609,0.000018664661,0.00009395197,0.000049152044,0.0017250363],"genre_scores_gemma":[0.2662813,0.015171066,0.7062295,0.0014062258,0.0009286743,0.00049917924,0.00081022014,0.00021832308,0.008455515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98956573,0.007886868,0.0002669275,0.0010945571,0.0009577885,0.00022808908],"domain_scores_gemma":[0.967481,0.028424192,0.0013655714,0.0014541174,0.0010726367,0.00020249092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017077686,0.0012342663,0.0017964015,0.0025380962,0.00070473365,0.0019475558,0.002453587,0.0019564126,0.0032888472],"category_scores_gemma":[0.049341995,0.0008721053,0.0015844912,0.0048529226,0.0043551787,0.0029131589,0.0031188563,0.0026757976,0.00058743265],"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":[0.000019231742,0.000020059984,0.0020250373,0.00019592371,0.0001318058,0.00010523086,0.00026510918,0.15275155,0.00023066023,0.7831948,0.001528415,0.05953216],"study_design_scores_gemma":[0.00001082981,0.000026403357,0.0011578217,0.00011125455,0.000040982628,0.00009426521,0.000042029107,0.21859145,0.00013196643,0.7724273,0.0073274774,0.000038177866],"about_ca_topic_score_codex":0.01911893,"about_ca_topic_score_gemma":0.014224357,"teacher_disagreement_score":0.01911893,"about_ca_system_score_codex":0.0022710662,"about_ca_system_score_gemma":0.0028245396,"threshold_uncertainty_score":0.090316534},"labels":[],"label_agreement":null},{"id":"W2005098449","doi":"10.1002/bimj.200510299","title":"A Nonparametric Procedure for the Two‐Factor Mixed Model with Missing Data","year":2007,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian 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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Missing data; Imputation (statistics); Mathematics; Statistics; Covariance; Econometrics","score_opus":0.2487398269463149,"score_gpt":0.45213614917901157,"score_spread":0.20339632223269666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005098449","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":[0.00029862984,0.00003871605,0.9992924,0.00004277963,0.000021603708,0.00005211613,0.000039484436,0.000094048024,0.00012028191],"genre_scores_gemma":[0.020832548,0.00014030287,0.9766981,0.000109627465,0.00007107986,0.0011190295,0.00021997342,0.000088096465,0.0007212616],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9822437,0.014373509,0.0004693737,0.0010173413,0.0016515604,0.00024443085],"domain_scores_gemma":[0.98541117,0.00990641,0.0011401195,0.0022465773,0.0011229619,0.00017273007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01680307,0.0010704182,0.002064471,0.002053255,0.0010381424,0.001089606,0.003663992,0.0016580517,0.0048392396],"category_scores_gemma":[0.0416286,0.0006764573,0.0028991343,0.0024324814,0.0016472625,0.0017575452,0.0024686733,0.0042154477,0.0012246782],"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":[0.00023843451,0.0002445469,0.0025394943,0.00075420423,0.00080411515,0.00039909562,0.00061726954,0.09859954,0.0034696767,0.3567458,0.007892352,0.52769554],"study_design_scores_gemma":[0.00013372279,0.00039412724,0.0021846027,0.00019000442,0.00021974342,0.0006648909,0.00011823074,0.6784722,0.0037733787,0.28774053,0.025897112,0.00021149736],"about_ca_topic_score_codex":0.0020552971,"about_ca_topic_score_gemma":0.0025895182,"teacher_disagreement_score":0.01680307,"about_ca_system_score_codex":0.0009371217,"about_ca_system_score_gemma":0.0035999685,"threshold_uncertainty_score":0.08886421},"labels":[],"label_agreement":null},{"id":"W2005099207","doi":"10.1016/s0378-3758(02)00257-4","title":"Chi-squared tests for and against uniform stochastic ordering on multinomial parameters","year":2002,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"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; Multinomial distribution; Stochastic ordering; Statistics; Applied mathematics; Mean squared error; Chi-square test; Econometrics","score_opus":0.12609179581487281,"score_gpt":0.3890733578008482,"score_spread":0.26298156198597533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005099207","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":[0.555342,0.001966684,0.42041734,0.0020989599,0.0010817953,0.00077009917,0.0043349057,0.0014114412,0.012576743],"genre_scores_gemma":[0.9335206,0.0003419675,0.05837564,0.00068733445,0.0005179623,0.0009308983,0.0027798924,0.00030728726,0.002538391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83992916,0.11312561,0.0073939273,0.020650862,0.014257865,0.0046426468],"domain_scores_gemma":[0.21229883,0.73078847,0.010824594,0.037346464,0.005670611,0.0030711552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1145492,0.0022160502,0.0039117215,0.005131399,0.0025703656,0.0049192254,0.0076155285,0.0064757983,0.028583806],"category_scores_gemma":[0.46644872,0.0012489781,0.004748514,0.0064924294,0.010270977,0.011779497,0.0038196323,0.008134875,0.0022805075],"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":[0.022602549,0.0014864407,0.2700649,0.002484697,0.012891532,0.0032317748,0.005420567,0.060340002,0.0046311715,0.3924799,0.021058109,0.2033084],"study_design_scores_gemma":[0.0031732551,0.0049563535,0.12999994,0.0007678904,0.0033914084,0.003273899,0.0037895823,0.30143085,0.006575927,0.53163624,0.010492375,0.0005123037],"about_ca_topic_score_codex":0.0030662694,"about_ca_topic_score_gemma":0.0026614491,"teacher_disagreement_score":0.1145492,"about_ca_system_score_codex":0.0018588735,"about_ca_system_score_gemma":0.0042635514,"threshold_uncertainty_score":0.6058013},"labels":[],"label_agreement":null},{"id":"W2005218877","doi":"10.1007/s11222-011-9234-3","title":"Smooth functional tempering for nonlinear differential equation models","year":2011,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":45,"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; Simon Fraser University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Applied mathematics; Differential equation; Mathematics; Parallel tempering; Markov chain Monte Carlo; Smoothing; Robustness (evolution); Nonlinear system; Mathematical optimization; Markov chain; Computer science; Population; Convergence (economics); Monte Carlo method; Algorithm; Mathematical analysis; Statistics; Physics; Hybrid Monte Carlo","score_opus":0.17627517806685983,"score_gpt":0.3451306417402916,"score_spread":0.1688554636734318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005218877","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":[0.009089001,0.00095144333,0.9883432,0.0005054039,0.000078394085,0.000016621663,0.000058256544,0.00012722578,0.0008305679],"genre_scores_gemma":[0.51254964,0.0040842737,0.4624346,0.0007043888,0.000902483,0.00038162767,0.0008425957,0.00089366914,0.01720682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979079,0.0012511929,0.00011409556,0.0003285847,0.0002841438,0.00011410775],"domain_scores_gemma":[0.9807246,0.014443864,0.0014680362,0.0014153435,0.0011079343,0.000840145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008024119,0.0015266176,0.0029413735,0.0018970013,0.0011332219,0.0025136312,0.002425349,0.0032737073,0.0026851988],"category_scores_gemma":[0.03792739,0.00170184,0.0019333545,0.0013867649,0.0044082985,0.004395647,0.003500124,0.0043001026,0.00045379848],"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":[0.00006124809,0.000033512028,0.0010027554,0.00016961872,0.000121383804,0.00010105173,0.0001353841,0.22597177,0.0010929175,0.7587036,0.0013908754,0.011215846],"study_design_scores_gemma":[0.000007936869,0.000010332918,0.00016399444,0.000015430516,0.000015597807,0.000017869575,0.000007159069,0.6560883,0.00011062849,0.3428728,0.0006738012,0.000016175407],"about_ca_topic_score_codex":0.0073688347,"about_ca_topic_score_gemma":0.0049843434,"teacher_disagreement_score":0.008024119,"about_ca_system_score_codex":0.002119004,"about_ca_system_score_gemma":0.0016788681,"threshold_uncertainty_score":0.042436063},"labels":[],"label_agreement":null},{"id":"W2005308861","doi":"10.1080/01621459.2014.946034","title":"Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model","year":2014,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"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":"Estimating equations; Estimator; Accelerated failure time model; Covariate; Proportional hazards model; Independent and identically distributed random variables; Semiparametric model; Statistics; Mathematics; Event (particle physics); Semiparametric regression; Likelihood function; Econometrics; Computer science; Maximum likelihood; Random variable","score_opus":0.04563765243633032,"score_gpt":0.3451408181056067,"score_spread":0.29950316566927637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005308861","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":[0.0038152393,0.00019888603,0.99525154,0.00013529441,0.000014379885,0.000045878347,0.00010795894,0.000091436414,0.00033944362],"genre_scores_gemma":[0.16378845,0.0019855772,0.8238494,0.00022658653,0.00024129581,0.0012918093,0.0020223383,0.00026717316,0.006327346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949189,0.0032537198,0.0002766052,0.0005441887,0.0008086846,0.00019792869],"domain_scores_gemma":[0.96806127,0.024854654,0.0019940163,0.0019076237,0.0028656102,0.000316812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013568051,0.001451178,0.002289881,0.0016112914,0.00044437675,0.0018937242,0.0026948892,0.001810977,0.0045023873],"category_scores_gemma":[0.05292371,0.00085737073,0.0019664753,0.0025511237,0.001519297,0.003534141,0.0035119338,0.004066465,0.0013474053],"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":[0.000108971195,0.00007718443,0.0042326823,0.00028498413,0.00018455942,0.00026614944,0.00041643358,0.34808335,0.0011098081,0.5312783,0.0036554148,0.11030226],"study_design_scores_gemma":[0.000056286797,0.000046896363,0.0011191245,0.00004648453,0.000050019753,0.00011207201,0.000026624664,0.82017505,0.0003570674,0.17504509,0.0029271496,0.00003808468],"about_ca_topic_score_codex":0.0045018406,"about_ca_topic_score_gemma":0.003986526,"teacher_disagreement_score":0.013568051,"about_ca_system_score_codex":0.0012893364,"about_ca_system_score_gemma":0.002496615,"threshold_uncertainty_score":0.07175559},"labels":[],"label_agreement":null},{"id":"W2005628926","doi":"10.1002/sim.4182","title":"Estimation of reliability in a three‐factor model","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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 Northern British Columbia","funders":"","keywords":"Intraclass correlation; Reliability (semiconductor); Statistics; Interval estimation; Point estimation; Confidence interval; Markov chain Monte Carlo; Markov chain; Variance (accounting); Monte Carlo method; Random effects model; Estimation; Econometrics; Computer science; Mathematics; Medicine; Psychometrics; Meta-analysis","score_opus":0.14374485287821784,"score_gpt":0.4220546455536928,"score_spread":0.27830979267547495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005628926","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":[0.02369242,0.0004544972,0.9746552,0.00029162026,0.000030680698,0.00008373382,0.00013674967,0.00014686104,0.00050823786],"genre_scores_gemma":[0.5995353,0.0015072606,0.39496818,0.00018922087,0.00018478838,0.0008975262,0.0008351524,0.00008989332,0.0017925967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96812606,0.0241127,0.0010538409,0.0032672381,0.0026919574,0.0007482999],"domain_scores_gemma":[0.9052067,0.08243018,0.0043640505,0.004989626,0.0025674498,0.0004419956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030978423,0.0015642957,0.0030357838,0.002615155,0.00060678576,0.0022133621,0.002942891,0.0024087767,0.0020350204],"category_scores_gemma":[0.0953797,0.0011903147,0.002662918,0.0033268488,0.0029637292,0.0024641964,0.002288818,0.0028241137,0.0009206224],"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":[0.00055645616,0.00029374287,0.02188746,0.0007973904,0.0017466984,0.0005148759,0.0019740185,0.49302584,0.0016610883,0.31995058,0.0031532964,0.1544386],"study_design_scores_gemma":[0.00006223841,0.00018922643,0.005470328,0.00012888994,0.00014507219,0.00015495995,0.00011098489,0.7792011,0.000308274,0.21268988,0.0014576735,0.00008135826],"about_ca_topic_score_codex":0.0060707456,"about_ca_topic_score_gemma":0.002414749,"teacher_disagreement_score":0.030978423,"about_ca_system_score_codex":0.0013442463,"about_ca_system_score_gemma":0.0023543872,"threshold_uncertainty_score":0.16383153},"labels":[],"label_agreement":null},{"id":"W2005712685","doi":"10.1002/cjs.10040","title":"Modeling multiple‐response categorical data from complex surveys","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Categorical variable; Statistics; Log-linear model; Sampling (signal processing); Simple random sample; Computer science; Econometrics; Mathematics; Linear model","score_opus":0.27539225570972947,"score_gpt":0.38736054654993085,"score_spread":0.11196829084020138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005712685","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":[0.05558438,0.00034778938,0.94147235,0.00077661796,0.00005743513,0.0002602807,0.00063367846,0.0002150391,0.0006523916],"genre_scores_gemma":[0.5910186,0.00055226113,0.40316054,0.00038377996,0.00012249364,0.0017447746,0.0012735411,0.000079534024,0.0016644095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96041536,0.032730468,0.0009134065,0.003137599,0.00224293,0.0005601734],"domain_scores_gemma":[0.8024466,0.17601295,0.0123483315,0.0060994155,0.0024725404,0.00062023016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041544355,0.00086865533,0.0021124291,0.0025361679,0.00074505387,0.002419424,0.003278617,0.001778042,0.0033879827],"category_scores_gemma":[0.12410124,0.0009686508,0.0020633442,0.004421857,0.0023516584,0.0023541332,0.0026334657,0.0025956589,0.00046901652],"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":[0.00039455591,0.00032237763,0.056424253,0.00070700556,0.00090407906,0.000676764,0.001496411,0.62444377,0.00083679444,0.21762928,0.0032206203,0.09294412],"study_design_scores_gemma":[0.000049848742,0.00011017266,0.0046924343,0.00008225756,0.00007054265,0.000087092594,0.00016080355,0.85095966,0.00021545019,0.14197044,0.0015609378,0.000040393425],"about_ca_topic_score_codex":0.00838014,"about_ca_topic_score_gemma":0.007057985,"teacher_disagreement_score":0.041544355,"about_ca_system_score_codex":0.0024250734,"about_ca_system_score_gemma":0.0014642447,"threshold_uncertainty_score":0.21971023},"labels":[],"label_agreement":null},{"id":"W2006031679","doi":"10.1186/1471-2288-7-34","title":"A simulation study of sample size for multilevel logistic regression models","year":2007,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":400,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; St. Michael's Hospital; University of Toronto","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Statistics; Sample size determination; Multilevel model; Logistic regression; Random effects model; Variance (accounting); Covariance; Mathematics; Hierarchical database model; Sample (material); Econometrics; Regression; Medicine; Computer science; Meta-analysis; Data mining","score_opus":0.8745461031859402,"score_gpt":0.6879301196957895,"score_spread":0.18661598349015063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006031679","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":[0.5004105,0.0013490834,0.48516005,0.002603449,0.00018723498,0.0013685523,0.0010155059,0.00045920393,0.0074464153],"genre_scores_gemma":[0.8857378,0.00033559834,0.11066317,0.0002445157,0.00004077831,0.0013741291,0.00061828113,0.00007007657,0.00091570395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9714386,0.025301281,0.0005071524,0.0011062105,0.0010838662,0.0005629908],"domain_scores_gemma":[0.48513922,0.49127758,0.0074301404,0.00661793,0.008253897,0.0012811762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054618455,0.000915547,0.001807696,0.0016835916,0.0011238588,0.001774447,0.002634742,0.0024612446,0.0033907872],"category_scores_gemma":[0.23558392,0.00071967463,0.002125658,0.0021259533,0.0016425943,0.0024583312,0.0020562646,0.0031977633,0.00022987508],"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":[0.0009392232,0.00034176657,0.039247554,0.00039332215,0.00047233235,0.00055448245,0.00078024756,0.9027356,0.0003450244,0.03671673,0.0019918897,0.015481882],"study_design_scores_gemma":[0.000111558635,0.00016665725,0.0016707117,0.00006925201,0.00007640014,0.0000813584,0.00013848467,0.98935074,0.00014449349,0.007817791,0.0003536364,0.000019011964],"about_ca_topic_score_codex":0.013063104,"about_ca_topic_score_gemma":0.006774671,"teacher_disagreement_score":0.054618455,"about_ca_system_score_codex":0.002700877,"about_ca_system_score_gemma":0.002232731,"threshold_uncertainty_score":0.28885347},"labels":[],"label_agreement":null},{"id":"W2006070241","doi":"10.2147/oams.s33060","title":"Comparison of various modeling approaches in the analysis of longitudinal data with a binary outcome: The Ontario Mother and Infant Study (TOMIS) III","year":2012,"lang":"en","type":"article","venue":"Open Access Medical Statistics","topic":"Statistical Methods and Bayesian Inference","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":true,"ca_institutions":"York University; University of Toronto; McMaster University; St. Joseph’s Healthcare Hamilton","funders":"Canadian Institutes of Health Research","keywords":"Gee; Postpartum depression; Biostatistics; Generalized estimating equation; Longitudinal study; Multilevel model; Longitudinal data; Psychology; Statistics; Gerontology; Demography; Medicine; Mathematics; Public health; Sociology; Nursing","score_opus":0.5087971337204172,"score_gpt":0.5401671055933287,"score_spread":0.03136997187291146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006070241","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":[0.45867687,0.017746612,0.50389653,0.0061280318,0.0005852421,0.003961807,0.00519244,0.0007304191,0.0030821555],"genre_scores_gemma":[0.66743255,0.0060756216,0.31334502,0.0009771693,0.00018287604,0.0068768025,0.0037436301,0.00023725646,0.0011290691],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91084737,0.082805,0.0016446859,0.0016767062,0.0024352882,0.00059096457],"domain_scores_gemma":[0.88682604,0.09958014,0.0050214157,0.0039768727,0.004002853,0.00059269293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11616658,0.0013629469,0.001566756,0.0026950059,0.0012350443,0.0017746767,0.0034256787,0.0011180653,0.0013261789],"category_scores_gemma":[0.123921834,0.000862008,0.007187589,0.0032203821,0.0007333499,0.00085432,0.0020154505,0.0016690562,0.00013636038],"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":[0.008076928,0.00064722664,0.5111484,0.0040718247,0.045653347,0.0010513011,0.0051512737,0.16737561,0.001324988,0.030681925,0.010921476,0.21389562],"study_design_scores_gemma":[0.0018896819,0.0023534757,0.24538848,0.0023890345,0.010629663,0.00048641136,0.0026358217,0.69212,0.0009987482,0.03007682,0.01054186,0.00048995105],"about_ca_topic_score_codex":0.12205273,"about_ca_topic_score_gemma":0.13583186,"teacher_disagreement_score":0.12205273,"about_ca_system_score_codex":0.004888673,"about_ca_system_score_gemma":0.006939607,"threshold_uncertainty_score":0.6143549},"labels":[],"label_agreement":null},{"id":"W2007011920","doi":"10.1016/j.jspi.2004.06.005","title":"Comparison of methods for incomplete repeated measures data analysis in small samples","year":2004,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"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 Alberta; Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Missing data; Multivariate statistics; Statistics; Inference; Mathematics; Repeated measures design; Sample size determination; Data mining; Computer science; Artificial intelligence","score_opus":0.483232859096804,"score_gpt":0.553904635177514,"score_spread":0.07067177608070996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007011920","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":[0.012813469,0.0024759595,0.9819324,0.0003179492,0.0003392301,0.0010141713,0.00021792535,0.00035376588,0.00053518044],"genre_scores_gemma":[0.07481381,0.002317465,0.9148846,0.00024292745,0.00022947282,0.0055942754,0.00044588503,0.00059266144,0.0008788454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.80954176,0.16494101,0.0057985527,0.0067775464,0.012060099,0.0008809597],"domain_scores_gemma":[0.2015765,0.76733005,0.0059108064,0.015028493,0.009004385,0.0011497381],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19282277,0.0025289874,0.006076315,0.0049391007,0.0017805581,0.003790962,0.007408847,0.0046537425,0.0075357277],"category_scores_gemma":[0.55616826,0.0017309824,0.005915282,0.004087235,0.0031868005,0.0073998715,0.0037914447,0.0060788244,0.0008271648],"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":[0.021163682,0.0013844201,0.009119897,0.007556353,0.01422932,0.00032549127,0.0044755545,0.043070633,0.0021849836,0.106408685,0.006126287,0.78395474],"study_design_scores_gemma":[0.010759847,0.0098183835,0.028942682,0.003404968,0.0107850665,0.0016187184,0.0017405674,0.51295507,0.0061972425,0.396076,0.01684863,0.0008527785],"about_ca_topic_score_codex":0.0031590746,"about_ca_topic_score_gemma":0.0037518619,"teacher_disagreement_score":0.19282277,"about_ca_system_score_codex":0.0028450424,"about_ca_system_score_gemma":0.0056954823,"threshold_uncertainty_score":0.9953932},"labels":[],"label_agreement":null},{"id":"W2007493872","doi":"10.1002/sim.2737","title":"Biased odds ratios from dichotomization of age","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":74,"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 Health Services; Columbia College","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Mental Health","keywords":"Odds; Odds ratio; Statistics; Demography; Medicine; Econometrics; Computer science; Logistic regression; Mathematics","score_opus":0.05253306517601675,"score_gpt":0.3872355764733088,"score_spread":0.33470251129729206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007493872","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":[0.13495298,0.011277853,0.8284968,0.01178915,0.0007995826,0.00051406166,0.00092769664,0.0005678055,0.010674017],"genre_scores_gemma":[0.8321137,0.0026204772,0.15678549,0.0050027617,0.0006423759,0.00073515845,0.00054512144,0.00018675157,0.0013682053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.903351,0.08016694,0.002715092,0.005729166,0.0069417977,0.0010959784],"domain_scores_gemma":[0.5620477,0.38887975,0.020514537,0.022330955,0.0053621246,0.00086490094],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08406337,0.001136472,0.0025190758,0.0027594063,0.00077208114,0.003318683,0.0025348992,0.0026012843,0.0034476519],"category_scores_gemma":[0.44553235,0.00074810244,0.0020679976,0.0026906736,0.0063459203,0.0058108675,0.0035584962,0.0044627544,0.00066708174],"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":[0.0045506917,0.0001713406,0.1777812,0.002009297,0.0030407987,0.0020747136,0.004361209,0.037382055,0.0023373363,0.47771877,0.011993074,0.27657956],"study_design_scores_gemma":[0.00033857292,0.00030064603,0.031125657,0.0006886202,0.00077903667,0.0024162184,0.00070535485,0.07047093,0.0027607097,0.8825906,0.007678115,0.00014559229],"about_ca_topic_score_codex":0.001436657,"about_ca_topic_score_gemma":0.00092115137,"teacher_disagreement_score":0.91593665,"about_ca_system_score_codex":0.001681373,"about_ca_system_score_gemma":0.0012678572,"threshold_uncertainty_score":0.44457495},"labels":[],"label_agreement":null},{"id":"W2007834077","doi":"10.1080/00949650008812035","title":"Comparison of permutation methods for the partial correlation and partial mantel tests","year":2000,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":286,"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":"Mathematics; Partial correlation; Permutation (music); Statistics; Type I and type II errors; Resampling; Outlier; Population; Covariance matrix; Partial permutation; Applied mathematics; Correlation; Permutation matrix; Algorithm; Medicine","score_opus":0.12327636417891526,"score_gpt":0.5152636500600378,"score_spread":0.3919872858811226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007834077","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":[0.049136605,0.002938397,0.9312595,0.00083954696,0.0012303536,0.0023560121,0.0009723037,0.0017208799,0.009546364],"genre_scores_gemma":[0.26705173,0.0019165529,0.71579695,0.00046898512,0.00042121662,0.008419606,0.0014410081,0.0013616581,0.003122226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8577738,0.116008244,0.004802549,0.0063999332,0.013918009,0.0010975241],"domain_scores_gemma":[0.5276433,0.4211433,0.01115699,0.024061345,0.01510316,0.0008918742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08986937,0.0015482303,0.0024005156,0.00452895,0.0014877454,0.002572562,0.0027827027,0.0019945987,0.00884984],"category_scores_gemma":[0.4103569,0.0008571988,0.0032601228,0.0051205433,0.0029958868,0.0039357194,0.0023684832,0.0033403342,0.0017011428],"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":[0.0055591585,0.00064389565,0.024510775,0.0025623967,0.004341807,0.0005903227,0.002753934,0.043572694,0.0031631116,0.099020295,0.016846517,0.7964351],"study_design_scores_gemma":[0.0031945219,0.012113943,0.06651308,0.0025221584,0.0034874498,0.0039499123,0.0029601837,0.53249735,0.01678822,0.27144715,0.08320955,0.0013164697],"about_ca_topic_score_codex":0.0017106665,"about_ca_topic_score_gemma":0.0016093532,"teacher_disagreement_score":0.08986937,"about_ca_system_score_codex":0.0013450864,"about_ca_system_score_gemma":0.0033973774,"threshold_uncertainty_score":0.47528034},"labels":[],"label_agreement":null},{"id":"W2008333663","doi":"10.1007/s10985-012-9241-5","title":"Estimation of finite population duration distributions from longitudinal survey panels with intermittent followup","year":2013,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"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 Waterloo","funders":"","keywords":"Censoring (clinical trials); Statistics; Duration (music); Population; Survival analysis; Demography; Longitudinal study; Sample (material); Longitudinal data; Mathematics; Estimation; Econometrics","score_opus":0.0953934276544864,"score_gpt":0.369740184758983,"score_spread":0.27434675710449663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008333663","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":[0.047964517,0.00064925785,0.9497175,0.00042242015,0.00003637531,0.00006811043,0.00052506506,0.00018940125,0.00042732983],"genre_scores_gemma":[0.75031567,0.0019957216,0.23613483,0.0003809367,0.0004069209,0.00083744904,0.0032504548,0.00017233613,0.006505778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99333316,0.0045895213,0.0002844491,0.0011404731,0.00037881488,0.00027364987],"domain_scores_gemma":[0.8154794,0.16695866,0.0067796195,0.008269628,0.0014947738,0.0010179748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032470737,0.0010837694,0.002985496,0.0022300347,0.0008867393,0.0028931417,0.0055412757,0.0027646336,0.0036084363],"category_scores_gemma":[0.11716708,0.002316782,0.002636699,0.0024170408,0.0034508149,0.0044649197,0.0030885993,0.0031070153,0.0005559996],"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":[0.00058295706,0.00027282222,0.03467889,0.00051172887,0.0011500479,0.00069829205,0.0010754737,0.57331395,0.00080879865,0.30568218,0.0035725709,0.077652365],"study_design_scores_gemma":[0.000056654142,0.00005696615,0.0031857581,0.000081939776,0.00013185515,0.00013840829,0.000086737185,0.7935987,0.00023338865,0.20146462,0.0009275139,0.00003740848],"about_ca_topic_score_codex":0.006796183,"about_ca_topic_score_gemma":0.0061005624,"teacher_disagreement_score":0.032470737,"about_ca_system_score_codex":0.0012949056,"about_ca_system_score_gemma":0.0016059437,"threshold_uncertainty_score":0.17172372},"labels":[],"label_agreement":null},{"id":"W2008363918","doi":"10.1080/09652140020004287","title":"Multivariate modeling of missing data within and across assessment waves","year":2000,"lang":"en","type":"review","venue":"Addiction","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":88,"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":"Missing data; Multivariate statistics; Imputation (statistics); Latent variable; Multivariate analysis; Computer science; Latent variable model; Data mining; Context (archaeology); Statistics; Longitudinal data; Econometrics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.2957922079733491,"score_gpt":0.5143796784453989,"score_spread":0.2185874704720498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008363918","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":[0.0018776284,0.20818903,0.7752472,0.0068394276,0.00059479073,0.00021791978,0.0006383979,0.0005086896,0.0058869827],"genre_scores_gemma":[0.05422509,0.5517264,0.38352436,0.002093929,0.0015779601,0.0011368692,0.001444579,0.0002291903,0.0040417546],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98360485,0.012167362,0.00065368623,0.001086822,0.0023318673,0.00015540111],"domain_scores_gemma":[0.9269527,0.06322295,0.0031748884,0.003352944,0.0030117948,0.00028466777],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.025724038,0.001476565,0.0038562117,0.0044328435,0.000716794,0.0026162858,0.0061430973,0.0028853924,0.0039447793],"category_scores_gemma":[0.06381491,0.0008588543,0.0023162772,0.008515859,0.002451648,0.004312353,0.0018129296,0.0034848116,0.0028037906],"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":[0.00006799644,0.0000801862,0.003959159,0.005077321,0.0007199174,0.00024285157,0.00044567417,0.013876403,0.00024166498,0.1359222,0.0155471,0.8238195],"study_design_scores_gemma":[0.00006897215,0.00019522429,0.0083005065,0.0070781726,0.0005793348,0.0025562947,0.00063051993,0.05963258,0.0011436814,0.72507906,0.19452395,0.00021162334],"about_ca_topic_score_codex":0.0030899388,"about_ca_topic_score_gemma":0.0040423083,"teacher_disagreement_score":0.97427595,"about_ca_system_score_codex":0.001775383,"about_ca_system_score_gemma":0.0040091295,"threshold_uncertainty_score":0.13604337},"labels":[],"label_agreement":null},{"id":"W2008954435","doi":"10.1007/s00184-011-0359-3","title":"Robust analysis of longitudinal data with nonignorable missing responses","year":2011,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Outlier; Estimator; Longitudinal data; Statistics; Mathematics; Robust statistics; Maximum likelihood; Econometrics; Computer science; Data mining","score_opus":0.41964674663354634,"score_gpt":0.41038221634336625,"score_spread":0.009264530290180095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008954435","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":[0.0019812963,0.0006990237,0.9964714,0.00032566453,0.000054204073,0.00003306749,0.00016331399,0.00013729314,0.00013475069],"genre_scores_gemma":[0.18082175,0.0031491201,0.80709183,0.00048438986,0.00071497646,0.0012425956,0.0016263883,0.0004314844,0.0044375504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9663105,0.026159948,0.0013926411,0.0034552256,0.002051348,0.00063024595],"domain_scores_gemma":[0.8390761,0.1374448,0.007469251,0.011763502,0.0035123462,0.0007341157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052574854,0.0018656584,0.004435734,0.002807071,0.0010306083,0.0025366852,0.0062348093,0.0024162082,0.00413537],"category_scores_gemma":[0.18196677,0.0020659163,0.0037330254,0.0030966483,0.002993492,0.0031182724,0.0033544288,0.004575608,0.0008894654],"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":[0.00088791765,0.00026368035,0.005845984,0.0018304074,0.00482446,0.0007476556,0.0005545851,0.16665053,0.0023045167,0.614821,0.008127007,0.19314227],"study_design_scores_gemma":[0.00012103493,0.00011132931,0.0016537805,0.00014337632,0.00033475083,0.00017455779,0.00005602491,0.3873316,0.00082574715,0.6055059,0.0036789405,0.000062984735],"about_ca_topic_score_codex":0.005243961,"about_ca_topic_score_gemma":0.0041253963,"teacher_disagreement_score":0.052574854,"about_ca_system_score_codex":0.0015083539,"about_ca_system_score_gemma":0.004144647,"threshold_uncertainty_score":0.27804577},"labels":[],"label_agreement":null},{"id":"W2009378006","doi":"10.2307/3315996","title":"Combining information from multiple surveys through the empirical likelihood method","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Consistency (knowledge bases); Construct (python library); Computer science; Empirical likelihood; Maximum likelihood; Mathematics; Statistics; Quasi-maximum likelihood; Econometrics; Likelihood function; Artificial intelligence","score_opus":0.13442138034341397,"score_gpt":0.3737763162867002,"score_spread":0.23935493594328625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009378006","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":[0.0052611097,0.00040334155,0.9930131,0.00031147362,0.00002819346,0.000046212695,0.000065421984,0.000091340626,0.00077990245],"genre_scores_gemma":[0.3376546,0.0018258955,0.65517366,0.00043873786,0.0003919736,0.000398099,0.0006206239,0.00012700689,0.0033693893],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9704023,0.023887834,0.00073085015,0.0014291315,0.003204753,0.00034508578],"domain_scores_gemma":[0.95309114,0.038359262,0.00275773,0.0032067355,0.0021906337,0.00039450664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022739915,0.0011441556,0.0027050788,0.005092232,0.00048641136,0.003265411,0.002480195,0.0018201526,0.003227443],"category_scores_gemma":[0.08095817,0.0014955343,0.0015411628,0.006340177,0.0018153767,0.0056370473,0.004227408,0.0021050991,0.0006855439],"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":[0.00031070085,0.00016670952,0.010057051,0.0008599404,0.0011479893,0.000532373,0.00055387546,0.29095152,0.001285548,0.33557242,0.0026782213,0.3558837],"study_design_scores_gemma":[0.00006681458,0.00013456932,0.002481657,0.0001774297,0.00015545748,0.00017733852,0.00010361638,0.64942735,0.0008673948,0.34134376,0.004973729,0.00009086132],"about_ca_topic_score_codex":0.0019845576,"about_ca_topic_score_gemma":0.001544113,"teacher_disagreement_score":0.022739915,"about_ca_system_score_codex":0.0011408683,"about_ca_system_score_gemma":0.0016159036,"threshold_uncertainty_score":0.12026161},"labels":[],"label_agreement":null},{"id":"W2009397745","doi":"10.1002/cjs.5550340207","title":"Interval estimation via tail functions","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidence interval; Credible interval; Inference; Bernoulli's principle; Function (biology); Interval estimation; Mathematics; Coverage probability; Statistics; Interval (graph theory); Bayesian probability; Cutoff; Confidence distribution; Algorithm; Confidence region; Point estimation; Bayesian inference; Computer science; Artificial intelligence; Combinatorics; Physics","score_opus":0.039014192836897915,"score_gpt":0.3077683457835654,"score_spread":0.2687541529466675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009397745","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":[0.0013555456,0.00030401634,0.9971685,0.00007953197,0.000039564307,0.00001598637,0.000039581064,0.00027462994,0.0007227663],"genre_scores_gemma":[0.22098842,0.001506975,0.77190435,0.00047730477,0.00038787068,0.00045736448,0.0005783796,0.00051099533,0.003188347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98991525,0.0060652723,0.00048645,0.00085471664,0.0023487108,0.00032971185],"domain_scores_gemma":[0.92397964,0.06258769,0.0028241365,0.0057042106,0.00442323,0.0004810505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01388361,0.0011694484,0.0015906964,0.0054340926,0.00058544957,0.0030659474,0.0022109791,0.0020431392,0.0046017044],"category_scores_gemma":[0.10586926,0.00080976903,0.0015132049,0.0032810643,0.0022281713,0.0038945337,0.002936247,0.0045207646,0.0015409485],"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":[0.0003152729,0.00009236652,0.0039174054,0.00047946846,0.00029560542,0.00024157164,0.00039160813,0.16337775,0.0037720832,0.49551457,0.0053521767,0.32625017],"study_design_scores_gemma":[0.00006562352,0.00007171584,0.0011245881,0.00017303268,0.00007333515,0.00019080556,0.000031147527,0.696492,0.0033172362,0.29211468,0.0062668314,0.00007906924],"about_ca_topic_score_codex":0.0018299678,"about_ca_topic_score_gemma":0.0007355564,"teacher_disagreement_score":0.01388361,"about_ca_system_score_codex":0.0012125295,"about_ca_system_score_gemma":0.0011143703,"threshold_uncertainty_score":0.0734244},"labels":[],"label_agreement":null},{"id":"W2009470745","doi":"10.1897/08-480.1","title":"Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests","year":2009,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Heteroscedasticity; Statistics; Count data; Power transform; Regression analysis; Homogeneity (statistics); Statistical inference; Econometrics; Mathematics; Regression; Data transformation; Variance (accounting); Poisson distribution; Computer science; Data mining","score_opus":0.04387319346725209,"score_gpt":0.3519121392488207,"score_spread":0.3080389457815686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009470745","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":[0.014245635,0.00022239643,0.9842523,0.00018766795,0.000046719186,0.0001430544,0.00011498527,0.00029164317,0.0004956052],"genre_scores_gemma":[0.28299505,0.00061985187,0.7123931,0.00038975928,0.0001044986,0.001247783,0.0006360198,0.0003927816,0.0012212343],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98153573,0.0110805,0.001157566,0.0016657095,0.0042811222,0.00027936584],"domain_scores_gemma":[0.92141277,0.06309065,0.0064076753,0.0054378854,0.0033294647,0.00032155306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027713891,0.0012289674,0.0019408088,0.0021612584,0.0010995332,0.0015391822,0.0021716654,0.0011803489,0.0028107292],"category_scores_gemma":[0.09688131,0.0004968314,0.0013721975,0.0021290935,0.0029010272,0.0019688115,0.0020603214,0.0046084584,0.0004942444],"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":[0.0009625487,0.00052485906,0.062406998,0.00275889,0.000799297,0.0027065838,0.0032697378,0.110731065,0.13969266,0.1866316,0.0054338127,0.48408192],"study_design_scores_gemma":[0.00011071965,0.0012980095,0.032287415,0.0002781565,0.00027332356,0.0021205063,0.0007487513,0.49480316,0.10230038,0.3460072,0.019425388,0.00034698445],"about_ca_topic_score_codex":0.0018447023,"about_ca_topic_score_gemma":0.0029479899,"teacher_disagreement_score":0.027713891,"about_ca_system_score_codex":0.0012192903,"about_ca_system_score_gemma":0.0022544125,"threshold_uncertainty_score":0.14656681},"labels":[],"label_agreement":null},{"id":"W2009687799","doi":"10.1002/cjs.11220","title":"Bayesian sensitivity analyses for hidden sub‐populations in weighted sampling","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Medical Expenditure Panel Survey; Statistics; Bayesian probability; Econometrics; Population; Sampling (signal processing); Sample (material); Sensitivity (control systems); Health care; Computer science; Mathematics; Medicine; Environmental health; Economics; Health insurance","score_opus":0.2100766677044757,"score_gpt":0.41858946097468996,"score_spread":0.20851279327021427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009687799","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":[0.015568377,0.00076141214,0.98080724,0.0006241355,0.00007694443,0.00040829662,0.00020989616,0.00014088872,0.0014028827],"genre_scores_gemma":[0.688023,0.0017205575,0.30228904,0.0012576313,0.00032533126,0.003143459,0.0005894922,0.00020158246,0.0024499244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8351281,0.15128829,0.002514835,0.005077805,0.0046986835,0.0012922194],"domain_scores_gemma":[0.38566008,0.5803069,0.012408071,0.016395535,0.004423446,0.0008059094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1792568,0.0020307358,0.0041015185,0.004724428,0.0012811011,0.0038645251,0.004464021,0.0034821548,0.006077513],"category_scores_gemma":[0.46243888,0.0020952467,0.005757268,0.002987126,0.005008545,0.0058454447,0.0066258577,0.006098718,0.00032355337],"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":[0.0006758573,0.0001904122,0.01005907,0.0009130237,0.003446938,0.00071748614,0.0007424359,0.52830833,0.00069554534,0.4058251,0.0022479699,0.04617775],"study_design_scores_gemma":[0.000090330395,0.00012776649,0.0016799995,0.00019762588,0.00046997218,0.0001392105,0.00009441273,0.63679093,0.00045703867,0.35839278,0.0014801756,0.00007984396],"about_ca_topic_score_codex":0.005386649,"about_ca_topic_score_gemma":0.002301485,"teacher_disagreement_score":0.1792568,"about_ca_system_score_codex":0.0040838495,"about_ca_system_score_gemma":0.0017823823,"threshold_uncertainty_score":0.9480119},"labels":[],"label_agreement":null},{"id":"W2010210508","doi":"10.1002/cjs.5550360111","title":"Proportional hazards models based on biased samples and estimated selection probabilities","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Health Resources and Services Administration; National Institutes of Health","keywords":"Statistics; Mathematics; Proportional hazards model; Estimator; Population; Inverse probability weighting; Model selection; Logistic regression; Weighting; Regression; Econometrics; Medicine","score_opus":0.16055998558484938,"score_gpt":0.3280504479686865,"score_spread":0.1674904623838371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010210508","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":[0.018643875,0.00063768827,0.97755617,0.0010554901,0.00011349953,0.0003320302,0.00016522605,0.00013724815,0.0013587995],"genre_scores_gemma":[0.60898,0.0021111234,0.37461025,0.0013361187,0.0006154912,0.0035424882,0.00077910186,0.00017312852,0.007852338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95673037,0.03501602,0.0008445546,0.0027141601,0.0035415972,0.0011533134],"domain_scores_gemma":[0.7431511,0.2323771,0.010006497,0.008499897,0.0051771416,0.00078823307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.074457675,0.0014717473,0.0030549366,0.003528386,0.001051426,0.002823868,0.0047615455,0.0035177825,0.0054425937],"category_scores_gemma":[0.24304639,0.0017980387,0.0025549582,0.0031086686,0.005688666,0.0049883863,0.0035169718,0.005072749,0.00069609564],"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":[0.00032264044,0.00009827293,0.0086124595,0.0002518723,0.00034432526,0.00057191186,0.000716004,0.29491594,0.0003062044,0.6554555,0.002681283,0.03572361],"study_design_scores_gemma":[0.0001918839,0.000086819564,0.0010556427,0.00010633126,0.00010463853,0.00012180166,0.000061513056,0.61134803,0.00025724695,0.3849365,0.0016890703,0.00004062141],"about_ca_topic_score_codex":0.007148838,"about_ca_topic_score_gemma":0.0034720264,"teacher_disagreement_score":0.074457675,"about_ca_system_score_codex":0.0031887202,"about_ca_system_score_gemma":0.0021580884,"threshold_uncertainty_score":0.3937745},"labels":[],"label_agreement":null},{"id":"W2010547933","doi":"10.1111/j.1467-9868.2007.00603.x","title":"Nested Generalized Linear Mixed Models: An Orthodox Best Linear Unbiased Predictor Approach","year":2007,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":63,"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 New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Applied mathematics; Best linear unbiased prediction; Generalized linear mixed model; Generalized linear model; Range (aeronautics); Inference; Exponential function; Linear regression; Parametric statistics; Linear model; Exponential family; Statistics; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.20436723264623652,"score_gpt":0.4050345134045369,"score_spread":0.20066728075830037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010547933","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":[0.0007515366,0.00024860035,0.99837506,0.00019149567,0.000026572356,0.000015256878,0.00003504164,0.000051158484,0.0003053294],"genre_scores_gemma":[0.077791534,0.00089692586,0.9177194,0.00040704384,0.00020716932,0.00043693298,0.00018546236,0.00016619398,0.002189319],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9804514,0.016187456,0.00037446455,0.0011652383,0.0015868018,0.00023465628],"domain_scores_gemma":[0.9842865,0.011855154,0.00091687846,0.0016933233,0.0010460854,0.000202169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021754164,0.0010793565,0.002493176,0.0016192503,0.0006450305,0.0020925605,0.003669166,0.0016914159,0.00282768],"category_scores_gemma":[0.039528072,0.0011119092,0.0023068178,0.0016865836,0.001989614,0.0025796082,0.002718625,0.0034664334,0.0007014076],"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":[0.00006248623,0.000046297726,0.0013041027,0.00026463775,0.00039580252,0.00018704947,0.00034206928,0.07915975,0.0007844831,0.837559,0.0022068515,0.07768751],"study_design_scores_gemma":[0.00002254541,0.00006173823,0.0003189074,0.00009822856,0.00008940409,0.00008938695,0.000041701252,0.31114507,0.0003797326,0.6809431,0.0067762746,0.000033871984],"about_ca_topic_score_codex":0.0020979512,"about_ca_topic_score_gemma":0.0033857634,"teacher_disagreement_score":0.021754164,"about_ca_system_score_codex":0.001192639,"about_ca_system_score_gemma":0.0025093108,"threshold_uncertainty_score":0.11504835},"labels":[],"label_agreement":null},{"id":"W2010713129","doi":"10.1177/1471082x0800900203","title":"Clustered binary data with random cluster sizes","year":2009,"lang":"en","type":"article","venue":"Statistical Modelling","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Binary data; Random effects model; Cluster (spacecraft); Poisson distribution; Inference; Statistics; Binary number; Mathematics; Best linear unbiased prediction; Moment (physics); Overdispersion; Computer science; Count data; Selection (genetic algorithm); Artificial intelligence","score_opus":0.12350467163338766,"score_gpt":0.38117321083704786,"score_spread":0.2576685392036602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010713129","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":[0.008362012,0.00019704652,0.98941296,0.00023925518,0.00010850863,0.0002604325,0.0005286088,0.00027469592,0.000616454],"genre_scores_gemma":[0.17715509,0.00039507696,0.81421995,0.0005858431,0.00020711086,0.0029805754,0.002054088,0.00022274873,0.0021793996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9697734,0.016778054,0.001338963,0.007384777,0.004090699,0.00063407933],"domain_scores_gemma":[0.90540546,0.061436422,0.006597318,0.021460379,0.004541164,0.000559295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040351275,0.00087679666,0.0024338157,0.0029258402,0.0017122601,0.0027555383,0.0054570823,0.0027770055,0.0045855287],"category_scores_gemma":[0.1528627,0.001115001,0.0020259945,0.005336879,0.0052463943,0.004516738,0.0033577317,0.004201334,0.0013769357],"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":[0.0008304616,0.00024483717,0.016416864,0.0013237383,0.0008653002,0.0005246657,0.0017259876,0.07668452,0.0057221353,0.702357,0.007768616,0.18553586],"study_design_scores_gemma":[0.00013283575,0.00022194751,0.0068083154,0.00025383005,0.00021558182,0.00048085907,0.00023342544,0.27725458,0.003272211,0.7007608,0.010224138,0.00014158386],"about_ca_topic_score_codex":0.002101829,"about_ca_topic_score_gemma":0.0019788211,"teacher_disagreement_score":0.040351275,"about_ca_system_score_codex":0.0019069059,"about_ca_system_score_gemma":0.0015589234,"threshold_uncertainty_score":0.21340048},"labels":[],"label_agreement":null},{"id":"W2011216343","doi":"10.1136/jech-2013-203098.4","title":"DEVELOPMENTAL TRAJECTORIES OF BODY MASS INDEX THROUGHOUT ADULTHOOD: EVIDENCE FROM THE NATIONAL POPULATION HEALTH SURVEY","year":2013,"lang":"en","type":"article","venue":"Journal of Epidemiology & Community Health","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Body mass index; Medicine; Population; Trajectory; Demography; Covariate; Gerontology; Statistics; Environmental health","score_opus":0.35587215549819434,"score_gpt":0.5100353415601062,"score_spread":0.15416318606191187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011216343","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":[0.8165462,0.10146141,0.0035973818,0.009522918,0.00027781862,0.00026498715,0.048803367,0.00012907104,0.019396812],"genre_scores_gemma":[0.94838077,0.034728017,0.0022028536,0.0005254593,0.000058892747,0.000074999036,0.01362388,0.000025598352,0.00037950746],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99744475,0.00042802643,0.00023920537,0.00048132634,0.001148349,0.00025827912],"domain_scores_gemma":[0.9833714,0.0053106905,0.002704191,0.0010934724,0.006725059,0.0007952279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005868239,0.00040357347,0.0006288832,0.0025917508,0.0010824616,0.0013939728,0.0015810649,0.0004190848,0.002008723],"category_scores_gemma":[0.025970358,0.0003482138,0.0009705691,0.009089757,0.000864539,0.000657133,0.0012144424,0.0011016866,0.0002025645],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009704815,0.000018122362,0.966347,0.0006041946,0.0005147054,0.0000279464,0.0004946747,0.0002600566,0.000016225196,0.00044695608,0.0025203219,0.028652841],"study_design_scores_gemma":[0.0000063835932,0.000018800934,0.9948665,0.0004365424,0.00035325205,0.000030466299,0.0005024323,0.00022923762,0.000023387052,0.00016524094,0.0033592016,0.000008606808],"about_ca_topic_score_codex":0.9044683,"about_ca_topic_score_gemma":0.8926578,"teacher_disagreement_score":0.9044683,"about_ca_system_score_codex":0.00480563,"about_ca_system_score_gemma":0.011937554,"threshold_uncertainty_score":0.19218856},"labels":[],"label_agreement":null},{"id":"W2012731203","doi":"10.1080/03610920802618392","title":"Regression Analysis with Covariates Missing at Random: A Piece-wise Nonparametric Model for Missing Covariates","year":2009,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada; WILEY","keywords":"Covariate; Missing data; Statistics; Nonparametric statistics; Mathematics; Regression analysis; Nonparametric regression; Econometrics; Parametric statistics; Semiparametric regression","score_opus":0.08670862013605163,"score_gpt":0.4580745081038819,"score_spread":0.37136588796783027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012731203","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":[0.0036949147,0.00046146152,0.9931613,0.0009148068,0.00012825009,0.00014237044,0.0005685804,0.00021059255,0.000717716],"genre_scores_gemma":[0.27768424,0.0020536892,0.70219535,0.0014658563,0.0006851055,0.0040697986,0.002472879,0.00028486925,0.009088251],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9505445,0.037530884,0.0013692805,0.006275328,0.0032991245,0.0009808876],"domain_scores_gemma":[0.90531814,0.07051639,0.007294042,0.012967705,0.0032384575,0.00066527363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049279027,0.0020218347,0.004024838,0.0024709417,0.0010798795,0.0033962703,0.010451574,0.0061356723,0.0061312513],"category_scores_gemma":[0.1284698,0.0016354814,0.0041297474,0.005787579,0.003796163,0.0060205734,0.0036571126,0.007306,0.0024481206],"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":[0.000735431,0.00030900957,0.014282518,0.0012920134,0.0014434031,0.0013712029,0.002405477,0.16015752,0.0013995834,0.6147943,0.0134596,0.18835005],"study_design_scores_gemma":[0.00015434508,0.00048396576,0.0042021275,0.0003803033,0.0003785866,0.00078883383,0.00024242878,0.48262182,0.0006339504,0.49231556,0.017625464,0.00017258551],"about_ca_topic_score_codex":0.0026754695,"about_ca_topic_score_gemma":0.0021788948,"teacher_disagreement_score":0.049279027,"about_ca_system_score_codex":0.0017553028,"about_ca_system_score_gemma":0.0028004677,"threshold_uncertainty_score":0.26061547},"labels":[],"label_agreement":null},{"id":"W2012843612","doi":"10.1111/j.1751-5823.2009.00093.x","title":"A General Algorithm for Univariate Stratification","year":2009,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"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; Canada Research Chairs","keywords":"Stratum; Univariate; Stratification (seeds); Mathematics; Statistics; Population; Variable (mathematics); Population stratification; Mathematical optimization; Applied mathematics; Algorithm; Multivariate statistics; Geology; Mathematical analysis","score_opus":0.09225431997941459,"score_gpt":0.4599714786362926,"score_spread":0.36771715865687804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012843612","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":[0.0006750963,0.000048490325,0.99573827,0.00008354304,0.000033129425,0.0001104349,0.00016571792,0.0016476808,0.0014976359],"genre_scores_gemma":[0.019478079,0.00007993284,0.9745447,0.00010277429,0.000050321745,0.0004523842,0.0007032893,0.0005938006,0.0039946185],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970573,0.0009110838,0.00027190382,0.0006374742,0.0007874519,0.00033471908],"domain_scores_gemma":[0.9969902,0.00117116,0.00013982298,0.00077175145,0.00081254705,0.000114495735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00335136,0.0015251427,0.0014473591,0.0020092672,0.0014579028,0.0025555186,0.0026773824,0.0013299177,0.036144912],"category_scores_gemma":[0.012164663,0.00090998795,0.0021971893,0.0023587998,0.00090799987,0.0028028109,0.004117482,0.0022315003,0.015624562],"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":[0.00030772592,0.00011429648,0.0014142465,0.00024505405,0.000092281065,0.00012846767,0.00024720485,0.0674672,0.0024400963,0.1269791,0.02673433,0.77383],"study_design_scores_gemma":[0.0001943226,0.00011875224,0.0005725903,0.00008654277,0.000057502322,0.00022133536,0.00009241363,0.6295168,0.0035807209,0.3172644,0.048240867,0.000053775315],"about_ca_topic_score_codex":0.0039515127,"about_ca_topic_score_gemma":0.0036689811,"teacher_disagreement_score":0.036144912,"about_ca_system_score_codex":0.001460431,"about_ca_system_score_gemma":0.0036196576,"threshold_uncertainty_score":0.120916784},"labels":[],"label_agreement":null},{"id":"W2013385108","doi":"10.1198/jasa.2010.tm09757","title":"Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning","year":2010,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Generalized linear mixed model; Estimator; Likelihood function; Binary data; Random effects model; Restricted maximum likelihood; Mathematics; Quasi-likelihood; Frequentist inference; Computer science; Markov chain Monte Carlo; Generalized linear model; Statistics; Poisson distribution; Bayesian probability; Bayesian inference; Binary number; Count data; Estimation theory","score_opus":0.11425867505733692,"score_gpt":0.43789684397732387,"score_spread":0.323638168919987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013385108","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":[0.0013160467,0.00010578489,0.99794465,0.000219692,0.00001840074,0.000055860048,0.000051103183,0.00007001671,0.00021840371],"genre_scores_gemma":[0.051795613,0.0003036678,0.9455339,0.00030240344,0.00008926677,0.0011472027,0.00031404404,0.000104442675,0.00040960708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.882992,0.103394195,0.0032735667,0.0048875185,0.0048182462,0.0006344749],"domain_scores_gemma":[0.56050026,0.39341488,0.01121588,0.02868749,0.0055080624,0.0006735229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09822444,0.0020877079,0.0037113624,0.0044264654,0.002241716,0.0046475753,0.006336994,0.0040026857,0.0035245756],"category_scores_gemma":[0.40062046,0.00202444,0.0052115973,0.0054955985,0.008031869,0.008153896,0.009625861,0.008515417,0.00083331333],"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":[0.00013580926,0.000080192825,0.0046505188,0.0004650015,0.00045105608,0.00031387212,0.0009292871,0.050482098,0.00047567475,0.8571988,0.0015816438,0.083236024],"study_design_scores_gemma":[0.00008967384,0.00006739783,0.00057699706,0.00016645937,0.00010906236,0.00021835626,0.0000985644,0.2293388,0.00090994354,0.764832,0.0035368132,0.00005598058],"about_ca_topic_score_codex":0.0032242064,"about_ca_topic_score_gemma":0.0025152876,"teacher_disagreement_score":0.09822444,"about_ca_system_score_codex":0.0025789973,"about_ca_system_score_gemma":0.0041216584,"threshold_uncertainty_score":0.5194667},"labels":[],"label_agreement":null},{"id":"W2013623941","doi":"10.1002/cjs.5550360206","title":"Local influence in multilevel models","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multilevel model; Computation; Measure (data warehouse); Random effects model; Econometrics; Mathematics; Simple (philosophy); Statistics; Regression analysis; Matrix (chemical analysis); Regression; Computer science; Algorithm; Data mining","score_opus":0.09810041417147447,"score_gpt":0.3278599594412723,"score_spread":0.2297595452697978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013623941","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":[0.08832409,0.0014484841,0.9016031,0.0010489082,0.00006309998,0.00007514232,0.00011819922,0.00020139797,0.0071175625],"genre_scores_gemma":[0.9433809,0.00071550516,0.052361198,0.00023373181,0.00014823637,0.00014624122,0.00010953765,0.00011227716,0.0027925137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9801618,0.014558628,0.00038254843,0.002139185,0.0019938448,0.00076398684],"domain_scores_gemma":[0.8990806,0.08514643,0.0063532568,0.0055771004,0.002747616,0.0010950132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015493448,0.00095408555,0.0026674708,0.0022989265,0.0016062907,0.0026126127,0.0022869753,0.0020795956,0.00404837],"category_scores_gemma":[0.0737555,0.0009545507,0.0030704483,0.00223615,0.0042895786,0.0028750037,0.0050638886,0.0034438544,0.00042077995],"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":[0.00025848334,0.00009837698,0.016947389,0.00043325854,0.0011892116,0.0010345705,0.0020582634,0.43525755,0.0017246172,0.50019723,0.0018881878,0.038912844],"study_design_scores_gemma":[0.00003634756,0.00013654151,0.004516008,0.00010480498,0.00028021575,0.0001733245,0.00022464097,0.62467027,0.00069290464,0.36673892,0.002369431,0.00005655018],"about_ca_topic_score_codex":0.007961302,"about_ca_topic_score_gemma":0.0077865543,"teacher_disagreement_score":0.015493448,"about_ca_system_score_codex":0.002531263,"about_ca_system_score_gemma":0.0009779106,"threshold_uncertainty_score":0.08193821},"labels":[],"label_agreement":null},{"id":"W2014057084","doi":"10.1016/j.csda.2010.08.010","title":"Analyzing dependent proportions in cluster randomized trials: Modeling inter-cluster correlation via copula function","year":2010,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Northern British Columbia","funders":"","keywords":"Copula (linguistics); Mathematics; Correlation; Cluster (spacecraft); Statistics; Econometrics; Computer science","score_opus":0.12220970426581963,"score_gpt":0.4206989497888344,"score_spread":0.29848924552301476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014057084","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":[0.013022396,0.00042466805,0.98557615,0.00025920005,0.000044796405,0.00024068986,0.00007140132,0.00014729852,0.00021341271],"genre_scores_gemma":[0.44640926,0.001217483,0.5461045,0.0007011768,0.00022196816,0.002682637,0.00047433455,0.00046217977,0.0017265397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9055906,0.079914324,0.0018488398,0.007842207,0.0037865648,0.0010175479],"domain_scores_gemma":[0.46954486,0.49368548,0.011890139,0.01838119,0.004957888,0.0015404514],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12389893,0.0024780899,0.007446444,0.0031556785,0.0011746493,0.003821384,0.007087413,0.0048408937,0.0029465817],"category_scores_gemma":[0.35199514,0.0023945086,0.0041173194,0.0044335,0.005589395,0.005374979,0.0042878296,0.0066794367,0.0006281733],"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":[0.0022548109,0.00048527753,0.01167149,0.0010313602,0.003926677,0.0004076058,0.0014714848,0.68072844,0.0011480385,0.179748,0.0034019006,0.11372486],"study_design_scores_gemma":[0.00014821537,0.00015751916,0.0012431515,0.00008052906,0.00034386944,0.000087068365,0.000051794126,0.9150831,0.00043801247,0.08180801,0.0005163136,0.000042376305],"about_ca_topic_score_codex":0.0038780575,"about_ca_topic_score_gemma":0.00240385,"teacher_disagreement_score":0.8761011,"about_ca_system_score_codex":0.0025207696,"about_ca_system_score_gemma":0.004519634,"threshold_uncertainty_score":0.65524805},"labels":[],"label_agreement":null},{"id":"W2014601668","doi":"10.1080/00220973.2014.919569","title":"Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials","year":2014,"lang":"en","type":"article","venue":"The Journal of Experimental Education","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université du Québec à Montréal","funders":"","keywords":"Psychology; Cluster (spacecraft); Mediation; Statistics; Sample size determination; Econometrics; Mathematics; Computer science","score_opus":0.05754630073440449,"score_gpt":0.4567522910505695,"score_spread":0.39920599031616505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014601668","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":[0.001964167,0.001815966,0.9821844,0.0009784339,0.0011059215,0.005042725,0.002172571,0.0014705153,0.0032653974],"genre_scores_gemma":[0.03955348,0.0008658294,0.89786327,0.0010930839,0.00042152187,0.05717254,0.0010730464,0.0010643912,0.0008929598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.56250846,0.3700908,0.029785411,0.010884672,0.025354685,0.0013758534],"domain_scores_gemma":[0.2496608,0.6619286,0.026009029,0.045055766,0.016480215,0.0008655545],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.28544164,0.003083352,0.0049242927,0.009074732,0.0017470889,0.0041880845,0.0054737506,0.004947709,0.03743432],"category_scores_gemma":[0.6897496,0.0021291573,0.009818542,0.009653399,0.005058179,0.0065841996,0.0054830797,0.011771041,0.00345082],"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":[0.0031883852,0.0004914635,0.007398016,0.015344918,0.01055751,0.00039231274,0.0030521573,0.014132124,0.0015360184,0.5007312,0.060156833,0.38301897],"study_design_scores_gemma":[0.0056374744,0.0044629555,0.01170788,0.007870916,0.0063195545,0.0010604043,0.0008270603,0.05077302,0.0067707784,0.7695451,0.13434802,0.0006768125],"about_ca_topic_score_codex":0.0011273012,"about_ca_topic_score_gemma":0.00088764256,"teacher_disagreement_score":0.71455836,"about_ca_system_score_codex":0.003134624,"about_ca_system_score_gemma":0.004954173,"threshold_uncertainty_score":0.88117766},"labels":[],"label_agreement":null},{"id":"W2015303556","doi":"10.2307/3315963","title":"Conjugate analysis of multivariate normal data with incomplete observations","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Conjugate prior; Gibbs sampling; Prior probability; Multivariate normal distribution; Normal-Wishart distribution; Mathematics; Inverse-Wishart distribution; Conjugate; Multivariate statistics; Statistics; Posterior probability; Matrix t-distribution; Sampling (signal processing); Applied mathematics; Computer science; Bayesian probability; Mathematical analysis","score_opus":0.13844204255421785,"score_gpt":0.3470553374672173,"score_spread":0.20861329491299943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015303556","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":[0.017839517,0.00013972248,0.98084474,0.00022808646,0.00003177035,0.000038942217,0.00007913134,0.0000744587,0.000723544],"genre_scores_gemma":[0.65940434,0.0008883279,0.3319978,0.0003642347,0.00026350975,0.00024462878,0.00072588475,0.00023762912,0.005873631],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923124,0.0044868235,0.00026378685,0.001217123,0.001381973,0.00033782737],"domain_scores_gemma":[0.95118695,0.036573395,0.003715115,0.0045779548,0.0033053868,0.0006411312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01694867,0.00094339537,0.0022217827,0.0025616467,0.00063771795,0.002648078,0.0027607083,0.0017429746,0.0034720947],"category_scores_gemma":[0.06849991,0.0012968798,0.0017311068,0.0017879996,0.003927751,0.0042137466,0.0032633427,0.0025968195,0.00050322426],"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":[0.000349237,0.00012635216,0.0041150586,0.00023944177,0.00029972725,0.0004418463,0.000359541,0.40184113,0.0028860853,0.5369471,0.0019413708,0.050453123],"study_design_scores_gemma":[0.000029105884,0.000036090994,0.0009782223,0.000033304565,0.000029049479,0.00007159497,0.000029905483,0.78966945,0.0013859927,0.20663428,0.0010625969,0.000040471416],"about_ca_topic_score_codex":0.0017877988,"about_ca_topic_score_gemma":0.0012373055,"teacher_disagreement_score":0.01694867,"about_ca_system_score_codex":0.0017214294,"about_ca_system_score_gemma":0.0015306162,"threshold_uncertainty_score":0.08963418},"labels":[],"label_agreement":null},{"id":"W2015341825","doi":"10.1007/s12561-010-9031-0","title":"Cox Regression with Covariates Missing Not at Random","year":2010,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; BC Centre for Disease Control; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Public Health Agency","keywords":"Covariate; Proportional hazards model; Missing data; Estimation; Econometrics; Statistics; Biostatistics; Computer science; Regression; Event (particle physics); Regression analysis; Data mining; Mathematics; Public health; Engineering; Medicine","score_opus":0.051099391357166526,"score_gpt":0.38576229698868225,"score_spread":0.33466290563151574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015341825","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":[0.0111632515,0.003616509,0.97731704,0.0041745505,0.0007121685,0.000119908254,0.0016109021,0.0003256586,0.0009600719],"genre_scores_gemma":[0.545195,0.009756204,0.40763092,0.0034647856,0.0038991943,0.0025341152,0.0041865394,0.00033481888,0.022998383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9782004,0.015527117,0.00096460595,0.0025760827,0.001968759,0.0007631323],"domain_scores_gemma":[0.8556361,0.116129525,0.005900452,0.019128963,0.0024183958,0.00078647106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04621983,0.0016620005,0.0045510107,0.0018128805,0.0010683861,0.0028208,0.0049483073,0.0039362297,0.0068266704],"category_scores_gemma":[0.15403526,0.0018701578,0.0023716076,0.004102486,0.0030736174,0.0058393436,0.0020626434,0.007043127,0.0013925014],"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":[0.0012925094,0.00017503754,0.016216356,0.0010604667,0.0015076743,0.0011354807,0.0005031897,0.044601273,0.00048384562,0.8257819,0.0155696785,0.091672555],"study_design_scores_gemma":[0.00035241776,0.00016414508,0.0028460799,0.00018052344,0.0006233665,0.00062450656,0.00006927298,0.13943289,0.0005092895,0.84491277,0.010205398,0.00007936477],"about_ca_topic_score_codex":0.0046847565,"about_ca_topic_score_gemma":0.0041742744,"teacher_disagreement_score":0.04621983,"about_ca_system_score_codex":0.0014239243,"about_ca_system_score_gemma":0.0051181074,"threshold_uncertainty_score":0.24443674},"labels":[],"label_agreement":null},{"id":"W2015353734","doi":"10.1007/s10651-011-0186-8","title":"Inference about the ratio of means from Negative Binomial paired count data","year":2011,"lang":"en","type":"article","venue":"Environmental and Ecological Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"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 Saskatchewan; Memorial University of Newfoundland; Fisheries and Oceans Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Negative binomial distribution; Statistics; Quasi-likelihood; Mathematics; Binomial proportion confidence interval; Binomial distribution; Poisson distribution; Overdispersion; Negative multinomial distribution; Estimator; Beta-binomial distribution; Confidence interval; Econometrics","score_opus":0.1487085357037323,"score_gpt":0.321909832739767,"score_spread":0.17320129703603468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015353734","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":[0.010986611,0.00022952423,0.98746735,0.00022925384,0.00007017118,0.000035832378,0.00017183342,0.00020929814,0.00060013065],"genre_scores_gemma":[0.33525512,0.0007810291,0.65772396,0.0006663304,0.00073801744,0.0006574846,0.0016799307,0.00034792692,0.0021501665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95816326,0.027944528,0.0017650691,0.0068227206,0.004672906,0.00063147856],"domain_scores_gemma":[0.76758593,0.20744784,0.008444986,0.012711099,0.0029722098,0.0008378787],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04721272,0.0014598005,0.003637229,0.0046310765,0.001176293,0.004626816,0.005504709,0.0036089334,0.004434144],"category_scores_gemma":[0.21976095,0.001873282,0.0027360246,0.003057805,0.006718733,0.008304516,0.0029641504,0.0058736578,0.0012987333],"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":[0.0011416905,0.00044138945,0.022035215,0.001145763,0.0014438069,0.00075584813,0.00093560515,0.08722502,0.0066648806,0.57807446,0.005879221,0.29425713],"study_design_scores_gemma":[0.0000978164,0.00011047011,0.0033597643,0.00009582617,0.00012946314,0.0007076579,0.00009310077,0.2029497,0.0020478829,0.78865606,0.0016855705,0.000066704735],"about_ca_topic_score_codex":0.0005870549,"about_ca_topic_score_gemma":0.00066988176,"teacher_disagreement_score":0.9527873,"about_ca_system_score_codex":0.0011066715,"about_ca_system_score_gemma":0.0012632344,"threshold_uncertainty_score":0.24968773},"labels":[],"label_agreement":null},{"id":"W2015678583","doi":"10.1186/1742-7622-10-14","title":"Log-binomial models: exploring failed convergence","year":2013,"lang":"en","type":"article","venue":"Emerging Themes in Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":150,"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; Queen's University","funders":"Alberta Innovates","keywords":"Binomial (polynomial); Quasi-likelihood; Convergence (economics); Log-linear model; Binomial distribution; Statistics; Metric (unit); Negative binomial distribution; Function (biology); Mathematics; Set (abstract data type); Econometrics; Binary number; Computer science; Linear model","score_opus":0.3515251721728177,"score_gpt":0.42190606145362763,"score_spread":0.07038088928080993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015678583","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":[0.017239312,0.0011603329,0.97662944,0.0020380402,0.00009522159,0.00014760971,0.00013102787,0.00029445364,0.002264643],"genre_scores_gemma":[0.34481198,0.0023036296,0.6444727,0.0016747456,0.0003877014,0.0015634361,0.0005896693,0.0005395696,0.0036565643],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94788414,0.042907063,0.0015760445,0.0030037959,0.0037962995,0.00083255995],"domain_scores_gemma":[0.4605619,0.51271814,0.010205085,0.007965025,0.0073719886,0.0011779229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11283659,0.001888432,0.0037204304,0.0039875465,0.0017601319,0.0042396905,0.006302193,0.0044701854,0.0060124006],"category_scores_gemma":[0.36863008,0.0015604857,0.0036815107,0.0031823593,0.0060517313,0.0072447644,0.0050888583,0.008056037,0.0009504895],"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":[0.0003118236,0.00013123374,0.017806645,0.00088182074,0.00058325275,0.001142975,0.002284605,0.29498303,0.00046310417,0.6208274,0.0052103326,0.055373803],"study_design_scores_gemma":[0.000050626157,0.000080217535,0.00086913904,0.00026660727,0.0000650137,0.00031765676,0.00018336147,0.5593117,0.00024643153,0.43584335,0.0027161138,0.0000498659],"about_ca_topic_score_codex":0.0061586746,"about_ca_topic_score_gemma":0.0037411156,"teacher_disagreement_score":0.11283659,"about_ca_system_score_codex":0.003441126,"about_ca_system_score_gemma":0.0030134832,"threshold_uncertainty_score":0.59674406},"labels":[],"label_agreement":null},{"id":"W2016120509","doi":"10.2307/3315998","title":"Analyzing multivariate longitudinal binary data: A generalized estimating equations approach","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"Carleton University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Generalized estimating equation; Multivariate statistics; Random effects model; Binary data; Binary number; Statistics; Variance (accounting); Mathematics; Estimating equations; Longitudinal data; Applied mathematics; Mixed model; Maximum likelihood; Computer science; Econometrics; Data mining","score_opus":0.19332790836125138,"score_gpt":0.3871777933494236,"score_spread":0.1938498849881722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016120509","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":[0.0056059663,0.00052823126,0.9925884,0.000510252,0.00004470849,0.00008283527,0.00021107288,0.00015323068,0.00027523583],"genre_scores_gemma":[0.12223632,0.001271205,0.8734454,0.00024968828,0.00017946833,0.0008213534,0.0008093957,0.000091437156,0.0008957745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9693731,0.027420394,0.0005750132,0.0011342465,0.0011649489,0.00033224557],"domain_scores_gemma":[0.95283717,0.040997434,0.0022032142,0.0023961938,0.0013281577,0.00023774977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021741204,0.0015286278,0.0026971162,0.0035125064,0.00080943306,0.0018519502,0.0038749857,0.0018753132,0.002715582],"category_scores_gemma":[0.068101734,0.001197165,0.0027637149,0.004903872,0.001247662,0.0017706014,0.0026295534,0.0031616027,0.0006113633],"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":[0.00020570385,0.00035399315,0.020936683,0.0007090346,0.004105106,0.0005536468,0.0006585232,0.3741947,0.0010297196,0.34509465,0.007873742,0.24428457],"study_design_scores_gemma":[0.000118268086,0.00012554874,0.0027335002,0.000108864755,0.0003260113,0.0000872982,0.00009273289,0.76112586,0.00030072834,0.23084684,0.004072571,0.000061720304],"about_ca_topic_score_codex":0.013342575,"about_ca_topic_score_gemma":0.010283772,"teacher_disagreement_score":0.021741204,"about_ca_system_score_codex":0.0014046644,"about_ca_system_score_gemma":0.0025870206,"threshold_uncertainty_score":0.1149798},"labels":[],"label_agreement":null},{"id":"W2016895228","doi":"10.1007/bf02595715","title":"On the probability of a model","year":2002,"lang":"en","type":"article","venue":"Test","topic":"Statistical Methods and Bayesian Inference","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":"University of the Fraser Valley; Simon Fraser University","funders":"","keywords":"Mathematics; Bayesian information criterion; Akaike information criterion; Prior probability; Applied mathematics; Statistics; Geometric distribution; Statistic; Truncation (statistics); Bayesian linear regression; Bayesian probability; Bayesian inference; Probability distribution","score_opus":0.23398621401155506,"score_gpt":0.371325601320931,"score_spread":0.13733938730937595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016895228","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":[0.01310265,0.0010668044,0.97934765,0.003364211,0.0001734071,0.00008030725,0.00024427095,0.00018785537,0.0024328788],"genre_scores_gemma":[0.6011278,0.003858111,0.38102132,0.0027855597,0.0027197986,0.001607955,0.001245053,0.0003131369,0.0053212843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9484006,0.03883288,0.0014109897,0.0053631254,0.0049402383,0.001052292],"domain_scores_gemma":[0.44655198,0.53022325,0.006673761,0.011742645,0.0030716755,0.0017367144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06192035,0.0022879534,0.0063227233,0.0075310254,0.002440108,0.0067853187,0.0074875765,0.008177686,0.011272906],"category_scores_gemma":[0.30214736,0.0017823356,0.0037279425,0.0052033025,0.023989262,0.013823166,0.006826221,0.011738758,0.00098874],"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":[0.00021635868,0.000054596625,0.002371555,0.00028821413,0.0003019876,0.0002687891,0.0002530481,0.028577235,0.00020668296,0.9442643,0.0018408342,0.021356318],"study_design_scores_gemma":[0.000074057716,0.000051717976,0.00039426694,0.00010329306,0.000072162875,0.00017451838,0.00004395384,0.095256396,0.0001889783,0.9026704,0.00093856093,0.000031621286],"about_ca_topic_score_codex":0.0033689283,"about_ca_topic_score_gemma":0.0013708724,"teacher_disagreement_score":0.06192035,"about_ca_system_score_codex":0.0033834744,"about_ca_system_score_gemma":0.0036865952,"threshold_uncertainty_score":0.32747},"labels":[],"label_agreement":null},{"id":"W2016919144","doi":"10.1002/sim.2711","title":"Bayesian sensitivity analysis for unmeasured confounding in observational studies","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":184,"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":"","keywords":"Confounding; Statistics; Markov chain Monte Carlo; Bayesian probability; Observational study; Econometrics; Logistic regression; Prior probability; Sample size determination; Sensitivity (control systems); Mathematics; Computer science","score_opus":0.2203225194206481,"score_gpt":0.47620137534415125,"score_spread":0.2558788559235031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016919144","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":[0.017517373,0.0023400553,0.97508085,0.002014596,0.0001175295,0.00042277988,0.00022816389,0.00010730737,0.0021713565],"genre_scores_gemma":[0.739243,0.0029514662,0.25300166,0.0013037664,0.00029625627,0.001983278,0.00026676024,0.00009065766,0.0008630661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83659905,0.15007588,0.0025557834,0.0034951828,0.006378083,0.00089601474],"domain_scores_gemma":[0.39808345,0.5747087,0.011629346,0.010942355,0.0040790658,0.0005570442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16287005,0.0013851692,0.0032669397,0.0040063187,0.0010066346,0.0031571668,0.0030281025,0.0035227756,0.0028960784],"category_scores_gemma":[0.48156366,0.00096366165,0.0036800893,0.0030213213,0.003157869,0.0033721623,0.0036115563,0.0036136105,0.00017849226],"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":[0.00067086006,0.00009664701,0.010567918,0.0015351665,0.0034358467,0.00091787387,0.00076006877,0.5666932,0.0006471625,0.35674554,0.002211173,0.055718504],"study_design_scores_gemma":[0.00018851893,0.00017490724,0.002877858,0.00048777644,0.0008702548,0.00036965837,0.00011552694,0.5058632,0.0005926207,0.48534966,0.003016271,0.000093805655],"about_ca_topic_score_codex":0.0039459257,"about_ca_topic_score_gemma":0.001653432,"teacher_disagreement_score":0.16287005,"about_ca_system_score_codex":0.0030125924,"about_ca_system_score_gemma":0.002308714,"threshold_uncertainty_score":0.86134946},"labels":[],"label_agreement":null},{"id":"W2016979665","doi":"10.1080/01621459.2000.10474271","title":"Bayesian Regression Modeling with Interactions and Smooth Effects","year":2000,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Computer science; Bayesian linear regression; Bayesian probability; Interpretation (philosophy); Machine learning; Regression; Computation; Artificial intelligence; Bivariate analysis; Model selection; Regression analysis; Bayesian inference; Econometrics; Data mining; Algorithm; Mathematics; Statistics","score_opus":0.017252388416330745,"score_gpt":0.3433515887195779,"score_spread":0.3260992003032471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016979665","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":[0.023398109,0.0006095719,0.9735449,0.000503394,0.00004984563,0.000038988663,0.000122125,0.0002556345,0.0014773703],"genre_scores_gemma":[0.685663,0.0011411145,0.30572513,0.00033669267,0.0002125879,0.00031006933,0.00036426168,0.00018309365,0.006064084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908647,0.0064731366,0.00022349418,0.001022081,0.0010245242,0.00039201186],"domain_scores_gemma":[0.9694101,0.024326704,0.0026914538,0.0018809689,0.0011516656,0.00053902995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014752652,0.0012415164,0.0024106519,0.0019895572,0.00078648725,0.0022459882,0.002392002,0.0022973577,0.0055286908],"category_scores_gemma":[0.04265577,0.0010092696,0.0021608954,0.0026544868,0.002963842,0.0028631778,0.0027923312,0.0039164713,0.0007563243],"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":[0.0003724966,0.0001460987,0.007452515,0.00021107888,0.0005245029,0.0003408091,0.0004139431,0.4335341,0.0015618899,0.49193457,0.0018643907,0.061643686],"study_design_scores_gemma":[0.000056126635,0.00009602836,0.0019722935,0.000054747146,0.00013271769,0.00006397319,0.00003962659,0.6494,0.00026094008,0.345551,0.0023186887,0.000053853677],"about_ca_topic_score_codex":0.008406886,"about_ca_topic_score_gemma":0.006991939,"teacher_disagreement_score":0.014752652,"about_ca_system_score_codex":0.0010699389,"about_ca_system_score_gemma":0.0012919682,"threshold_uncertainty_score":0.07802045},"labels":[],"label_agreement":null},{"id":"W2017784796","doi":"10.1002/cjs.10078","title":"Mean squared error estimators of small area means using survey weights","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Carleton University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mean squared error; Estimator; Mathematics; Statistics; Small area estimation; Bias of an estimator; Best linear unbiased prediction; Efficient estimator; Consistency (knowledge bases); Minimum-variance unbiased estimator; Econometrics; Computer science; Discrete mathematics; Artificial intelligence","score_opus":0.12846755018034683,"score_gpt":0.34432871596177717,"score_spread":0.21586116578143033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017784796","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":[0.015706388,0.00033446477,0.9825449,0.0001589992,0.000046551937,0.000062489846,0.000164037,0.00015132145,0.0008309571],"genre_scores_gemma":[0.43345731,0.0006535532,0.56098205,0.00020150938,0.0001609904,0.0006431159,0.0009832854,0.00011560128,0.002802586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9721248,0.022099596,0.0007193161,0.0026058883,0.0020918413,0.00035856038],"domain_scores_gemma":[0.9091461,0.06969776,0.005583076,0.008793854,0.0063541355,0.00042503257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028073585,0.00075698266,0.0016756329,0.0024744656,0.0004161155,0.0016783791,0.0019178658,0.0012804064,0.0023283781],"category_scores_gemma":[0.14818455,0.00074115087,0.0010744429,0.004315784,0.0015266605,0.002707183,0.0020450836,0.0017446447,0.00053549896],"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":[0.00031377378,0.00013959447,0.034045275,0.00041007344,0.0011406746,0.000105629,0.00044821415,0.4745004,0.0008622532,0.21276124,0.0066405674,0.2686323],"study_design_scores_gemma":[0.00009141251,0.00015718421,0.010379874,0.00014135109,0.000113985276,0.0000801784,0.00011386544,0.79544824,0.0011571897,0.18547964,0.006771011,0.000066105145],"about_ca_topic_score_codex":0.0073370906,"about_ca_topic_score_gemma":0.005167226,"teacher_disagreement_score":0.028073585,"about_ca_system_score_codex":0.001215407,"about_ca_system_score_gemma":0.0014793003,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2017916848","doi":"10.1080/03610920902871453","title":"Predictive Inference from a Two-Parameter Rayleigh Life Model Given a Doubly Censored Sample","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Western University","funders":"","keywords":"Hyperparameter; Predictive inference; Inference; Bayesian inference; Conjugate prior; Scale parameter; Mathematics; Statistics; Bayesian probability; Sample (material); Applied mathematics; Computer science; Prior probability; Frequentist inference; Algorithm; Artificial intelligence; Physics","score_opus":0.08132746619311328,"score_gpt":0.4560546645984665,"score_spread":0.37472719840535323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017916848","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":[0.044731952,0.000290885,0.95277214,0.00052758056,0.000029973495,0.000097254255,0.00020249744,0.00015332055,0.0011944729],"genre_scores_gemma":[0.8031672,0.0011232381,0.1893176,0.00048396608,0.00023827916,0.0005155668,0.0008873939,0.000094335155,0.004172371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99170154,0.005396553,0.00028580357,0.0012299933,0.0009982691,0.00038776407],"domain_scores_gemma":[0.928076,0.06248966,0.003112671,0.0041628755,0.0016461422,0.00051255245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026886206,0.0010962334,0.0025949564,0.0018545337,0.00058012985,0.0024385091,0.0028411082,0.0020539947,0.0031526166],"category_scores_gemma":[0.081519894,0.0008353819,0.0017171386,0.0014820788,0.0044196118,0.0048185503,0.002323661,0.0036239056,0.00059825723],"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":[0.00049462967,0.00012560078,0.009091239,0.0002059885,0.00027289146,0.00075302355,0.0007464812,0.5512132,0.0013988123,0.3911241,0.001445599,0.043128464],"study_design_scores_gemma":[0.000037359678,0.00007412851,0.0014247465,0.00005235192,0.000041663505,0.00014394027,0.000087848726,0.828115,0.0006301441,0.16876659,0.0005749424,0.000051206047],"about_ca_topic_score_codex":0.0026927781,"about_ca_topic_score_gemma":0.0019333664,"teacher_disagreement_score":0.026886206,"about_ca_system_score_codex":0.0014671261,"about_ca_system_score_gemma":0.0010123898,"threshold_uncertainty_score":0.14218956},"labels":[],"label_agreement":null},{"id":"W2018795457","doi":"10.3758/bf03206555","title":"Fitting distributions using maximum likelihood: Methods and packages","year":2004,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":167,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Weibull distribution; Log-normal distribution; Gumbel distribution; Mathematics; Statistics; Estimator; Gaussian; Applied mathematics; Algorithm; Computer science; Extreme value theory","score_opus":0.2979135059293795,"score_gpt":0.5754612622245866,"score_spread":0.2775477562952071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018795457","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":[0.00020719017,0.000063600004,0.99760264,0.000063879925,0.000010563056,0.00004401525,0.00018584775,0.0015845688,0.00023772442],"genre_scores_gemma":[0.0062127733,0.00017170228,0.9905434,0.00004785137,0.00002751993,0.0005331689,0.00041617293,0.0014278351,0.00061969087],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9934383,0.0045993035,0.00033090674,0.0005809375,0.00090575666,0.0001448919],"domain_scores_gemma":[0.9702533,0.024736825,0.0008370364,0.0024720074,0.0014435354,0.00025727923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012723756,0.0022810488,0.002956723,0.0035386144,0.0012642316,0.0033377572,0.0055792797,0.0030464805,0.02715645],"category_scores_gemma":[0.06467499,0.0029259638,0.002798471,0.004526506,0.0013831325,0.0042671524,0.0035194363,0.0050323918,0.010912873],"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":[0.00034712703,0.0004477701,0.0015787419,0.001442115,0.0007317618,0.00018747935,0.0008255671,0.15090366,0.0016332704,0.23660155,0.06090335,0.5443976],"study_design_scores_gemma":[0.00014331253,0.00003653579,0.0005092445,0.00020848619,0.00012288356,0.00022252528,0.00007924847,0.5874226,0.0026666361,0.37626463,0.032201856,0.00012206888],"about_ca_topic_score_codex":0.0029729882,"about_ca_topic_score_gemma":0.0032123346,"teacher_disagreement_score":0.02715645,"about_ca_system_score_codex":0.0010028501,"about_ca_system_score_gemma":0.0028165034,"threshold_uncertainty_score":0.09084737},"labels":[],"label_agreement":null},{"id":"W2019346058","doi":"10.1002/cjs.11144","title":"Likelihood‐based and marginal inference methods for recurrent event data with covariate measurement error","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Inference; Statistics; Event (particle physics); Statistical inference; Observational error; Econometrics; Computer science; Mathematics; Artificial intelligence","score_opus":0.2729706254577828,"score_gpt":0.44539463999981743,"score_spread":0.17242401454203465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019346058","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":[0.0020934914,0.0002920593,0.9968407,0.00025726526,0.000027765547,0.00005053578,0.00008919529,0.00007732103,0.00027150323],"genre_scores_gemma":[0.1633021,0.0013768254,0.8302244,0.00032350398,0.0002989584,0.0007812743,0.00069707405,0.00018986486,0.0028059913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97857326,0.016433004,0.0007932972,0.0019726467,0.001841716,0.00038604208],"domain_scores_gemma":[0.8295857,0.15060809,0.006905849,0.007676635,0.0042078993,0.001015833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042230614,0.0011860444,0.002563714,0.0032199793,0.0007181872,0.002092006,0.005355851,0.0019352547,0.005872466],"category_scores_gemma":[0.18666384,0.0010587015,0.0030530249,0.0032181663,0.0039992807,0.004313287,0.004223149,0.0047476976,0.00082018017],"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":[0.00019590632,0.00013841652,0.0067486144,0.000449064,0.0005272996,0.0003013329,0.00065245613,0.11352666,0.0005267031,0.7426759,0.0035597375,0.13069801],"study_design_scores_gemma":[0.00006174232,0.000066320245,0.0016719202,0.00010529228,0.00011278354,0.00012307719,0.00006188332,0.45099616,0.00041956158,0.5429694,0.0033632598,0.00004864273],"about_ca_topic_score_codex":0.0063511035,"about_ca_topic_score_gemma":0.005749314,"teacher_disagreement_score":0.042230614,"about_ca_system_score_codex":0.002410575,"about_ca_system_score_gemma":0.003789756,"threshold_uncertainty_score":0.22333956},"labels":[],"label_agreement":null},{"id":"W2019790321","doi":"10.1111/1467-9469.00244","title":"Approximate Inference for the Factor Loading of a Simple Factor Analysis Model","year":2001,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Inference; Factor (programming language); Statistic; Simple (philosophy); Measure (data warehouse); Statistics; Factor analysis; Likelihood-ratio test; Maximum likelihood; Applied mathematics; Computer science; Data mining; Artificial intelligence","score_opus":0.1083295081046766,"score_gpt":0.40346843658106113,"score_spread":0.2951389284763845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019790321","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":[0.018354408,0.00013938828,0.9805762,0.000234634,0.000026284064,0.000042081003,0.000054041324,0.00008819636,0.00048483306],"genre_scores_gemma":[0.5008158,0.000935278,0.49370736,0.00025540663,0.00021565375,0.0006244655,0.0005087832,0.00015380423,0.0027833886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99141145,0.00598246,0.00028179563,0.0010488136,0.00093703804,0.0003384267],"domain_scores_gemma":[0.9000783,0.090160444,0.0036991169,0.003698757,0.0018639889,0.0004994731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02042741,0.001258761,0.001969296,0.0016116197,0.0007891381,0.002300188,0.002194641,0.0026067446,0.0042235223],"category_scores_gemma":[0.13163625,0.0013224285,0.0016786252,0.0023371063,0.002650105,0.004352682,0.0020306585,0.003132506,0.0009899867],"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":[0.00026519716,0.00016633545,0.005483012,0.00047346007,0.00037790192,0.00044197313,0.00069959555,0.57207775,0.001798312,0.3436907,0.0026540805,0.07187169],"study_design_scores_gemma":[0.000045374345,0.000039389895,0.00068303995,0.00003067653,0.00002963242,0.00007522775,0.000044910597,0.8037755,0.0002578191,0.19447795,0.00051332795,0.000027234279],"about_ca_topic_score_codex":0.0039176065,"about_ca_topic_score_gemma":0.0028827884,"teacher_disagreement_score":0.02042741,"about_ca_system_score_codex":0.001262084,"about_ca_system_score_gemma":0.0017497662,"threshold_uncertainty_score":0.10803175},"labels":[],"label_agreement":null},{"id":"W2019928099","doi":"10.1016/s0895-4356(01)00433-4","title":"Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses","year":2002,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":132,"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; University of Calgary","funders":"","keywords":"Missing data; Observational study; Imputation (statistics); Computer science; Statistics; Data mining; Medicine; Mathematics","score_opus":0.932162341780281,"score_gpt":0.7061193995434054,"score_spread":0.2260429422368756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019928099","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":[0.006634646,0.0013000863,0.9900502,0.0011675513,0.0001295482,0.00015434607,0.00014661995,0.00022347749,0.00019368254],"genre_scores_gemma":[0.122948594,0.0010628228,0.8733006,0.0005975506,0.0004464883,0.00081221043,0.0002520473,0.00013703298,0.00044260427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8692313,0.11828533,0.005121592,0.0037145987,0.003138539,0.0005086222],"domain_scores_gemma":[0.53058416,0.42293143,0.012187739,0.0299779,0.0035599095,0.0007587824],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15973292,0.0013469972,0.004049141,0.0019631193,0.00072387076,0.002419965,0.0046905186,0.003193187,0.0028161001],"category_scores_gemma":[0.37422934,0.0017624921,0.004044209,0.003409652,0.002001701,0.0051962114,0.0033627173,0.004945416,0.0003783076],"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":[0.0061399387,0.0006609756,0.026370095,0.005728357,0.0067899628,0.0010463415,0.0021467567,0.08768607,0.0025247715,0.17282695,0.009873476,0.67820626],"study_design_scores_gemma":[0.0018473868,0.0014300258,0.007916071,0.0013023674,0.0037142558,0.0015286774,0.00023694587,0.5039157,0.0060632117,0.46274683,0.009063171,0.00023527576],"about_ca_topic_score_codex":0.00053886935,"about_ca_topic_score_gemma":0.000681324,"teacher_disagreement_score":0.84026706,"about_ca_system_score_codex":0.0005122469,"about_ca_system_score_gemma":0.0024012602,"threshold_uncertainty_score":0.8447585},"labels":[],"label_agreement":null},{"id":"W2020357043","doi":"10.1016/j.csda.2008.06.015","title":"A method for bias-reduction of sample-based MLE of the autologistic model","year":2008,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","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":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Statistics; Mathematics; Row; Sample size determination; Population; Sample (material); Confidence interval; Cluster (spacecraft); Coverage probability; Mean squared error; Computer science; Physics","score_opus":0.3315027015479352,"score_gpt":0.4586701654503985,"score_spread":0.1271674639024633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020357043","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":[0.00029042718,0.00006078159,0.9991924,0.00004180072,0.000031599575,0.000016557791,0.000027377213,0.00022426192,0.000114741095],"genre_scores_gemma":[0.025491651,0.00019789516,0.9715161,0.00022833969,0.00023293076,0.00033187616,0.00028328525,0.0005045638,0.001213305],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99232703,0.004492627,0.00032919866,0.00096150406,0.0016904221,0.00019921412],"domain_scores_gemma":[0.9753829,0.017334947,0.00067711895,0.0038767254,0.0024076153,0.00032073382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01143977,0.0012958476,0.00221936,0.002582477,0.0009889021,0.0017336295,0.0035677606,0.0020519414,0.0055844965],"category_scores_gemma":[0.05755465,0.0012831294,0.002547298,0.0018050685,0.00181665,0.002123835,0.0037575462,0.005174737,0.0020840196],"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":[0.00040032752,0.00021015757,0.0019143755,0.000745738,0.00078195165,0.00028510348,0.00044743062,0.12356764,0.014485037,0.25886056,0.012352151,0.58594954],"study_design_scores_gemma":[0.0000835054,0.00009975881,0.0008223434,0.00008359656,0.00013742331,0.00039529987,0.000029080711,0.79209113,0.006091116,0.18886317,0.011211539,0.00009195525],"about_ca_topic_score_codex":0.0015693763,"about_ca_topic_score_gemma":0.0020241567,"teacher_disagreement_score":0.01143977,"about_ca_system_score_codex":0.00092924945,"about_ca_system_score_gemma":0.0018989687,"threshold_uncertainty_score":0.060500026},"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":"W2021179080","doi":"10.1890/08-0549.1","title":"Bayesian methods for hierarchical models: Are ecologists making a Faustian bargain","year":2009,"lang":"en","type":"letter","venue":"Ecological Applications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"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; Wildlife; Library science; Fish <Actinopterygii>; Computer science; Operations research; Geography; History; Ecology; Artificial intelligence; Mathematics; Fishery; Biology","score_opus":0.15168375102284545,"score_gpt":0.45498548077758094,"score_spread":0.30330172975473546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021179080","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":[0.00024148227,0.0070982696,0.020522263,0.9630727,0.0046635056,0.000006165895,0.000060073933,0.000056461686,0.004279043],"genre_scores_gemma":[0.03808194,0.018473495,0.05176995,0.7649056,0.11199736,0.0002070202,0.00012150167,0.00051479804,0.01392841],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98583883,0.010030938,0.00042275767,0.00093576947,0.0025411537,0.00023064448],"domain_scores_gemma":[0.8305846,0.15402244,0.0020910234,0.005832461,0.0059759123,0.0014934523],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.025314117,0.0009064526,0.0021047657,0.001498152,0.0023620888,0.0059949597,0.0029488225,0.020731851,0.006321711],"category_scores_gemma":[0.13296056,0.0008848183,0.0007930226,0.0013975691,0.021119907,0.018707681,0.0025367578,0.043847367,0.004754205],"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":[0.00005411667,0.00003109966,0.00027434673,0.000121327845,0.000046811572,0.00011508953,0.0002764688,0.0007857142,0.00007397535,0.46895203,0.48647198,0.0427971],"study_design_scores_gemma":[0.000026637616,0.000003689275,0.00010286463,0.00007393279,0.0000046895125,0.00007123981,0.00005801153,0.001612293,0.000032092266,0.93318564,0.06480869,0.0000203029],"about_ca_topic_score_codex":0.0045642112,"about_ca_topic_score_gemma":0.0057231793,"teacher_disagreement_score":0.9746859,"about_ca_system_score_codex":0.0041282633,"about_ca_system_score_gemma":0.0021856283,"threshold_uncertainty_score":0.13387549},"labels":[],"label_agreement":null},{"id":"W2021803007","doi":"10.1214/13-ba824","title":"Hypothesis Assessment and Inequalities for Bayes Factors and Relative Belief Ratios","year":2013,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Bayes factor; Bayes' rule; Bayes' theorem; Bayes error rate; Bayesian probability; Bayesian programming; A priori and a posteriori; Mathematics; Bayesian inference; Econometrics; Statistics; Point estimation; Computer science; Bayes classifier","score_opus":0.07725196141600667,"score_gpt":0.3691396893848857,"score_spread":0.2918877279688791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021803007","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":[0.0018090574,0.0073586428,0.9718469,0.004691975,0.00047327066,0.00012897489,0.00022293002,0.00007056479,0.013397679],"genre_scores_gemma":[0.16824576,0.013307627,0.8012037,0.0038304662,0.0041694846,0.0031043186,0.00048739262,0.00026180665,0.0053894194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9185035,0.05891044,0.0041468395,0.0052255574,0.0118178995,0.0013956577],"domain_scores_gemma":[0.60320204,0.36472136,0.012814534,0.0083957175,0.009723277,0.0011430022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.091993794,0.0028675527,0.0033368673,0.009381948,0.0026070806,0.008540365,0.0049191928,0.00554682,0.011588565],"category_scores_gemma":[0.31335616,0.001298742,0.0034141156,0.008046337,0.01634288,0.016315583,0.0051068515,0.011901747,0.0025008603],"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":[0.000019271303,0.000012414043,0.00025767687,0.00019683687,0.000056643403,0.000058911843,0.0002050828,0.0035657343,0.000082711966,0.97899985,0.0011257455,0.015419221],"study_design_scores_gemma":[0.000011634186,0.000021162803,0.00017946433,0.00019060186,0.000025106536,0.00007084593,0.00003923944,0.007882287,0.00011894414,0.98810285,0.0033359458,0.00002190142],"about_ca_topic_score_codex":0.0034853516,"about_ca_topic_score_gemma":0.0018808705,"teacher_disagreement_score":0.091993794,"about_ca_system_score_codex":0.007125705,"about_ca_system_score_gemma":0.0037449794,"threshold_uncertainty_score":0.48651552},"labels":[],"label_agreement":null},{"id":"W2021864359","doi":"10.1080/15598608.2009.10411957","title":"The Life and Work of Michael A. Stephens: A Conversation with Richard A. Lockhart and John J. Spinelli","year":2009,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia; BC Cancer Agency; Simon Fraser University","funders":"","keywords":"Conversation; Sociology; Communication","score_opus":0.036825672278899345,"score_gpt":0.3559824964881776,"score_spread":0.31915682420927827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021864359","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":[0.0005394929,0.030265726,0.0010607999,0.9596897,0.006194806,0.00000533396,0.000016554557,0.000013288785,0.0022143847],"genre_scores_gemma":[0.048770625,0.09379842,0.0062332223,0.80760735,0.021707602,0.00008311323,0.000043295266,0.00027854147,0.02147786],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98927784,0.007915089,0.00033875828,0.0006574637,0.0014689513,0.00034187507],"domain_scores_gemma":[0.9549533,0.03445215,0.0013962797,0.00068127544,0.004314907,0.0042020967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023569103,0.00088089745,0.0016827143,0.0014257507,0.010823364,0.007670087,0.0018329622,0.008651291,0.0048773796],"category_scores_gemma":[0.046294883,0.0010942833,0.00073738146,0.001999633,0.015443311,0.0138519965,0.0027937058,0.02951146,0.0024266543],"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":[0.000045825098,0.000024634954,0.00032313477,0.00017069578,0.000020776035,0.00040922165,0.01916634,0.00010847291,0.0002299545,0.040400594,0.9249849,0.014115576],"study_design_scores_gemma":[0.000010746188,0.00003096365,0.00051227974,0.00071060087,0.000010990635,0.00070041453,0.017155902,0.00015359338,0.00021157938,0.026825376,0.9535986,0.00007895994],"about_ca_topic_score_codex":0.016541444,"about_ca_topic_score_gemma":0.032213785,"teacher_disagreement_score":0.023569103,"about_ca_system_score_codex":0.0047228364,"about_ca_system_score_gemma":0.0049277027,"threshold_uncertainty_score":0.12464684},"labels":[],"label_agreement":null},{"id":"W2022075199","doi":"10.3758/brm.42.4.957","title":"Estimating the probability and fidelity of memory","year":2010,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Fidelity; Variance (accounting); Probability distribution; Algorithm; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.444713486248623,"score_gpt":0.6297663182607204,"score_spread":0.18505283201209738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022075199","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":[0.21254312,0.0009910592,0.7818425,0.0012980653,0.00006138027,0.00009798722,0.00042565633,0.00020040471,0.002539847],"genre_scores_gemma":[0.9342704,0.0006139945,0.0631065,0.00015456295,0.00009080881,0.00010838344,0.0003573365,0.000041364703,0.0012566962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99351573,0.003689716,0.0003313437,0.001559504,0.00060393434,0.00029973284],"domain_scores_gemma":[0.82975197,0.14688016,0.007527174,0.013060727,0.0020674416,0.0007126119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015266066,0.00070359657,0.0019727985,0.0024942553,0.00066122494,0.0037105053,0.0028922008,0.0027872226,0.0034104977],"category_scores_gemma":[0.17704718,0.00118204,0.0013683954,0.0018315075,0.0036127437,0.007206693,0.0027061878,0.0029248858,0.00036212648],"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":[0.0008845615,0.0003908485,0.08879284,0.00071502384,0.0017979135,0.00028881984,0.0013323454,0.35062414,0.002109773,0.33515212,0.0021772324,0.21573438],"study_design_scores_gemma":[0.00009695524,0.00012377619,0.020045128,0.00016237321,0.00029104794,0.0002897184,0.00017492,0.48196667,0.0013413228,0.49428985,0.0011359344,0.00008220931],"about_ca_topic_score_codex":0.006892066,"about_ca_topic_score_gemma":0.0057437513,"teacher_disagreement_score":0.015266066,"about_ca_system_score_codex":0.0016973725,"about_ca_system_score_gemma":0.0013021127,"threshold_uncertainty_score":0.080735624},"labels":[],"label_agreement":null},{"id":"W2023600974","doi":"10.1016/j.otohns.2010.04.128","title":"S5– Generalizability of results from randomized trials","year":2010,"lang":"en","type":"article","venue":"Otolaryngology","topic":"Statistical Methods and Bayesian 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":"McMaster University","funders":"","keywords":"Generalizability theory; Randomized controlled trial; Psychology; Medicine; Internal medicine; Developmental psychology","score_opus":0.10153657422275163,"score_gpt":0.4057704131418187,"score_spread":0.3042338389190671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023600974","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":[0.012717675,0.25047907,0.55614173,0.06277491,0.017375177,0.043741293,0.0156088285,0.0030333218,0.038128],"genre_scores_gemma":[0.5225725,0.05468136,0.2929589,0.039346248,0.010560583,0.06496507,0.009554058,0.0011064324,0.0042548375],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.17627905,0.6071386,0.13678382,0.021587528,0.056696326,0.0015147313],"domain_scores_gemma":[0.080733724,0.7794868,0.05395949,0.06267583,0.02203615,0.0011081027],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.60344774,0.004370168,0.012389175,0.014339719,0.0017502619,0.008886613,0.0063402974,0.012071787,0.026824469],"category_scores_gemma":[0.83971864,0.0038470128,0.039062824,0.011986952,0.010269562,0.008356371,0.007086011,0.010311974,0.0036826862],"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":[0.009950535,0.0002622598,0.018402321,0.19110765,0.11190692,0.0013009568,0.0027072849,0.008410334,0.0012751542,0.059457578,0.0553421,0.53987694],"study_design_scores_gemma":[0.0131660085,0.005176423,0.03824382,0.15363471,0.0932253,0.0031972402,0.0017274715,0.025121832,0.006745716,0.4885025,0.17030354,0.00095547904],"about_ca_topic_score_codex":0.002558605,"about_ca_topic_score_gemma":0.0021475744,"teacher_disagreement_score":0.39655226,"about_ca_system_score_codex":0.00902517,"about_ca_system_score_gemma":0.008701141,"threshold_uncertainty_score":0.48901957},"labels":[],"label_agreement":null},{"id":"W2023717335","doi":"10.1007/s11336-010-9165-5","title":"A General Family of Limited Information Goodness-of-Fit Statistics for Multinomial Data","year":2010,"lang":"en","type":"article","venue":"Psychometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":52,"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":"Multinomial distribution; Mathematics; Statistics; Goodness of fit; Quadratic equation; Chi-square test; Square (algebra); Binary number; Null hypothesis; Binary data; Statistical hypothesis testing; Applied mathematics; Arithmetic","score_opus":0.2157104244704096,"score_gpt":0.4410081733007281,"score_spread":0.2252977488303185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023717335","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":[0.0064583663,0.00048395497,0.99127746,0.00034243468,0.000035765806,0.000100433375,0.00020335654,0.00025311508,0.00084517465],"genre_scores_gemma":[0.28344366,0.0018011524,0.70736855,0.000973058,0.00037753128,0.0018291832,0.0015732226,0.00039710302,0.002236528],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9746412,0.018490685,0.0011649423,0.0022630512,0.0029801356,0.00045990688],"domain_scores_gemma":[0.88307875,0.09689287,0.0050147604,0.009686967,0.0046039317,0.0007226442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0435574,0.0017179619,0.002824639,0.0057115043,0.0008617793,0.002713887,0.0039136005,0.0026190197,0.0052146213],"category_scores_gemma":[0.1840428,0.0009686167,0.0027586862,0.0056164907,0.004797748,0.0068085925,0.0036479793,0.004823281,0.0017038365],"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":[0.00026283623,0.00016025998,0.012012475,0.00089442375,0.00065988454,0.00062240724,0.0010170403,0.08615431,0.0025519654,0.6329916,0.0069930055,0.2556798],"study_design_scores_gemma":[0.00009427168,0.00027718718,0.004054262,0.00027223295,0.0000931239,0.00086231926,0.00014921218,0.33786324,0.001091806,0.64913774,0.005978186,0.00012641234],"about_ca_topic_score_codex":0.0008162137,"about_ca_topic_score_gemma":0.000675145,"teacher_disagreement_score":0.0435574,"about_ca_system_score_codex":0.0011351986,"about_ca_system_score_gemma":0.0014025852,"threshold_uncertainty_score":0.23035634},"labels":[],"label_agreement":null},{"id":"W2023846227","doi":"10.1111/j.1467-9892.2007.00570.x","title":"GQL Versus Conditional GQL Inferences for Non‐Stationary Time Series of Counts with Overdispersion","year":2008,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overdispersion; Mathematics; Statistics; Autoregressive model; Covariate; Series (stratigraphy); Count data; Econometrics; Negative binomial distribution; Time series; Applied mathematics; Poisson distribution","score_opus":0.032644538385159655,"score_gpt":0.3239532888587129,"score_spread":0.2913087504735532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023846227","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":[0.033611298,0.00017382394,0.9646358,0.00043328595,0.000030119807,0.00007286515,0.0001616786,0.00025934173,0.00062179565],"genre_scores_gemma":[0.72118175,0.00027364914,0.2751561,0.00056339765,0.00011182018,0.00034743125,0.0009466884,0.00014303807,0.001276127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9872421,0.009487398,0.00043659302,0.0014806363,0.0011281642,0.00022508844],"domain_scores_gemma":[0.89221054,0.09559445,0.0043345406,0.005585432,0.0017963883,0.00047854125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02717046,0.00060182763,0.0014258648,0.0019716742,0.00070444425,0.0019431139,0.0030832598,0.0013517278,0.0036459798],"category_scores_gemma":[0.12718856,0.0006857161,0.0016872992,0.0016891886,0.0028862196,0.0045540035,0.0033516672,0.0028289494,0.00040852302],"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":[0.00045079162,0.00024816935,0.022965437,0.0004996211,0.000648224,0.00058090803,0.0010611471,0.30615583,0.0014085856,0.4557655,0.0029635113,0.20725231],"study_design_scores_gemma":[0.00006997598,0.00008998463,0.0033125633,0.000044745477,0.00005571304,0.0001082488,0.00010299585,0.8192179,0.0006328517,0.17496546,0.0013564836,0.00004302055],"about_ca_topic_score_codex":0.006685773,"about_ca_topic_score_gemma":0.005589164,"teacher_disagreement_score":0.02717046,"about_ca_system_score_codex":0.0020350784,"about_ca_system_score_gemma":0.001747919,"threshold_uncertainty_score":0.14369285},"labels":[],"label_agreement":null},{"id":"W2023969011","doi":"10.1111/1368-423x.00091","title":"Multinomial probit estimation without nuisance parameters","year":2002,"lang":"ca","type":"article","venue":"Econometrics Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Multinomial probit; Covariance; Mathematics; Multinomial distribution; Statistics; Monte Carlo method; Rank (graph theory); Econometrics; Covariance matrix; Probit; Law of total covariance; Estimation of covariance matrices; Set (abstract data type); Probit model; Covariance intersection; Computer science","score_opus":0.14305975602852325,"score_gpt":0.341287233149511,"score_spread":0.19822747712098773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023969011","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":[0.0021643946,0.00010324641,0.9962063,0.000116598436,0.000018801888,0.000051222913,0.00009930455,0.0001244778,0.0011155916],"genre_scores_gemma":[0.14371184,0.00060049497,0.8470208,0.00014013117,0.00009825039,0.00075230433,0.00064783514,0.00014791742,0.006880462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953837,0.0030217164,0.00021440923,0.00053781294,0.0006724496,0.00016988233],"domain_scores_gemma":[0.99182206,0.0058323215,0.0006886657,0.00082659785,0.0007431765,0.00008711243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00664656,0.0011405732,0.0017536619,0.0010482465,0.00081367046,0.0018436725,0.0017536677,0.0015766233,0.0072605642],"category_scores_gemma":[0.041979015,0.0008506135,0.001073544,0.002129516,0.000958985,0.0023664571,0.00212171,0.0020680598,0.0025097856],"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":[0.000111517,0.0001447791,0.005113812,0.0003667793,0.00024685432,0.0003771525,0.00034884445,0.277949,0.0011379292,0.47411597,0.0063991514,0.23368816],"study_design_scores_gemma":[0.000032357068,0.000044278015,0.00093736785,0.00007861917,0.000052344094,0.00015160181,0.000046616486,0.82678074,0.00061353226,0.16480578,0.00642869,0.000028145812],"about_ca_topic_score_codex":0.008516528,"about_ca_topic_score_gemma":0.009906081,"teacher_disagreement_score":0.008516528,"about_ca_system_score_codex":0.0010415482,"about_ca_system_score_gemma":0.003848526,"threshold_uncertainty_score":0.035150766},"labels":[],"label_agreement":null},{"id":"W2024159897","doi":"10.2307/3316084","title":"Inequalities between expected marginal log‐likelihoods, with implications for likelihood‐based model complexity and comparison measures","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Marginal likelihood; Likelihood function; Mathematics; A priori and a posteriori; Likelihood principle; Econometrics; Statistics; Maximum likelihood; Bayesian probability; Bayesian inference; Marginal model; Quasi-maximum likelihood; Regression analysis","score_opus":0.21415528983965182,"score_gpt":0.37306319667029586,"score_spread":0.15890790683064404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024159897","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":[0.12382131,0.0020037738,0.846718,0.0057282127,0.00012488582,0.00015785903,0.0003969787,0.00014688591,0.02090207],"genre_scores_gemma":[0.8556958,0.00066774443,0.13973059,0.00047998963,0.00027691718,0.00044979763,0.0004264709,0.00013982102,0.0021328498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96764874,0.023121921,0.0014320838,0.0024128775,0.00459152,0.000792884],"domain_scores_gemma":[0.4191553,0.5520156,0.012019998,0.009416093,0.0047095167,0.0026834644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05431589,0.0010764966,0.0018563609,0.0044442005,0.0014146455,0.007088393,0.0036578458,0.0029890097,0.007694824],"category_scores_gemma":[0.29132354,0.0008264069,0.0018723301,0.0032322647,0.009589337,0.015209626,0.0069711464,0.0053818617,0.0003488491],"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":[0.00016298966,0.00006661199,0.0036009606,0.00017624373,0.000110359644,0.00013629768,0.00039452393,0.041776273,0.00029781394,0.9381202,0.00075309677,0.014404529],"study_design_scores_gemma":[0.000026662356,0.000079137,0.0018545808,0.000067952635,0.000026083995,0.000105903666,0.00011042748,0.108792424,0.00027507698,0.88809043,0.0005352127,0.00003611244],"about_ca_topic_score_codex":0.0011653518,"about_ca_topic_score_gemma":0.00094749266,"teacher_disagreement_score":0.05431589,"about_ca_system_score_codex":0.004673954,"about_ca_system_score_gemma":0.0014230127,"threshold_uncertainty_score":0.28725332},"labels":[],"label_agreement":null},{"id":"W2024233572","doi":"10.1016/j.spl.2010.05.015","title":"Bootstrap procedures for the pseudo empirical likelihood method in sample surveys","year":2010,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Empirical likelihood; Statistics; Mathematics; Confidence interval; Sample size determination; Statistic; Coverage probability; CDF-based nonparametric confidence interval; Sampling design; Sampling (signal processing); Variance (accounting); Benchmark (surveying); Population; Econometrics; Computer science","score_opus":0.09791854182274352,"score_gpt":0.4215707332005172,"score_spread":0.3236521913777737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024233572","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":[0.0008034428,0.00038112863,0.99779093,0.00020234892,0.000057487847,0.000044699096,0.000060179616,0.0001338487,0.0005260092],"genre_scores_gemma":[0.060301587,0.0014544425,0.93158,0.00041730606,0.0004995538,0.0013181387,0.00061642966,0.0005745828,0.0032378852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9705502,0.025312975,0.0006998223,0.0009295833,0.0021454731,0.000361836],"domain_scores_gemma":[0.8813822,0.10146069,0.0023198004,0.009638727,0.0043778378,0.00082071184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032658745,0.001539273,0.0029150113,0.004362204,0.0018960255,0.0028970537,0.005043923,0.003473484,0.010749539],"category_scores_gemma":[0.19055258,0.0019586,0.0026696878,0.004647397,0.0043484475,0.0064021433,0.003925526,0.0065343357,0.0030673714],"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":[0.00013052947,0.000078785844,0.0010450342,0.00029607437,0.00018843284,0.00010002526,0.0002936409,0.027828433,0.00045199067,0.89276755,0.005666698,0.071152836],"study_design_scores_gemma":[0.00006748345,0.000037480655,0.00057105283,0.00012327937,0.000042881107,0.00010166886,0.000047044643,0.2039008,0.00037238366,0.7870822,0.007608717,0.000045053548],"about_ca_topic_score_codex":0.0028262765,"about_ca_topic_score_gemma":0.0030304026,"teacher_disagreement_score":0.032658745,"about_ca_system_score_codex":0.0016892249,"about_ca_system_score_gemma":0.0026299036,"threshold_uncertainty_score":0.17271799},"labels":[],"label_agreement":null},{"id":"W2024304065","doi":"10.1081/sac-200033260","title":"A Comparison of Two General Approaches to Mixed Model Longitudinal Analyses Under Small Sample Size Conditions","year":2004,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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":"Simon Fraser University","funders":"","keywords":"Mathematics; Sample size determination; Statistics; Mixed model; Inference; Restricted maximum likelihood; Asymptotic analysis; Generalized linear mixed model; Type I and type II errors; Linear model; Random effects model; Applied mathematics; Combinatorics; Maximum likelihood","score_opus":0.7463782541497181,"score_gpt":0.5823892205180258,"score_spread":0.16398903363169237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024304065","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":[0.004975106,0.0011760867,0.9916556,0.00054532854,0.00011756079,0.00025800764,0.00008315989,0.00023814476,0.00095100055],"genre_scores_gemma":[0.0716027,0.0014560564,0.9229077,0.000552083,0.0002856218,0.0015712569,0.0002218446,0.0002594888,0.0011433351],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8713883,0.114649534,0.0020354998,0.0040144967,0.0070771514,0.00083495013],"domain_scores_gemma":[0.7556487,0.20979193,0.005473185,0.015821962,0.01150502,0.0017591427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12985784,0.0021644311,0.0037196246,0.0059577413,0.0012418305,0.004069993,0.0072891326,0.0039002597,0.00706529],"category_scores_gemma":[0.29182723,0.001812802,0.0049301935,0.00489478,0.003922745,0.007592647,0.007837636,0.004680369,0.00097537687],"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":[0.0024076025,0.0003444634,0.009369675,0.0020905298,0.0042975596,0.0005615199,0.0026308035,0.071627535,0.0019862005,0.48348957,0.0046446407,0.41654986],"study_design_scores_gemma":[0.00051754987,0.0011089299,0.007712526,0.0005548196,0.0014671558,0.0006089777,0.00052712153,0.60112154,0.0015113357,0.37144956,0.0131558515,0.00026463674],"about_ca_topic_score_codex":0.004299935,"about_ca_topic_score_gemma":0.006634312,"teacher_disagreement_score":0.12985784,"about_ca_system_score_codex":0.0031811676,"about_ca_system_score_gemma":0.004543857,"threshold_uncertainty_score":0.6867621},"labels":[],"label_agreement":null},{"id":"W2025425088","doi":"10.1007/s00362-010-0344-3","title":"Pseudolikelihood ratio test with biased observations","year":2010,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Statistical Methods and Bayesian Inference","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":"Null hypothesis; Construct (python library); Null (SQL); Statistics; Test (biology); Statistical hypothesis testing; Mathematics; Association (psychology); Alternative hypothesis; Sample size determination; Computer science; Econometrics; Psychology; Data mining; Biology","score_opus":0.05115523037100574,"score_gpt":0.3385866532938162,"score_spread":0.2874314229228105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025425088","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":[0.010398004,0.00073708704,0.98525095,0.0010764351,0.00015839504,0.0000960055,0.00021757229,0.00030196927,0.0017636388],"genre_scores_gemma":[0.42152777,0.001031992,0.56515,0.0018012797,0.0010054917,0.001169607,0.001123685,0.00052174495,0.006668359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.940307,0.04741061,0.0017866681,0.0037414648,0.0060543446,0.00069983245],"domain_scores_gemma":[0.65282255,0.3152974,0.007676907,0.01765655,0.0054266676,0.0011199716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05067228,0.0016072447,0.003381507,0.0036835452,0.0010555016,0.0039185314,0.005368304,0.004590313,0.0102971755],"category_scores_gemma":[0.3277251,0.0015040403,0.002177647,0.0036289196,0.0050714714,0.0083060125,0.004277228,0.004020507,0.0022267988],"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":[0.0016677675,0.0002470908,0.014100901,0.0008826727,0.0013499619,0.0022337572,0.00058152684,0.052189063,0.0016819481,0.7196403,0.009836123,0.19558898],"study_design_scores_gemma":[0.00039671332,0.00023806785,0.0029345318,0.00016407807,0.0002536441,0.0022547971,0.000090265785,0.2590547,0.0022007106,0.72704583,0.005275753,0.00009084268],"about_ca_topic_score_codex":0.0007908876,"about_ca_topic_score_gemma":0.0004941233,"teacher_disagreement_score":0.05067228,"about_ca_system_score_codex":0.0014910046,"about_ca_system_score_gemma":0.0025156825,"threshold_uncertainty_score":0.2679838},"labels":[],"label_agreement":null},{"id":"W2028152993","doi":"10.1002/cjs.5550340112","title":"Imputation using response probability","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Mathematics; Estimator; Statistics; Missing data","score_opus":0.07531166977525713,"score_gpt":0.34414066757024686,"score_spread":0.2688289977949897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028152993","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":[0.0008092039,0.00042256434,0.99583733,0.00066061073,0.00026726184,0.00028171894,0.00024394697,0.0003598453,0.0011174262],"genre_scores_gemma":[0.055146657,0.0010572019,0.93438214,0.0015312314,0.0008551265,0.0019128169,0.0011694159,0.00031959685,0.0036258146],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8863647,0.089422464,0.0045613083,0.008106395,0.010478055,0.0010671006],"domain_scores_gemma":[0.87806994,0.087886676,0.006755635,0.017372856,0.009174022,0.00074075727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05352198,0.0017913474,0.0044907928,0.0072874594,0.0010365375,0.0038391012,0.0071508186,0.00475939,0.012235327],"category_scores_gemma":[0.2187174,0.0013033784,0.0054870467,0.008910596,0.0014966896,0.0045554177,0.0040531964,0.0055365926,0.0073989327],"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":[0.0006103672,0.0004344851,0.00929337,0.001692768,0.0025347488,0.00041750725,0.00069303514,0.013361444,0.001164613,0.15403719,0.025256827,0.7905036],"study_design_scores_gemma":[0.0016543012,0.001515581,0.008132989,0.0013093165,0.0025580544,0.0040044305,0.00032175164,0.28689834,0.00798277,0.54943275,0.1355663,0.00062338635],"about_ca_topic_score_codex":0.0011095903,"about_ca_topic_score_gemma":0.0007501163,"teacher_disagreement_score":0.05352198,"about_ca_system_score_codex":0.0010623074,"about_ca_system_score_gemma":0.0018696381,"threshold_uncertainty_score":0.28305465},"labels":[],"label_agreement":null},{"id":"W2029593772","doi":"10.1093/biomet/asn028","title":"A new approach to weighting and inference in sample surveys","year":2008,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","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":"Statistics Canada","funders":"University of Southampton","keywords":"Estimator; Weighting; Mathematics; Inference; Variable (mathematics); Smoothing; Focus (optics); Calibration; Statistics; Mathematical optimization; Computer science; Artificial intelligence","score_opus":0.1871175162063903,"score_gpt":0.3925625544718294,"score_spread":0.2054450382654391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029593772","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":[0.00070018595,0.00019510515,0.99820244,0.00019037374,0.000053783482,0.000056231405,0.000034612218,0.00004116748,0.00052609964],"genre_scores_gemma":[0.072640784,0.0009540238,0.92170876,0.0006527343,0.00043069146,0.001154892,0.00016673966,0.000071362,0.0022198705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.956612,0.03246755,0.0014236147,0.0034846875,0.005508069,0.0005040341],"domain_scores_gemma":[0.94800127,0.038590346,0.0029655208,0.0070829545,0.0029530653,0.00040691908],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037465986,0.0015985966,0.0030359137,0.004592372,0.0011020257,0.002958464,0.0048144246,0.0035035652,0.0035306094],"category_scores_gemma":[0.111618586,0.0014024068,0.0027464684,0.0046773828,0.003520577,0.005479659,0.003895783,0.0047194217,0.0006555342],"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":[0.0000419727,0.000064402746,0.0014225232,0.00031088875,0.0002721524,0.00007018106,0.0003989192,0.030345598,0.0005470434,0.8785319,0.0015706235,0.08642379],"study_design_scores_gemma":[0.00006207641,0.000107178654,0.0005595446,0.000097087686,0.00007400495,0.00011144839,0.00004072181,0.123262316,0.00039550962,0.86511993,0.010131309,0.00003878978],"about_ca_topic_score_codex":0.0020205267,"about_ca_topic_score_gemma":0.0018476176,"teacher_disagreement_score":0.962534,"about_ca_system_score_codex":0.0022852242,"about_ca_system_score_gemma":0.002718793,"threshold_uncertainty_score":0.19814146},"labels":[],"label_agreement":null},{"id":"W2030152850","doi":"10.1081/sta-120014915","title":"LOG-LINEAR MODELLING OF CHANGE USING LONGITUDINAL SURVEY DATA","year":2002,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Multinomial distribution; Statistics; Variance (accounting); Sampling (signal processing); Mathematics; Longitudinal data; Sampling design; Econometrics; Log-linear model; Independence (probability theory); Linear model; Computer science; Demography; Data mining; Economics; Population","score_opus":0.7854312983125642,"score_gpt":0.5559545184830741,"score_spread":0.2294767798294901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030152850","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":[0.041437138,0.0004650353,0.95519626,0.0006591375,0.00008121981,0.000089623405,0.00076344516,0.00035474304,0.0009533586],"genre_scores_gemma":[0.8260105,0.000993175,0.16085012,0.00026833502,0.00015917711,0.0008679954,0.0015203708,0.00016782804,0.009162349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916847,0.006176029,0.00022475446,0.00092212006,0.00064258673,0.00034984882],"domain_scores_gemma":[0.9720777,0.022856174,0.0018084624,0.0015748648,0.0014630661,0.00021969476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018373584,0.0007024914,0.0012163538,0.0019909297,0.00064297125,0.002202761,0.0030023127,0.0012436658,0.0033876144],"category_scores_gemma":[0.041063074,0.0006489172,0.0015918677,0.002973439,0.0016005567,0.0018744102,0.0016485323,0.0018238071,0.0006154921],"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":[0.0002975234,0.00016099699,0.037377607,0.00032508397,0.0006263069,0.00044229635,0.00095704896,0.64037085,0.00089667406,0.22237085,0.003181351,0.092993475],"study_design_scores_gemma":[0.000015943328,0.000062589876,0.0036157148,0.00003594207,0.000041703828,0.0000559103,0.00006738084,0.9226724,0.00014776931,0.07151471,0.0017458154,0.000024155252],"about_ca_topic_score_codex":0.02105509,"about_ca_topic_score_gemma":0.01753416,"teacher_disagreement_score":0.02105509,"about_ca_system_score_codex":0.0017663126,"about_ca_system_score_gemma":0.0016905223,"threshold_uncertainty_score":0.097169936},"labels":[],"label_agreement":null},{"id":"W2030605340","doi":"10.1111/cdep.12043","title":"Planned Missing Data Designs for Developmental Researchers","year":2013,"lang":"en","type":"article","venue":"Child Development Perspectives","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":292,"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":"Social Sciences and Humanities Research Council of Canada; University of Kansas; National Science Foundation","keywords":"Missing data; Data collection; Research design; Longitudinal data; Psychology; Computer science; Statistics; Data mining; Econometrics; Machine learning; Mathematics","score_opus":0.3455343248455911,"score_gpt":0.4424078329821734,"score_spread":0.0968735081365823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030605340","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":[0.0011956547,0.00052906736,0.99393386,0.0006615487,0.0003437153,0.0018103364,0.0003644274,0.00027565198,0.0008856591],"genre_scores_gemma":[0.02841107,0.0006971601,0.94994575,0.00057014835,0.00027219107,0.018967837,0.00037397447,0.00009565956,0.00066625985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7089699,0.2667975,0.006910047,0.006889964,0.00948058,0.00095212617],"domain_scores_gemma":[0.42919594,0.48444763,0.023142688,0.041136082,0.02049273,0.0015849001],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25911596,0.0026060455,0.0036105008,0.0035724891,0.0018301874,0.0037766907,0.006722041,0.004356856,0.01601243],"category_scores_gemma":[0.49227545,0.0021179717,0.0035075431,0.0051068417,0.0059270137,0.005766788,0.0042044614,0.0078075803,0.0021843007],"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":[0.0013152511,0.00030230483,0.0042117885,0.0040304027,0.0009675545,0.00033985384,0.0032084682,0.014168501,0.00058781373,0.8105416,0.017101929,0.14322452],"study_design_scores_gemma":[0.0012636288,0.0013378497,0.0016350498,0.0020356383,0.0005026576,0.0003085867,0.00043872066,0.05435751,0.0013215434,0.9043358,0.03228286,0.00018024199],"about_ca_topic_score_codex":0.0011496899,"about_ca_topic_score_gemma":0.0010520222,"teacher_disagreement_score":0.74088407,"about_ca_system_score_codex":0.00282741,"about_ca_system_score_gemma":0.008036098,"threshold_uncertainty_score":0.9136419},"labels":[],"label_agreement":null},{"id":"W2031038069","doi":"10.1038/jes.2012.22","title":"A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies","year":2012,"lang":"en","type":"article","venue":"Journal of Exposure Science & Environmental Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"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":"Statistics; Logistic regression; Bayesian probability; Econometrics; Mathematics","score_opus":0.08603609547104074,"score_gpt":0.41158330186284187,"score_spread":0.3255472063918011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031038069","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":[0.004201358,0.0009861325,0.9939657,0.0002257646,0.00003730756,0.0000992607,0.00010763136,0.00015584301,0.00022107405],"genre_scores_gemma":[0.15474495,0.0023943954,0.838163,0.0003907567,0.00033948314,0.0014171435,0.0007295142,0.00014919769,0.0016715254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9537101,0.037185885,0.0018413602,0.0036582055,0.0030650245,0.00053943665],"domain_scores_gemma":[0.8457655,0.14376777,0.0033543915,0.004692208,0.0018047717,0.00061544025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.066008545,0.0028190275,0.0056132157,0.0076263766,0.0016231246,0.003723415,0.008444345,0.0048049525,0.0036707907],"category_scores_gemma":[0.1537182,0.0030233774,0.006879358,0.005760512,0.0027930122,0.0037840945,0.0041757454,0.0046062083,0.00056086923],"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":[0.0012513399,0.000490136,0.018348655,0.0011055998,0.007991769,0.0010268724,0.0011573378,0.3726002,0.0023063465,0.3321779,0.0033354126,0.25820836],"study_design_scores_gemma":[0.00037345878,0.00029953505,0.003209724,0.00024407628,0.0019022955,0.00063577486,0.00008241991,0.6937701,0.0004811554,0.2960201,0.002837031,0.00014429378],"about_ca_topic_score_codex":0.010683684,"about_ca_topic_score_gemma":0.0069208625,"teacher_disagreement_score":0.066008545,"about_ca_system_score_codex":0.0017016408,"about_ca_system_score_gemma":0.003131336,"threshold_uncertainty_score":0.34909075},"labels":[],"label_agreement":null},{"id":"W2031106492","doi":"10.1007/s10661-005-4437-8","title":"A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies","year":2005,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Canadian Forest Service","funders":"","keywords":"Estimator; Statistics; Mathematics; Categorical variable; Mean squared error; Efficient estimator; Bias of an estimator; Extremum estimator; M-estimator; Invariant estimator; Variance (accounting); Econometrics; Minimum-variance unbiased estimator","score_opus":0.09803681634191229,"score_gpt":0.4010959194807351,"score_spread":0.3030591031388228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031106492","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":[0.0010480089,0.00009166894,0.9985935,0.00004481541,0.000016330934,0.0000116162155,0.000041488212,0.00006279637,0.00008970834],"genre_scores_gemma":[0.07939367,0.00040650618,0.9170283,0.00022653716,0.00020981736,0.00043188955,0.000673618,0.00025828858,0.0013714449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98790306,0.0069285217,0.0005800223,0.0022925856,0.0019783946,0.00031747093],"domain_scores_gemma":[0.9171156,0.07057952,0.0024499353,0.0060695433,0.0033567043,0.00042868705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02314278,0.000966275,0.0022608065,0.003298929,0.0007057949,0.002382924,0.004055014,0.0025825484,0.002620501],"category_scores_gemma":[0.10373598,0.001477789,0.0021275927,0.0033040298,0.0025462129,0.0038503455,0.0032581666,0.0040552816,0.00076352723],"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":[0.00028862784,0.00017891462,0.0056365994,0.00048816184,0.00085472077,0.0001681177,0.00035521507,0.2282544,0.0065725865,0.44416106,0.005435675,0.30760604],"study_design_scores_gemma":[0.000070772985,0.000076120596,0.0026706823,0.00013832885,0.00014947617,0.00022879726,0.000039531016,0.6909215,0.0018656168,0.30005732,0.0036963406,0.000085609994],"about_ca_topic_score_codex":0.003190109,"about_ca_topic_score_gemma":0.0029730687,"teacher_disagreement_score":0.02314278,"about_ca_system_score_codex":0.0010438985,"about_ca_system_score_gemma":0.0017224835,"threshold_uncertainty_score":0.12239218},"labels":[],"label_agreement":null},{"id":"W2031114469","doi":"10.1007/s40300-014-0041-4","title":"Approximate Bayesian computation with modified log-likelihood ratios","year":2014,"lang":"en","type":"article","venue":"METRON","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"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":"Approximate Bayesian computation; Univariate; Computation; Prior probability; Mathematics; Bayesian probability; Nuisance parameter; Matching (statistics); Marginal likelihood; Applied mathematics; Restricted maximum likelihood; Posterior probability; Simple (philosophy); Asymptotic analysis; Statistics; Algorithm; Maximum likelihood; Computer science; Multivariate statistics; Artificial intelligence","score_opus":0.038399201016857544,"score_gpt":0.3258608813276355,"score_spread":0.28746168031077796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031114469","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":[0.0012045038,0.0002134473,0.9974934,0.0001795058,0.000032338616,0.000011295035,0.000027002565,0.00014963667,0.00068885344],"genre_scores_gemma":[0.12260451,0.00066809467,0.8681909,0.00039434322,0.00035030386,0.0003050186,0.00028607526,0.0005660656,0.0066347206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936473,0.0042424584,0.00023264963,0.0005405362,0.0011547615,0.00018226847],"domain_scores_gemma":[0.9772512,0.018600866,0.0007793661,0.0019023956,0.0011447323,0.00032141924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009025771,0.0013694123,0.0024412537,0.00219693,0.000896838,0.0037379728,0.00466714,0.0025911925,0.009881949],"category_scores_gemma":[0.06333082,0.0014459491,0.0015322361,0.0026673223,0.0024944558,0.0070449645,0.003598959,0.0037880877,0.0025252432],"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":[0.0003184967,0.00010030339,0.00085551897,0.0003186398,0.00014976377,0.00016767503,0.00016418651,0.22901171,0.00091480126,0.66463625,0.005665004,0.097697735],"study_design_scores_gemma":[0.000027017193,0.000019097677,0.00012545369,0.000028525066,0.0000195383,0.00007642197,0.000012119582,0.6611754,0.00047643267,0.33597723,0.0020428763,0.000019857951],"about_ca_topic_score_codex":0.002383671,"about_ca_topic_score_gemma":0.0028600323,"teacher_disagreement_score":0.009881949,"about_ca_system_score_codex":0.002169377,"about_ca_system_score_gemma":0.0018728292,"threshold_uncertainty_score":0.047733366},"labels":[],"label_agreement":null},{"id":"W2032234757","doi":"10.1198/jasa.2010.tm08551","title":"Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random","year":2010,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":62,"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; Estimating equations; Econometrics; Mathematics; Random effects model; Generalized estimating equation; Computer science; Meta-analysis; Medicine","score_opus":0.10742718450881653,"score_gpt":0.40376227920922436,"score_spread":0.29633509470040786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032234757","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":[0.0008218063,0.00031987502,0.9981046,0.00014553264,0.00002585376,0.000086453896,0.00017195016,0.00010689374,0.00021700727],"genre_scores_gemma":[0.028263353,0.0018746449,0.9632667,0.00024190365,0.000119381424,0.002156477,0.0012893392,0.00016916367,0.0026190828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97749215,0.01895285,0.00074611127,0.001319516,0.0011960382,0.0002933725],"domain_scores_gemma":[0.9502816,0.03991207,0.0029245617,0.0047208294,0.001989639,0.00017114823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040624384,0.00236529,0.0027579935,0.0036011776,0.0006038961,0.0014612797,0.00507222,0.0027949854,0.007542885],"category_scores_gemma":[0.11640006,0.0014994879,0.0034116535,0.0048778774,0.0015922224,0.0046702866,0.0024995795,0.00475986,0.003080302],"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":[0.00009603959,0.00008407582,0.0038244394,0.0007063301,0.0011191652,0.00030651793,0.00050654815,0.09852992,0.0006833775,0.70219463,0.008989864,0.18295915],"study_design_scores_gemma":[0.00008936339,0.00011151957,0.0019023845,0.0003374257,0.0003435887,0.00034152466,0.00012392677,0.351539,0.0006373004,0.62716556,0.017301844,0.000106631334],"about_ca_topic_score_codex":0.005295298,"about_ca_topic_score_gemma":0.0073041054,"teacher_disagreement_score":0.040624384,"about_ca_system_score_codex":0.0015893711,"about_ca_system_score_gemma":0.0028675827,"threshold_uncertainty_score":0.21484482},"labels":[],"label_agreement":null},{"id":"W2032301741","doi":"10.1198/jasa.2003.s307","title":"Multivariate Dispersion, Central Regions and Depth: the Lift Zonoid Approach. Karl Mosler","year":2003,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lift (data mining); Multivariate statistics; Dispersion (optics); Geology; Mathematics; Geodesy; Statistics; Geography; Environmental science; Computer science; Physics; Optics; Data mining","score_opus":0.03287875679416995,"score_gpt":0.32827403949704836,"score_spread":0.2953952827028784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032301741","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":[0.007777365,0.25454083,0.6848222,0.03542151,0.0023346364,0.000055051612,0.0006537251,0.00038047796,0.014014159],"genre_scores_gemma":[0.4163351,0.24433158,0.28890544,0.00822964,0.014125582,0.0005460044,0.001153976,0.001229467,0.025143115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976286,0.0014614144,0.00012078559,0.00027745776,0.0003802136,0.00013151062],"domain_scores_gemma":[0.98679346,0.0105431145,0.00081328815,0.0004280446,0.0010157003,0.00040631744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075469376,0.0014610766,0.0019361078,0.0037922626,0.0016274444,0.003333618,0.00161,0.002137917,0.0063044094],"category_scores_gemma":[0.024355225,0.001439842,0.0013919843,0.005570958,0.005402453,0.008622678,0.00385907,0.005383763,0.0013341276],"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":[0.00012257387,0.000038314127,0.0024612506,0.00037037092,0.0002036977,0.00023291355,0.0008543235,0.015870769,0.00021842238,0.77804303,0.088433534,0.113150835],"study_design_scores_gemma":[0.000021096232,0.000019391753,0.0011854212,0.00018586412,0.000066541936,0.000105562576,0.00017751202,0.011572568,0.00011198607,0.957413,0.029081544,0.000059438324],"about_ca_topic_score_codex":0.012065869,"about_ca_topic_score_gemma":0.010594569,"teacher_disagreement_score":0.012065869,"about_ca_system_score_codex":0.0030317844,"about_ca_system_score_gemma":0.0016362049,"threshold_uncertainty_score":0.039912462},"labels":[],"label_agreement":null},{"id":"W2032526975","doi":"10.1002/sim.2002","title":"The utility of prior information and stratification for parameter estimation with two screening tests but no gold standard","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Gold standard (test); Prior information; Estimation; Statistics; Computer science; Stratification (seeds); Econometrics; Risk stratification; Mathematics; Medicine; Artificial intelligence; Internal medicine; Biology; Economics","score_opus":0.05101232799794388,"score_gpt":0.398043093680317,"score_spread":0.3470307656823731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032526975","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":[0.0075608105,0.00071917864,0.98929906,0.0011914056,0.000056064888,0.00006218952,0.000052465355,0.000085494125,0.0009732463],"genre_scores_gemma":[0.31666577,0.0026232211,0.67675936,0.00074675854,0.00049602205,0.00050819037,0.00035206607,0.0001196855,0.0017289704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9688118,0.025325635,0.0008956886,0.002096916,0.002426711,0.00044334136],"domain_scores_gemma":[0.7422467,0.2391447,0.0053213504,0.010090842,0.002320399,0.000876056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07040168,0.002175902,0.0028556662,0.004577096,0.0012728422,0.0034601828,0.0028092847,0.0036360763,0.0019243886],"category_scores_gemma":[0.30150953,0.0014588375,0.0025527258,0.0026516884,0.008423052,0.0073229107,0.004956421,0.00456831,0.0004660639],"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":[0.0003208589,0.00008880858,0.0072235344,0.0003373804,0.00034939856,0.00031079573,0.00066334713,0.17804489,0.00055012584,0.72256744,0.0012794773,0.08826391],"study_design_scores_gemma":[0.00004913578,0.000098202465,0.0014831362,0.00012867873,0.00006773098,0.0001038983,0.000040011808,0.40034813,0.00038452697,0.59613055,0.0011056938,0.000060253766],"about_ca_topic_score_codex":0.004822091,"about_ca_topic_score_gemma":0.003243171,"teacher_disagreement_score":0.07040168,"about_ca_system_score_codex":0.0024366502,"about_ca_system_score_gemma":0.0026865222,"threshold_uncertainty_score":0.3723241},"labels":[],"label_agreement":null},{"id":"W2033133921","doi":"10.1139/f06-178","title":"Variance estimation in integrated assessment models and its importance for hypothesis testing","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Exxon Mobil Corporation","keywords":"Statistics; Mathematics; Likelihood function; Restricted maximum likelihood; Variance (accounting); Likelihood-ratio test; Population; Econometrics; Estimation theory","score_opus":0.14321281875849548,"score_gpt":0.3599358491468565,"score_spread":0.21672303038836102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033133921","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":[0.0020296234,0.00052613084,0.9963413,0.00033720717,0.000047662757,0.000032044154,0.000022520197,0.000059287962,0.0006041913],"genre_scores_gemma":[0.21618785,0.0021268893,0.7781813,0.00048239774,0.0004163588,0.0010134254,0.0002291188,0.00023951342,0.0011230275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9316419,0.05353544,0.0021119304,0.005020005,0.006943912,0.00074679643],"domain_scores_gemma":[0.70552605,0.27505296,0.006351084,0.0075818026,0.005042419,0.00044563788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07519448,0.001683675,0.003592228,0.003590372,0.0013032561,0.0042121485,0.003654356,0.0033178504,0.0017909978],"category_scores_gemma":[0.29507494,0.0014588444,0.0023641651,0.0038400604,0.008080232,0.008332953,0.0049150866,0.00633452,0.00038301127],"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":[0.00010343307,0.00007225132,0.0050073313,0.00052081264,0.00047355765,0.00025363808,0.0005831882,0.15034233,0.0008156018,0.7184142,0.0016492194,0.12176437],"study_design_scores_gemma":[0.000021643638,0.00006202802,0.00079076295,0.00012640218,0.00004994464,0.000076474455,0.000060179787,0.22158279,0.00045727455,0.7749054,0.0018238074,0.00004332265],"about_ca_topic_score_codex":0.0029064917,"about_ca_topic_score_gemma":0.0018527614,"teacher_disagreement_score":0.07519448,"about_ca_system_score_codex":0.0022197878,"about_ca_system_score_gemma":0.0029226437,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2033317193","doi":"10.1016/j.csda.2007.03.027","title":"Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs","year":2007,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Covariance; Sensitivity (control systems); Drop (telecommunication); Mathematics; Drop out; Econometrics; Statistics; Longitudinal data; Computer science; Data mining; Engineering; Economics","score_opus":0.17932447230720955,"score_gpt":0.43589605519466423,"score_spread":0.2565715828874547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033317193","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":[0.034441646,0.0010380845,0.9616453,0.00077017606,0.00010371994,0.00015943791,0.00021056867,0.00031135557,0.0013196324],"genre_scores_gemma":[0.86073405,0.0006323608,0.1340357,0.0005907374,0.00027806248,0.00070693414,0.00042687674,0.00040983973,0.0021854378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.78218436,0.19517021,0.004689734,0.00964303,0.0061503523,0.002162395],"domain_scores_gemma":[0.28081682,0.6744544,0.010253357,0.029075429,0.0043309103,0.0010691091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18627587,0.0019246038,0.0048980443,0.004258645,0.0023236377,0.0032220602,0.0057973657,0.0035448575,0.0038532324],"category_scores_gemma":[0.494093,0.0022862386,0.0070191734,0.0036706827,0.0053413427,0.0059173345,0.005892719,0.00651021,0.0002956531],"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":[0.0016444435,0.00040692062,0.066656955,0.0013126773,0.0115054585,0.0036926551,0.0051752585,0.33476356,0.0015350003,0.43374065,0.0050917156,0.13447477],"study_design_scores_gemma":[0.0001637592,0.00042034968,0.0116050765,0.00019386206,0.0018722293,0.0006128599,0.0005072874,0.5808031,0.0016700363,0.39906478,0.00291007,0.00017656191],"about_ca_topic_score_codex":0.008796828,"about_ca_topic_score_gemma":0.007175995,"teacher_disagreement_score":0.18627587,"about_ca_system_score_codex":0.0021230914,"about_ca_system_score_gemma":0.003654587,"threshold_uncertainty_score":0.98513275},"labels":[],"label_agreement":null},{"id":"W2033898478","doi":"10.1007/s00180-008-0121-0","title":"Numerical approximation of conditional asymptotic variances using Monte Carlo simulation","year":2008,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Estimator; Delta method; Mathematics; Monte Carlo method; Applied mathematics; Control variates; Statistic; Asymptotic distribution; Extrapolation; Variance (accounting); Sample size determination; Asymptotic analysis; Population; Statistics; Hybrid Monte Carlo","score_opus":0.12325246805482289,"score_gpt":0.3929568412781948,"score_spread":0.26970437322337193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033898478","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":[0.0024934642,0.0001227827,0.99604553,0.000110264264,0.000023475204,0.00001386475,0.000020132558,0.0001348571,0.0010356274],"genre_scores_gemma":[0.29703566,0.0008582749,0.6971744,0.00023357992,0.0001885254,0.0003699881,0.00033929668,0.00047068755,0.0033295027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666154,0.0018172045,0.00014127773,0.00025090209,0.0009700718,0.00015896073],"domain_scores_gemma":[0.96388453,0.030412914,0.001189237,0.0019809497,0.0021770194,0.00035543749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076880297,0.00091236946,0.0018880599,0.0028247393,0.000861018,0.0033078855,0.0029297823,0.0021912854,0.0050226934],"category_scores_gemma":[0.060494397,0.0010787213,0.0010758456,0.0025584665,0.0026335707,0.0038436626,0.0024472054,0.0035887877,0.000996387],"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":[0.000052274427,0.000047524696,0.00071447843,0.0001113361,0.000046570873,0.00008118969,0.00008356617,0.64348346,0.00045885998,0.33724755,0.0011014962,0.016571756],"study_design_scores_gemma":[0.000007877259,0.0000033965237,0.00006421213,0.00001680509,0.000004381324,0.000019446281,0.000005369119,0.93633455,0.00013150091,0.063057296,0.0003467958,0.000008408978],"about_ca_topic_score_codex":0.0057823593,"about_ca_topic_score_gemma":0.004867078,"teacher_disagreement_score":0.0076880297,"about_ca_system_score_codex":0.001611543,"about_ca_system_score_gemma":0.0025024454,"threshold_uncertainty_score":0.040658653},"labels":[],"label_agreement":null},{"id":"W2034610158","doi":"10.1002/cjs.11130","title":"Restricted maximum likelihood estimation of joint mean‐covariance models","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Science Foundation Ireland; National University of Ireland","keywords":"Cholesky decomposition; Covariance; Restricted maximum likelihood; Covariance matrix; Mathematics; Statistics; Degrees of freedom (physics and chemistry); Estimation of covariance matrices; Estimation theory","score_opus":0.08162468802370972,"score_gpt":0.315337180927269,"score_spread":0.23371249290355928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034610158","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":[0.0035319338,0.00019448259,0.99531907,0.00013892062,0.0000141912415,0.000038860257,0.00012423903,0.00019300151,0.00044528974],"genre_scores_gemma":[0.24126548,0.0006516032,0.752946,0.0001969005,0.000120113575,0.000650548,0.0013328432,0.00033527974,0.002501276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9804283,0.016167765,0.0004649857,0.0013904618,0.001187527,0.00036096823],"domain_scores_gemma":[0.9566489,0.03630245,0.0017856425,0.00354808,0.001502048,0.00021294206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018379563,0.0011842714,0.002834847,0.002409032,0.00065307383,0.0028888078,0.004034669,0.0018830784,0.0031384933],"category_scores_gemma":[0.09171474,0.0012837163,0.00232788,0.0029333434,0.0018029492,0.0028469476,0.0025940211,0.0036494425,0.0012382227],"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":[0.00035886478,0.00015717988,0.004784125,0.00055739086,0.00089748506,0.00034747343,0.00048122645,0.5963647,0.0016293147,0.21639688,0.0068847327,0.17114066],"study_design_scores_gemma":[0.000047450038,0.00003245677,0.0012199057,0.00006445289,0.000051274714,0.00008108557,0.000031180076,0.8454579,0.00043152526,0.15080355,0.0017248036,0.00005440319],"about_ca_topic_score_codex":0.0073810345,"about_ca_topic_score_gemma":0.0069752936,"teacher_disagreement_score":0.018379563,"about_ca_system_score_codex":0.0013111227,"about_ca_system_score_gemma":0.0029683837,"threshold_uncertainty_score":0.097201586},"labels":[],"label_agreement":null},{"id":"W2034828560","doi":"10.1016/j.jmva.2009.02.007","title":"Asymptotics for non-parametric likelihood estimation with doubly censored multivariate failure times","year":2009,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Censoring (clinical trials); Mathematics; Statistics; Parametric statistics; Consistency (knowledge bases); Econometrics; Nonparametric statistics; Multivariate analysis","score_opus":0.024061921736543555,"score_gpt":0.3498397854135701,"score_spread":0.32577786367702655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034828560","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":[0.003515996,0.000588875,0.99432397,0.0003673888,0.00004835689,0.00004119414,0.000069646274,0.00019419497,0.0008504415],"genre_scores_gemma":[0.34905437,0.005144592,0.6236078,0.0010506223,0.0016165354,0.002141539,0.0018303277,0.0012361736,0.01431809],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.976404,0.015685715,0.0011311688,0.0020720304,0.003877386,0.00082970347],"domain_scores_gemma":[0.6197722,0.34327197,0.009697932,0.017014705,0.00839126,0.0018518944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06373446,0.0027987661,0.0052766018,0.00526224,0.0016372778,0.0058550322,0.010145753,0.0055079185,0.010528937],"category_scores_gemma":[0.31791532,0.002782626,0.0038172416,0.004717872,0.011033799,0.012644073,0.0075831604,0.010493398,0.0018736246],"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":[0.00015385287,0.000111760426,0.0022403453,0.00044550613,0.0002495693,0.00032008512,0.0006126925,0.08986011,0.0005688261,0.8616155,0.0028386463,0.04098324],"study_design_scores_gemma":[0.000053678763,0.00005184606,0.00094295776,0.00012807352,0.00008278224,0.00023371982,0.000089695655,0.41453066,0.0003224091,0.58193123,0.0015603307,0.000072636365],"about_ca_topic_score_codex":0.0039218194,"about_ca_topic_score_gemma":0.003160566,"teacher_disagreement_score":0.06373446,"about_ca_system_score_codex":0.0034021258,"about_ca_system_score_gemma":0.0039329072,"threshold_uncertainty_score":0.33706403},"labels":[],"label_agreement":null},{"id":"W2037011349","doi":"10.1080/02664763.2014.960372","title":"A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"York University","funders":"National Natural Science Foundation of China","keywords":"Covariate; Imputation (statistics); Missing data; Markov chain Monte Carlo; Statistics; Computer science; Gibbs sampling; Econometrics; Observational error; Markov chain; Monte Carlo method; Mathematics; Bayesian probability","score_opus":0.07023764708469796,"score_gpt":0.3212560569575242,"score_spread":0.2510184098728262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037011349","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":[0.00040465494,0.0002320518,0.9988757,0.00013941649,0.000041224877,0.000033976117,0.00006261298,0.00007944932,0.00013086181],"genre_scores_gemma":[0.03667961,0.0009818295,0.95889574,0.00031388996,0.00026857544,0.00058392156,0.00047463586,0.00010975731,0.0016919767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9855297,0.01121391,0.0005465684,0.0014837412,0.0009951446,0.00023100397],"domain_scores_gemma":[0.98757195,0.009448528,0.0008214463,0.0010133849,0.00094590546,0.00019873731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01720132,0.0014359847,0.0030252724,0.0018412541,0.0012851238,0.0016708107,0.0059830775,0.0024882876,0.003839059],"category_scores_gemma":[0.03471332,0.0011963181,0.0041636475,0.0044247448,0.0011670381,0.0023707568,0.002687542,0.0045471624,0.001138225],"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":[0.00023471279,0.00017082413,0.0060742972,0.0011985911,0.0019343864,0.0010089497,0.00090884045,0.2452584,0.0014349621,0.4434046,0.009503669,0.2888677],"study_design_scores_gemma":[0.00008955204,0.00010458518,0.00075016264,0.00014676151,0.00032951505,0.00040494668,0.00006141577,0.7231028,0.00051603524,0.2620661,0.012333998,0.00009405446],"about_ca_topic_score_codex":0.006564924,"about_ca_topic_score_gemma":0.008891502,"teacher_disagreement_score":0.01720132,"about_ca_system_score_codex":0.0012779861,"about_ca_system_score_gemma":0.0032980884,"threshold_uncertainty_score":0.09097034},"labels":[],"label_agreement":null},{"id":"W2037696238","doi":"10.6000/1929-6029.2013.02.04.5","title":"Snapshot of Statistical Methods Used in Geriatric Cohort Studies: How Do We Treat Missing Data in Publications?","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Longitudinal data; Computer science; Data set; Data mining; Cohort; Medicine; Cohort study; Snapshot (computer storage); Statistics; Data science; Artificial intelligence; Machine learning; Mathematics","score_opus":0.3901992935197678,"score_gpt":0.6153466726185004,"score_spread":0.22514737909873261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037696238","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":[0.033984464,0.11742321,0.7690458,0.062434103,0.0075537814,0.0019477389,0.0032208862,0.00095074065,0.0034392804],"genre_scores_gemma":[0.44676122,0.04259454,0.47541773,0.016528217,0.0061682356,0.007977089,0.0027245511,0.0006914447,0.0011369702],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.48681787,0.41891578,0.050445177,0.0133529,0.028909499,0.0015587687],"domain_scores_gemma":[0.1470684,0.700998,0.060753945,0.05433403,0.034469463,0.0023761913],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.47494954,0.0012316075,0.004230625,0.0124185905,0.002227955,0.01033198,0.0053267577,0.0035023335,0.003483507],"category_scores_gemma":[0.78255343,0.0013601565,0.0042142128,0.018817408,0.004945756,0.013840095,0.006353764,0.0048842235,0.0009191464],"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":[0.0012812234,0.0001732249,0.17038551,0.03008343,0.014393334,0.0005698702,0.014801745,0.004388261,0.00056224136,0.06336842,0.04355888,0.6564339],"study_design_scores_gemma":[0.0012146591,0.0023348595,0.11397415,0.10021037,0.011284906,0.002844122,0.0135334935,0.037693333,0.0035335973,0.5579416,0.1545687,0.00086622284],"about_ca_topic_score_codex":0.0024014076,"about_ca_topic_score_gemma":0.0030352785,"teacher_disagreement_score":0.52505046,"about_ca_system_score_codex":0.002906102,"about_ca_system_score_gemma":0.008721277,"threshold_uncertainty_score":0.6474807},"labels":[],"label_agreement":null},{"id":"W2037740528","doi":"10.1016/j.csda.2012.12.012","title":"Simulation-based Bayesian inference for epidemic models","year":2013,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":88,"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 Guelph","funders":"National Medical Research Council; Biotechnology and Biological Sciences Research Council","keywords":"Markov chain Monte Carlo; Computer science; Missing data; Bayesian inference; Inference; Bayesian probability; Reversible-jump Markov chain Monte Carlo; Monte Carlo method; Marginal likelihood; Sampling (signal processing); Posterior probability; Algorithm; Data mining; Statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.17149250930281654,"score_gpt":0.4445010993073115,"score_spread":0.27300859000449496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037740528","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":[0.0051656654,0.00039235753,0.9928115,0.0004635483,0.00003451984,0.000026273234,0.00007775651,0.00013242112,0.00089590054],"genre_scores_gemma":[0.48390895,0.0027763725,0.5057856,0.00059338816,0.00033382734,0.00058875186,0.0011660584,0.00041483977,0.0044322433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937913,0.004626777,0.00023659406,0.00044083424,0.0007445622,0.00016001626],"domain_scores_gemma":[0.9395259,0.054385796,0.0016911471,0.0021590942,0.0016776657,0.0005604544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013205394,0.0010336471,0.0023867278,0.0028167472,0.0012207748,0.0027987051,0.0031980188,0.0025556486,0.004464869],"category_scores_gemma":[0.08475896,0.0019252488,0.0019360615,0.0023347794,0.002885679,0.004932321,0.0031223318,0.0043953694,0.00073886814],"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":[0.00007846473,0.000055672204,0.0009605915,0.00013688813,0.00012228657,0.00006492745,0.00012329224,0.68662274,0.00039219833,0.29282555,0.0014345171,0.017182903],"study_design_scores_gemma":[0.000019319854,0.000005277027,0.00007418541,0.000021829386,0.000010793249,0.000019314917,0.0000053636204,0.79662246,0.00009438442,0.20262837,0.0004898124,0.0000089470905],"about_ca_topic_score_codex":0.0072279107,"about_ca_topic_score_gemma":0.0055454164,"teacher_disagreement_score":0.013205394,"about_ca_system_score_codex":0.002322715,"about_ca_system_score_gemma":0.0035208445,"threshold_uncertainty_score":0.06983763},"labels":[],"label_agreement":null},{"id":"W2038522769","doi":"10.1111/j.1751-5823.2007.00017.x","title":"Methods for Generating Longitudinally Correlated Binary Data","year":2007,"lang":"en","type":"article","venue":"International Statistical Review","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Binary data; Binary number; Computer science; Flexibility (engineering); Contrast (vision); Range (aeronautics); Sample (material); Longitudinal data; Sample size determination; Statistics; Data mining; Algorithm; Mathematics; Econometrics; Artificial intelligence; Arithmetic","score_opus":0.2787789056899614,"score_gpt":0.5783747127488411,"score_spread":0.2995958070588797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038522769","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":[0.0024950078,0.0003456534,0.9955996,0.00029415538,0.000054238113,0.00014998518,0.00021298378,0.0001427758,0.00070540217],"genre_scores_gemma":[0.07812216,0.0010236624,0.91499287,0.00029422494,0.00017311856,0.0020662856,0.00095308473,0.00012874615,0.0022458816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890688,0.007939205,0.0004705236,0.0008917893,0.0014355595,0.00019415728],"domain_scores_gemma":[0.9564423,0.032547463,0.0028271836,0.0050014653,0.0027782498,0.00040342016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02137909,0.00056794164,0.0010835124,0.0027306434,0.0006088527,0.0013426208,0.0025970493,0.0010564417,0.009334268],"category_scores_gemma":[0.0771261,0.0006413186,0.0013479275,0.0025752883,0.0012485755,0.0016258645,0.002643309,0.0023371852,0.0020372528],"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":[0.00029939634,0.00013718531,0.006954197,0.000677271,0.00028630483,0.0002032246,0.0005830696,0.043476693,0.0015347851,0.53746533,0.0090833595,0.3992992],"study_design_scores_gemma":[0.0002178675,0.00013971125,0.0025033113,0.00039984816,0.000085650754,0.00035461932,0.00012010542,0.2445536,0.0020064474,0.7322802,0.017242203,0.00009644522],"about_ca_topic_score_codex":0.0008770967,"about_ca_topic_score_gemma":0.00092204177,"teacher_disagreement_score":0.02137909,"about_ca_system_score_codex":0.00075833575,"about_ca_system_score_gemma":0.0014337453,"threshold_uncertainty_score":0.113064826},"labels":[],"label_agreement":null},{"id":"W2038999742","doi":"10.1007/s11749-010-0206-2","title":"Estimation of mean squared error of model-based small area estimators","year":2010,"lang":"en","type":"article","venue":"Test","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Estimator; Mean squared error; Small area estimation; Mathematics; Statistics; Efficient estimator; Estimation; Bias of an estimator; Efficiency; Minimum mean square error; Minimum-variance unbiased estimator","score_opus":0.09447748159691625,"score_gpt":0.3715502187633022,"score_spread":0.2770727371663859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038999742","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":[0.007418437,0.00028998396,0.9914842,0.00013307134,0.000030810646,0.000016842268,0.000041016443,0.00018275998,0.00040281928],"genre_scores_gemma":[0.41555512,0.0008366851,0.57842,0.00029366883,0.00027245405,0.00041298816,0.00065949705,0.000496429,0.0030532314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98867726,0.008267733,0.00040348008,0.001063038,0.0013775802,0.00021105923],"domain_scores_gemma":[0.84843385,0.13691108,0.0034604077,0.006663841,0.0039279973,0.00060280925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024397762,0.0012195644,0.0031478144,0.0025183558,0.0006248977,0.0021141104,0.0030732506,0.0029570144,0.003611894],"category_scores_gemma":[0.16121092,0.0009467478,0.0012363242,0.0018776541,0.0029929343,0.0040509505,0.0027821602,0.0024641072,0.0007712576],"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":[0.0006438545,0.00016859129,0.007881599,0.00063845847,0.0010149124,0.00020566212,0.00023372422,0.59207994,0.0038438172,0.23377708,0.0035344623,0.15597787],"study_design_scores_gemma":[0.00004172303,0.00009190091,0.0013849627,0.000051577314,0.00006136287,0.00011916452,0.000026801219,0.8969075,0.0015743497,0.0988167,0.0008932177,0.000030721127],"about_ca_topic_score_codex":0.0016827593,"about_ca_topic_score_gemma":0.0011728755,"teacher_disagreement_score":0.024397762,"about_ca_system_score_codex":0.0011418087,"about_ca_system_score_gemma":0.0016457723,"threshold_uncertainty_score":0.12902921},"labels":[],"label_agreement":null},{"id":"W2040030112","doi":"10.2307/3315965","title":"Score tests for zero inflation in generalized linear models","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zero (linguistics); Inflation (cosmology); Goodness of fit; Generalized linear model; Poisson distribution; Mathematics; Binomial (polynomial); Applied mathematics; Score test; Negative binomial distribution; Statistics; Linear model; Econometrics; Statistical hypothesis testing; Physics","score_opus":0.11317774050703258,"score_gpt":0.3573222353182051,"score_spread":0.24414449481117254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040030112","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":[0.24630515,0.0006591113,0.741894,0.002285208,0.00020748809,0.00012829894,0.0006029942,0.0008010827,0.0071167117],"genre_scores_gemma":[0.96557695,0.00012065624,0.032396168,0.00019181115,0.00014729187,0.00011116752,0.0005226274,0.00008768934,0.00084567757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93908733,0.047999002,0.0018915785,0.003233674,0.0063594924,0.0014288886],"domain_scores_gemma":[0.58453846,0.37224126,0.016077735,0.012415363,0.011695311,0.0030319004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034240864,0.0012007224,0.002226657,0.004331625,0.0010534422,0.003987717,0.0027908005,0.0020663491,0.00691383],"category_scores_gemma":[0.32265803,0.0005171083,0.0016417822,0.0043122894,0.0060626087,0.0041370946,0.0045983833,0.003440668,0.0008936074],"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":[0.0018121194,0.00044911462,0.1362611,0.0006437548,0.0022799256,0.0006777226,0.0014311059,0.1472281,0.0014494468,0.5374933,0.00854174,0.1617326],"study_design_scores_gemma":[0.00016973601,0.000599314,0.021167869,0.00012889458,0.00016532913,0.00023945152,0.0004513033,0.39509848,0.0008165872,0.57947725,0.0015769881,0.00010876476],"about_ca_topic_score_codex":0.0019994073,"about_ca_topic_score_gemma":0.0010074304,"teacher_disagreement_score":0.034240864,"about_ca_system_score_codex":0.0011990977,"about_ca_system_score_gemma":0.0018687582,"threshold_uncertainty_score":0.18108511},"labels":[],"label_agreement":null},{"id":"W2040384528","doi":"10.1080/10705511.2014.935266","title":"Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models","year":2014,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":36,"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":"Social Sciences and Humanities Research Council of Canada; Western Canada Research Grid","keywords":"Latent variable; Inference; Interval estimation; Latent variable model; Estimation; Econometrics; Interval (graph theory); Statistics; Computer science; Confidence interval; Mathematics; Artificial intelligence; Economics","score_opus":0.10197084436957725,"score_gpt":0.4250064667261869,"score_spread":0.32303562235660965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040384528","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":[0.0032760387,0.0003201605,0.99502385,0.00014188148,0.00004422142,0.000114089155,0.00011243526,0.00021619082,0.000751132],"genre_scores_gemma":[0.14878905,0.00077254977,0.84679425,0.0001554507,0.00018937328,0.0014945699,0.000709918,0.0002645171,0.0008303171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89728594,0.08928973,0.0021070156,0.0045380755,0.006099358,0.0006799078],"domain_scores_gemma":[0.43958965,0.52306575,0.010449224,0.018545128,0.007695254,0.0006549485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09890927,0.0021689949,0.002571296,0.008024146,0.0013549236,0.0042540194,0.006281473,0.002545433,0.011663983],"category_scores_gemma":[0.425139,0.0012501815,0.004040896,0.0074547953,0.004424902,0.0060658073,0.0056861253,0.007349854,0.001174284],"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":[0.00031225322,0.00024756766,0.013876925,0.00092072244,0.001291001,0.00019116359,0.0016211849,0.10933348,0.00047575918,0.6039735,0.003893429,0.26386306],"study_design_scores_gemma":[0.00011586428,0.0001337297,0.0027006543,0.0006258829,0.00024195814,0.00014079968,0.00029490818,0.46545082,0.0011800188,0.5250509,0.003969162,0.000095273004],"about_ca_topic_score_codex":0.004121858,"about_ca_topic_score_gemma":0.002629756,"teacher_disagreement_score":0.09890927,"about_ca_system_score_codex":0.0021590998,"about_ca_system_score_gemma":0.0026855334,"threshold_uncertainty_score":0.52308846},"labels":[],"label_agreement":null},{"id":"W2040467543","doi":"10.1007/s10985-006-9030-0","title":"Nonparametric estimation of the mean function of a stochastic process with missing observations","year":2006,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":28,"is_retracted":false,"has_abstract":false,"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 Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Nonparametric statistics; Censoring (clinical trials); Mathematics; Statistics; Binary data; Gaussian process; Covariance; Survival function; Missing data; Econometrics; Gaussian; Binary number","score_opus":0.0686326042531449,"score_gpt":0.35191805595979875,"score_spread":0.28328545170665387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040467543","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":[0.014103984,0.00024733803,0.9850118,0.0002235971,0.000019148096,0.000012419378,0.00010519377,0.000096873286,0.00017962832],"genre_scores_gemma":[0.64745945,0.0015935734,0.3457492,0.0001899176,0.00030287536,0.00029470833,0.0013712877,0.00019910105,0.0028398982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960912,0.0020392102,0.00019827475,0.0007972334,0.00065890263,0.00021516098],"domain_scores_gemma":[0.948248,0.042283837,0.0035464265,0.0040260656,0.0014483143,0.00044734427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017123003,0.0009558899,0.0022538677,0.002213257,0.0007019406,0.0022570824,0.0035239342,0.0024935366,0.0013471487],"category_scores_gemma":[0.07705998,0.0013195109,0.0013749904,0.0019778598,0.003185995,0.005073053,0.0021741374,0.0034365898,0.00036963436],"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":[0.0003695254,0.00017430069,0.009636358,0.00045025264,0.00051132636,0.0002799571,0.00043288773,0.5104322,0.0033254274,0.37523833,0.0022702687,0.09687923],"study_design_scores_gemma":[0.00001905759,0.000031369666,0.0017007898,0.000045356945,0.000041802952,0.00011242097,0.00003567493,0.79763824,0.0009848317,0.19853525,0.000817531,0.000037734786],"about_ca_topic_score_codex":0.0026919958,"about_ca_topic_score_gemma":0.0020199365,"teacher_disagreement_score":0.017123003,"about_ca_system_score_codex":0.001466728,"about_ca_system_score_gemma":0.0023612923,"threshold_uncertainty_score":0.090556145},"labels":[],"label_agreement":null},{"id":"W2040615921","doi":"10.2307/3316036","title":"A semi‐Markov model for binary longitudinal responses subject to misclassification","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unobservable; Binary number; Markov chain; Markov process; Mathematics; Statistics; Binary data; Applied mathematics; Counting process; Markov model; Computer science; Econometrics","score_opus":0.13782541658622907,"score_gpt":0.3724950606746295,"score_spread":0.23466964408840044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040615921","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":[0.0757785,0.00038727775,0.91866225,0.0018244853,0.00014115716,0.00018419285,0.0009474353,0.00033255317,0.0017422148],"genre_scores_gemma":[0.89086276,0.00072751875,0.0894085,0.00044079343,0.00024135069,0.0012089997,0.0018918873,0.00008240758,0.015135856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950113,0.002526578,0.0002554227,0.0009768987,0.00062360073,0.0006062983],"domain_scores_gemma":[0.96428764,0.02880354,0.0029364352,0.0014196937,0.0019181839,0.00063448865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017999653,0.001065831,0.0024259342,0.0016562846,0.00090603984,0.0020836955,0.0050306693,0.0031262585,0.008661562],"category_scores_gemma":[0.02632321,0.0014632605,0.0016714075,0.0013419483,0.0025493684,0.0026670166,0.0017669424,0.0032351573,0.0013436902],"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":[0.00087232667,0.00022079074,0.012393775,0.00019721473,0.00029582196,0.00053810515,0.00082809565,0.61522627,0.0013255703,0.34446353,0.002119014,0.021519553],"study_design_scores_gemma":[0.00007825104,0.000062228224,0.0011213655,0.000030076768,0.000042311978,0.000055882552,0.00003312012,0.95576775,0.00016587626,0.042073127,0.0005323858,0.000037740447],"about_ca_topic_score_codex":0.020008303,"about_ca_topic_score_gemma":0.013502525,"teacher_disagreement_score":0.020008303,"about_ca_system_score_codex":0.0025441367,"about_ca_system_score_gemma":0.002228751,"threshold_uncertainty_score":0.09519237},"labels":[],"label_agreement":null},{"id":"W2040620182","doi":"10.1007/bf02595814","title":"Posterior distributions for functions of variance components","year":2003,"lang":"en","type":"article","venue":"Test","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"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":"Posterior probability; Variance (accounting); Mathematics; Bayesian probability; Bayesian linear regression; Percentile; Monte Carlo method; Applied mathematics; Statistics; Markov chain Monte Carlo; Statistical physics; Bayesian inference; Physics","score_opus":0.09603092973850022,"score_gpt":0.36967506526974686,"score_spread":0.27364413553124667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040620182","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":[0.004172445,0.00041329055,0.9932094,0.0005658766,0.000044542423,0.0000749758,0.00024153509,0.0002348841,0.0010430182],"genre_scores_gemma":[0.2773711,0.0035996635,0.69829375,0.00096669514,0.0010082993,0.0026597911,0.0032933885,0.0009620066,0.0118453875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97852707,0.014695084,0.00065511157,0.0027139354,0.0027066155,0.0007021749],"domain_scores_gemma":[0.7235776,0.25658154,0.0046596914,0.0090884585,0.0050411103,0.0010515642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04449466,0.003121189,0.0046954076,0.0077131665,0.0016628308,0.007209788,0.0072461357,0.0061031044,0.014460798],"category_scores_gemma":[0.25568795,0.0019322985,0.0036195903,0.00451626,0.010046387,0.0121490415,0.0036930386,0.010141205,0.0028920881],"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":[0.00020137911,0.00010056974,0.0022978408,0.00036484213,0.0002873486,0.00016484558,0.0003478086,0.056492604,0.00046341697,0.8827927,0.0053805523,0.05110615],"study_design_scores_gemma":[0.00007797336,0.00003373336,0.00094456715,0.00017270316,0.00009896269,0.00017815917,0.00008594813,0.20601417,0.000499383,0.78968304,0.0021547282,0.000056662804],"about_ca_topic_score_codex":0.0034041023,"about_ca_topic_score_gemma":0.00209551,"teacher_disagreement_score":0.04449466,"about_ca_system_score_codex":0.0033469254,"about_ca_system_score_gemma":0.00297721,"threshold_uncertainty_score":0.23531306},"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":"W2042297468","doi":"10.1080/00949655.2011.569721","title":"A consistent simulation-based estimator in generalized linear mixed models","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian 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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Asymptotic distribution; Applied mathematics; Consistency (knowledge bases); Outlier; Invariant estimator; Bias of an estimator; Consistent estimator; Parametric statistics; Efficient estimator; Weak consistency; Generalized linear mixed model; Trimmed estimator; Statistics; Minimum-variance unbiased estimator; Strong consistency; Discrete mathematics","score_opus":0.18402526252368234,"score_gpt":0.4142985681483103,"score_spread":0.23027330562462794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042297468","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":[0.0011272797,0.00004385832,0.99856085,0.00005958096,0.000009674224,0.000020678199,0.000014158604,0.00004878785,0.00011509278],"genre_scores_gemma":[0.11497365,0.00029529788,0.88289964,0.0002081182,0.000083323655,0.0006302662,0.00026167132,0.00009483155,0.0005532198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97990614,0.017046403,0.00041126105,0.00099127,0.0014477342,0.00019703917],"domain_scores_gemma":[0.9583199,0.033626687,0.0022068021,0.0028874355,0.0026133675,0.00034585572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019084621,0.00090870174,0.0019436558,0.0016111655,0.00058868254,0.0017086206,0.0028216976,0.0018923185,0.0018466654],"category_scores_gemma":[0.07266423,0.0010069194,0.001904837,0.0015903314,0.0022505203,0.002012153,0.0027762095,0.0021352048,0.0006799005],"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":[0.00019041623,0.0001013115,0.0060310694,0.00032553024,0.00047352538,0.00019039781,0.00029556343,0.4962322,0.003077691,0.39544407,0.0016841467,0.09595404],"study_design_scores_gemma":[0.000068805755,0.000106783096,0.0004385335,0.000062477404,0.000054069427,0.000058874226,0.00002221188,0.86582625,0.0010676562,0.12999958,0.0022489722,0.000045809906],"about_ca_topic_score_codex":0.0011192407,"about_ca_topic_score_gemma":0.0010186844,"teacher_disagreement_score":0.019084621,"about_ca_system_score_codex":0.0007543086,"about_ca_system_score_gemma":0.002272283,"threshold_uncertainty_score":0.10093033},"labels":[],"label_agreement":null},{"id":"W2043478295","doi":"10.1024/1662-9647/a000047","title":"Joint Modeling of Longitudinal Change and Survival","year":2011,"lang":"en","type":"article","venue":"GeroPsych","topic":"Statistical Methods and Bayesian 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 Victoria","funders":"National Institute on Aging","keywords":"Longitudinal study; Joint (building); Association (psychology); Recall; Dropout (neural networks); Weibull distribution; Longitudinal data; Random effects model; Psychology; Demography; Cognitive psychology; Computer science; Statistics; Medicine; Engineering; Mathematics; Data mining; Machine learning","score_opus":0.466142779231477,"score_gpt":0.3955173349640249,"score_spread":0.0706254442674521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043478295","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":[0.022568276,0.00060004956,0.97389823,0.0008568852,0.00009751862,0.00011804863,0.0005349844,0.00026395745,0.0010620351],"genre_scores_gemma":[0.6667888,0.001756816,0.31581065,0.000515975,0.00031577217,0.0019332323,0.0024283505,0.0002431517,0.01020719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9843268,0.011262868,0.00057737273,0.0021209407,0.0011386273,0.0005734142],"domain_scores_gemma":[0.96455806,0.026662195,0.0028390936,0.0039537125,0.0015580246,0.0004289271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037148662,0.00089486584,0.0024396146,0.0021745998,0.0008949503,0.0026586233,0.0028656865,0.0017942529,0.004197617],"category_scores_gemma":[0.07320175,0.00095525716,0.0025009257,0.002943718,0.0023933004,0.003351266,0.0030022003,0.0030978569,0.0007187976],"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":[0.0003933778,0.00014698865,0.037847564,0.00033778598,0.0011249287,0.000292271,0.0019360019,0.33939502,0.00063452515,0.52367365,0.003308603,0.09090929],"study_design_scores_gemma":[0.000046964713,0.00014488143,0.006896588,0.00009264102,0.00018456174,0.00013336453,0.00014897539,0.6164942,0.00023090797,0.3706582,0.0049046734,0.00006403509],"about_ca_topic_score_codex":0.011833026,"about_ca_topic_score_gemma":0.01039908,"teacher_disagreement_score":0.037148662,"about_ca_system_score_codex":0.0018665805,"about_ca_system_score_gemma":0.0020520526,"threshold_uncertainty_score":0.19646329},"labels":[],"label_agreement":null},{"id":"W2043593191","doi":"10.1016/j.cct.2013.07.003","title":"Covariate-adjusted confidence interval for the intraclass correlation coefficient","year":2013,"lang":"en","type":"article","venue":"Contemporary Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"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":"","keywords":"Covariate; Statistics; Confidence interval; Intraclass correlation; Coverage probability; Sample size determination; Estimator; Cluster sampling; Standard error; Sampling design; Population; Econometrics; Medicine; Mathematics","score_opus":0.6589154888184945,"score_gpt":0.547937651841365,"score_spread":0.11097783697712948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043593191","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":[0.041266236,0.00845577,0.933356,0.0010436968,0.00087271683,0.0007467412,0.004133577,0.0020154172,0.008109843],"genre_scores_gemma":[0.5326825,0.002418686,0.4516813,0.001009629,0.0005434964,0.004505129,0.0048872917,0.0007834806,0.001488534],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9290441,0.04553208,0.0041375794,0.009307245,0.011078471,0.00090050016],"domain_scores_gemma":[0.59682286,0.31596133,0.021764271,0.037901156,0.026224604,0.0013258472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0725654,0.00106406,0.0032761982,0.0057336786,0.000836995,0.0024062674,0.003556943,0.004320364,0.005694956],"category_scores_gemma":[0.37955448,0.0004955494,0.0031614904,0.0050155716,0.0024592667,0.0025322842,0.0029701272,0.0051533855,0.0014269467],"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":[0.00601656,0.0006979075,0.10212788,0.00515513,0.007967463,0.0013322181,0.0038345621,0.053242043,0.007094874,0.25395066,0.0398605,0.5187203],"study_design_scores_gemma":[0.0014768433,0.00457194,0.26395488,0.0046172026,0.0056845844,0.00771419,0.0016371692,0.29254496,0.013940883,0.29832748,0.1042072,0.0013226722],"about_ca_topic_score_codex":0.0020823404,"about_ca_topic_score_gemma":0.00080528593,"teacher_disagreement_score":0.0725654,"about_ca_system_score_codex":0.0009429128,"about_ca_system_score_gemma":0.00148538,"threshold_uncertainty_score":0.38376707},"labels":[],"label_agreement":null},{"id":"W2043714498","doi":"10.2202/1557-4679.1177","title":"Interval Estimation of Some Epidemiological Measures of Association","year":2010,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian 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":"University of Windsor; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Confidence interval; Mathematics; Estimator; Relative risk; Interval estimation; Coverage probability; Econometrics","score_opus":0.09281863447476195,"score_gpt":0.4080686945668315,"score_spread":0.3152500600920695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043714498","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":[0.0065794857,0.004051316,0.9852749,0.00050326,0.0002627745,0.0002259678,0.0006028061,0.00028646027,0.002213028],"genre_scores_gemma":[0.21944177,0.005593911,0.7675391,0.0006422818,0.00081082765,0.0023620185,0.0020613298,0.00016879811,0.0013799459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9444697,0.041161045,0.0028943727,0.0049503515,0.0059462003,0.0005783812],"domain_scores_gemma":[0.66346306,0.2957512,0.016672721,0.013851859,0.009434486,0.0008266534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.067350894,0.0019146148,0.0029110308,0.00656517,0.0006766143,0.0034505054,0.0047222087,0.0031656795,0.003894419],"category_scores_gemma":[0.31200066,0.00083578646,0.0028161712,0.005827919,0.0026010505,0.0053389496,0.0033586416,0.0057871807,0.0007215222],"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":[0.00081592426,0.000268941,0.03585869,0.0030263022,0.0032935427,0.00058652833,0.0018418207,0.12154598,0.0012618175,0.46984088,0.007990014,0.3536694],"study_design_scores_gemma":[0.0002878382,0.00087425567,0.019577764,0.0016932201,0.001060868,0.0010799957,0.00068860786,0.40705532,0.0023493588,0.5375309,0.027431965,0.0003699315],"about_ca_topic_score_codex":0.0021397246,"about_ca_topic_score_gemma":0.00066665845,"teacher_disagreement_score":0.067350894,"about_ca_system_score_codex":0.001433251,"about_ca_system_score_gemma":0.0013757325,"threshold_uncertainty_score":0.35618985},"labels":[],"label_agreement":null},{"id":"W2043862209","doi":"10.1081/sta-120002855","title":"ON PSEUDO-LIKELIHOOD INFERENCE IN THE BINARY LONGITUDINAL MIXED MODEL","year":2002,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","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":true,"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Binary data; Mixed model; Poisson distribution; Mathematics; Statistics; Restricted maximum likelihood; Random effects model; Generalized linear mixed model; Binary number; Multivariate statistics; Poisson regression; Inference; Quasi-likelihood; Likelihood-ratio test; Count data; Applied mathematics; Estimation theory; Computer science; Artificial intelligence","score_opus":0.14441060829227265,"score_gpt":0.46054246014263606,"score_spread":0.3161318518503634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043862209","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":[0.0017222122,0.00014344713,0.9974795,0.0002210035,0.000028314545,0.000031208558,0.00004505821,0.0000610636,0.00026813053],"genre_scores_gemma":[0.11779657,0.0007916146,0.8770833,0.0006020242,0.00037246945,0.0007282472,0.00050600054,0.00017190838,0.0019479764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9472635,0.04637739,0.0010254624,0.0016792293,0.0032244013,0.0004301129],"domain_scores_gemma":[0.7658545,0.21736687,0.005091289,0.0061770147,0.0046223216,0.00088796654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047777675,0.0012580724,0.002426524,0.0033420331,0.0010682859,0.0027898771,0.005887917,0.0028307107,0.0044128885],"category_scores_gemma":[0.19698824,0.0015344527,0.002409525,0.003811172,0.0046197968,0.004977322,0.00588562,0.0053524734,0.0010660209],"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":[0.00024826257,0.00010372928,0.004678944,0.00048991124,0.00025507,0.00045863324,0.0005224533,0.17433856,0.0005835749,0.7406895,0.0024888024,0.07514257],"study_design_scores_gemma":[0.000040505856,0.00004015263,0.00050590205,0.00007056664,0.000025526922,0.000112604175,0.00003602146,0.7268312,0.00037969282,0.27045903,0.0014618931,0.000036952788],"about_ca_topic_score_codex":0.004123489,"about_ca_topic_score_gemma":0.0032898039,"teacher_disagreement_score":0.047777675,"about_ca_system_score_codex":0.0019150157,"about_ca_system_score_gemma":0.0022128683,"threshold_uncertainty_score":0.25267547},"labels":[],"label_agreement":null},{"id":"W2044478943","doi":"10.1177/1094428103254672","title":"How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method","year":2003,"lang":"en","type":"article","venue":"Organizational Research Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":69,"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; University of Waterloo","funders":"","keywords":"Categorical variable; Missing data; Imputation (statistics); Bayesian probability; Computer science; Regression; Statistics; Regression analysis; Data mining; Econometrics; Mathematics","score_opus":0.2853940616044742,"score_gpt":0.5576315963265147,"score_spread":0.2722375347220405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044478943","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":[0.056633234,0.00088246504,0.9283861,0.0047755637,0.00036095438,0.0009205301,0.00031940974,0.0005554719,0.007166335],"genre_scores_gemma":[0.48922917,0.0006714789,0.5021169,0.0021021916,0.0004585983,0.0020451325,0.00074387353,0.00042875097,0.0022038806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.77876425,0.19108126,0.005411193,0.00871221,0.01418011,0.0018509411],"domain_scores_gemma":[0.12020708,0.8464698,0.008478763,0.015820924,0.0075327936,0.0014907119],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2292475,0.0017348785,0.0043678633,0.0046889163,0.002018428,0.004393498,0.005867321,0.005907318,0.01379709],"category_scores_gemma":[0.6935367,0.0011878461,0.0050524063,0.004494958,0.006952363,0.011537217,0.0052123833,0.0057000667,0.0017671678],"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":[0.0058114687,0.0015915725,0.05141714,0.0019466343,0.0076572807,0.00084874354,0.003255806,0.08569056,0.0011188909,0.35916644,0.016945425,0.4645501],"study_design_scores_gemma":[0.0016181439,0.001850434,0.014189594,0.0005956928,0.0011354324,0.00059039507,0.0010943183,0.48122138,0.001953547,0.4867821,0.008721984,0.0002470068],"about_ca_topic_score_codex":0.0026845222,"about_ca_topic_score_gemma":0.0010448413,"teacher_disagreement_score":0.2292475,"about_ca_system_score_codex":0.001692131,"about_ca_system_score_gemma":0.00514635,"threshold_uncertainty_score":0.95047504},"labels":[],"label_agreement":null},{"id":"W2046161665","doi":"10.1073/pnas.98.3.837","title":"A stochastic model for the self-similar heterogeneity of regional organ blood flow","year":2001,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"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 Regional Cancer Foundation","funders":"","keywords":"Poisson distribution; Scaling; Stochastic modelling; Gamma distribution; Exponential function; Standard deviation; Exponential distribution; Flow (mathematics); Mathematics; Blood flow; Distribution (mathematics); Dispersion (optics); Statistical physics; Physics; Statistics; Mechanics; Mathematical analysis; Geometry; Medicine; Internal medicine","score_opus":0.15455752722204655,"score_gpt":0.39126170857339515,"score_spread":0.2367041813513486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046161665","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":[0.03967175,0.00018514969,0.9579808,0.00043188198,0.00003165774,0.000043093427,0.00018849091,0.00012652666,0.0013406386],"genre_scores_gemma":[0.8858788,0.0007524495,0.102523945,0.0002861719,0.00021607612,0.00047494355,0.00056496524,0.00009296642,0.009209603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914515,0.0002728632,0.000050995815,0.00021129358,0.00018313125,0.0001365186],"domain_scores_gemma":[0.99689794,0.002005779,0.00047413757,0.00019015506,0.00030384923,0.00012805425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028979091,0.00061685516,0.0011211338,0.0013018108,0.0005766126,0.0015005313,0.0024632998,0.002008748,0.0017081398],"category_scores_gemma":[0.005922428,0.00068586384,0.0014267131,0.000930845,0.0019597132,0.002110965,0.001019196,0.0018063766,0.00037826068],"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":[0.00003830694,0.000030626532,0.001318099,0.000046245652,0.000052265685,0.00014659113,0.000121411074,0.67840976,0.0025586926,0.31201532,0.00053926266,0.004723476],"study_design_scores_gemma":[0.000018933666,0.000017900136,0.00029400177,0.000006288958,0.0000125310125,0.000052753963,0.000008610065,0.9363863,0.00020361433,0.06236335,0.00061975216,0.000016084246],"about_ca_topic_score_codex":0.005288175,"about_ca_topic_score_gemma":0.003721937,"teacher_disagreement_score":0.005288175,"about_ca_system_score_codex":0.00179168,"about_ca_system_score_gemma":0.0012233579,"threshold_uncertainty_score":0.015325785},"labels":[],"label_agreement":null},{"id":"W2046177289","doi":"10.1186/1756-0500-3-231","title":"Problems encountered with the use of simulation in an attempt to enhance interpretation of a secondary data source in epidemiologic mental health research","year":2010,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Statistical Methods and Bayesian 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":"University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Fondation pour la Recherche Médicale","keywords":"Mental health; Interpretation (philosophy); Data science; Data source; Medicine; Computer science; Management science; Psychiatry; Data mining; Engineering","score_opus":0.6929301302786729,"score_gpt":0.6105454313762415,"score_spread":0.08238469890243139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046177289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006340103,0.0011270853,0.98047316,0.007332364,0.00042960778,0.0009862332,0.00012413586,0.00040244384,0.002784918],"genre_scores_gemma":[0.10751698,0.00095946266,0.8857141,0.00175987,0.000297152,0.002989651,0.00009940502,0.00026599853,0.0003974684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.32220346,0.644655,0.0148547115,0.0046429452,0.013085342,0.0005585539],"domain_scores_gemma":[0.07442375,0.8809406,0.0090743955,0.023129215,0.011779661,0.00065232476],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.52423054,0.0026081062,0.003192929,0.0056679645,0.0031140505,0.009754212,0.0060018883,0.0055253175,0.0038935111],"category_scores_gemma":[0.775076,0.0024642774,0.0033243373,0.0070077027,0.010481685,0.010351012,0.009568456,0.011653109,0.0010249907],"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":[0.0023874096,0.00059454417,0.03240264,0.008214165,0.0031420928,0.0012886738,0.024471588,0.13058119,0.0017192063,0.4247876,0.012475417,0.35793555],"study_design_scores_gemma":[0.00057421095,0.0013020338,0.0052055535,0.0058794296,0.00060608896,0.0016086561,0.0035079413,0.42409933,0.0026926305,0.5142156,0.039830737,0.00047774194],"about_ca_topic_score_codex":0.007263816,"about_ca_topic_score_gemma":0.0071257353,"teacher_disagreement_score":0.47576946,"about_ca_system_score_codex":0.0048140613,"about_ca_system_score_gemma":0.011020293,"threshold_uncertainty_score":0.5867085},"labels":[],"label_agreement":null},{"id":"W2046204761","doi":"10.1002/cjs.10039","title":"Inference after variable selection using restricted permutation methods","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Covariate; Inference; Statistics; Statistical inference; Variable (mathematics); Computer science; Data set; Feature selection; Resampling; Set (abstract data type); Selection (genetic algorithm); Permutation (music); Model selection; Data mining; Sample size determination; Econometrics; Mathematics; Machine learning; Artificial intelligence","score_opus":0.07041852243096688,"score_gpt":0.39726305578088866,"score_spread":0.3268445333499218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046204761","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":[0.0015595859,0.000082892795,0.9972289,0.00011355999,0.000045676097,0.000116367235,0.00005137505,0.0002137816,0.00058780384],"genre_scores_gemma":[0.088295,0.00028562965,0.9075064,0.0002853141,0.0001629598,0.0012423703,0.00046124824,0.00024835326,0.0015127333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94596547,0.044906713,0.0013916232,0.0038819432,0.0031309447,0.00072323217],"domain_scores_gemma":[0.8348089,0.14195211,0.002587835,0.015684366,0.0044191694,0.00054761383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048793137,0.0018889173,0.0030119065,0.003785434,0.0019389801,0.0028360107,0.0038436607,0.0021300875,0.009268845],"category_scores_gemma":[0.20352045,0.0011873358,0.0032038856,0.0044451477,0.00353306,0.0038349838,0.0031278636,0.005409229,0.0015640529],"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":[0.00052698166,0.00029185822,0.004894588,0.00055894087,0.0009174824,0.0005334751,0.0006415651,0.11360173,0.0013507691,0.5017437,0.0084008295,0.36653814],"study_design_scores_gemma":[0.00023641213,0.00013135673,0.00070955773,0.00008231712,0.00010978282,0.00015426928,0.00005884528,0.35794672,0.0010702264,0.6346137,0.0048436667,0.000043201748],"about_ca_topic_score_codex":0.006256529,"about_ca_topic_score_gemma":0.0058189584,"teacher_disagreement_score":0.048793137,"about_ca_system_score_codex":0.0016091664,"about_ca_system_score_gemma":0.0042394246,"threshold_uncertainty_score":0.25804585},"labels":[],"label_agreement":null},{"id":"W2046249327","doi":"10.1111/j.1467-9469.2004.02-064.x","title":"Maximum Likelihood Estimation for Cox's Regression Model Under Case–Cohort Sampling","year":2004,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre","funders":"National Institutes of Health; Aalborg Universitet","keywords":"Statistics; Mathematics; Estimator; Proportional hazards model; Cohort; Sampling (signal processing); Regression analysis; Sample size determination; Maximum likelihood; Econometrics; Mean squared error; Computer science","score_opus":0.08153168265338802,"score_gpt":0.3998308058810042,"score_spread":0.3182991232276162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046249327","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":[0.004146383,0.000388311,0.99456894,0.0002589076,0.00002468096,0.0001280228,0.00012127708,0.000092673145,0.00027089895],"genre_scores_gemma":[0.24459907,0.002176608,0.7437922,0.00032365997,0.00033155474,0.0030071512,0.0017257329,0.00015585266,0.0038881837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98210883,0.015317449,0.0003412455,0.0009549329,0.0010065255,0.00027101894],"domain_scores_gemma":[0.92451346,0.06904266,0.002074585,0.0026784777,0.0013823775,0.00030841425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04021589,0.000941647,0.0025395309,0.0021429434,0.0007041331,0.0015094335,0.0033566009,0.0019168997,0.0040885317],"category_scores_gemma":[0.1123129,0.0011830808,0.0018916486,0.0026755712,0.0014865084,0.002134948,0.0022018075,0.0027954027,0.0011760662],"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":[0.00078631856,0.00020829208,0.01424891,0.000960689,0.0011282285,0.0010707371,0.0005399615,0.44493935,0.0010599898,0.31151733,0.009167868,0.2143723],"study_design_scores_gemma":[0.0001382751,0.00008915707,0.0017060072,0.00009314754,0.000098832264,0.00023505827,0.000042105417,0.85199,0.00031909166,0.14289968,0.002351373,0.000037361864],"about_ca_topic_score_codex":0.003717263,"about_ca_topic_score_gemma":0.002858405,"teacher_disagreement_score":0.04021589,"about_ca_system_score_codex":0.0011017523,"about_ca_system_score_gemma":0.0020820145,"threshold_uncertainty_score":0.21268451},"labels":[],"label_agreement":null},{"id":"W2046382281","doi":"10.1002/cjs.11180","title":"Multivariate one‐sided tests for nonlinear mixed‐effects models","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multivariate statistics; Multivariate analysis; Wald test; Contingency table; Multivariate analysis of variance; Statistics; Econometrics; Likelihood-ratio test; Statistical hypothesis testing; Score test; Test (biology); Multivariate normal distribution; Computer science; Mathematics; Geology","score_opus":0.09191539880402065,"score_gpt":0.34340782729320424,"score_spread":0.2514924284891836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046382281","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":[0.020217817,0.0010062086,0.97469825,0.0008792291,0.00018384354,0.00029803437,0.000617832,0.00047285517,0.0016260089],"genre_scores_gemma":[0.47760144,0.00074025907,0.51647013,0.0004252021,0.0005207201,0.0016537124,0.0008415614,0.000192726,0.001554245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9079986,0.07719236,0.0024357173,0.005837329,0.0056448686,0.0008910074],"domain_scores_gemma":[0.5091672,0.46398008,0.011061475,0.010638896,0.003848908,0.0013035343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.063236535,0.0019327777,0.0035624008,0.0043594916,0.001187944,0.0030162053,0.0042739185,0.0025317771,0.01586039],"category_scores_gemma":[0.29307473,0.000707033,0.0033378126,0.005166986,0.005963358,0.004798791,0.0031262585,0.0047523347,0.0012020717],"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":[0.0019472444,0.0005150706,0.040123988,0.0024790002,0.0044580456,0.0018218458,0.0010062637,0.09836546,0.0025237056,0.48608652,0.010242856,0.35043004],"study_design_scores_gemma":[0.00041525028,0.001180967,0.009855965,0.00033345382,0.00047779793,0.00070155633,0.00033663545,0.4094131,0.00214627,0.56902945,0.0059416657,0.00016798681],"about_ca_topic_score_codex":0.0018661878,"about_ca_topic_score_gemma":0.001419199,"teacher_disagreement_score":0.063236535,"about_ca_system_score_codex":0.0016269695,"about_ca_system_score_gemma":0.003433072,"threshold_uncertainty_score":0.33443075},"labels":[],"label_agreement":null},{"id":"W2046657333","doi":"10.1002/cjs.11249","title":"Multiple imputation for the analysis of incomplete compound variables","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Missing data; Estimator; Multivariate statistics; Statistics; Econometrics; Mathematics; Computer science","score_opus":0.12703087820378292,"score_gpt":0.35598180791201056,"score_spread":0.22895092970822764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046657333","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":[0.0024724733,0.00051315647,0.99622405,0.00022338553,0.000045824265,0.000044610304,0.00013748216,0.00009158924,0.00024730747],"genre_scores_gemma":[0.14561182,0.001309598,0.8498552,0.0002149099,0.00019088724,0.00052024715,0.0007316492,0.00012604993,0.0014396083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97113085,0.022768736,0.0011083095,0.0019070118,0.0026913988,0.00039366572],"domain_scores_gemma":[0.8886818,0.087025225,0.009404346,0.010119108,0.004226081,0.0005434193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047627155,0.00095387746,0.0036955397,0.0030052115,0.0011370016,0.002233816,0.0046541644,0.0021408612,0.0043949853],"category_scores_gemma":[0.12603037,0.0010580608,0.0023425282,0.007342899,0.002008096,0.0026013127,0.0028416826,0.004649466,0.0008593157],"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":[0.00033858663,0.00013812963,0.016421355,0.00123572,0.0017252453,0.00092566415,0.0006473385,0.27153596,0.0008923088,0.42790204,0.009870738,0.2683669],"study_design_scores_gemma":[0.00007305563,0.000074139236,0.0022137044,0.00022663597,0.0001489644,0.00020594253,0.00004766572,0.62752056,0.0005733215,0.3637002,0.0051615573,0.000054237444],"about_ca_topic_score_codex":0.00649327,"about_ca_topic_score_gemma":0.00769367,"teacher_disagreement_score":0.047627155,"about_ca_system_score_codex":0.002093617,"about_ca_system_score_gemma":0.004284014,"threshold_uncertainty_score":0.25187945},"labels":[],"label_agreement":null},{"id":"W2047330460","doi":"10.1177/0962280212446326","title":"Log Gaussian Cox processes and spatially aggregated disease incidence data","year":2012,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian 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":"Cancer Care Ontario; Public Health Ontario; University of Toronto","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Cox process; Markov chain Monte Carlo; Inference; Computer science; Gaussian; Statistics; Mixture model; Econometrics; Gaussian network model; Gaussian process; Data mining; Mathematics; Monte Carlo method; Poisson distribution; Artificial intelligence","score_opus":0.3339326463437911,"score_gpt":0.6084049746751412,"score_spread":0.2744723283313501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047330460","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":[0.016562944,0.00033793284,0.98055637,0.000544949,0.000059983602,0.00008126996,0.00046907843,0.00022698731,0.0011604987],"genre_scores_gemma":[0.69344866,0.001820427,0.2922913,0.00043881036,0.00038104842,0.0010501987,0.0015679569,0.00010139071,0.008900103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99553275,0.0026369984,0.00017272931,0.0006817311,0.000724428,0.00025141268],"domain_scores_gemma":[0.97746915,0.017478002,0.002192218,0.0015787529,0.0009841179,0.00029770847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010199003,0.0007116124,0.0010405513,0.0020073853,0.0005855502,0.0020809646,0.002326436,0.0016657796,0.0032354863],"category_scores_gemma":[0.029937113,0.0007069427,0.0013520367,0.0026762101,0.0020089573,0.0030489406,0.0017779049,0.0027909493,0.0005542181],"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":[0.00009446469,0.00006603546,0.009287133,0.00012673979,0.00013432557,0.00031848392,0.00043758957,0.44109532,0.00041036095,0.51798093,0.0022072932,0.02784136],"study_design_scores_gemma":[0.00003249352,0.000045814733,0.0013968449,0.00002479062,0.000030323103,0.000085276326,0.00006770017,0.7915693,0.0001683031,0.2033999,0.0031519136,0.000027394692],"about_ca_topic_score_codex":0.012593952,"about_ca_topic_score_gemma":0.008713711,"teacher_disagreement_score":0.012593952,"about_ca_system_score_codex":0.0019152544,"about_ca_system_score_gemma":0.001954849,"threshold_uncertainty_score":0.05393809},"labels":[],"label_agreement":null},{"id":"W2047555822","doi":"10.1080/16066350802582714","title":"Reply to Stockwell and Kerr","year":2009,"lang":"en","type":"article","venue":"Addiction Research & Theory","topic":"Statistical Methods and Bayesian Inference","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":"Concordia University","funders":"","keywords":"Psychology","score_opus":0.11680041492902352,"score_gpt":0.46626571253491317,"score_spread":0.3494652976058896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047555822","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":[0.000052918367,0.0014797773,0.00008411338,0.9851199,0.012989748,0.0000025803672,0.000025693995,0.000007672087,0.00023758133],"genre_scores_gemma":[0.0012814585,0.00091060565,0.00014619273,0.97207594,0.024257746,0.000014384725,0.00001136753,0.000019183995,0.0012830831],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9907804,0.0035731362,0.0011764376,0.0014248261,0.0024013738,0.00064369926],"domain_scores_gemma":[0.89829046,0.08000375,0.0036165973,0.0033784271,0.011501521,0.0032093264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018793277,0.0013444346,0.0032656358,0.0019289604,0.0044516413,0.0075460626,0.0051874905,0.061905526,0.0092551755],"category_scores_gemma":[0.13523573,0.0013591684,0.0017648763,0.0024084924,0.010892572,0.009568207,0.004365345,0.07824514,0.008642964],"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":[0.000021825139,0.0000090748135,0.00012184699,0.0000570244,0.000021030246,0.00007277926,0.00014406831,0.000026070353,0.00003138291,0.0047339434,0.9919517,0.0028093595],"study_design_scores_gemma":[0.000114944436,0.000033477194,0.0018123976,0.0011100376,0.00010180037,0.0008576985,0.0016244809,0.00047053568,0.00027124977,0.05185721,0.94159037,0.00015573042],"about_ca_topic_score_codex":0.0072386554,"about_ca_topic_score_gemma":0.0076181884,"teacher_disagreement_score":0.061905526,"about_ca_system_score_codex":0.0046475995,"about_ca_system_score_gemma":0.004769214,"threshold_uncertainty_score":0.09938949},"labels":[],"label_agreement":null},{"id":"W2047815880","doi":"10.1016/j.csda.2015.03.013","title":"Likelihood inference for small area estimation using data cloning","year":2015,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Alberta; University of Manitoba; Manitoba Health","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Small area estimation; Generalized linear mixed model; Markov chain Monte Carlo; Statistics; Inference; Mathematics; Bayesian probability; Computer science; Econometrics; Sample size determination; Bayesian inference; Algorithm; Artificial intelligence; Estimator","score_opus":0.4510083262298539,"score_gpt":0.4857792121440956,"score_spread":0.03477088591424171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047815880","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":[0.0010587891,0.00007227592,0.9985025,0.00006225449,0.000012502895,0.000011391009,0.000033406235,0.000094903306,0.00015204157],"genre_scores_gemma":[0.09856118,0.00040795823,0.89565843,0.00021539086,0.00015885638,0.00045363637,0.0006553351,0.00045986305,0.0034293397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911724,0.00546771,0.00043269878,0.0015648155,0.0011041097,0.00025830136],"domain_scores_gemma":[0.89091223,0.09399891,0.0024620665,0.009106845,0.0028754359,0.0006444972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016792472,0.0013941574,0.0029622612,0.0025006605,0.0016855074,0.003496552,0.0057600173,0.002835024,0.0062187836],"category_scores_gemma":[0.109046556,0.0024137134,0.00269385,0.003991863,0.003998386,0.007835251,0.00625851,0.0056477753,0.0019442516],"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":[0.00031865796,0.00011531267,0.0031412388,0.00040526013,0.00031320582,0.00026797122,0.0005453444,0.24248834,0.0028870073,0.5974835,0.004871869,0.1471623],"study_design_scores_gemma":[0.00003200769,0.00002284236,0.00034466127,0.000034833625,0.000034445668,0.00008053418,0.000029372606,0.65842026,0.0008753483,0.33805263,0.0020489297,0.000024099343],"about_ca_topic_score_codex":0.0039014365,"about_ca_topic_score_gemma":0.0031840783,"teacher_disagreement_score":0.016792472,"about_ca_system_score_codex":0.0015962023,"about_ca_system_score_gemma":0.0024741313,"threshold_uncertainty_score":0.08880818},"labels":[],"label_agreement":null},{"id":"W2047817271","doi":"10.5539/ijsp.v1n2p229","title":"Bayesian Simultaneous Intervals for Small Areas: An Application to Variation in Maps","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Credible interval; Mathematics; Interval (graph theory); Bayesian probability; Poisson distribution; Nonparametric statistics; Statistics; Coverage probability; Bayesian inference; Parametric statistics; Applied mathematics; Inference; Confidence interval; Algorithm; Mathematical optimization; Computer science; Artificial intelligence; Combinatorics","score_opus":0.0474310433929793,"score_gpt":0.371207203625952,"score_spread":0.3237761602329727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047817271","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":[0.0012649879,0.00018878568,0.9979494,0.000094949755,0.000011908351,0.00002445379,0.000025843972,0.000106523104,0.00033311543],"genre_scores_gemma":[0.075125314,0.0004582292,0.92267716,0.000102870225,0.00014123833,0.00028796354,0.00013788314,0.00023144345,0.0008377823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97959375,0.014032894,0.0006637111,0.0020620723,0.0032400077,0.0004075292],"domain_scores_gemma":[0.8818174,0.10880743,0.0027559772,0.0033567136,0.002625403,0.000637143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029154548,0.0011223541,0.0024938304,0.0051607857,0.0013769127,0.0027061647,0.0042072893,0.0025914896,0.0042716665],"category_scores_gemma":[0.14266638,0.0013603463,0.0034590815,0.0051693446,0.003934417,0.0041965707,0.00515155,0.005085035,0.00049671234],"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":[0.00014283172,0.000069794616,0.0030183296,0.00042999937,0.00042006068,0.00036641714,0.0012028228,0.2596038,0.0017952062,0.53926015,0.0025779512,0.19111261],"study_design_scores_gemma":[0.000042505584,0.000041543008,0.00082473725,0.0000748372,0.00006797856,0.0001842612,0.000090234054,0.56231344,0.0005916027,0.43139297,0.0043079946,0.00006789342],"about_ca_topic_score_codex":0.007951569,"about_ca_topic_score_gemma":0.005373529,"teacher_disagreement_score":0.029154548,"about_ca_system_score_codex":0.001684723,"about_ca_system_score_gemma":0.0022345134,"threshold_uncertainty_score":0.15418589},"labels":[],"label_agreement":null},{"id":"W2048108181","doi":"10.1155/2012/640153","title":"Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues","year":2011,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":150,"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; York University; University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Covariate; Inference; Longitudinal data; Econometrics; Survival analysis; Joint (building); Longitudinal study; Process (computing); Computer science; Statistics; Missing data; Data mining; Mathematics; Machine learning; Artificial intelligence; Engineering","score_opus":0.3512865377508967,"score_gpt":0.47051411707112684,"score_spread":0.11922757932023015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048108181","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":[0.0011726352,0.0068326746,0.9880822,0.0028842865,0.00017108954,0.000053575426,0.00014015847,0.00008935316,0.0005740581],"genre_scores_gemma":[0.079249844,0.021539705,0.88990617,0.001803983,0.0028719616,0.0011409261,0.0009131083,0.00023918293,0.0023352054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92039675,0.06503103,0.0026763212,0.005085075,0.006077205,0.00073376636],"domain_scores_gemma":[0.6656328,0.3097802,0.007878236,0.010268329,0.0054459246,0.0009944915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.102929294,0.0018853282,0.004645028,0.004406568,0.0017462823,0.0049565574,0.006519305,0.0051679984,0.003564429],"category_scores_gemma":[0.2215237,0.0022073751,0.0033000957,0.0074488064,0.006539544,0.010147094,0.0051228395,0.010059328,0.0009496961],"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":[0.00007428726,0.00014147266,0.004844875,0.00091408764,0.0006425971,0.00023798032,0.0007843468,0.023289124,0.00018711899,0.8547201,0.0052270014,0.10893705],"study_design_scores_gemma":[0.000026147243,0.00003591579,0.0008711742,0.00024522486,0.00007993803,0.0001666301,0.0001187985,0.08205228,0.00014672094,0.9095007,0.006704526,0.00005194404],"about_ca_topic_score_codex":0.0077189137,"about_ca_topic_score_gemma":0.0037334333,"teacher_disagreement_score":0.102929294,"about_ca_system_score_codex":0.00263582,"about_ca_system_score_gemma":0.005386049,"threshold_uncertainty_score":0.5443486},"labels":[],"label_agreement":null},{"id":"W2048248779","doi":"10.1111/j.1467-842x.2012.00679.x","title":"Shrinkage and Penalty Estimators of a Poisson Regression Model","year":2012,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Brock University; University of Winnipeg","funders":"","keywords":"Estimator; Lasso (programming language); Mathematics; Shrinkage; Extremum estimator; Shrinkage estimator; Subspace topology; Poisson distribution; Mean squared error; Penalty method; Statistics; Applied mathematics; Regression; M-estimator; Mathematical optimization; Efficient estimator; Computer science; Mathematical analysis","score_opus":0.07798474966300933,"score_gpt":0.38609591453745107,"score_spread":0.30811116487444173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048248779","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":[0.024372235,0.00013200993,0.9742329,0.00025426853,0.000027426453,0.000025528378,0.000031671323,0.00008334699,0.00084053463],"genre_scores_gemma":[0.63117003,0.0005423108,0.36319548,0.00024223936,0.000158924,0.0002948937,0.00039020076,0.00014792326,0.0038580273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99499536,0.003272719,0.00016636135,0.00041645428,0.000988227,0.00016076052],"domain_scores_gemma":[0.98180276,0.012894597,0.0016352643,0.0014789847,0.0019407229,0.0002476747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015476248,0.0005452556,0.0009745447,0.001232239,0.00037990336,0.0010330412,0.0017619209,0.0009886671,0.0016173959],"category_scores_gemma":[0.046580162,0.00048961997,0.00093223713,0.00095340324,0.0013985004,0.0015385207,0.0018695837,0.002001077,0.00044350012],"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":[0.0002532552,0.00011050613,0.008170622,0.00017470549,0.00017318234,0.00027612114,0.00028096052,0.6399669,0.0032933054,0.19943722,0.0029470564,0.1449162],"study_design_scores_gemma":[0.000013321346,0.000027924034,0.0007525894,0.000015656928,0.000009944815,0.00004271291,0.000017476214,0.95964223,0.0004523454,0.038310282,0.000698255,0.000017153792],"about_ca_topic_score_codex":0.0012107366,"about_ca_topic_score_gemma":0.00083074375,"teacher_disagreement_score":0.015476248,"about_ca_system_score_codex":0.0005630876,"about_ca_system_score_gemma":0.0010795982,"threshold_uncertainty_score":0.08184719},"labels":[],"label_agreement":null},{"id":"W2049727490","doi":"10.3389/fpubh.2015.00057","title":"Threshold-Free Measures for Assessing the Performance of Medical Screening Tests","year":2015,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Statistical Methods and Bayesian Inference","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 Waterloo; MacEwan University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Receiver operating characteristic; Medicine; Screening test; Prevalence; Test (biology); Population; Statistics; Disease; Measure (data warehouse); Predictive power; Predictive value; Computer science; Internal medicine; Mathematics; Data mining; Pediatrics; Environmental health","score_opus":0.22562283809648717,"score_gpt":0.44362208983644175,"score_spread":0.21799925173995457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049727490","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":[0.037177917,0.00585735,0.94905514,0.0012335968,0.00030033116,0.00035618964,0.0012855269,0.00054144487,0.0041924855],"genre_scores_gemma":[0.6209349,0.002592222,0.36995688,0.000690254,0.0008752397,0.0015182626,0.0020966285,0.00032676416,0.0010088301],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9441779,0.034549914,0.0040025846,0.0036880327,0.012947998,0.0006336664],"domain_scores_gemma":[0.6746664,0.2751837,0.022920683,0.014178195,0.011305174,0.0017458365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060706943,0.002498903,0.0020082137,0.014362085,0.000985517,0.0040345946,0.0031806796,0.003902732,0.0028661485],"category_scores_gemma":[0.2759483,0.00066709955,0.002643275,0.007843251,0.0036601787,0.005306735,0.0024740677,0.0032696973,0.0007418098],"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":[0.0016119955,0.0005177372,0.122279264,0.0033239531,0.0040462553,0.00056969293,0.0014139167,0.27024284,0.0034276114,0.2607593,0.014061268,0.31774622],"study_design_scores_gemma":[0.00020358854,0.001588865,0.03354362,0.00089872116,0.0007288982,0.001174714,0.00034350308,0.52572274,0.0041991407,0.41967025,0.0116129685,0.00031296405],"about_ca_topic_score_codex":0.0016287767,"about_ca_topic_score_gemma":0.0010373153,"teacher_disagreement_score":0.060706943,"about_ca_system_score_codex":0.0029844346,"about_ca_system_score_gemma":0.0020047638,"threshold_uncertainty_score":0.32105285},"labels":[],"label_agreement":null},{"id":"W2050204377","doi":"10.1002/bimj.200900093","title":"Pattern‐Mixture Zero‐Inflated Mixed Models for Longitudinal Unbalanced Count Data with Excessive Zeros","year":2009,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Dropout (neural networks); Longitudinal data; Autoregressive model; Longitudinal study; Statistics; Poisson distribution; Random effects model; Mixed model; Zero (linguistics); Mathematics; Missing data; Econometrics; Computer science; Medicine; Data mining; Machine learning; Meta-analysis","score_opus":0.1483950334913557,"score_gpt":0.39588429263509023,"score_spread":0.24748925914373454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050204377","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":[0.018163918,0.00045502215,0.9793529,0.00042638276,0.000103469065,0.00024378214,0.0005522841,0.00027560323,0.0004266502],"genre_scores_gemma":[0.41260886,0.001011221,0.57132936,0.00067431125,0.00038113724,0.004218119,0.0027790326,0.00023473315,0.0067633097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.967292,0.025609842,0.0011613918,0.0034424001,0.0017020716,0.0007922002],"domain_scores_gemma":[0.9058929,0.07582718,0.0067218393,0.0077389847,0.0030635628,0.0007555124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04929614,0.0022458408,0.0038497255,0.0039732303,0.0016327174,0.0031803122,0.0070343614,0.0036575573,0.0052475287],"category_scores_gemma":[0.094389334,0.0020473124,0.005517151,0.004050056,0.0030754209,0.0042944774,0.003935011,0.0040627066,0.0011838181],"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":[0.0014532139,0.00038661688,0.033469766,0.00083167653,0.0026329216,0.0015149697,0.0024020658,0.34552133,0.0013085819,0.5101118,0.004391652,0.09597537],"study_design_scores_gemma":[0.00019874464,0.00028306636,0.0036004246,0.000111373745,0.0003839722,0.00023368113,0.00015173327,0.806967,0.00033523882,0.18433034,0.003291478,0.00011287179],"about_ca_topic_score_codex":0.0064047948,"about_ca_topic_score_gemma":0.007090541,"teacher_disagreement_score":0.04929614,"about_ca_system_score_codex":0.002286131,"about_ca_system_score_gemma":0.002112194,"threshold_uncertainty_score":0.260706},"labels":[],"label_agreement":null},{"id":"W2050304616","doi":"10.1016/s0167-7152(00)00126-7","title":"Is variance larger if and only if tails are larger? [Statist. Probab. Lett. 47 (2000) 141–147]","year":2000,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","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":"Royal Victoria Hospital","funders":"","keywords":"Mathematics; Variance (accounting); Statistical physics; Statistics; Pure mathematics; Physics","score_opus":0.03400635743949688,"score_gpt":0.31363716923739937,"score_spread":0.2796308117979025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050304616","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":[0.67455876,0.0153201725,0.21416523,0.03290199,0.0019410085,0.00007589418,0.002229594,0.000852305,0.057955038],"genre_scores_gemma":[0.98141795,0.002634385,0.008151074,0.0028252217,0.0013507648,0.000046102687,0.00048217832,0.0002172749,0.002874976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962214,0.00094064453,0.00023281231,0.0014567848,0.0005123173,0.0006360502],"domain_scores_gemma":[0.9628822,0.025822427,0.005437286,0.0033896358,0.0016609501,0.0008074401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047001964,0.00054331066,0.0016613022,0.0014989912,0.0008191659,0.0042225095,0.0013639787,0.002098946,0.013468643],"category_scores_gemma":[0.047613617,0.000538657,0.001110615,0.0015075519,0.0050111283,0.0052334894,0.0012097262,0.0020389268,0.0016048722],"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":[0.001971265,0.00023691992,0.06185215,0.0010565459,0.0013347536,0.0016800065,0.0013448823,0.0051716887,0.016515033,0.7087638,0.03134774,0.16872516],"study_design_scores_gemma":[0.00010832241,0.00009366686,0.041970253,0.00013074704,0.000379518,0.0010322598,0.00038785496,0.0074645416,0.0025315816,0.94159746,0.0041855215,0.000118340875],"about_ca_topic_score_codex":0.0015323901,"about_ca_topic_score_gemma":0.0010281977,"teacher_disagreement_score":0.013468643,"about_ca_system_score_codex":0.00067200337,"about_ca_system_score_gemma":0.0007127234,"threshold_uncertainty_score":0.045057118},"labels":[],"label_agreement":null},{"id":"W2050638343","doi":"10.1016/j.csda.2009.02.007","title":"Effects of ignoring baseline on modeling transitions from intact cognition to dementia","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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 Waterloo","funders":"Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation; National Institute on Aging; University of Kentucky","keywords":"Dementia; Cognition; Covariate; Multinomial logistic regression; Baseline (sea); Multinomial distribution; Cohort; Cognitive decline; Psychology; Logistic regression; Markov chain; Medicine; Gerontology; Audiology; Demography; Statistics; Psychiatry; Internal medicine; Mathematics; Disease","score_opus":0.07790374400610788,"score_gpt":0.38888932124822967,"score_spread":0.3109855772421218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050638343","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":[0.9636173,0.00044594656,0.03362779,0.000681771,0.00009316639,0.00011072797,0.0005404467,0.0002668222,0.0006159088],"genre_scores_gemma":[0.9920963,0.00006422599,0.0068705813,0.00021298576,0.000015685537,0.000072264345,0.0003431237,0.000048323935,0.0002763439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98980814,0.0071743485,0.0004878147,0.001834759,0.00024732258,0.00044761275],"domain_scores_gemma":[0.7480528,0.23322718,0.0048113144,0.009132387,0.002283541,0.0024927587],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05040631,0.00072255783,0.0014477873,0.0005238718,0.0012840807,0.0024531796,0.001599497,0.0028213556,0.0020223411],"category_scores_gemma":[0.20442006,0.0009260221,0.0020206163,0.00068695855,0.0014969191,0.0035472095,0.0016250677,0.0052000578,0.00019981599],"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":[0.081299804,0.003775184,0.275406,0.0008784231,0.0043186974,0.0010286872,0.0054692836,0.5358192,0.011596778,0.011672225,0.0024340055,0.06630167],"study_design_scores_gemma":[0.0015813682,0.009910745,0.14599131,0.0002442093,0.0060994015,0.0007070209,0.0012085947,0.7818184,0.008818254,0.041595712,0.0016076621,0.00041734907],"about_ca_topic_score_codex":0.02068668,"about_ca_topic_score_gemma":0.016687771,"teacher_disagreement_score":0.94959366,"about_ca_system_score_codex":0.0018874184,"about_ca_system_score_gemma":0.0027699564,"threshold_uncertainty_score":0.26657724},"labels":[],"label_agreement":null},{"id":"W2051129785","doi":"10.2202/1557-4679.1171","title":"Inference in Epidemic Models without Likelihoods","year":2009,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":194,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Inference; Markov chain Monte Carlo; Computer science; Monte Carlo method; Econometrics; Data mining; Statistics; Machine learning; Artificial intelligence; Mathematics; Bayesian probability","score_opus":0.0851133174261388,"score_gpt":0.42630224393751154,"score_spread":0.3411889265113727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051129785","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":[0.008483788,0.00043079007,0.9892057,0.0006734136,0.000031650394,0.000045233533,0.00012227692,0.00029210493,0.0007150568],"genre_scores_gemma":[0.36789656,0.0016408286,0.6255092,0.00063364755,0.00025540355,0.00044574574,0.00077185687,0.00033157502,0.0025152147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9820424,0.013669171,0.0008179413,0.001700049,0.0013139091,0.00045660668],"domain_scores_gemma":[0.7836256,0.19968055,0.0050362223,0.008092329,0.0026950322,0.0008702226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045771305,0.0015119726,0.0035023682,0.0034182502,0.0016175153,0.0046590553,0.0037811839,0.0039606523,0.004756791],"category_scores_gemma":[0.22900455,0.0024616073,0.002411136,0.0030840088,0.0050353585,0.010357741,0.0038155485,0.005053356,0.00089204893],"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":[0.00013186349,0.00005142834,0.0038587295,0.00020636235,0.00022117418,0.00022405786,0.00026330352,0.7121635,0.00030877136,0.25435045,0.00093772454,0.027282614],"study_design_scores_gemma":[0.00003204515,0.000014729466,0.00023098929,0.00003749089,0.000017855284,0.00005260181,0.000022295046,0.7747467,0.0001494413,0.22415599,0.0005202933,0.00001956788],"about_ca_topic_score_codex":0.01516664,"about_ca_topic_score_gemma":0.009574087,"teacher_disagreement_score":0.045771305,"about_ca_system_score_codex":0.0028015345,"about_ca_system_score_gemma":0.0038692974,"threshold_uncertainty_score":0.24206465},"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":"W2052847579","doi":"10.1038/sj.bjc.6606078","title":"Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer","year":2011,"lang":"en","type":"review","venue":"British Journal of Cancer","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia; BC Cancer Agency","funders":"NIHR Cambridge Biomedical Research Centre; BC Cancer Agency; Cancer Council Victoria; National Institute for Health and Care Research; National Health and Medical Research Council; Cancer Research UK","keywords":"Missing data; Imputation (statistics); Proportional hazards model; Breast cancer; Hazard ratio; Statistics; Oncology; Medicine; Data mining; Computer science; Internal medicine; Cancer; Mathematics; Confidence interval","score_opus":0.3276888214765711,"score_gpt":0.579606906818396,"score_spread":0.2519180853418249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052847579","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28039086,0.0060918448,0.7090771,0.001027007,0.0002734219,0.0010846581,0.0006463079,0.0006882744,0.000720435],"genre_scores_gemma":[0.5565383,0.002173709,0.43700823,0.00026970945,0.00018415877,0.0020128048,0.0010636766,0.00029424054,0.00045518356],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83987695,0.14605679,0.0044285506,0.0033044515,0.0056032436,0.0007300056],"domain_scores_gemma":[0.37398472,0.59632486,0.011222298,0.009203429,0.008529007,0.0007356819],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18164945,0.0015918859,0.0021087904,0.0047164527,0.000711705,0.0017459773,0.0029355409,0.0020774463,0.0014058421],"category_scores_gemma":[0.28094396,0.0011470945,0.0068357475,0.003510524,0.0014852406,0.002670315,0.0031833034,0.002697364,0.00026039092],"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":[0.020076951,0.0010715771,0.17421992,0.0038265528,0.03531883,0.0002792594,0.0023857513,0.2926443,0.0024030304,0.008745641,0.0023472277,0.45668095],"study_design_scores_gemma":[0.0021567189,0.004160268,0.07098396,0.0006725569,0.004699814,0.0004160976,0.00069016975,0.893856,0.0022827475,0.017935717,0.0017883562,0.00035757525],"about_ca_topic_score_codex":0.0033402261,"about_ca_topic_score_gemma":0.003349766,"teacher_disagreement_score":0.81835055,"about_ca_system_score_codex":0.0019803047,"about_ca_system_score_gemma":0.0025871634,"threshold_uncertainty_score":0.9606656},"labels":[],"label_agreement":null},{"id":"W2054140640","doi":"10.1002/sim.2210","title":"An appraisal of methods for the analysis of longitudinal categorical data with MAR drop‐outs","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","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 Guelph; University of Waterloo","funders":"","keywords":"Categorical variable; Statistics; Generalized estimating equation; Marginal model; Mathematics; Gee; Missing data; Econometrics; Contingency table; Applied mathematics; Regression analysis","score_opus":0.1568631137230554,"score_gpt":0.5382222258091557,"score_spread":0.3813591120861003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054140640","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":[0.0016247865,0.23912911,0.7383711,0.013313429,0.0022294384,0.0006227828,0.00027620196,0.0004834416,0.003949711],"genre_scores_gemma":[0.015954943,0.16755633,0.80530214,0.0031256187,0.0025343855,0.0025001133,0.00033299765,0.0004185886,0.0022747864],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8113067,0.13863188,0.009584349,0.0036147728,0.036281757,0.00058048434],"domain_scores_gemma":[0.478027,0.46318272,0.009003396,0.009696177,0.039073005,0.001017629],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19024833,0.0018639925,0.004176542,0.01285615,0.0014652733,0.005067888,0.006215861,0.00357869,0.0032354235],"category_scores_gemma":[0.3559669,0.0020744295,0.0044334116,0.011297375,0.005302486,0.0047898884,0.0026121482,0.0069105695,0.0015990401],"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":[0.00030873303,0.00014748753,0.005982672,0.011311498,0.00091404206,0.00027738832,0.0012641671,0.010864082,0.0006845898,0.1253437,0.027610682,0.8152909],"study_design_scores_gemma":[0.0005354531,0.0012793024,0.029284712,0.030170426,0.00090054295,0.0035505472,0.0018817399,0.11772027,0.0026133496,0.32857403,0.48247582,0.0010138272],"about_ca_topic_score_codex":0.006763146,"about_ca_topic_score_gemma":0.0077449647,"teacher_disagreement_score":0.19024833,"about_ca_system_score_codex":0.00500204,"about_ca_system_score_gemma":0.007173662,"threshold_uncertainty_score":0.998568},"labels":[],"label_agreement":null},{"id":"W2054292793","doi":"10.1097/00042752-200603000-00004","title":"Understanding the Relationship Between Risks and Odds Ratios","year":2006,"lang":"en","type":"article","venue":"Clinical Journal of Sport Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital","funders":"","keywords":"Relative risk; Odds ratio; Medicine; Confidence interval; Confounding; Statistics; Logistic regression; Odds; Zhàng; Mathematics; Internal medicine","score_opus":0.6649133274792299,"score_gpt":0.5362153587461209,"score_spread":0.128697968733109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054292793","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":[0.02756184,0.089403674,0.7773288,0.072216146,0.0019768097,0.00020144362,0.001015571,0.0005981113,0.02969761],"genre_scores_gemma":[0.48252982,0.097957246,0.39388493,0.012659131,0.0059968466,0.0006393206,0.0008375891,0.00035661802,0.0051385476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97349644,0.017177356,0.0021547389,0.002704154,0.004084578,0.0003827846],"domain_scores_gemma":[0.8273693,0.15563455,0.008624547,0.0030138427,0.004912408,0.00044544876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028846439,0.0012807114,0.0010205271,0.0058651385,0.00042521747,0.0050669117,0.0022456911,0.0019856554,0.0048712157],"category_scores_gemma":[0.17272443,0.00088354497,0.0012289071,0.0038139597,0.0041461946,0.009244444,0.0022146546,0.0058850395,0.0013240189],"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":[0.00018215897,0.00011325586,0.05278032,0.0027005887,0.0008028083,0.00078731973,0.0024105038,0.014020338,0.0011512965,0.6119581,0.017301764,0.29579166],"study_design_scores_gemma":[0.000034253448,0.00011616758,0.015688054,0.0014094575,0.0002870726,0.0010669581,0.00056429347,0.015324482,0.0008456302,0.9104319,0.054133106,0.0000985924],"about_ca_topic_score_codex":0.0028088894,"about_ca_topic_score_gemma":0.001216527,"teacher_disagreement_score":0.028846439,"about_ca_system_score_codex":0.0025155281,"about_ca_system_score_gemma":0.002060743,"threshold_uncertainty_score":0.15255642},"labels":[],"label_agreement":null},{"id":"W2054584201","doi":"10.1002/sim.3547","title":"Bayesian multivariate disease mapping and ecological regression with errors in covariates: Bayesian estimation of DALYs and ‘preventable’ DALYs","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"Child and Family Research Institute; University of British Columbia","funders":"Canadian Institutes of Health Research; University of British Columbia; Ministry of Health, British Columbia","keywords":"Multivariate statistics; Covariate; Bayesian probability; Estimation; Disease; Bayesian inference; Multivariate analysis; Regression analysis; Statistics; Geography; Computer science; Environmental health; Medicine; Machine learning; Engineering; Mathematics","score_opus":0.03902389800503935,"score_gpt":0.3799327395614607,"score_spread":0.3409088415564213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054584201","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":[0.004925994,0.0006148239,0.9925861,0.00065389957,0.000030782146,0.000024409694,0.000115751805,0.000054993827,0.0009932905],"genre_scores_gemma":[0.38633758,0.0049283104,0.60050946,0.0006755209,0.00045466088,0.0006380841,0.00080849644,0.00015002422,0.0054978356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99292433,0.0049750363,0.00020013595,0.0008022688,0.00085067877,0.00024753305],"domain_scores_gemma":[0.98236084,0.014207069,0.0015623783,0.0010389138,0.0006518399,0.00017890261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014243052,0.0011320258,0.0019303464,0.0022489864,0.00058065815,0.0020276392,0.002406028,0.001515533,0.0025070785],"category_scores_gemma":[0.05365126,0.0008126542,0.0017453241,0.0031817516,0.002140636,0.0031516908,0.0026513797,0.0027139708,0.0003497594],"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":[0.000056753124,0.000050040628,0.008193427,0.00015840275,0.00023431024,0.00012258872,0.00025814297,0.31950903,0.00022949708,0.58310705,0.0018694351,0.08621125],"study_design_scores_gemma":[0.000023924098,0.00002887928,0.002251374,0.00006863615,0.0000530918,0.00010709825,0.00004362076,0.4605391,0.00013691203,0.53213674,0.0045723845,0.000038276747],"about_ca_topic_score_codex":0.013851262,"about_ca_topic_score_gemma":0.009487349,"teacher_disagreement_score":0.014243052,"about_ca_system_score_codex":0.0017626036,"about_ca_system_score_gemma":0.0021580532,"threshold_uncertainty_score":0.07532537},"labels":[],"label_agreement":null},{"id":"W2054970172","doi":"10.1177/0013164407305589","title":"Population Validity and Cross-Validity","year":2007,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Statistical Methods and Bayesian Inference","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 Manitoba","funders":"","keywords":"Statistics; Confidence interval; Correlation coefficient; Sample size determination; Cross-validation; Mathematics; Population; Econometrics; Demography","score_opus":0.5024942114162912,"score_gpt":0.5054909374568204,"score_spread":0.0029967260405291407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054970172","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":[0.07861874,0.0216645,0.7195072,0.00627124,0.002389026,0.0026828488,0.0019106431,0.0006276176,0.16632822],"genre_scores_gemma":[0.7964696,0.0058572288,0.18001689,0.0032082067,0.000958878,0.005590814,0.0014659371,0.0003532435,0.0060791373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8470782,0.091522984,0.0095486045,0.015648669,0.034457017,0.0017444422],"domain_scores_gemma":[0.62603897,0.26780143,0.017796258,0.045035418,0.04156229,0.0017656519],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11569013,0.0008757047,0.0023381917,0.007816762,0.0022034508,0.005660276,0.002167103,0.0022726313,0.006533147],"category_scores_gemma":[0.42415634,0.000676861,0.0023782288,0.0058823097,0.010062091,0.007285752,0.0076786675,0.003067211,0.0013858897],"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":[0.00022015524,0.00020762162,0.11592708,0.0023464432,0.002171598,0.00035427613,0.0070472257,0.0054172105,0.00054083293,0.5184434,0.010662771,0.3366615],"study_design_scores_gemma":[0.00014934018,0.000390878,0.13568386,0.0033877161,0.0010270714,0.001838777,0.0048049456,0.014382504,0.0018908144,0.7394696,0.09675703,0.000217457],"about_ca_topic_score_codex":0.0035833507,"about_ca_topic_score_gemma":0.00317894,"teacher_disagreement_score":0.8843099,"about_ca_system_score_codex":0.003045771,"about_ca_system_score_gemma":0.005637296,"threshold_uncertainty_score":0.6118352},"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":"W2057163547","doi":"10.1002/cjs.5550330203","title":"Estimation of a finite population distribution function based on a linear model with unknown heteroscedastic errors","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Regional Development Fund; Xunta de Galicia; Ministerio de Ciencia y Tecnología","keywords":"Heteroscedasticity; Mathematics; Estimator; Statistics; Mean squared error; Nonparametric statistics; Asymptotic distribution; Applied mathematics; Estimation; Population; Distribution (mathematics); Mathematical analysis","score_opus":0.04322635767420027,"score_gpt":0.3077736477000427,"score_spread":0.26454729002584243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057163547","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":[0.022694463,0.00009420687,0.9765866,0.000106114865,0.000007190956,0.000016465678,0.00003642679,0.00004875954,0.00040981782],"genre_scores_gemma":[0.6658504,0.0006335173,0.32873407,0.00016140916,0.000047282723,0.00029766813,0.00033792213,0.00006655227,0.0038711517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980361,0.0012705653,0.00006368392,0.0003206563,0.00019977656,0.00010923191],"domain_scores_gemma":[0.9817585,0.015962455,0.000909456,0.0006620287,0.0005619242,0.00014560235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064280904,0.00056603615,0.0016064981,0.001139396,0.00049058726,0.0013782621,0.0025806648,0.0011939275,0.0014222751],"category_scores_gemma":[0.023041481,0.0005243209,0.0010630793,0.0011630689,0.001845691,0.00205044,0.001760044,0.0012222228,0.0002686695],"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":[0.000040119037,0.000036172358,0.0027297365,0.00007362087,0.000082710096,0.00011082019,0.00018898526,0.8752482,0.00065406645,0.095833324,0.00028775012,0.024714561],"study_design_scores_gemma":[0.0000065740237,0.000019999694,0.00038955116,0.000012490138,0.00001280631,0.000026541087,0.000024383291,0.97386235,0.00032426027,0.025075844,0.0002313678,0.0000137410525],"about_ca_topic_score_codex":0.009182147,"about_ca_topic_score_gemma":0.006168176,"teacher_disagreement_score":0.009182147,"about_ca_system_score_codex":0.0014210999,"about_ca_system_score_gemma":0.001301085,"threshold_uncertainty_score":0.03399539},"labels":[],"label_agreement":null},{"id":"W2059020892","doi":"10.2202/1557-4679.1179","title":"A Comparison of the Statistical Power of Different Methods for the Analysis of Repeated Cross-Sectional Cluster Randomization Trials with Binary Outcomes","year":2010,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","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":"Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Statistics; Sample size determination; Cluster randomised controlled trial; Statistical power; Generalized estimating equation; Baseline (sea); Cluster (spacecraft); Mathematics; Random effects model; Marginal model; Randomization; Homogeneity (statistics); Econometrics; Regression analysis; Medicine; Randomized controlled trial; Computer science; Meta-analysis; Internal medicine","score_opus":0.10719052713115843,"score_gpt":0.5200359822670371,"score_spread":0.41284545513587867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059020892","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":[0.046881624,0.013117357,0.9243819,0.0020496524,0.0011619721,0.007877056,0.000739493,0.00064892997,0.003141885],"genre_scores_gemma":[0.2846161,0.0037172632,0.682432,0.0011180626,0.00034477445,0.025840765,0.0005929899,0.00045981637,0.0008781697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.38693956,0.5687589,0.014704387,0.010935724,0.017692007,0.0009693619],"domain_scores_gemma":[0.13133016,0.81846225,0.015552125,0.024737312,0.009197387,0.000720829],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.48846915,0.0027059976,0.006070864,0.0061498582,0.0011966537,0.0037255648,0.004162981,0.0057283207,0.0064538512],"category_scores_gemma":[0.72804064,0.0016707447,0.009633243,0.0060391095,0.005353931,0.005466018,0.003909849,0.005290841,0.00062854885],"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":[0.06342996,0.0018517888,0.031040002,0.020099107,0.07821622,0.00050629355,0.0047553624,0.06325827,0.0042784535,0.13870798,0.0100431,0.5838135],"study_design_scores_gemma":[0.048100688,0.030455645,0.06305693,0.013493017,0.036621034,0.002044187,0.0012339392,0.39870286,0.0152849825,0.35403422,0.03482623,0.0021461954],"about_ca_topic_score_codex":0.0010455198,"about_ca_topic_score_gemma":0.00095865846,"teacher_disagreement_score":0.5115309,"about_ca_system_score_codex":0.002792958,"about_ca_system_score_gemma":0.003281375,"threshold_uncertainty_score":0.6308086},"labels":[],"label_agreement":null},{"id":"W2059088896","doi":"10.1002/cjs.5550350405","title":"General mixed‐data model: Extension of general location and grouped continuous models","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extension (predicate logic); Pairwise comparison; Inference; Computer science; Construct (python library); Maximization; Expectation–maximization algorithm; Statistical inference; Econometrics; Maximum likelihood; Statistical model; Grouped data; Joint probability distribution; Data mining; Statistics; Machine learning; Mathematics; Artificial intelligence; Mathematical optimization","score_opus":0.0991206201952849,"score_gpt":0.33950170862470114,"score_spread":0.24038108842941625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059088896","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":[0.018741706,0.00046234104,0.97528434,0.0013692789,0.00014554242,0.00021208676,0.0008704301,0.00027441356,0.002639743],"genre_scores_gemma":[0.5826973,0.0011905775,0.39356163,0.0010069838,0.0005868133,0.0020398705,0.0023111345,0.00029213817,0.016313612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98680925,0.008864032,0.00049555826,0.002220961,0.0009967958,0.0006133393],"domain_scores_gemma":[0.97004426,0.020480622,0.0028691888,0.003501411,0.0023663305,0.00073818985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020797983,0.001424235,0.0029840034,0.0029498613,0.0009828175,0.0038433087,0.008105058,0.0040152194,0.013926494],"category_scores_gemma":[0.045590363,0.0012642394,0.0043373154,0.0055041714,0.0027157834,0.0054809484,0.0039467844,0.003936553,0.0024566292],"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":[0.00036871803,0.00022678493,0.007050818,0.00041090065,0.0007673564,0.0009998193,0.0010218889,0.30284166,0.0006280946,0.6362896,0.0052226763,0.044171587],"study_design_scores_gemma":[0.00009699615,0.00014379177,0.0012958574,0.00008661937,0.00013819657,0.00024092017,0.00014364853,0.6389119,0.00015737634,0.35308132,0.0056424974,0.00006083529],"about_ca_topic_score_codex":0.007039702,"about_ca_topic_score_gemma":0.0055129877,"teacher_disagreement_score":0.020797983,"about_ca_system_score_codex":0.0024430288,"about_ca_system_score_gemma":0.001763845,"threshold_uncertainty_score":0.10999155},"labels":[],"label_agreement":null},{"id":"W2059804431","doi":"10.1016/s0895-7177(00)00134-5","title":"Classical and Bayesian approaches to compartment models based on in vivo cadmium data","year":2000,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","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":"McMaster University","funders":"","keywords":"Compartment (ship); Bayesian probability; Computer science; Consistency (knowledge bases); Mathematical model; Set (abstract data type); Experimental data; Applied mathematics; Mathematics; Algorithm; Statistics; Artificial intelligence; Geology","score_opus":0.34398347331781237,"score_gpt":0.3331665104352761,"score_spread":0.010816962882536252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059804431","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":[0.0029928437,0.00080671,0.99503607,0.00034369144,0.00003293331,0.000010983849,0.0000986008,0.00005631853,0.0006218536],"genre_scores_gemma":[0.38094306,0.00954586,0.58937997,0.00076603243,0.00064111437,0.00042683686,0.0007806097,0.0003538052,0.017162757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819934,0.0009152921,0.000099179786,0.00031591704,0.00036320693,0.000107126274],"domain_scores_gemma":[0.99449664,0.0042404947,0.0003877695,0.0003364274,0.00041945197,0.000119186254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042905724,0.0011165858,0.0016430841,0.0013927931,0.00057293416,0.0023828922,0.0041106595,0.0021488278,0.002301369],"category_scores_gemma":[0.012427611,0.001035545,0.0016152199,0.001646034,0.0022705216,0.0033610573,0.0015687511,0.003367547,0.0005701513],"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":[0.000096448275,0.000045265868,0.00054025324,0.00029003815,0.00017333328,0.00014846523,0.00017822167,0.49410683,0.002614135,0.47329825,0.001502307,0.027006453],"study_design_scores_gemma":[0.000010543925,0.000014254288,0.00024509948,0.000020131945,0.00003917562,0.00008614212,0.00001670666,0.6641982,0.0006499911,0.33252248,0.0021559552,0.00004122114],"about_ca_topic_score_codex":0.0072475146,"about_ca_topic_score_gemma":0.007202491,"teacher_disagreement_score":0.0072475146,"about_ca_system_score_codex":0.002720654,"about_ca_system_score_gemma":0.0019993628,"threshold_uncertainty_score":0.022690952},"labels":[],"label_agreement":null},{"id":"W2061062996","doi":"10.2307/3315865","title":"Bayesian methods for generalized linear models with covariates missing at random","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Bayesian linear regression; Mathematics; Bayesian probability; Statistics; Missing data; Generalized linear model; Conditional probability distribution; Bayesian inference; Joint probability distribution; Posterior probability; Linear model; Calibration","score_opus":0.1071731295981301,"score_gpt":0.36949358203707805,"score_spread":0.26232045243894797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061062996","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":[0.00034737177,0.0004237367,0.998604,0.00017040301,0.000029672807,0.000034442994,0.00006245719,0.00009327969,0.00023459997],"genre_scores_gemma":[0.036006372,0.0019991726,0.95760685,0.00028763825,0.0002895737,0.0009190705,0.00054287614,0.00019915836,0.0021493193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9780586,0.017564368,0.00054532837,0.0012693298,0.0022255986,0.00033672788],"domain_scores_gemma":[0.9614089,0.033714764,0.0016728654,0.0013687576,0.0015359393,0.00029881505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025201323,0.0022943856,0.0032080929,0.0037346252,0.0011631009,0.0025252588,0.0051785926,0.0031172065,0.0066196374],"category_scores_gemma":[0.07206579,0.0018158123,0.0028618795,0.0046282546,0.0027356811,0.0031190754,0.003706631,0.005463289,0.001694921],"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":[0.00011142373,0.00009580999,0.00081728236,0.0005385564,0.0005184193,0.00017029313,0.00033654738,0.28302112,0.00042529238,0.59032524,0.0056139426,0.11802608],"study_design_scores_gemma":[0.000088092725,0.000028684797,0.0002256602,0.00014278066,0.000064944164,0.00007357777,0.00003427491,0.45552224,0.00017950275,0.5375796,0.006014381,0.000046165584],"about_ca_topic_score_codex":0.008362379,"about_ca_topic_score_gemma":0.008636242,"teacher_disagreement_score":0.025201323,"about_ca_system_score_codex":0.0024685992,"about_ca_system_score_gemma":0.004071816,"threshold_uncertainty_score":0.1332789},"labels":[],"label_agreement":null},{"id":"W2061663271","doi":"10.1016/j.jspi.2012.04.006","title":"Marginal methods for clustered longitudinal binary data with incomplete covariates","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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 Waterloo","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Mathematics; Estimator; Statistics; Missing data; Marginal model; Estimating equations; Longitudinal data; Econometrics; Binary data; Random effects model; Generalized estimating equation; Data set; Binary number; Regression analysis; Data mining; Medicine; Computer science; Meta-analysis","score_opus":0.24570018443948302,"score_gpt":0.5004273527028614,"score_spread":0.2547271682633784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061663271","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":[0.0011386684,0.00041036925,0.9977507,0.00018920223,0.00002665952,0.000042293737,0.00016884,0.00010914435,0.00016405944],"genre_scores_gemma":[0.05590569,0.0015864223,0.93585414,0.00027919037,0.00032054115,0.0014210356,0.0013623551,0.0003394687,0.0029310887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98151416,0.01422478,0.0007635466,0.0018548461,0.0012394603,0.00040322664],"domain_scores_gemma":[0.8439218,0.1387138,0.0038609628,0.009578968,0.0028033613,0.0011211862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04271713,0.002061623,0.004372947,0.0039545395,0.0014777045,0.0030035034,0.0088526085,0.002739214,0.0108275255],"category_scores_gemma":[0.13146712,0.0029458867,0.0044249524,0.00499511,0.004144777,0.006088835,0.0053162533,0.007206898,0.0012000947],"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":[0.00034065478,0.00015314195,0.0036489093,0.0005866062,0.000981321,0.00019880528,0.0004868853,0.10320768,0.00033564348,0.7948107,0.005176698,0.090073034],"study_design_scores_gemma":[0.000099911595,0.000057937395,0.00074371987,0.0001177681,0.00014727052,0.00008714121,0.000056273973,0.28510815,0.00019505186,0.70990187,0.0034380371,0.00004677376],"about_ca_topic_score_codex":0.011446292,"about_ca_topic_score_gemma":0.015608531,"teacher_disagreement_score":0.04271713,"about_ca_system_score_codex":0.002904214,"about_ca_system_score_gemma":0.0063513834,"threshold_uncertainty_score":0.22591245},"labels":[],"label_agreement":null},{"id":"W2061932305","doi":"10.1007/s13571-011-0026-8","title":"Generalized confidence interval and p-value in location and scale family","year":2011,"lang":"en","type":"article","venue":"Sankhya B","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Inference; Scale (ratio); Location parameter; Mathematics; Confidence interval; Statistics; Set (abstract data type); Sample (material); Generalized linear model; Interval (graph theory); Econometrics; Computer science; Geography; Artificial intelligence; Cartography","score_opus":0.10002734224081626,"score_gpt":0.35491718329464395,"score_spread":0.25488984105382767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061932305","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":[0.010337112,0.0013475161,0.986361,0.00037808245,0.000055808687,0.000015414938,0.00009194157,0.00015758829,0.0012554615],"genre_scores_gemma":[0.6496257,0.0025942125,0.34175897,0.00035697667,0.00054348976,0.00039858202,0.0004929415,0.00027792715,0.0039512096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895756,0.0062909317,0.00043234607,0.0019092137,0.0014667236,0.00032513775],"domain_scores_gemma":[0.8435747,0.13849777,0.004606147,0.008381281,0.0040403414,0.0008997536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017944416,0.000939964,0.0021037504,0.003126563,0.0009062807,0.0028591864,0.0035827274,0.0027832466,0.0037342936],"category_scores_gemma":[0.14344038,0.0007242958,0.0016822122,0.0034890152,0.0050429013,0.0054014563,0.0026903898,0.0045514056,0.0005475732],"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":[0.000107766646,0.000025611325,0.002912633,0.0002185483,0.00017629126,0.00039737267,0.00046272986,0.038431194,0.0005856716,0.9185373,0.00181086,0.03633409],"study_design_scores_gemma":[0.000027547268,0.00004018982,0.001310564,0.000058067555,0.000071554394,0.0005214607,0.00008062259,0.15928654,0.00040649375,0.83581024,0.0023458295,0.000040875],"about_ca_topic_score_codex":0.0019492129,"about_ca_topic_score_gemma":0.000629788,"teacher_disagreement_score":0.017944416,"about_ca_system_score_codex":0.0013162111,"about_ca_system_score_gemma":0.0010806785,"threshold_uncertainty_score":0.09490025},"labels":[],"label_agreement":null},{"id":"W2063585734","doi":"10.1186/1471-2288-8-28","title":"Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database","year":2008,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; University of Guelph; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Gee; Generalized estimating equation; Confidence interval; Covariate; Structural equation modeling; Statistics; Population; Estimating equations; Longitudinal study; Psychology; Demography; Medicine; Mathematics","score_opus":0.9273639114767059,"score_gpt":0.6624919635322225,"score_spread":0.26487194794448343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063585734","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":[0.22641647,0.0059383516,0.7312798,0.004625569,0.00044220383,0.0027572305,0.017035516,0.0030168816,0.008487892],"genre_scores_gemma":[0.5044753,0.0029961185,0.46213534,0.0008206265,0.000111913745,0.0045608883,0.022018798,0.0009276786,0.0019534184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8843195,0.10476797,0.0028718547,0.0035952504,0.003630172,0.0008152506],"domain_scores_gemma":[0.6045981,0.3591187,0.007929919,0.012176181,0.015469411,0.0007077097],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11634215,0.0012908059,0.0022374974,0.0038122113,0.0008011398,0.0024798797,0.003058407,0.0015174038,0.0049363547],"category_scores_gemma":[0.3678858,0.0008206704,0.004931354,0.008249655,0.0009583498,0.003262383,0.0021115418,0.0028582609,0.0007890144],"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":[0.0039825193,0.0010804805,0.20744431,0.0046541938,0.011497861,0.0006539835,0.0037426099,0.23521069,0.0003581146,0.0966713,0.056831475,0.37787256],"study_design_scores_gemma":[0.0017762296,0.0016148888,0.1103097,0.0015907,0.002910843,0.0005424776,0.0034955726,0.79715896,0.0006269108,0.047806855,0.031762067,0.000404821],"about_ca_topic_score_codex":0.06459802,"about_ca_topic_score_gemma":0.04688057,"teacher_disagreement_score":0.8836579,"about_ca_system_score_codex":0.0034256787,"about_ca_system_score_gemma":0.0054751863,"threshold_uncertainty_score":0.6152835},"labels":[],"label_agreement":null},{"id":"W2064422634","doi":"10.1111/1467-9868.00279","title":"Local Influence for Incomplete Data Models","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":215,"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 Victoria","funders":"","keywords":"Likelihood function; Variety (cybernetics); Computer science; Maximum likelihood; Algorithm; Mathematics; Applied mathematics; Mathematical optimization; Data mining; Estimation theory; Artificial intelligence; Statistics","score_opus":0.27338020882958636,"score_gpt":0.4303219885770805,"score_spread":0.15694177974749413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064422634","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":[0.005619186,0.00033308676,0.99213916,0.00023209679,0.000021797685,0.000018601284,0.000025499026,0.00008462922,0.001525917],"genre_scores_gemma":[0.62522787,0.001205623,0.36701435,0.00042705375,0.00041388412,0.00034786324,0.00023370396,0.0002930656,0.004836521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9885288,0.00717184,0.00027062665,0.0011670657,0.0025403367,0.00032130876],"domain_scores_gemma":[0.9526178,0.03769259,0.0030983177,0.0032715846,0.0024599982,0.00085969915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014369133,0.001009259,0.002008858,0.0026485287,0.0008628511,0.0021954388,0.0022661046,0.0014356142,0.0026076338],"category_scores_gemma":[0.06310428,0.0007181567,0.0020740624,0.0012879776,0.00397313,0.003264691,0.0036108217,0.0029951078,0.0004496248],"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":[0.00011105321,0.000040738872,0.0027290275,0.00032543036,0.0003851336,0.0005897696,0.00054077175,0.33176208,0.0021813011,0.6186038,0.0016973981,0.04103348],"study_design_scores_gemma":[0.000007942721,0.000032977347,0.00057497364,0.000042919564,0.000036090336,0.00012803386,0.000047210757,0.68064594,0.0008488164,0.3154279,0.002180156,0.000027058162],"about_ca_topic_score_codex":0.0025884889,"about_ca_topic_score_gemma":0.0019172801,"teacher_disagreement_score":0.014369133,"about_ca_system_score_codex":0.0021082368,"about_ca_system_score_gemma":0.001200296,"threshold_uncertainty_score":0.07599211},"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":"W2065883168","doi":"10.1155/2012/931416","title":"Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood","year":2012,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","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":"McGill University","funders":"Terveyden ja hyvinvoinnin laitos; European Commission","keywords":"Covariate; Inverse probability weighting; Statistics; Event (particle physics); Cohort; Mathematics; Sampling (signal processing); Econometrics; Weighting; Inverse probability; Estimator; Computer science; Medicine; Bayesian probability; Posterior probability","score_opus":0.1775559486848967,"score_gpt":0.41087691367565643,"score_spread":0.23332096499075974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065883168","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":[0.0025713823,0.00022687098,0.9956546,0.00021059498,0.000053836142,0.00041660544,0.00018826642,0.00010777301,0.0005700254],"genre_scores_gemma":[0.11191067,0.0007531293,0.87848496,0.00043048055,0.00026667555,0.004640457,0.0008727072,0.00015806392,0.0024829472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9069087,0.08162362,0.002064569,0.0043972866,0.004323696,0.0006820063],"domain_scores_gemma":[0.74622285,0.21978146,0.008478579,0.017128188,0.0074575385,0.00093148643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11689284,0.0017981929,0.0031290413,0.0031634367,0.0010507817,0.002473862,0.0039955177,0.0025653436,0.008735147],"category_scores_gemma":[0.23836207,0.00096343213,0.0036559901,0.0033624156,0.0026703265,0.0035724961,0.0038137855,0.0035177225,0.0012293953],"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":[0.0013795352,0.0003994205,0.014653529,0.0019856186,0.0019201762,0.0006067122,0.0013834627,0.09609415,0.0014525357,0.6856987,0.008919743,0.18550646],"study_design_scores_gemma":[0.0005709509,0.0005516934,0.0032174238,0.00032218546,0.00047508854,0.0003657337,0.00014005459,0.3664252,0.0011606665,0.617184,0.009496178,0.00009086397],"about_ca_topic_score_codex":0.002040402,"about_ca_topic_score_gemma":0.0016920561,"teacher_disagreement_score":0.11689284,"about_ca_system_score_codex":0.0015867696,"about_ca_system_score_gemma":0.0035448775,"threshold_uncertainty_score":0.6181958},"labels":[],"label_agreement":null},{"id":"W2065974896","doi":"10.1136/bmj.b2393","title":"Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls","year":2009,"lang":"en","type":"article","venue":"BMJ","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7164,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"Economic and Social Research Council; British Heart Foundation","keywords":"Imputation (statistics); Missing data; Computer science; Data science; Data mining; Statistics; Econometrics; Mathematics; Machine learning","score_opus":0.6024689602522153,"score_gpt":0.6126207473716742,"score_spread":0.010151787119458855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065974896","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":[0.0059103756,0.09038762,0.6684355,0.22571388,0.004901252,0.0007488788,0.0006502328,0.00062313257,0.0026292067],"genre_scores_gemma":[0.13455442,0.0406627,0.7555287,0.053953975,0.010498492,0.002665421,0.00038397565,0.00048632763,0.0012660018],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.3045496,0.625974,0.032069605,0.008136102,0.027881516,0.0013891658],"domain_scores_gemma":[0.13524711,0.7857723,0.025826696,0.031793106,0.019571882,0.0017889141],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5608573,0.0020987412,0.007949745,0.008115331,0.003558868,0.008471162,0.011231623,0.010866203,0.0023285022],"category_scores_gemma":[0.7528582,0.003219603,0.006426312,0.019785257,0.011986037,0.015203949,0.009367724,0.018361945,0.0013162011],"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":[0.001296147,0.000265233,0.03718862,0.015138942,0.007749138,0.0019306336,0.0136993015,0.0097396765,0.000548099,0.15413593,0.086913444,0.6713949],"study_design_scores_gemma":[0.00058299233,0.00056959,0.011726555,0.021495968,0.0017649821,0.0060577486,0.003141255,0.037661877,0.0013034352,0.83826864,0.07670487,0.00072207284],"about_ca_topic_score_codex":0.0051413327,"about_ca_topic_score_gemma":0.007906439,"teacher_disagreement_score":0.4391427,"about_ca_system_score_codex":0.0032570097,"about_ca_system_score_gemma":0.008750628,"threshold_uncertainty_score":0.5415412},"labels":[],"label_agreement":null},{"id":"W2066828743","doi":"10.1002/cjs.11182","title":"Empirical likelihood confidence regions for the evaluation of continuous‐scale diagnostic tests in the presence of verification bias","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Empirical likelihood; Nuisance parameter; Statistics; Computer science; Confidence interval; Test (biology); Sensitivity (control systems); Econometrics; Scale (ratio); Empirical research; Statistical hypothesis testing; Sample size determination; Focus (optics); Sampling bias; Data mining; Mathematics; Engineering","score_opus":0.19158558205314727,"score_gpt":0.40322631268672793,"score_spread":0.21164073063358066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066828743","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":[0.021824488,0.0025072012,0.9723752,0.0007688486,0.000045339944,0.00022546687,0.0002536658,0.00033274497,0.0016670196],"genre_scores_gemma":[0.65581673,0.0018047947,0.33882946,0.00043269905,0.00022972563,0.0009689314,0.00074494735,0.00022530985,0.0009474407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9256036,0.06050447,0.0023746225,0.004187314,0.0063018575,0.0010279875],"domain_scores_gemma":[0.27543756,0.6896301,0.014437703,0.010349532,0.008911395,0.0012336434],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12665121,0.0018075102,0.0036570742,0.0055455896,0.0007434995,0.0050342004,0.0061528087,0.0041530635,0.0043267133],"category_scores_gemma":[0.5827374,0.0013925803,0.0030112425,0.0033817594,0.007565722,0.005635369,0.0050114705,0.0044504073,0.0005567608],"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":[0.0018830019,0.00017184352,0.025547354,0.001437197,0.0012179136,0.0010387298,0.0011361366,0.50383174,0.0014405727,0.33649153,0.0032342314,0.12256974],"study_design_scores_gemma":[0.00021629069,0.00032014752,0.0048408555,0.00056290615,0.0002829509,0.0006495126,0.00016602462,0.8232466,0.0018431039,0.16532986,0.002394346,0.00014730613],"about_ca_topic_score_codex":0.003421091,"about_ca_topic_score_gemma":0.0009601877,"teacher_disagreement_score":0.8733488,"about_ca_system_score_codex":0.003226558,"about_ca_system_score_gemma":0.0024436251,"threshold_uncertainty_score":0.6698036},"labels":[],"label_agreement":null},{"id":"W2066948187","doi":"10.1002/sim.3953","title":"A binning method for analyzing mixed longitudinal data measured at distinct time points","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Mixed model; Smoothing; Poisson regression; Generalized linear mixed model; Longitudinal data; Computer science; Statistics; Preprocessor; Event (particle physics); Generalized linear model; Econometrics; Mathematics; Data mining; Artificial intelligence; Medicine","score_opus":0.14269810999708532,"score_gpt":0.45700158563559795,"score_spread":0.31430347563851263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066948187","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":[0.0005776917,0.0001039801,0.9988519,0.000029377417,0.000032030493,0.00004475238,0.00007082669,0.00022070076,0.00006876966],"genre_scores_gemma":[0.013540616,0.00026288274,0.9842689,0.00009081355,0.00006634512,0.0006111705,0.00042571287,0.00016550753,0.00056807016],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9927499,0.0046262834,0.00047492335,0.0009187811,0.0010522677,0.00017768808],"domain_scores_gemma":[0.98299384,0.012516375,0.00089382875,0.0021144731,0.0012038819,0.00027746568],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015099998,0.0010449776,0.0019363066,0.003840272,0.0009571006,0.0014258376,0.0025885974,0.0012974852,0.0050283493],"category_scores_gemma":[0.030739158,0.0010165549,0.0020462507,0.0049810195,0.0014067328,0.0021332013,0.0020883468,0.0031602357,0.0011664496],"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":[0.0006561188,0.0001868351,0.006226985,0.0008553412,0.0011104257,0.00040502666,0.0009096029,0.061748,0.008538937,0.3015878,0.007912522,0.6098624],"study_design_scores_gemma":[0.00016398192,0.00025037653,0.0035702446,0.00018348299,0.00029382162,0.00072034827,0.00014018615,0.6452652,0.004785388,0.3135103,0.030919006,0.00019763083],"about_ca_topic_score_codex":0.0034892522,"about_ca_topic_score_gemma":0.0032896742,"teacher_disagreement_score":0.9849,"about_ca_system_score_codex":0.0008763198,"about_ca_system_score_gemma":0.0022843208,"threshold_uncertainty_score":0.07985741},"labels":[],"label_agreement":null},{"id":"W2067188960","doi":"10.1016/s0167-7152(00)00215-7","title":"A higher-order approximation to likelihood inference in the Poisson mixed model","year":2001,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"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; Random effects model; Poisson distribution; Statistics; Poisson regression; Estimator; Variance components; Variance (accounting); Component (thermodynamics); Applied mathematics; Inference; Restricted maximum likelihood; Quasi-likelihood; Mixed model; Econometrics; Maximum likelihood; Count data; Computer science; Artificial intelligence","score_opus":0.07112994660665108,"score_gpt":0.35676995419153773,"score_spread":0.28564000758488667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067188960","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":[0.0021882833,0.0003829137,0.99568707,0.0004433565,0.00007076217,0.000013506124,0.00005388916,0.00009290873,0.0010672772],"genre_scores_gemma":[0.18520422,0.0022677884,0.7872269,0.0015748087,0.0013751815,0.00040898958,0.0007938302,0.00076394086,0.02038434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9912753,0.0057429504,0.000340699,0.0006970798,0.001465308,0.0004786552],"domain_scores_gemma":[0.9427186,0.04975065,0.0014140581,0.0029709167,0.0022169673,0.00092887465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018612636,0.0013184431,0.0031844962,0.0025818176,0.001583293,0.0046527046,0.006653632,0.0037945835,0.0066193673],"category_scores_gemma":[0.08084329,0.0024271058,0.00330776,0.003320152,0.0047107083,0.0067478274,0.0046748263,0.007328739,0.0020812775],"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":[0.00011909997,0.000073561176,0.0012236647,0.00019193193,0.00014764219,0.00025836725,0.00029589608,0.24846303,0.00062909856,0.7215132,0.0034878217,0.023596723],"study_design_scores_gemma":[0.000016223277,0.000010378125,0.00018271843,0.000024585,0.000022821938,0.00006376681,0.00001589669,0.7820688,0.00013582261,0.21618678,0.0012448634,0.000027338523],"about_ca_topic_score_codex":0.013789145,"about_ca_topic_score_gemma":0.01438168,"teacher_disagreement_score":0.018612636,"about_ca_system_score_codex":0.00348875,"about_ca_system_score_gemma":0.0037190118,"threshold_uncertainty_score":0.09843421},"labels":[],"label_agreement":null},{"id":"W2067223891","doi":"10.1111/1467-9469.00210","title":"Sampling Bias in Population Studies—How to Use the Lexis Diagram","year":2000,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre","funders":"Chalmers Tekniska Högskola","keywords":"Mathematics; Censoring (clinical trials); Sampling (signal processing); Statistics; Population; Renewal theory; Truncation (statistics); Poisson sampling; Poisson distribution; Conditional probability distribution; Applied mathematics; Importance sampling; Algorithm; Computer science; Slice sampling; Monte Carlo method","score_opus":0.26285386366058566,"score_gpt":0.44122922983846535,"score_spread":0.1783753661778797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067223891","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":[0.0008422153,0.0007381836,0.99517006,0.001284429,0.00025503375,0.000117584634,0.000121209625,0.00031566198,0.0011555816],"genre_scores_gemma":[0.028265527,0.0015388001,0.9652147,0.0009895435,0.0004212038,0.0012407408,0.00022956758,0.00036391866,0.0017359261],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95002353,0.04036473,0.003060821,0.0024907223,0.0037539895,0.00030624986],"domain_scores_gemma":[0.81676835,0.15916297,0.0051752753,0.009984375,0.007735425,0.001173526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.070363805,0.0010154316,0.0023541232,0.0037733102,0.0014027922,0.0045370143,0.0030754427,0.0034685226,0.0075063948],"category_scores_gemma":[0.22187647,0.0013562203,0.0019599034,0.0031144384,0.004195218,0.010292546,0.0037670457,0.0051698154,0.0023331372],"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":[0.000092827315,0.000035925106,0.0026364496,0.00046916498,0.00012530044,0.00027040025,0.0007194621,0.006315338,0.00051006075,0.8553303,0.008642203,0.12485263],"study_design_scores_gemma":[0.000048882328,0.00004422329,0.0007051146,0.00026932344,0.00004276135,0.00034469378,0.00009816438,0.025146361,0.0005620731,0.94409907,0.02858407,0.000055251392],"about_ca_topic_score_codex":0.0018414275,"about_ca_topic_score_gemma":0.0014720822,"teacher_disagreement_score":0.070363805,"about_ca_system_score_codex":0.0019471406,"about_ca_system_score_gemma":0.0030723931,"threshold_uncertainty_score":0.37212384},"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":"W2068394532","doi":"10.1002/cjs.10138","title":"Approximate jackknife empirical likelihood method for estimating equations","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Security Agency; National Science Foundation","keywords":"Jackknife resampling; Empirical likelihood; Estimator; Estimating equations; Nuisance; Mathematics; Statistics; Nuisance parameter; Econometrics; Computation; Algorithm","score_opus":0.13946598311820033,"score_gpt":0.421718548225866,"score_spread":0.2822525651076657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068394532","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":[0.0006542702,0.000095650445,0.9987488,0.000046912566,0.000011882606,0.000029576579,0.000027931465,0.000069200694,0.00031570953],"genre_scores_gemma":[0.06798916,0.00046245908,0.9267575,0.00015466289,0.00007772172,0.00059603836,0.0004011243,0.00020305718,0.0033581704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98023397,0.015145521,0.0005618762,0.0014965951,0.002180776,0.00038131193],"domain_scores_gemma":[0.95488966,0.036101453,0.0020731254,0.003253958,0.0033031285,0.00037867253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01884957,0.0014253713,0.0032480776,0.0026725791,0.0011418663,0.0025742045,0.005405331,0.0024534923,0.007516309],"category_scores_gemma":[0.10818046,0.0014135863,0.0019318672,0.003411245,0.0024108973,0.004135932,0.0023935349,0.0044859033,0.0023667002],"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":[0.00027781632,0.00011571485,0.00391226,0.00055957894,0.00039704065,0.0005587665,0.00073281146,0.33137363,0.0012006646,0.4548866,0.0065631126,0.1994221],"study_design_scores_gemma":[0.000034848556,0.000035617515,0.0005134844,0.00010890586,0.000042173757,0.00017448924,0.0000715918,0.83988124,0.0005692469,0.15241711,0.0061056926,0.000045558103],"about_ca_topic_score_codex":0.01047954,"about_ca_topic_score_gemma":0.009196792,"teacher_disagreement_score":0.01884957,"about_ca_system_score_codex":0.0019573262,"about_ca_system_score_gemma":0.0032946842,"threshold_uncertainty_score":0.09968722},"labels":[],"label_agreement":null},{"id":"W2068729564","doi":"10.1016/j.csda.2004.02.001","title":"On likelihood inference in binary mixed model with an application to COPD data","year":2004,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Estimator; Statistics; Inference; Random effects model; Mathematics; Binary data; Monte Carlo method; Binary number; Mixed model; Statistical inference; Moment (physics); Computer science; Artificial intelligence; Medicine","score_opus":0.10092335098585019,"score_gpt":0.4170239513736352,"score_spread":0.316100600387785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068729564","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":[0.0016545005,0.0003351573,0.99697506,0.00054337626,0.000035773304,0.00003215668,0.00005449931,0.000095584066,0.00027387423],"genre_scores_gemma":[0.073332116,0.0016074828,0.91842705,0.00070859917,0.0006773447,0.0007462312,0.0004942816,0.00033836177,0.003668454],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9836299,0.013478201,0.0005248534,0.00094725826,0.0011576209,0.0002622487],"domain_scores_gemma":[0.8124045,0.17859943,0.00218341,0.0036799205,0.0024251798,0.00070746575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037493035,0.001831354,0.0039181146,0.0035124344,0.0017582531,0.003211437,0.0059330855,0.004877875,0.0064480193],"category_scores_gemma":[0.15046902,0.0023194938,0.0033261313,0.005440379,0.00485869,0.0055006333,0.0068950555,0.007177266,0.0010540311],"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":[0.00028492298,0.00016415757,0.0030821715,0.0006157861,0.00041394873,0.00069620943,0.0006904062,0.28632122,0.00083819183,0.567517,0.005695159,0.13368088],"study_design_scores_gemma":[0.000067197565,0.000034371405,0.00041071977,0.00004854482,0.000052117608,0.00014784276,0.00003287148,0.671021,0.00019726012,0.32649487,0.0014541318,0.00003893034],"about_ca_topic_score_codex":0.008738827,"about_ca_topic_score_gemma":0.007728002,"teacher_disagreement_score":0.037493035,"about_ca_system_score_codex":0.0017131844,"about_ca_system_score_gemma":0.0031776165,"threshold_uncertainty_score":0.1982845},"labels":[],"label_agreement":null},{"id":"W2068988403","doi":"10.1118/1.4894923","title":"Poster - Thur Eve - 63: Prostate IMRT: <b> <i>Product-Mixture</i> </b> model of a two-dimensional probability density function integrating the variability of the motion of the rectum and the rectal wall thickness","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Statistical Methods and Bayesian 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":"Ottawa Regional Cancer Foundation; Princess Margaret Cancer Centre; University of Waterloo; University Health Network","funders":"","keywords":"Rectum; Probability density function; Prostate; Function (biology); Mathematics; Probability distribution; Motion (physics); Nuclear medicine; Statistics; Medicine; Computer science; Artificial intelligence; Surgery","score_opus":0.022893718545603053,"score_gpt":0.2780999795603565,"score_spread":0.2552062610147534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068988403","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":[0.1047365,0.0011283696,0.8847782,0.00059954554,0.00018243397,0.00016493455,0.0010834171,0.00073450984,0.006592017],"genre_scores_gemma":[0.88048613,0.0010921694,0.0921944,0.00029659734,0.00025882697,0.00032595807,0.0020371263,0.000366256,0.022942541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992499,0.00024190667,0.000032344877,0.00021063004,0.00019388406,0.00007128918],"domain_scores_gemma":[0.99872714,0.00060416246,0.00016111927,0.00019540198,0.00024646652,0.000065703316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018020836,0.0008861582,0.0011244842,0.00078139955,0.0003404029,0.0016620173,0.0015436368,0.0011545605,0.0033435647],"category_scores_gemma":[0.0027979268,0.00064684136,0.002180299,0.00088232884,0.00075845857,0.001331355,0.00077421,0.0012865106,0.0016542004],"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":[0.00035766378,0.00014054806,0.006866509,0.00017747363,0.000301898,0.00029908164,0.00015927701,0.9186934,0.006057593,0.021573458,0.0029568675,0.04241623],"study_design_scores_gemma":[0.000008538477,0.000051345763,0.0014869849,0.000011589815,0.000037754922,0.00013495077,0.000008822053,0.9924775,0.0010662843,0.0033642303,0.0013280719,0.000023957831],"about_ca_topic_score_codex":0.0069022686,"about_ca_topic_score_gemma":0.0037630552,"teacher_disagreement_score":0.0069022686,"about_ca_system_score_codex":0.0008881992,"about_ca_system_score_gemma":0.00077995186,"threshold_uncertainty_score":0.013724208},"labels":[],"label_agreement":null},{"id":"W2069182847","doi":"10.1111/1467-985x.00213","title":"A Simple Method for Estimating a Regression Model for κ Between a Pair of Raters","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario","funders":"National Institute of Mental Health; National Institutes of Health","keywords":"Multinomial logistic regression; Covariate; Logistic regression; Statistics; Linear regression; Econometrics; Function (biology); Psychology; Mathematics; Panel data; Social psychology","score_opus":0.07857670896700181,"score_gpt":0.41311960480382165,"score_spread":0.33454289583681984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069182847","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":[0.0020328688,0.00008142384,0.9930754,0.0001768856,0.00017982423,0.0010376978,0.00094523735,0.001677104,0.0007935348],"genre_scores_gemma":[0.03616379,0.000090919864,0.9497603,0.00017632572,0.00011694332,0.0074641937,0.001401519,0.0008381896,0.0039877766],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.82723254,0.13285461,0.00935248,0.019318348,0.009826622,0.0014153891],"domain_scores_gemma":[0.72563267,0.20724991,0.011718109,0.040066816,0.014526178,0.00080629985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12918335,0.0034717268,0.003630353,0.0051249154,0.0017077349,0.0034876512,0.0070413062,0.004011423,0.030317094],"category_scores_gemma":[0.30992717,0.0028558972,0.0073753214,0.005922122,0.0021865822,0.0043301038,0.004179919,0.008856873,0.016892726],"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":[0.0016073178,0.0005541118,0.026298804,0.001689611,0.0059040627,0.0004960517,0.002785509,0.054480158,0.0030783694,0.105000116,0.0564792,0.7416268],"study_design_scores_gemma":[0.0012403824,0.0019449254,0.034026444,0.0014549227,0.0017029045,0.0015012271,0.0011784121,0.5747531,0.008342549,0.22168924,0.15112929,0.0010365923],"about_ca_topic_score_codex":0.009555454,"about_ca_topic_score_gemma":0.0065400708,"teacher_disagreement_score":0.12918335,"about_ca_system_score_codex":0.0025370943,"about_ca_system_score_gemma":0.0034775052,"threshold_uncertainty_score":0.683195},"labels":[],"label_agreement":null},{"id":"W2071030162","doi":"10.1002/cjs.10012","title":"Bootstrap tests for variance components in generalized linear mixed models","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Statistics; Test statistic; Mathematics; Generalized linear mixed model; Random effects model; Null hypothesis; Parametric statistics; Statistical hypothesis testing; Mixed model; Statistic; Score test; Generalized least squares; Econometrics; Variance (accounting); Chi-square test; Applied mathematics; Meta-analysis","score_opus":0.19354758001250655,"score_gpt":0.3750452436926864,"score_spread":0.18149766368017983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071030162","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":[0.031798344,0.0009866239,0.9632199,0.00064361823,0.00016336987,0.00031595532,0.00025051038,0.000550191,0.002071478],"genre_scores_gemma":[0.66951007,0.00076228066,0.32497358,0.0005080141,0.00032021845,0.001673229,0.00092458783,0.00024304117,0.0010849352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9275455,0.06224279,0.0013937136,0.002311704,0.005856229,0.00065010664],"domain_scores_gemma":[0.7034433,0.27462167,0.006586983,0.008896393,0.0053964956,0.0010551693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055510152,0.0013945883,0.003000923,0.0046994356,0.0012953423,0.0025248842,0.0029667867,0.0023293209,0.005814301],"category_scores_gemma":[0.32301944,0.0007447337,0.0028383161,0.004414644,0.00442347,0.003185506,0.0032753977,0.0036348035,0.0008650924],"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":[0.0018193722,0.0004325193,0.030065326,0.0010846399,0.0035993266,0.0010767422,0.0012218327,0.10493997,0.0014952372,0.51531434,0.008665521,0.33028522],"study_design_scores_gemma":[0.00034028533,0.0008120766,0.009451781,0.00038031474,0.00033480593,0.00044565927,0.0004378615,0.3942957,0.0013797759,0.58676594,0.0052170516,0.00013867008],"about_ca_topic_score_codex":0.0022327811,"about_ca_topic_score_gemma":0.0012617126,"teacher_disagreement_score":0.055510152,"about_ca_system_score_codex":0.0010980666,"about_ca_system_score_gemma":0.0022559597,"threshold_uncertainty_score":0.2935692},"labels":[],"label_agreement":null},{"id":"W2071164098","doi":"10.1111/j.1467-9892.2007.00567.x","title":"Improved inference for first‐order autocorrelation using likelihood analysis","year":2008,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Simon Fraser University","funders":"","keywords":"Autocorrelation; Mathematics; Statistics; Inference; Autocorrelation technique; Likelihood-ratio test; Applied mathematics; Statistical hypothesis testing; Econometrics; Value (mathematics); Computer science; Artificial intelligence","score_opus":0.042322817362984995,"score_gpt":0.3509703510651057,"score_spread":0.3086475337021207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071164098","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":[0.009026443,0.00024944218,0.9895214,0.00020068567,0.000030252457,0.000013680529,0.00003275747,0.0002278032,0.000697541],"genre_scores_gemma":[0.5027165,0.00065368786,0.49294567,0.00025561982,0.00021217836,0.0001509193,0.0003272943,0.00031205604,0.0024260122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99352866,0.0046929084,0.00018067726,0.0004762358,0.0009686895,0.0001527279],"domain_scores_gemma":[0.9179334,0.073410854,0.0020640886,0.0034224298,0.002724214,0.00044497658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013872381,0.00069885945,0.0015613729,0.001852719,0.00060246553,0.0017263622,0.0015619945,0.0011585557,0.0035487942],"category_scores_gemma":[0.10306831,0.00055711623,0.0011141851,0.0017362747,0.0018284433,0.0026070164,0.0019435699,0.0030617046,0.0007920718],"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":[0.00050302246,0.0001540117,0.009831102,0.0004860159,0.00040325272,0.00065051496,0.00047482576,0.5111591,0.0049068644,0.25444853,0.004749605,0.21223316],"study_design_scores_gemma":[0.00005042082,0.000042836426,0.0012911608,0.00003486897,0.0000354093,0.0001259435,0.000017546501,0.89541936,0.001160352,0.10068897,0.0011031657,0.000030015048],"about_ca_topic_score_codex":0.0030289667,"about_ca_topic_score_gemma":0.0026149622,"teacher_disagreement_score":0.013872381,"about_ca_system_score_codex":0.0009632133,"about_ca_system_score_gemma":0.00185583,"threshold_uncertainty_score":0.07336503},"labels":[],"label_agreement":null},{"id":"W2071391129","doi":"10.1007/s00213-013-3344-x","title":"The impact of missing data on clinical trials: a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder","year":2013,"lang":"en","type":"article","venue":"Psychopharmacology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Mental Health","keywords":"Sertraline; Placebo; Hypericum perforatum; Hypericum; Missing data; Medicine; Randomized controlled trial; Fluoxetine; Clinical trial; Confidence interval; Internal medicine; Psychology; Psychiatry; Pharmacology; Traditional medicine; Statistics; Antidepressant; Mathematics; Alternative medicine","score_opus":0.19382973156288902,"score_gpt":0.5439673325423596,"score_spread":0.3501376009794706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071391129","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":[0.58092195,0.28093833,0.07597542,0.031452797,0.008490707,0.005158773,0.0089145005,0.001297226,0.0068504307],"genre_scores_gemma":[0.9706041,0.008528658,0.011285304,0.004319431,0.0012572011,0.0009105932,0.0016509129,0.00029413615,0.0011496068],"study_design_codex":"meta_analysis","study_design_gemma":"observational","domain_scores_codex":[0.6864457,0.25419202,0.03323175,0.009255,0.014689207,0.0021863652],"domain_scores_gemma":[0.32699084,0.55756,0.048538737,0.053292196,0.01173195,0.0018863179],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27069908,0.0026204374,0.009283828,0.0030486002,0.0010936966,0.0041370336,0.0038723643,0.003962409,0.003556706],"category_scores_gemma":[0.4185788,0.0020475988,0.028500425,0.003364116,0.0028527621,0.00382448,0.0023185285,0.008135491,0.00050116447],"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":[0.31600657,0.0006573539,0.029014828,0.02406374,0.47774544,0.001817193,0.0011543777,0.0067095645,0.0041599176,0.0038203397,0.009408876,0.12544185],"study_design_scores_gemma":[0.10073003,0.012143506,0.105531774,0.0045900275,0.71866745,0.001775634,0.00057327846,0.016854374,0.0040756185,0.014044346,0.02052235,0.0004915681],"about_ca_topic_score_codex":0.002739855,"about_ca_topic_score_gemma":0.0040737675,"teacher_disagreement_score":0.7293009,"about_ca_system_score_codex":0.002907805,"about_ca_system_score_gemma":0.0024845365,"threshold_uncertainty_score":0.89935786},"labels":[],"label_agreement":null},{"id":"W2071767222","doi":"10.1080/08839510902872223","title":"AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES","year":2009,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Society of Intestinal Research","funders":"","keywords":"Missing data; Imputation (statistics); Computer science; Spurious relationship; Decision tree; Robustness (evolution); Data mining; Decision tree learning; Machine learning; Statistics; Artificial intelligence; Mathematics","score_opus":0.47976507396873247,"score_gpt":0.5540475740189142,"score_spread":0.07428250005018178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071767222","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":[0.4404857,0.010751471,0.53810966,0.002268281,0.00042433102,0.0009788476,0.001420952,0.00074870844,0.004812041],"genre_scores_gemma":[0.7432156,0.0030797154,0.2500071,0.00026009363,0.00016432318,0.0005600113,0.0019156906,0.0002666839,0.00053067243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8772705,0.09538129,0.0055979933,0.0054019648,0.0150423525,0.001305858],"domain_scores_gemma":[0.42979842,0.51304704,0.012676231,0.025141096,0.017737534,0.0015996484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13348469,0.0017660159,0.0019509038,0.0064013186,0.0015894558,0.0032250292,0.003320985,0.0027116628,0.0012260653],"category_scores_gemma":[0.38223106,0.00082667265,0.0037374862,0.0070881713,0.002476889,0.008221205,0.0033569757,0.0032700903,0.0006197022],"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":[0.0035249202,0.00083863246,0.094211146,0.0023148363,0.0037024578,0.00029391388,0.0040530493,0.42596433,0.0010901038,0.017324746,0.0064460216,0.44023585],"study_design_scores_gemma":[0.00035872203,0.002795449,0.044703882,0.0018029782,0.0013000182,0.0008343115,0.0024945058,0.87715435,0.0033652266,0.054484084,0.010336442,0.00036994572],"about_ca_topic_score_codex":0.003506488,"about_ca_topic_score_gemma":0.0034157643,"teacher_disagreement_score":0.13348469,"about_ca_system_score_codex":0.0024509146,"about_ca_system_score_gemma":0.0024032441,"threshold_uncertainty_score":0.705943},"labels":[],"label_agreement":null},{"id":"W2071834496","doi":"10.1002/sim.5536","title":"Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Covariate; Pairwise comparison; Computer science; Statistics; Robustness (evolution); Econometrics; Binary data; Data mining; Binary number; Mathematics; Machine learning","score_opus":0.26870786049482126,"score_gpt":0.5006180560725655,"score_spread":0.23191019557774423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071834496","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":[0.00090546446,0.0003357942,0.9982192,0.00019238258,0.00002707574,0.000042819924,0.00007575135,0.000067536435,0.00013394104],"genre_scores_gemma":[0.04107237,0.0017282204,0.95341116,0.00019824039,0.00026417145,0.0008579087,0.00072045793,0.00016860454,0.001578856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890814,0.008038207,0.00049464626,0.0012092944,0.00096861535,0.00020787733],"domain_scores_gemma":[0.93189776,0.05788461,0.003439171,0.003798699,0.0024472235,0.0005325369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040021483,0.0017158581,0.0022167969,0.004062342,0.0009786073,0.0018127951,0.00528402,0.0027188938,0.005955999],"category_scores_gemma":[0.13469627,0.0012374528,0.0029442066,0.004129124,0.002676555,0.0048839753,0.0033092129,0.0064450237,0.0017889871],"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":[0.00019233022,0.00016279155,0.009157925,0.00078895135,0.0006040957,0.00026302665,0.00086293806,0.14923897,0.0011911879,0.56251216,0.0060699414,0.26895577],"study_design_scores_gemma":[0.00006948256,0.00007990052,0.0020904094,0.00019647254,0.00013051982,0.00028338382,0.00014134217,0.3984043,0.0007453503,0.5901602,0.0076106754,0.000087909444],"about_ca_topic_score_codex":0.0026933623,"about_ca_topic_score_gemma":0.0035973573,"teacher_disagreement_score":0.040021483,"about_ca_system_score_codex":0.0013232013,"about_ca_system_score_gemma":0.0031792056,"threshold_uncertainty_score":0.21165633},"labels":[],"label_agreement":null},{"id":"W2071849850","doi":"10.3102/1076998609332756","title":"Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach","year":2009,"lang":"en","type":"article","venue":"Journal of Educational and Behavioral Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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":"","keywords":"Intraclass correlation; Bayes' theorem; Computer science; Cluster (spacecraft); Statistics; Sample size determination; Estimation; Sample (material); Data mining; Task (project management); Field (mathematics); Econometrics; Bayesian probability; Mathematics; Artificial intelligence; Psychometrics","score_opus":0.17298107137845747,"score_gpt":0.5008700679701498,"score_spread":0.3278889965916923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071849850","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":[0.0013277782,0.00077998714,0.99541986,0.00049573067,0.00010898776,0.001274565,0.000064178595,0.0001848704,0.0003439629],"genre_scores_gemma":[0.046786215,0.00077626103,0.9432451,0.0006357772,0.00023674694,0.0076875435,0.00015636874,0.00009665392,0.0003793122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.70711136,0.2725939,0.0061044325,0.006005429,0.007532097,0.0006527388],"domain_scores_gemma":[0.4853451,0.49092302,0.006569588,0.011562249,0.0047733234,0.00082682865],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23238614,0.0024863333,0.00945213,0.005913597,0.0013537904,0.0032596006,0.0066126045,0.005829132,0.004191216],"category_scores_gemma":[0.4642672,0.0021016852,0.0043287906,0.0041865674,0.0042220885,0.0038084793,0.0030971253,0.007025414,0.00087242323],"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":[0.0050512613,0.0006249609,0.0050513935,0.00515365,0.005032888,0.0004529592,0.0013528618,0.20811112,0.0008937987,0.3569687,0.012419473,0.39888695],"study_design_scores_gemma":[0.0021026435,0.00089136156,0.00094869424,0.0013591778,0.0010133258,0.000236837,0.00008255611,0.48694366,0.0010209993,0.49990848,0.00534665,0.00014563886],"about_ca_topic_score_codex":0.0027655435,"about_ca_topic_score_gemma":0.0021563978,"teacher_disagreement_score":0.7676139,"about_ca_system_score_codex":0.0025587135,"about_ca_system_score_gemma":0.005226885,"threshold_uncertainty_score":0.94660455},"labels":[],"label_agreement":null},{"id":"W2072457708","doi":"10.1080/15598608.2013.772830","title":"Bias Correction Methods for Misclassified Covariates in the Cox Model: Comparison of Five Correction Methods by Simulation and Data Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":22,"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","funders":"National Heart, Lung, and Blood Institute; University of Otago; Ryerson University","keywords":"Covariate; Categorical variable; Statistics; Imputation (statistics); Mathematics; Inference; Proportional hazards model; Regression analysis; Regression; Estimation; Data mining; Computer science; Econometrics; Missing data; Artificial intelligence","score_opus":0.26325416895915715,"score_gpt":0.566600147292207,"score_spread":0.3033459783330498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072457708","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":[0.061155885,0.0073093353,0.9265558,0.001226853,0.0006106246,0.0010370881,0.0003270479,0.0009912738,0.0007860153],"genre_scores_gemma":[0.46440104,0.0040271096,0.5262337,0.0004989871,0.00021648814,0.0020731394,0.00050715276,0.00080825615,0.0012341432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.85538816,0.12899318,0.0040685637,0.0039653885,0.0064547635,0.0011299389],"domain_scores_gemma":[0.28432894,0.6667943,0.013396263,0.020780802,0.013027093,0.0016726251],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17885286,0.0022660152,0.004613304,0.0050748726,0.0015553687,0.002956019,0.005920777,0.0049520116,0.0028953874],"category_scores_gemma":[0.44600683,0.0013753225,0.007086396,0.0040966403,0.002619229,0.0051761633,0.0032434436,0.0055181743,0.00038254765],"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":[0.029025618,0.0013605633,0.039480194,0.0054799938,0.017935965,0.00034914445,0.0034087813,0.18651107,0.0015681095,0.062853076,0.007574904,0.64445263],"study_design_scores_gemma":[0.0065618115,0.005517448,0.019586267,0.0021609354,0.009394701,0.0009950342,0.0008971708,0.83716536,0.0044640168,0.10546691,0.0070718755,0.0007184749],"about_ca_topic_score_codex":0.007836323,"about_ca_topic_score_gemma":0.0046937144,"teacher_disagreement_score":0.82114714,"about_ca_system_score_codex":0.002761391,"about_ca_system_score_gemma":0.0076203234,"threshold_uncertainty_score":0.94587564},"labels":[],"label_agreement":null},{"id":"W2072469512","doi":"10.1080/00949655.2010.496727","title":"On efficient inferences in familial-longitudinal binary models with two variance components","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Random effects model; Statistics; Estimator; Generalized linear mixed model; Mixed model; Binary number; Inference; Binary data; Delta method; Applied mathematics; Computer science","score_opus":0.15048506328710287,"score_gpt":0.38917084478519826,"score_spread":0.23868578149809538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072469512","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":[0.007382286,0.00051171135,0.99110436,0.0003308697,0.000015834454,0.000047319943,0.000050568873,0.00008007062,0.00047693265],"genre_scores_gemma":[0.14550589,0.0014403074,0.85036886,0.00036240742,0.00013223653,0.00051846635,0.000379285,0.00016527013,0.0011272262],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95762104,0.03789312,0.00087527366,0.0014264856,0.0018663079,0.0003177039],"domain_scores_gemma":[0.671697,0.3121258,0.0050447583,0.0076290844,0.0029390142,0.0005643902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06868812,0.0013519003,0.0021730163,0.0028696763,0.0010510444,0.0022080736,0.0028558688,0.0021694025,0.0028991096],"category_scores_gemma":[0.27178293,0.0013971119,0.001646052,0.0032045725,0.0039803563,0.0043735886,0.0043317424,0.002437049,0.0006980012],"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":[0.0003261712,0.00013310283,0.0067921113,0.0007827071,0.00065432844,0.00060390733,0.001186632,0.26492226,0.0012771556,0.54184914,0.0016511616,0.17982131],"study_design_scores_gemma":[0.000092963695,0.00008406752,0.0016640307,0.00018167573,0.000114235256,0.00020440968,0.000113099326,0.54374707,0.00087548373,0.45100164,0.001869848,0.000051460902],"about_ca_topic_score_codex":0.005192515,"about_ca_topic_score_gemma":0.0053445767,"teacher_disagreement_score":0.06868812,"about_ca_system_score_codex":0.0020159935,"about_ca_system_score_gemma":0.0031662632,"threshold_uncertainty_score":0.36326182},"labels":[],"label_agreement":null},{"id":"W2072533626","doi":"10.1016/j.jmva.2013.02.008","title":"Estimation of mean squared error of model-based estimators of small area means under a nested error linear regression model","year":2013,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Small area estimation; Mean squared error; Estimator; Statistics; Efficient estimator; Linear model; Minimum mean square error; Conditional expectation; Minimum-variance unbiased estimator","score_opus":0.11699843873935094,"score_gpt":0.3927812466780775,"score_spread":0.27578280793872656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072533626","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":[0.028608771,0.00013437374,0.97086334,0.00006621109,0.00001626776,0.00001284909,0.000034977948,0.00010996747,0.00015322784],"genre_scores_gemma":[0.48869243,0.000343523,0.50870657,0.00010262677,0.00006812245,0.0001669434,0.00047350477,0.0001842651,0.0012621098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9918394,0.00551657,0.00039814034,0.0010988778,0.000921123,0.0002258069],"domain_scores_gemma":[0.9363592,0.05339112,0.002717875,0.0040293317,0.0030872074,0.0004152303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025406733,0.0009057964,0.002240431,0.0011493553,0.00046641126,0.0015349333,0.0026538016,0.0019078918,0.00095779204],"category_scores_gemma":[0.0947832,0.0011614863,0.0013717636,0.0011365543,0.0018956058,0.00313979,0.0023974515,0.0019000699,0.0002543154],"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":[0.00032755453,0.00011945673,0.007819195,0.00020503938,0.0005086426,0.00006928443,0.00026573142,0.89757746,0.0025127593,0.045144238,0.0005904844,0.04486024],"study_design_scores_gemma":[0.000016739952,0.000052983774,0.0010224754,0.000014585122,0.000028474153,0.000029314462,0.000015855207,0.9811662,0.00061336975,0.016816543,0.00020676141,0.000016697892],"about_ca_topic_score_codex":0.0051945057,"about_ca_topic_score_gemma":0.0041253883,"teacher_disagreement_score":0.025406733,"about_ca_system_score_codex":0.0011666679,"about_ca_system_score_gemma":0.0022068878,"threshold_uncertainty_score":0.13436526},"labels":[],"label_agreement":null},{"id":"W2073629197","doi":"10.1002/bimj.200110052","title":"Modelling Heterogeneous Dispersion in Marginal Models for Longitudinal Proportional Data","year":2004,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian 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":"Children’s Health Research Institute; BC Research (Canada); York University","funders":"","keywords":"Dispersion (optics); Residual; Mathematics; Homogeneity (statistics); Statistics; Constant (computer programming); Inference; Marginal model; Applied mathematics; Econometrics; Statistical physics; Computer science; Regression analysis; Algorithm; Physics; Artificial intelligence","score_opus":0.33317165341404636,"score_gpt":0.4247078983422288,"score_spread":0.09153624492818241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073629197","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":[0.01688089,0.00017894301,0.9817602,0.00025698537,0.000028205175,0.00012728712,0.00017214756,0.00013263225,0.00046259264],"genre_scores_gemma":[0.6205021,0.0006945122,0.37100217,0.0003236039,0.0001913347,0.001441993,0.00088275224,0.00020614987,0.0047553605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9789165,0.015206361,0.0008420975,0.0027071661,0.0015537349,0.0007742119],"domain_scores_gemma":[0.8770646,0.1074679,0.0059655537,0.0065128435,0.0022888551,0.000700236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05561746,0.0015986101,0.0027437794,0.0026593253,0.0011167661,0.0030426811,0.0045766463,0.0028679234,0.004255908],"category_scores_gemma":[0.12625767,0.0013509806,0.003286735,0.002425303,0.0042346045,0.0041489457,0.0046292106,0.004437463,0.000717528],"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":[0.0003775024,0.00012045455,0.0068733776,0.0002553907,0.00043009993,0.00053857686,0.0012238306,0.53501934,0.00073372887,0.41762832,0.001325055,0.035474353],"study_design_scores_gemma":[0.000048980073,0.00009498893,0.0012966811,0.00004667849,0.00005880423,0.000116348914,0.00009497398,0.76284,0.00027068102,0.23393889,0.0011452859,0.00004775059],"about_ca_topic_score_codex":0.008697523,"about_ca_topic_score_gemma":0.006353565,"teacher_disagreement_score":0.05561746,"about_ca_system_score_codex":0.0026896207,"about_ca_system_score_gemma":0.0015487801,"threshold_uncertainty_score":0.29413676},"labels":[],"label_agreement":null},{"id":"W2073941082","doi":"10.1093/biomet/asr076","title":"A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error","year":2012,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"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 Institute of Neurological Disorders and Stroke; National Science Foundation; National Institutes of Health; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; King Abdullah University of Science and Technology","keywords":"Covariate; Missing data; Statistics; Observational error; Mathematics; Econometrics; Longitudinal data; Computer science; Data mining","score_opus":0.5341257932062304,"score_gpt":0.47150813527370466,"score_spread":0.06261765793252577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073941082","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":[0.00058927893,0.00035043719,0.99844474,0.00023590025,0.000047633326,0.000035657038,0.00006383497,0.000066857654,0.00016572193],"genre_scores_gemma":[0.044327296,0.0014406402,0.9502643,0.00044400047,0.00046874245,0.000988972,0.0003138419,0.00017424359,0.0015780767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9910534,0.007189056,0.00026855944,0.00057250046,0.00075931015,0.00015715927],"domain_scores_gemma":[0.9814384,0.014781081,0.0011292375,0.0014945043,0.00087394245,0.00028276947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01784837,0.0013209186,0.0017005766,0.0029035015,0.0006978675,0.0011214552,0.0030253772,0.001901225,0.0038623067],"category_scores_gemma":[0.04010529,0.000688823,0.0025272497,0.0025711395,0.0015790617,0.0018447646,0.0024992311,0.002823296,0.0008249171],"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":[0.00017405227,0.00009378069,0.00378409,0.0008663958,0.0010355412,0.00060833525,0.00057298975,0.07736488,0.0025957432,0.70899653,0.009701365,0.19420625],"study_design_scores_gemma":[0.0000855416,0.00016233254,0.0018993,0.00018179255,0.00021627305,0.00053162256,0.00008276741,0.3295565,0.00066021073,0.6485124,0.018006755,0.00010449746],"about_ca_topic_score_codex":0.0022041984,"about_ca_topic_score_gemma":0.0026845897,"teacher_disagreement_score":0.01784837,"about_ca_system_score_codex":0.0011513919,"about_ca_system_score_gemma":0.003165201,"threshold_uncertainty_score":0.09439236},"labels":[],"label_agreement":null},{"id":"W2074243634","doi":"10.1111/1467-9868.03712","title":"Discussion on the paper by Brooks, Giudici and Roberts","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Toronto","funders":"","keywords":"Library science; Art history; Art; Computer science","score_opus":0.0700473348179045,"score_gpt":0.3507921474211187,"score_spread":0.2807448126032142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074243634","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":[0.0018083218,0.10857769,0.013071431,0.713381,0.038329672,0.00004892425,0.00068746763,0.00027434388,0.12382114],"genre_scores_gemma":[0.07221412,0.09547369,0.014616207,0.58275765,0.0586578,0.00026194472,0.0010825767,0.0006378196,0.17429829],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976659,0.0008673844,0.0000960069,0.0004090868,0.0006759515,0.0002856361],"domain_scores_gemma":[0.99535877,0.0028017738,0.00026774887,0.00029516936,0.0009955612,0.00028100086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035534597,0.00079652085,0.0007186212,0.0020159474,0.002502531,0.003452298,0.002439386,0.006012324,0.024716416],"category_scores_gemma":[0.016798323,0.00026935688,0.0010484179,0.0024042616,0.0027311577,0.006011956,0.0023155657,0.005537559,0.010810226],"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":[0.000036748224,0.000026774884,0.00025315973,0.0002453762,0.000018311826,0.00011500353,0.00039927903,0.0002647225,0.000113269816,0.3516728,0.61722976,0.029624764],"study_design_scores_gemma":[0.000011386647,0.000010313097,0.00048007537,0.0002927835,0.000008682734,0.0001731868,0.00011329686,0.00018682987,0.000111775385,0.08745654,0.91113305,0.000022050846],"about_ca_topic_score_codex":0.009635017,"about_ca_topic_score_gemma":0.0061701555,"teacher_disagreement_score":0.024716416,"about_ca_system_score_codex":0.003098231,"about_ca_system_score_gemma":0.002311268,"threshold_uncertainty_score":0.082684636},"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":"W2074965526","doi":"10.2307/3316052","title":"On the application of extended quasi‐likelihood to the clustered data case","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Maximum likelihood; Quasi-maximum likelihood; Mathematics; Statistics; Quasi-likelihood; Estimating equations; Maximum likelihood sequence estimation; Sample size determination; Mean squared error; Restricted maximum likelihood; Generalized estimating equation; Likelihood function; Applied mathematics; Count data; Poisson distribution","score_opus":0.10114385865837701,"score_gpt":0.36439841084493335,"score_spread":0.26325455218655636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074965526","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":[0.013757068,0.0005159333,0.9826302,0.0007949851,0.000050111692,0.00004444055,0.000040845163,0.00005445765,0.002111939],"genre_scores_gemma":[0.46883065,0.0013689375,0.52536935,0.0007808001,0.00025437344,0.00027751882,0.0001558348,0.00013051591,0.0028320998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97689694,0.020414647,0.00034763996,0.00076712505,0.0013051631,0.00026857606],"domain_scores_gemma":[0.86108536,0.12564908,0.0037247264,0.0055477996,0.003451246,0.00054179644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025079764,0.0005204481,0.0011732308,0.0012410443,0.00054592564,0.0016829106,0.0022559974,0.0015757708,0.0034337656],"category_scores_gemma":[0.121528886,0.00056759315,0.0010827394,0.0024646842,0.0032363879,0.0028322092,0.0027653556,0.0025576719,0.0003252023],"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":[0.0001282849,0.00004792989,0.0030022233,0.00019704402,0.00020350816,0.00050989393,0.00071857724,0.19035542,0.00032869494,0.7365997,0.001749142,0.06615959],"study_design_scores_gemma":[0.000039958337,0.00006748468,0.0010817178,0.00007371259,0.00003923405,0.0001225582,0.00011987439,0.5014697,0.00025972503,0.4943113,0.0023820929,0.00003277365],"about_ca_topic_score_codex":0.0045556724,"about_ca_topic_score_gemma":0.0027314853,"teacher_disagreement_score":0.025079764,"about_ca_system_score_codex":0.0011075742,"about_ca_system_score_gemma":0.0012262027,"threshold_uncertainty_score":0.13263607},"labels":[],"label_agreement":null},{"id":"W2075875797","doi":"10.1002/sim.6198","title":"Small sample GEE estimation of regression parameters for longitudinal data","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Generalized estimating equation; Estimator; Mathematics; Marginal model; Sample size determination; Estimating equations; Regression; Regression analysis; Gee; Confidence interval; Standard error; Econometrics","score_opus":0.2614241119052628,"score_gpt":0.46521650086800426,"score_spread":0.20379238896274143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075875797","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":[0.0036354153,0.00038618938,0.99532676,0.00010832654,0.00001957329,0.000030361418,0.00008535136,0.00017752907,0.00023056804],"genre_scores_gemma":[0.18733059,0.002039765,0.80527395,0.00034448013,0.000111584326,0.0007527985,0.0010667368,0.000522271,0.002557891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952851,0.0033682145,0.00019512766,0.00055914506,0.00046864527,0.00012372849],"domain_scores_gemma":[0.9805818,0.015904449,0.001084885,0.0013769417,0.0009321457,0.00011970716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010318259,0.0011712203,0.0019864456,0.0015944897,0.0003237067,0.00096594956,0.0017081667,0.0012410708,0.002470299],"category_scores_gemma":[0.05662339,0.0006898761,0.0016585309,0.0016143295,0.0009310184,0.0023807157,0.0014106635,0.0024458682,0.00058710465],"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":[0.00045972943,0.00019517094,0.015494712,0.0017059078,0.00179797,0.0009801184,0.00061848824,0.40311104,0.00846296,0.19033414,0.0096458,0.36719388],"study_design_scores_gemma":[0.00010337679,0.00011845472,0.0045083296,0.00013514672,0.00025750426,0.0006350667,0.00009860313,0.7955953,0.0024581917,0.18622075,0.009769417,0.00009983987],"about_ca_topic_score_codex":0.0034394048,"about_ca_topic_score_gemma":0.003314829,"teacher_disagreement_score":0.010318259,"about_ca_system_score_codex":0.0005477831,"about_ca_system_score_gemma":0.0014888898,"threshold_uncertainty_score":0.054568827},"labels":[],"label_agreement":null},{"id":"W2076669807","doi":"10.1111/anzs.12047","title":"Therapeutic Hypothermia: Quantification of the Transition of Core Body Temperature Using the Flexible Mixture Bent‐Cable Model for Longitudinal Data","year":2013,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Saskatchewan","funders":"","keywords":"Hypothermia; Flexibility (engineering); Bayesian probability; Bent molecular geometry; Mathematics; Piecewise; Core (optical fiber); Transition (genetics); Segmented regression; Polynomial; Regression; Statistics; Polynomial regression; Chemistry; Internal medicine; Computer science; Medicine; Mathematical analysis","score_opus":0.23287333810570418,"score_gpt":0.4021234866741066,"score_spread":0.1692501485684024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076669807","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":[0.06571354,0.00017708227,0.9332009,0.00020223121,0.000012610073,0.00002796384,0.00019783308,0.00012787302,0.00033998318],"genre_scores_gemma":[0.8956552,0.00045294734,0.09958918,0.00007883456,0.000046148492,0.00019935891,0.00071885897,0.00009163844,0.0031678881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989262,0.0005229111,0.00004985766,0.00024381983,0.0001505729,0.000106665415],"domain_scores_gemma":[0.99242264,0.0053328304,0.0012514835,0.0003745206,0.00044002858,0.00017855888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049911113,0.0006997695,0.00068681425,0.0008444863,0.00024359513,0.00069455465,0.0014247951,0.000988038,0.0014450062],"category_scores_gemma":[0.0126839485,0.00049852993,0.001243904,0.00063251564,0.0007506169,0.0010930778,0.0010333955,0.0015562745,0.00029986037],"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":[0.000178157,0.000051350726,0.008442052,0.000069863556,0.00009381484,0.00010144121,0.00011509571,0.9483259,0.004007025,0.01870303,0.0003849951,0.019527292],"study_design_scores_gemma":[0.000002544486,0.000024615234,0.0009709233,0.0000052536766,0.0000072241432,0.000017123999,0.0000068974423,0.995252,0.000266315,0.0032772054,0.00016140226,0.000008485164],"about_ca_topic_score_codex":0.010159449,"about_ca_topic_score_gemma":0.0061448035,"teacher_disagreement_score":0.010159449,"about_ca_system_score_codex":0.00093487307,"about_ca_system_score_gemma":0.00093030767,"threshold_uncertainty_score":0.026395857},"labels":[],"label_agreement":null},{"id":"W2077461363","doi":"10.1016/j.jmva.2009.06.003","title":"Multiple imputation and other resampling schemes for imputing missing observations","year":2009,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"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":"Jackknife resampling; Mathematics; Estimator; Imputation (statistics); Statistics; Missing data; Resampling; Econometrics","score_opus":0.1377835391048527,"score_gpt":0.425771883766381,"score_spread":0.2879883446615283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077461363","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":[0.0013238887,0.00012559748,0.99803287,0.000100165606,0.000064279666,0.00007613769,0.000055990964,0.000114171686,0.00010690787],"genre_scores_gemma":[0.028474152,0.00018033,0.9697212,0.00014427087,0.00010864649,0.00034106194,0.00024646425,0.00009356226,0.0006903436],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96244556,0.031131973,0.0013703178,0.0020641158,0.002576668,0.00041136984],"domain_scores_gemma":[0.87982213,0.08254662,0.004528428,0.027254023,0.005119804,0.00072904024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06613216,0.0016435119,0.003039784,0.0036136345,0.0021433912,0.0023090984,0.008009698,0.0034370602,0.004670752],"category_scores_gemma":[0.21818691,0.0013848633,0.0053645708,0.005979131,0.0018617741,0.003630558,0.0027523413,0.005507409,0.0010469207],"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":[0.0013425706,0.00058783265,0.008785439,0.00093929394,0.0027035763,0.0007238738,0.001341424,0.1466265,0.0026340284,0.3029191,0.012359768,0.5190365],"study_design_scores_gemma":[0.00034271856,0.00026542618,0.0022183154,0.00023965427,0.0006184785,0.00072875834,0.00010946518,0.68870753,0.0023800943,0.29639754,0.0078052455,0.00018676782],"about_ca_topic_score_codex":0.0028887158,"about_ca_topic_score_gemma":0.004599687,"teacher_disagreement_score":0.06613216,"about_ca_system_score_codex":0.0009164839,"about_ca_system_score_gemma":0.0019090698,"threshold_uncertainty_score":0.34974444},"labels":[],"label_agreement":null},{"id":"W2077529047","doi":"10.1016/s0197-2456(02)00223-4","title":"Estimating sample size for tests on trends across repeated measurements with missing data based on the interaction term in a mixed model","year":2002,"lang":"en","type":"article","venue":"Controlled Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":28,"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 Health Network; Princess Margaret Cancer Centre","funders":"","keywords":"Missing data; Sample size determination; Statistics; Term (time); Mixed model; Repeated measures design; Sample (material); Correlation; Mathematics; Random effects model; Medicine; Physics","score_opus":0.7783710309113788,"score_gpt":0.5983992409499791,"score_spread":0.17997178996139973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077529047","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":[0.028625196,0.0051946677,0.9565128,0.0029452986,0.0007472645,0.0038942657,0.000590215,0.00050689734,0.0009832884],"genre_scores_gemma":[0.36030164,0.002208306,0.6144489,0.001775441,0.0007742817,0.018320762,0.00095867045,0.00021412894,0.0009977389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.62483615,0.34244516,0.010377658,0.012985064,0.00838196,0.0009739341],"domain_scores_gemma":[0.2043736,0.77127624,0.0077664102,0.013421899,0.0023815513,0.00078036427],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.34220254,0.003364361,0.01466967,0.0052447366,0.0011310795,0.0041845758,0.0064658113,0.009880653,0.004969941],"category_scores_gemma":[0.6263936,0.002950926,0.008735007,0.0036664354,0.005940075,0.006744408,0.0037998885,0.009135128,0.00052683026],"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":[0.06868093,0.0024367413,0.030828496,0.014072813,0.046121046,0.0015838359,0.0023031274,0.10796672,0.004213802,0.19749683,0.010920934,0.51337487],"study_design_scores_gemma":[0.02362194,0.009799036,0.009527364,0.0022554058,0.015587641,0.0008782086,0.00027584113,0.4786453,0.0037940186,0.44929916,0.006039654,0.000276474],"about_ca_topic_score_codex":0.001449344,"about_ca_topic_score_gemma":0.0011077024,"teacher_disagreement_score":0.65779746,"about_ca_system_score_codex":0.0023791217,"about_ca_system_score_gemma":0.0041108076,"threshold_uncertainty_score":0.81118137},"labels":[],"label_agreement":null},{"id":"W2078516591","doi":"10.1080/03610918.2014.950746","title":"A Comparative Study of Observation- and Parameter-driven Zero-inflated Poisson Models for Longitudinal Count Data","year":2014,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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":"Mount Saint Vincent University; University of New Brunswick","funders":"","keywords":"Count data; Poisson distribution; Correlation; Autocorrelation; Statistics; Zero (linguistics); Zero-inflated model; Random effects model; Overdispersion; Longitudinal data; Mathematics; Computer science; Poisson regression; Data mining","score_opus":0.5617932469651917,"score_gpt":0.5394372893436722,"score_spread":0.02235595762151954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078516591","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":[0.18161301,0.007618392,0.8022628,0.0031415347,0.00022879564,0.00026012328,0.00074143155,0.00048000022,0.003653849],"genre_scores_gemma":[0.77535564,0.0059646023,0.21132313,0.000680044,0.00031800216,0.00058043725,0.0021831698,0.00034354502,0.0032515689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9806842,0.01619452,0.00044838927,0.0011059238,0.00121743,0.00034958313],"domain_scores_gemma":[0.77430254,0.2088871,0.00471622,0.005268277,0.0055688852,0.0012570188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046000265,0.0009022682,0.001936185,0.002215501,0.0008646132,0.0029257177,0.0038800435,0.0025781794,0.0019246122],"category_scores_gemma":[0.12581351,0.00088371633,0.002626525,0.0023937752,0.0015532927,0.004931127,0.0020693955,0.0025607331,0.0003656219],"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":[0.0008795826,0.00023877235,0.02452285,0.0005907249,0.00071733946,0.00045245266,0.0012041414,0.78983206,0.000607379,0.13107413,0.0024669303,0.047413625],"study_design_scores_gemma":[0.000039040788,0.0001774466,0.0026760173,0.00005986445,0.00008579808,0.00010766428,0.00015550893,0.9664579,0.0001449041,0.028867485,0.0011654622,0.00006289586],"about_ca_topic_score_codex":0.011231643,"about_ca_topic_score_gemma":0.0072096447,"teacher_disagreement_score":0.046000265,"about_ca_system_score_codex":0.002746517,"about_ca_system_score_gemma":0.0032915322,"threshold_uncertainty_score":0.24327552},"labels":[],"label_agreement":null},{"id":"W2078600255","doi":"10.1016/j.jmva.2010.01.011","title":"On Bayes estimators with uniform priors on spheres and their comparative performance with maximum likelihood estimators for estimating bounded multivariate normal means","year":2010,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Mathematics; Estimator; Prior probability; Bounded function; Multivariate statistics; Applied mathematics; Multivariate normal distribution; Statistics; Bayes' theorem; Bayesian probability; Mathematical analysis","score_opus":0.0296139760245409,"score_gpt":0.3352590948327722,"score_spread":0.3056451188082313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078600255","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":[0.03591668,0.004442473,0.9565943,0.00048221328,0.00007210773,0.00007404439,0.000090700574,0.0002983292,0.0020292595],"genre_scores_gemma":[0.27417004,0.0057854177,0.7158162,0.00042538843,0.00031910112,0.0003360885,0.00067864556,0.0005383186,0.0019308178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9723126,0.02137422,0.0010187998,0.0012530908,0.0035083892,0.00053280033],"domain_scores_gemma":[0.5599899,0.41863176,0.00577331,0.007822645,0.006778385,0.0010039768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06123067,0.0018951055,0.0032672535,0.0050608316,0.0014621901,0.0041189482,0.0038820675,0.004815798,0.0033056838],"category_scores_gemma":[0.33323926,0.0019074914,0.0014032371,0.0044166315,0.005704647,0.008026598,0.0051889424,0.0026282195,0.000956947],"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":[0.0013181324,0.00015605964,0.0066211503,0.0006375151,0.00040500864,0.00014485938,0.00092085305,0.45311806,0.0016941441,0.3242226,0.0025218588,0.20823976],"study_design_scores_gemma":[0.00011132512,0.00015345207,0.0014871255,0.00017825112,0.00009885808,0.00015191994,0.000112773574,0.8598956,0.0014120768,0.13519126,0.0011035155,0.00010380047],"about_ca_topic_score_codex":0.008516636,"about_ca_topic_score_gemma":0.0051770573,"teacher_disagreement_score":0.06123067,"about_ca_system_score_codex":0.0025426417,"about_ca_system_score_gemma":0.0027411373,"threshold_uncertainty_score":0.32382262},"labels":[],"label_agreement":null},{"id":"W2079162592","doi":"10.1111/j.1467-9868.2006.00548.x","title":"Improved Likelihood Inference for Discrete Data","year":2006,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Contingency table; Estimator; Extension (predicate logic); Inference; Table (database); Count data; Flexibility (engineering); Exponential family; Contrast (vision); Computer science; Maximum likelihood; Algorithm; Mathematics; Statistics; Applied mathematics; Data mining; Poisson distribution; Artificial intelligence","score_opus":0.12224498421376277,"score_gpt":0.4138594176979046,"score_spread":0.2916144334841418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079162592","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":[0.0012624927,0.0003059555,0.9974463,0.00018979644,0.000035908433,0.000013286565,0.00008682622,0.00011584776,0.000543714],"genre_scores_gemma":[0.11275539,0.0014597382,0.880246,0.00033891996,0.00046890753,0.00030396107,0.0007967894,0.00027355913,0.00335666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97988874,0.014390387,0.00075573794,0.0020000432,0.0026202789,0.00034470076],"domain_scores_gemma":[0.883115,0.10009112,0.0037674354,0.008874375,0.0034589244,0.00069318427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02383758,0.0010781361,0.002305236,0.0033887865,0.00063974573,0.0030892822,0.0028423974,0.0014929866,0.0052221613],"category_scores_gemma":[0.13456234,0.000875147,0.001841737,0.0037647984,0.002824526,0.004659661,0.003387466,0.0056605525,0.0012785072],"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":[0.000103525796,0.00007251498,0.0030020874,0.0005255113,0.0002803451,0.00018012601,0.00046158762,0.10480029,0.00076810276,0.7487826,0.0047189505,0.13630445],"study_design_scores_gemma":[0.000035362937,0.0000390051,0.0008781402,0.00007084701,0.000048933107,0.00009245851,0.000032248226,0.33813265,0.00034566908,0.6553504,0.0049449876,0.000029372202],"about_ca_topic_score_codex":0.0029312507,"about_ca_topic_score_gemma":0.0032170743,"teacher_disagreement_score":0.02383758,"about_ca_system_score_codex":0.0018426024,"about_ca_system_score_gemma":0.002152831,"threshold_uncertainty_score":0.12606668},"labels":[],"label_agreement":null},{"id":"W2080989967","doi":"10.1002/cjs.11126","title":"Testing for generalized linear mixed models with cluster correlated data under linear inequality constraints","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Carleton University; Health Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized linear mixed model; Estimator; Mathematics; Inference; Generalized linear model; Applied mathematics; Statistical inference; Random effects model; Generalized estimating equation; Statistics; Statistical hypothesis testing; Computer science","score_opus":0.290386096729699,"score_gpt":0.3867316434641908,"score_spread":0.0963455467344918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080989967","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":[0.08209855,0.00047684758,0.91309637,0.00094439735,0.000083252446,0.00040950315,0.0009901631,0.00032041798,0.0015804506],"genre_scores_gemma":[0.67763925,0.0004504999,0.31670463,0.00048837176,0.00018607063,0.0016865664,0.0017632436,0.00014296561,0.0009384148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8612427,0.114775784,0.0037726115,0.011737443,0.006642156,0.0018293195],"domain_scores_gemma":[0.39560917,0.56832767,0.015947439,0.013858094,0.00500405,0.0012535874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07164099,0.0020471858,0.0045051817,0.0036211896,0.0020162726,0.0042853807,0.00541894,0.0030278135,0.008514796],"category_scores_gemma":[0.3860121,0.0015340918,0.0037910468,0.0070064426,0.0069072717,0.0046561793,0.0041256943,0.0041205976,0.00051788543],"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":[0.0027111475,0.0005157317,0.060584947,0.0018021966,0.006471659,0.0034445869,0.0016892059,0.37618726,0.0017823075,0.40152958,0.0047918856,0.13848957],"study_design_scores_gemma":[0.00037340747,0.000479585,0.010888661,0.00023486358,0.00037234355,0.00029128022,0.00037797668,0.65886784,0.0009674463,0.3250719,0.0019455384,0.00012908678],"about_ca_topic_score_codex":0.014873022,"about_ca_topic_score_gemma":0.008116286,"teacher_disagreement_score":0.07164099,"about_ca_system_score_codex":0.0032432338,"about_ca_system_score_gemma":0.006199077,"threshold_uncertainty_score":0.3788783},"labels":[],"label_agreement":null},{"id":"W2081060430","doi":"10.1093/biomet/asm093","title":"Studentization and deriving accurate p-values","year":2008,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Statistic; Null distribution; Conditional probability distribution; Noncentral chi-squared distribution; Applied mathematics; Test statistic; Statistical hypothesis testing; Asymptotic distribution; Ratio distribution","score_opus":0.13163577916910774,"score_gpt":0.4085972742877978,"score_spread":0.27696149511869006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081060430","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":[0.0028446247,0.00014089864,0.9953903,0.00022731055,0.00004461869,0.00004277875,0.00005696106,0.00024022865,0.0010122935],"genre_scores_gemma":[0.13112336,0.00040786836,0.8649125,0.00036270922,0.00031470737,0.00069172273,0.00037352616,0.00047914937,0.001334383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9735643,0.017238373,0.0015934384,0.003112526,0.003954215,0.0005370887],"domain_scores_gemma":[0.8412566,0.13069643,0.005748399,0.015190949,0.006258715,0.00084885483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03819577,0.0014316712,0.0019443404,0.0045279698,0.0009801652,0.0032219072,0.0034177792,0.002563085,0.005273339],"category_scores_gemma":[0.2815944,0.0009366544,0.0013589982,0.0036699907,0.005248736,0.0053975713,0.004052161,0.0062031583,0.00234306],"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":[0.00023787055,0.00007784987,0.0046527553,0.00050192734,0.00024719175,0.0007292018,0.0005805194,0.04578147,0.0032836264,0.74728763,0.0051239035,0.19149598],"study_design_scores_gemma":[0.00003523319,0.00010035471,0.0011719408,0.0001264469,0.000035234,0.00033814137,0.00008575292,0.12682602,0.0043923627,0.8592001,0.0076270914,0.000061323844],"about_ca_topic_score_codex":0.0007022521,"about_ca_topic_score_gemma":0.00053235155,"teacher_disagreement_score":0.03819577,"about_ca_system_score_codex":0.0011549043,"about_ca_system_score_gemma":0.0021686081,"threshold_uncertainty_score":0.20200098},"labels":[],"label_agreement":null},{"id":"W2082625612","doi":"10.1002/sim.4037","title":"Bayesian sample size for diagnostic test studies in the absence of a gold standard: Comparing identifiable with non‐identifiable models","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"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 Health Centre; Royal Victoria Regional Health Centre; McGill University; Royal Victoria Hospital","funders":"","keywords":"Sample size determination; Statistics; Sample (material); Bayesian probability; Conditional independence; Imperfect; Statistical hypothesis testing; Computer science; Gold standard (test); Econometrics; Independence (probability theory); Mathematics","score_opus":0.08947318568387154,"score_gpt":0.4160162086473315,"score_spread":0.32654302296346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082625612","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":[0.05109348,0.0047315275,0.93187135,0.005361651,0.0004937171,0.0015645686,0.00051067513,0.00024477864,0.004128265],"genre_scores_gemma":[0.6578589,0.0017442699,0.3317109,0.0016137059,0.0004430245,0.004685239,0.000712264,0.0001559398,0.0010757957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7101726,0.2569321,0.006894593,0.012205544,0.012239662,0.0015554716],"domain_scores_gemma":[0.15735207,0.8008322,0.013261362,0.021723425,0.0053203134,0.0015106146],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.34879985,0.0016621089,0.003961384,0.0034654054,0.0015276617,0.004679186,0.0063168122,0.0066301217,0.0031643708],"category_scores_gemma":[0.6523741,0.0018703741,0.0045986148,0.0016982422,0.009758399,0.009040403,0.006581864,0.0070006093,0.0003260703],"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":[0.0065529807,0.00044173707,0.048962798,0.003306294,0.0047971397,0.0010647351,0.0034050473,0.22851986,0.0016474387,0.52333987,0.0047555882,0.17320654],"study_design_scores_gemma":[0.0014417695,0.0014249698,0.010420397,0.0010755517,0.0013857867,0.00037733425,0.00042755206,0.4299618,0.001411271,0.54560536,0.0062854863,0.00018269329],"about_ca_topic_score_codex":0.0030981677,"about_ca_topic_score_gemma":0.0021584178,"teacher_disagreement_score":0.6512002,"about_ca_system_score_codex":0.0036968677,"about_ca_system_score_gemma":0.0034445783,"threshold_uncertainty_score":0.80304575},"labels":[],"label_agreement":null},{"id":"W2083504428","doi":"10.1080/02331880701600380","title":"Bayesian analysis of a 2×2 contingency table with dependent proportions and exact sample size","year":2008,"lang":"en","type":"article","venue":"Statistics","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Contingency table; Dirichlet distribution; Statistics; Bayesian probability; Marginal likelihood; Sample size determination; Bayesian average; Econometrics; Posterior probability; Bayesian inference; Bayesian statistics; Applied mathematics; Mathematical analysis","score_opus":0.04052479091243303,"score_gpt":0.33555756968226325,"score_spread":0.2950327787698302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083504428","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":[0.034097794,0.00019194081,0.96440315,0.00018249702,0.000027461376,0.000044613596,0.00013288621,0.0001363902,0.0007833571],"genre_scores_gemma":[0.55302405,0.00027766064,0.44381765,0.00021281987,0.00008893233,0.00021103074,0.00041470822,0.00012516222,0.0018281086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9888755,0.0074724103,0.00034208054,0.0015374325,0.0014349087,0.00033769046],"domain_scores_gemma":[0.880427,0.11154424,0.002672744,0.002599067,0.0023019826,0.00045496417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017849147,0.0004278861,0.0012918094,0.0015542398,0.00051244616,0.0015301818,0.0014444147,0.00080422463,0.004804207],"category_scores_gemma":[0.087915294,0.0006080371,0.000822897,0.0015930306,0.0016475596,0.0022414534,0.0011166974,0.0016825732,0.0003266104],"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":[0.00069755386,0.00013542418,0.021132462,0.00043112566,0.0004924501,0.0008907231,0.0006528196,0.40663892,0.0035775146,0.40778223,0.003782392,0.15378642],"study_design_scores_gemma":[0.000042846972,0.00006718659,0.0048293243,0.00006180835,0.0000536494,0.0002409539,0.00009303449,0.7914448,0.0012257118,0.20031025,0.00158391,0.000046473368],"about_ca_topic_score_codex":0.00438931,"about_ca_topic_score_gemma":0.0050271116,"teacher_disagreement_score":0.017849147,"about_ca_system_score_codex":0.0010655482,"about_ca_system_score_gemma":0.0010311813,"threshold_uncertainty_score":0.09439641},"labels":[],"label_agreement":null},{"id":"W2083675429","doi":"10.1002/1097-0258(20000730)19:14<1952::aid-sim474>3.0.co;2-k","title":"The theory of dispersion models. Bent J�rgensen, Chapman and Hall, 1997. No. of pages: 237. Price: �39.95. ISBN 0-412-99718-8","year":2000,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Waterloo","funders":"","keywords":"Citation; Library science; Mathematical economics; Statistics; Computer science; Mathematics","score_opus":0.04923718532831025,"score_gpt":0.3494584815210401,"score_spread":0.30022129619272986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083675429","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":[0.0025656624,0.47120774,0.45950446,0.011988222,0.00593922,0.00010448477,0.0036248537,0.0026233422,0.042442143],"genre_scores_gemma":[0.10666155,0.5395831,0.26178393,0.0029910882,0.007969433,0.0008174042,0.005449884,0.0031441182,0.071599476],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963862,0.0013475703,0.0003559189,0.0005539114,0.0012434489,0.00011304506],"domain_scores_gemma":[0.9884179,0.008371359,0.00097550446,0.0009502761,0.0011055926,0.00017936327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004376135,0.0039780755,0.0055186166,0.0042004464,0.0009404437,0.00482525,0.0024934914,0.0029482532,0.0253118],"category_scores_gemma":[0.018539414,0.0036301075,0.0019898536,0.0055832253,0.0033746907,0.007616008,0.0021693066,0.0063393237,0.013398427],"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":[0.00019743726,0.00009660039,0.0011529144,0.0036228988,0.0007199424,0.0003487001,0.00078051246,0.025447411,0.0016780557,0.23589614,0.3064924,0.4235671],"study_design_scores_gemma":[0.000040085913,0.00008000001,0.0021307312,0.0010743262,0.00027504613,0.00075352174,0.00019062645,0.017697794,0.0007066447,0.7242235,0.25264952,0.0001782207],"about_ca_topic_score_codex":0.0076062363,"about_ca_topic_score_gemma":0.00815415,"teacher_disagreement_score":0.0253118,"about_ca_system_score_codex":0.0028970537,"about_ca_system_score_gemma":0.0018217397,"threshold_uncertainty_score":0.084676385},"labels":[],"label_agreement":null},{"id":"W2084122520","doi":"10.1007/s13253-013-0155-9","title":"Gaussian Copula Mixed Models for Clustered Mixed Outcomes, With Application in Developmental Toxicology","year":2013,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"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 Calgary","funders":"","keywords":"Random effects model; Mixed model; Copula (linguistics); Generalized linear mixed model; Econometrics; Probit model; Developmental toxicity; Mixture model; Computer science; Binary data; Probit; Binary number; Statistics; Mathematics; Meta-analysis; Medicine; Biology","score_opus":0.047139749886037405,"score_gpt":0.2740384733498233,"score_spread":0.22689872346378587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084122520","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":[0.001424499,0.0003131191,0.99739695,0.00015195725,0.00005436106,0.000060592054,0.0001663826,0.0001796981,0.00025259916],"genre_scores_gemma":[0.06813742,0.0016768567,0.9172147,0.00051290257,0.00039695468,0.0017005431,0.001321976,0.00070569187,0.008332999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98672664,0.009832316,0.00043401928,0.0015029425,0.001072944,0.00043107974],"domain_scores_gemma":[0.9297113,0.058012888,0.0029881913,0.004453504,0.0037916044,0.0010425283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028965631,0.0032492776,0.004027888,0.0032971655,0.0016617958,0.0032694424,0.008091315,0.0037087835,0.007301195],"category_scores_gemma":[0.09958569,0.0019854787,0.005739575,0.004283912,0.0034061978,0.0038281989,0.004767259,0.00734631,0.0018577761],"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":[0.0003100086,0.00019331717,0.0026685672,0.00042356484,0.0009321907,0.0005575176,0.0005951058,0.1888049,0.0010810373,0.71537274,0.009890754,0.079170264],"study_design_scores_gemma":[0.00004975596,0.000077947254,0.00056418945,0.00007111013,0.00015432484,0.00014835868,0.000069785165,0.71499383,0.00039513878,0.2790209,0.004377652,0.00007706155],"about_ca_topic_score_codex":0.014925674,"about_ca_topic_score_gemma":0.019322967,"teacher_disagreement_score":0.028965631,"about_ca_system_score_codex":0.0024234038,"about_ca_system_score_gemma":0.0055422406,"threshold_uncertainty_score":0.15318674},"labels":[],"label_agreement":null},{"id":"W2084192089","doi":"10.1198/016214504000001006","title":"Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates","year":2004,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":46,"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; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Covariate; Missing data; Categorical variable; Convergence (economics); Monte Carlo method; Mathematics; Expectation–maximization algorithm; Applied mathematics; Computer science; Statistics; Maximum likelihood","score_opus":0.029116050359756826,"score_gpt":0.3410638414224675,"score_spread":0.31194779106271064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084192089","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":[0.0015809457,0.00022384964,0.99759716,0.00015576338,0.000010485265,0.000038364225,0.000038286737,0.000067263216,0.00028794416],"genre_scores_gemma":[0.084944345,0.0009211875,0.9113951,0.00032742028,0.00009977929,0.0006600958,0.00032146485,0.00012430495,0.0012063255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9816024,0.015171742,0.00053508906,0.0011090508,0.0014157554,0.00016590283],"domain_scores_gemma":[0.9021262,0.089621305,0.0025697818,0.0037564128,0.001588384,0.0003378159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029090043,0.0013488272,0.0019023773,0.002398202,0.0010233686,0.0017808812,0.0028025988,0.0021306744,0.0038679282],"category_scores_gemma":[0.16124186,0.0011990805,0.0018965084,0.002333671,0.0031241088,0.0055253934,0.0029968072,0.0034931109,0.00070392987],"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":[0.00014014654,0.00008511958,0.0036624058,0.0005457088,0.00035802234,0.00023581053,0.0006015686,0.3367783,0.00050041324,0.5114626,0.0020857332,0.14354402],"study_design_scores_gemma":[0.000049955815,0.000039528408,0.00053389854,0.000082907136,0.00004587042,0.00012497623,0.00006170709,0.51787657,0.0004204145,0.47816542,0.0025637907,0.000035029778],"about_ca_topic_score_codex":0.0034709496,"about_ca_topic_score_gemma":0.0055419593,"teacher_disagreement_score":0.029090043,"about_ca_system_score_codex":0.0015139701,"about_ca_system_score_gemma":0.0022865932,"threshold_uncertainty_score":0.15384471},"labels":[],"label_agreement":null},{"id":"W2084286900","doi":"10.1080/10485250108832855","title":"Distribution-free dispersion tests for data with ties","year":2001,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Victoria Hospital","funders":"","keywords":"Statistics; Wilcoxon signed-rank test; Mathematics; Categorical variable; Statistic; Test statistic; Dispersion (optics); Rank (graph theory); Statistical hypothesis testing; Econometrics; Combinatorics; Mann–Whitney U test","score_opus":0.11193933531760107,"score_gpt":0.40256117469220415,"score_spread":0.29062183937460306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084286900","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":[0.021306088,0.0010551966,0.962943,0.0008730556,0.00048945454,0.001546311,0.0015416375,0.00092526345,0.009319936],"genre_scores_gemma":[0.444839,0.0012104597,0.5280471,0.0013503674,0.0010289531,0.009366299,0.0054238755,0.0008538219,0.007880129],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9180968,0.0401749,0.0074130115,0.010740201,0.021756811,0.0018183003],"domain_scores_gemma":[0.5709362,0.35113612,0.019616278,0.038422022,0.017782085,0.0021073208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06754831,0.0015248451,0.0035403853,0.009477862,0.0029354054,0.0045580645,0.0047899247,0.0031981845,0.013888788],"category_scores_gemma":[0.4230819,0.00088177965,0.0034055987,0.009146984,0.005419893,0.0090586925,0.0058203954,0.0063944585,0.0029325094],"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":[0.0013496033,0.00046365857,0.047921915,0.0015379667,0.0020228014,0.0010854492,0.003570799,0.024665324,0.0019901458,0.47405076,0.022299096,0.4190424],"study_design_scores_gemma":[0.0005110573,0.0010859864,0.025450144,0.0008576726,0.0005336547,0.0013214246,0.001620916,0.1463942,0.0039918264,0.76265717,0.055229418,0.00034655482],"about_ca_topic_score_codex":0.0020247214,"about_ca_topic_score_gemma":0.0014437181,"teacher_disagreement_score":0.06754831,"about_ca_system_score_codex":0.0021200466,"about_ca_system_score_gemma":0.004204703,"threshold_uncertainty_score":0.35723388},"labels":[],"label_agreement":null},{"id":"W2084292194","doi":"10.1002/sim.2731","title":"Developments in cluster randomized trials and <i>Statistics in Medicine</i>","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":248,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Royal Society","keywords":"Sample size determination; Medical statistics; Statistics; Cluster (spacecraft); Cluster analysis; Randomized controlled trial; Computer science; Population; Psychological intervention; Research design; Medicine; Econometrics; Data science; Mathematics; Surgery","score_opus":0.08618382227126765,"score_gpt":0.4329159356107219,"score_spread":0.34673211333945425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084292194","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":[0.0005791133,0.16980775,0.76735836,0.038826525,0.010225883,0.0006341304,0.00025730074,0.00044331964,0.011867657],"genre_scores_gemma":[0.019045558,0.13632669,0.798959,0.019458594,0.020031631,0.0032775514,0.0002347149,0.00053586386,0.002130437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.7996178,0.16822897,0.008494695,0.0059692506,0.01669221,0.0009970459],"domain_scores_gemma":[0.5907113,0.36931247,0.009268369,0.016011726,0.012838517,0.0018576778],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14576566,0.002188136,0.0059994,0.0070496453,0.0013983446,0.007007676,0.005191343,0.007548613,0.0051487964],"category_scores_gemma":[0.22903499,0.0019018756,0.0038881295,0.012975643,0.014395131,0.0073122797,0.0043004085,0.022175634,0.0023449145],"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":[0.00018967903,0.00007970778,0.00047483935,0.0042249397,0.0005055555,0.0001243827,0.00049707334,0.004075204,0.00021460681,0.7620524,0.027495692,0.200066],"study_design_scores_gemma":[0.00017777782,0.00031173913,0.000605182,0.002757224,0.00018297978,0.0004756189,0.00013642598,0.009123604,0.000529565,0.8313168,0.15427335,0.00010966915],"about_ca_topic_score_codex":0.0025706894,"about_ca_topic_score_gemma":0.0013399113,"teacher_disagreement_score":0.85423434,"about_ca_system_score_codex":0.0059290593,"about_ca_system_score_gemma":0.010841206,"threshold_uncertainty_score":0.7708917},"labels":[],"label_agreement":null},{"id":"W2084293180","doi":"10.2307/3316085","title":"Loss functions for estimation of extrema with an application to disease mapping","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Iowa State University; National Institutes of Health; National Science Foundation","keywords":"Maxima and minima; Estimator; Mathematics; Bayes' theorem; Nonlinear system; Applied mathematics; Statistics; Bayesian probability; Mathematical analysis","score_opus":0.054783248712377726,"score_gpt":0.3275580132783424,"score_spread":0.27277476456596467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084293180","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":[0.0063330927,0.0005620974,0.99203813,0.00038237096,0.00003471862,0.0000334927,0.000030121515,0.00010378598,0.00048211895],"genre_scores_gemma":[0.26381797,0.0016759393,0.72804874,0.0004603948,0.00030150972,0.0006257325,0.00040079965,0.00038637192,0.0042825495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99446493,0.00414507,0.0002181985,0.0003229104,0.00068640517,0.00016240486],"domain_scores_gemma":[0.94674355,0.04721887,0.0018491638,0.001226922,0.0025477656,0.0004137935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023977742,0.0016087084,0.0019433653,0.0030182493,0.00079416984,0.0021189405,0.0021380181,0.0028348404,0.0018085625],"category_scores_gemma":[0.076760374,0.00089277385,0.0012026266,0.002104939,0.0024899004,0.0030085836,0.0031861675,0.003690785,0.0005351594],"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":[0.00017807755,0.00012636455,0.001626699,0.000255554,0.00015125213,0.00017761994,0.00020563108,0.7570412,0.0013840332,0.15581152,0.0030682422,0.07997372],"study_design_scores_gemma":[0.00001712651,0.000032365053,0.0002678797,0.00004208862,0.000014075824,0.0000465915,0.000019616979,0.9450401,0.00038949065,0.053295147,0.0008163961,0.000019120896],"about_ca_topic_score_codex":0.0022567378,"about_ca_topic_score_gemma":0.0011236388,"teacher_disagreement_score":0.023977742,"about_ca_system_score_codex":0.0017183177,"about_ca_system_score_gemma":0.0012668244,"threshold_uncertainty_score":0.12680793},"labels":[],"label_agreement":null},{"id":"W2085360857","doi":"10.1198/tas.2011.11077","title":"An Overview of Current Software Procedures for Fitting Linear Mixed Models","year":2011,"lang":"en","type":"article","venue":"The American Statistician","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Aging","funders":"National Institute on Aging","keywords":"Current (fluid); Software; Computer science; Statistics; Econometrics; Data science; Mathematics; Engineering; Programming language","score_opus":0.3300457036600927,"score_gpt":0.4672241114054354,"score_spread":0.13717840774534268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085360857","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":[0.00039021779,0.0013139799,0.98141414,0.00030935399,0.00012219066,0.00032169107,0.0016679852,0.012761657,0.0016987419],"genre_scores_gemma":[0.002472406,0.002208639,0.9863643,0.00019014024,0.000111756475,0.0018481738,0.0024108284,0.0032002283,0.0011935137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9799764,0.01250003,0.0023784756,0.0010379787,0.003822334,0.00028475438],"domain_scores_gemma":[0.9081771,0.07505353,0.0033936626,0.004916,0.007909861,0.0005499787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026624208,0.0037190928,0.0022707658,0.008086215,0.0011675879,0.003463343,0.0054443795,0.002427743,0.049806256],"category_scores_gemma":[0.11999969,0.0028936616,0.0035186152,0.008998307,0.0010360976,0.0034291965,0.00274972,0.0039276,0.035927605],"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":[0.0002491427,0.00025438363,0.002298908,0.004182309,0.00049083657,0.00027868617,0.0005372406,0.017195573,0.0026396238,0.032944456,0.117797494,0.8211313],"study_design_scores_gemma":[0.0005413129,0.00039251978,0.0057588844,0.0034801217,0.000581671,0.002453953,0.00049156253,0.2317892,0.015036246,0.17343174,0.56529945,0.0007433353],"about_ca_topic_score_codex":0.0046334704,"about_ca_topic_score_gemma":0.0067127277,"teacher_disagreement_score":0.049806256,"about_ca_system_score_codex":0.0013287074,"about_ca_system_score_gemma":0.0044033895,"threshold_uncertainty_score":0.16661853},"labels":[],"label_agreement":null},{"id":"W2085459152","doi":"10.1214/09-ba419","title":"Prediction of pregnancy: a joint model for longitudinal and binary data","year":2009,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian 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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Population; Linear model; Bayesian probability; Pregnancy; Generalized linear model; Random effects model; Statistics; Longitudinal study; Binary data; Joint (building); Computer science; Binary number; Mathematics; Medicine; Engineering","score_opus":0.20834621601040085,"score_gpt":0.38144529484090556,"score_spread":0.1730990788305047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085459152","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":[0.04088294,0.0013164341,0.95228004,0.0028938293,0.0001515494,0.0001848509,0.001177984,0.00029772328,0.00081465126],"genre_scores_gemma":[0.60720265,0.00341818,0.36993203,0.0012996778,0.0012950165,0.003076437,0.004063345,0.0002612506,0.009451363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9879308,0.007471008,0.00042675753,0.0024534592,0.0010394605,0.0006785808],"domain_scores_gemma":[0.9471538,0.04385362,0.004125099,0.0024071233,0.0017080199,0.0007523531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032588422,0.0023162134,0.0044899997,0.0030463212,0.0010705094,0.0029796087,0.0062032402,0.0043633264,0.003586193],"category_scores_gemma":[0.064625494,0.0017185443,0.0029961686,0.0033022058,0.0033677968,0.004744664,0.003720602,0.005495609,0.0011502244],"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":[0.0008734867,0.00074930687,0.047747005,0.00062083453,0.0012508563,0.0007576545,0.0010046926,0.6207162,0.0012033073,0.22554904,0.0049201455,0.09460741],"study_design_scores_gemma":[0.00011209062,0.00017239488,0.0034112595,0.00008375024,0.00018876324,0.00013443988,0.000050963572,0.8945351,0.00016728521,0.099714935,0.0013609086,0.00006797674],"about_ca_topic_score_codex":0.011460897,"about_ca_topic_score_gemma":0.006612119,"teacher_disagreement_score":0.032588422,"about_ca_system_score_codex":0.0018232387,"about_ca_system_score_gemma":0.002601121,"threshold_uncertainty_score":0.17234612},"labels":[],"label_agreement":null},{"id":"W2086012744","doi":"10.1037/a0029253","title":"Individual influence on model selection.","year":2012,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"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":"","keywords":"Akaike information criterion; Bayesian information criterion; Model selection; Selection (genetic algorithm); Ranking (information retrieval); Generality; Information Criteria; Computer science; Bayesian probability; Deviance information criterion; Multilevel model; Econometrics; Bayesian inference; Statistics; Data mining; Machine learning; Artificial intelligence; Psychology; Mathematics","score_opus":0.3007814790311631,"score_gpt":0.5548223446057223,"score_spread":0.25404086557455924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086012744","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":[0.07725093,0.0025553042,0.8895614,0.0040089665,0.0005734379,0.00080039824,0.0002841616,0.0008346066,0.024130655],"genre_scores_gemma":[0.7324887,0.0008589068,0.26198888,0.0012693128,0.00038645495,0.0008661279,0.00029185732,0.00042994268,0.0014196939],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7725892,0.18996902,0.005409789,0.010428481,0.020252325,0.0013513094],"domain_scores_gemma":[0.35322276,0.59258765,0.01076056,0.033499833,0.008301547,0.0016276005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14615585,0.0018190026,0.0021110545,0.0037677502,0.0018245608,0.004714647,0.0023436898,0.002227333,0.004834996],"category_scores_gemma":[0.546576,0.0010104453,0.0033254689,0.002495131,0.00533899,0.0050179656,0.0058477647,0.005408008,0.00068163057],"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":[0.00088531955,0.0003185941,0.10331663,0.0015905659,0.0051529496,0.0013091826,0.008750361,0.07748014,0.0026130106,0.45699328,0.010094955,0.3314951],"study_design_scores_gemma":[0.00012853857,0.00046521006,0.018131295,0.00056143495,0.000978865,0.00082270603,0.0010834045,0.39574823,0.004228465,0.56272453,0.014926584,0.00020079584],"about_ca_topic_score_codex":0.0031264827,"about_ca_topic_score_gemma":0.0039344635,"teacher_disagreement_score":0.14615585,"about_ca_system_score_codex":0.0026000352,"about_ca_system_score_gemma":0.0031054162,"threshold_uncertainty_score":0.77295524},"labels":[],"label_agreement":null},{"id":"W2086120770","doi":"10.1080/02664760120011563","title":"Prior distribution assessment for a multivariate normal distribution: An experimental study","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"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":"Kuwait University; Global Institute for Water Security, University of Saskatchewan","keywords":"Multivariate normal distribution; Statistics; Variance (accounting); Multivariate statistics; Distribution (mathematics); Normal distribution; Mathematics; Conjugate prior; Econometrics; Prior probability; Computer science; Bayesian probability","score_opus":0.05948208554205539,"score_gpt":0.4262112367468744,"score_spread":0.36672915120481897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086120770","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":[0.84377664,0.00039234688,0.144959,0.00066298194,0.00018072051,0.0020507874,0.00027131845,0.0003431691,0.0073629688],"genre_scores_gemma":[0.8661168,0.00035784653,0.12832622,0.00037517163,0.00013650497,0.0021161698,0.0003112518,0.0001394193,0.0021205132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96015716,0.029113846,0.0018592966,0.0032255745,0.005113756,0.00053043343],"domain_scores_gemma":[0.5198542,0.4485922,0.0071725803,0.0146852005,0.008230925,0.0014649502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04535807,0.0009944106,0.0011649944,0.00081082876,0.001291005,0.0022008626,0.0017505268,0.0021683273,0.008747951],"category_scores_gemma":[0.26979393,0.00050640997,0.00095210876,0.00085250865,0.0021017632,0.0041162595,0.0024163267,0.00287958,0.0010752431],"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":[0.019808734,0.02009499,0.043011565,0.006373404,0.0006766458,0.0014134776,0.0645756,0.04751639,0.11418185,0.042949658,0.01092988,0.6284679],"study_design_scores_gemma":[0.0050672763,0.079078,0.09165439,0.0020833537,0.0010546038,0.0035879975,0.023626622,0.5686181,0.092837006,0.08725353,0.043734122,0.0014050486],"about_ca_topic_score_codex":0.0008759075,"about_ca_topic_score_gemma":0.00082470535,"teacher_disagreement_score":0.04535807,"about_ca_system_score_codex":0.0010610345,"about_ca_system_score_gemma":0.00065128127,"threshold_uncertainty_score":0.23987931},"labels":[],"label_agreement":null},{"id":"W2086168102","doi":"10.1002/cjs.10057","title":"Modified weights based generalized quasilikelihood inferences in incomplete longitudinal binary models","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Covariate; Statistics; Generalized estimating equation; Estimating equations; Regression; Regression analysis; Missing data; Correlation; Econometrics; Maximum likelihood","score_opus":0.08984505911184888,"score_gpt":0.32767607988379704,"score_spread":0.23783102077194818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086168102","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":[0.011362129,0.00015586967,0.9873949,0.00029373882,0.000039013485,0.00008043816,0.0000918185,0.00010353039,0.00047851636],"genre_scores_gemma":[0.28164876,0.00046501748,0.71342754,0.00043654034,0.00021210448,0.00074759667,0.0005375484,0.00017641557,0.0023485138],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9713688,0.023318268,0.0008575581,0.0017468951,0.0023468514,0.00036164472],"domain_scores_gemma":[0.8798909,0.10382489,0.005596885,0.006672925,0.003443655,0.00057073636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040457632,0.0013054258,0.002665921,0.0028107013,0.0009336558,0.0025244108,0.005054384,0.0025891562,0.0032635182],"category_scores_gemma":[0.20352016,0.0017269695,0.0017171997,0.0032437427,0.0029888249,0.0050342595,0.003522044,0.0031266196,0.0005552129],"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":[0.00035425916,0.00016402623,0.004795779,0.00040480823,0.0006979101,0.000470223,0.0006509469,0.31353468,0.0009106479,0.5602833,0.0021406393,0.11559276],"study_design_scores_gemma":[0.00008068306,0.000058094327,0.0007081382,0.000058997695,0.000061143706,0.000045377907,0.000028583183,0.63985246,0.000366115,0.3577428,0.0009581488,0.000039440914],"about_ca_topic_score_codex":0.006093414,"about_ca_topic_score_gemma":0.0056507564,"teacher_disagreement_score":0.040457632,"about_ca_system_score_codex":0.0020100754,"about_ca_system_score_gemma":0.0020473045,"threshold_uncertainty_score":0.21396297},"labels":[],"label_agreement":null},{"id":"W2086696611","doi":"10.1198/016214506000000889","title":"Transition Models for Multivariate Longitudinal Binary Data","year":2007,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian 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":"Covariate; Multivariate statistics; Categorical variable; Binary data; Statistics; Econometrics; Mathematics; Marginal model; Logistic regression; Binary number; Regression analysis","score_opus":0.12608300461563685,"score_gpt":0.42921773164859145,"score_spread":0.30313472703295463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086696611","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":[0.0060189273,0.0004050172,0.991757,0.00042328716,0.000044596847,0.00008271027,0.00047729356,0.00029510373,0.00049604726],"genre_scores_gemma":[0.3427921,0.0026033665,0.63601035,0.00072296744,0.00046620672,0.002563741,0.0039344467,0.00035224034,0.01055456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9921137,0.0051696366,0.00035778116,0.001308759,0.0006920823,0.00035801256],"domain_scores_gemma":[0.94749635,0.044405375,0.0031290194,0.002764583,0.0016441682,0.00056039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020548305,0.001310657,0.0022065917,0.0032301578,0.000863665,0.0019162958,0.003971334,0.002395615,0.007893],"category_scores_gemma":[0.06888361,0.0009382908,0.0023747233,0.003347191,0.0022500877,0.004821258,0.0028650123,0.0054467404,0.0015921298],"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":[0.00023839241,0.00010420514,0.00601355,0.00029891357,0.00024536593,0.000278935,0.0005779783,0.13449176,0.000459087,0.8040856,0.003568397,0.049637888],"study_design_scores_gemma":[0.000047243397,0.000043435746,0.0007962123,0.000055438304,0.00005472856,0.000103764985,0.000052966534,0.4455507,0.00012954861,0.55002695,0.0031051603,0.00003380596],"about_ca_topic_score_codex":0.0064290804,"about_ca_topic_score_gemma":0.005242431,"teacher_disagreement_score":0.020548305,"about_ca_system_score_codex":0.0016731515,"about_ca_system_score_gemma":0.0016205034,"threshold_uncertainty_score":0.10867107},"labels":[],"label_agreement":null},{"id":"W2086919360","doi":"10.1080/03610920008832541","title":"Assessing conditional independence for log-linear poisson models with random effects","year":2000,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","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":"York University","funders":"National Science Foundation","keywords":"Conditional independence; Random effects model; Poisson regression; Mathematics; Inference; Context (archaeology); Statistics; Independence (probability theory); Poisson distribution; Econometrics; Linear regression; Statistical inference; Representation (politics); Applied mathematics; Computer science; Artificial intelligence","score_opus":0.09267713622293904,"score_gpt":0.4842993381443154,"score_spread":0.39162220192137637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086919360","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":[0.02599696,0.00025439967,0.97217035,0.00032113894,0.000018950224,0.00007568107,0.00011553518,0.00014302369,0.000903953],"genre_scores_gemma":[0.71368134,0.0007187099,0.28225577,0.0002773434,0.00021734965,0.00052073103,0.000748573,0.00013908718,0.0014411526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94428515,0.046051588,0.0014971767,0.0033695058,0.0038520119,0.00094460783],"domain_scores_gemma":[0.41002074,0.5570315,0.014038849,0.011543396,0.0061678137,0.0011977252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07646609,0.0010536053,0.0018968742,0.0028869088,0.00088458066,0.0025884889,0.003132637,0.0018440597,0.0057828804],"category_scores_gemma":[0.30252868,0.0010608076,0.0022395614,0.0027013253,0.0036736205,0.0042454335,0.00408118,0.0038815974,0.0005219777],"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":[0.0004862017,0.0003375247,0.065738074,0.0007035278,0.0015086487,0.00045463565,0.001462584,0.24331425,0.001308052,0.5358239,0.0028274613,0.14603515],"study_design_scores_gemma":[0.000043242766,0.00014806926,0.012575653,0.00014842313,0.00022338716,0.00016184697,0.0001866897,0.68170196,0.0009936801,0.3021604,0.0015839972,0.00007263692],"about_ca_topic_score_codex":0.0065764063,"about_ca_topic_score_gemma":0.004759101,"teacher_disagreement_score":0.07646609,"about_ca_system_score_codex":0.001647882,"about_ca_system_score_gemma":0.0031500538,"threshold_uncertainty_score":0.40439618},"labels":[],"label_agreement":null},{"id":"W2087047720","doi":"10.1002/(sici)1097-0258(20000315)19:5<715::aid-sim342>3.0.co;2-t","title":"GEE Analysis of negatively correlated binary responses: a caution","year":2000,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"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; Royal Victoria Hospital","funders":"National Cancer Institute","keywords":"Generalized estimating equation; Bounding overwatch; Binary number; Independence (probability theory); Statistics; Correlation; Binary data; Gee; Mathematics; Binomial (polynomial); Negative binomial distribution; Applied mathematics; Econometrics; Computer science","score_opus":0.05795689346548086,"score_gpt":0.41620766905050866,"score_spread":0.3582507755850278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087047720","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":[0.024311937,0.02506432,0.65492314,0.25745505,0.023113523,0.00094542006,0.0015632055,0.003523549,0.009099924],"genre_scores_gemma":[0.24284877,0.013934533,0.45102954,0.24782963,0.017391307,0.0024431453,0.000551916,0.002204539,0.021766663],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85711086,0.108587794,0.011160031,0.008990794,0.013552793,0.0005977634],"domain_scores_gemma":[0.5587799,0.3669347,0.0090837255,0.038097207,0.024131278,0.0029731926],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22383264,0.0027186933,0.0061689215,0.004354571,0.0018019566,0.0043788617,0.008742268,0.004351543,0.0026944908],"category_scores_gemma":[0.50540155,0.0013327713,0.0035771804,0.004771564,0.012846834,0.008093519,0.0050327694,0.023409907,0.0028241016],"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":[0.0020402195,0.0002989086,0.054006297,0.00783798,0.005245081,0.008132012,0.014573235,0.005487606,0.00629622,0.13895664,0.42766225,0.3294636],"study_design_scores_gemma":[0.000505916,0.00069250073,0.04242561,0.009590694,0.0016457891,0.008455878,0.0061798315,0.039617658,0.0065996386,0.6708314,0.21246213,0.000992952],"about_ca_topic_score_codex":0.009328613,"about_ca_topic_score_gemma":0.0151192155,"teacher_disagreement_score":0.22383264,"about_ca_system_score_codex":0.0015392269,"about_ca_system_score_gemma":0.002241218,"threshold_uncertainty_score":0.95715255},"labels":[],"label_agreement":null},{"id":"W2087713388","doi":"10.2333/bhmk.32.141","title":"An Extended Multivariate Random-Effects Growth Curve Model","year":2005,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Statistical Methods and Bayesian Inference","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":"McGill University; HEC Montréal","funders":"","keywords":"Growth curve (statistics); Multivariate statistics; Random effects model; Mathematics; Statistics; A priori and a posteriori; Multivariate analysis; Basis (linear algebra); Set (abstract data type); Econometrics; Computer science","score_opus":0.07029329654236934,"score_gpt":0.408737113280925,"score_spread":0.33844381673855567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087713388","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":[0.054857347,0.0007247495,0.9349852,0.0022457228,0.00011416414,0.00013490363,0.001963975,0.00063378835,0.004340101],"genre_scores_gemma":[0.72678787,0.002456548,0.22428705,0.00079602585,0.0003937342,0.0009872442,0.0031217595,0.0005315647,0.040638376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99551755,0.0025373856,0.00015864355,0.0011012225,0.00033194324,0.00035321765],"domain_scores_gemma":[0.9807858,0.014413323,0.0015906557,0.0016618578,0.0010490445,0.0004993772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009999168,0.0017783331,0.0034871988,0.0023993568,0.00072548364,0.0028167497,0.0057935603,0.003767924,0.01323049],"category_scores_gemma":[0.027927337,0.0014315459,0.0027332886,0.0031149907,0.0022692268,0.0048707817,0.002482683,0.0041398695,0.002215951],"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":[0.00044221096,0.000211354,0.0066832,0.0002890837,0.0005811567,0.0005218311,0.0006601982,0.46011117,0.00068434794,0.48235705,0.005285982,0.042172436],"study_design_scores_gemma":[0.00009989529,0.000058549933,0.0021523423,0.000053161613,0.00018313115,0.00019187808,0.000057100813,0.77798474,0.00009004421,0.21633336,0.0027296667,0.00006619743],"about_ca_topic_score_codex":0.014491473,"about_ca_topic_score_gemma":0.011635527,"teacher_disagreement_score":0.014491473,"about_ca_system_score_codex":0.0019659924,"about_ca_system_score_gemma":0.001973762,"threshold_uncertainty_score":0.0528813},"labels":[],"label_agreement":null},{"id":"W2088206359","doi":"10.5705/ss.2011.230","title":"Minimum description length principle for linear mixed effects models","year":2013,"lang":"en","type":"article","venue":"Statistica Sinica","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Applied mathematics; Generalized linear mixed model; Computer science; Mathematical optimization","score_opus":0.10795537373540492,"score_gpt":0.39003594190725416,"score_spread":0.28208056817184923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088206359","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":[0.0006714825,0.0003796403,0.99792814,0.00029776662,0.00001985123,0.000048090238,0.000177006,0.000060221533,0.00041769474],"genre_scores_gemma":[0.04997733,0.0021324542,0.9404924,0.0008643304,0.00047517705,0.0020232452,0.001488458,0.00024382601,0.0023028036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9798212,0.01432442,0.0010096015,0.0016392804,0.002837179,0.00036828624],"domain_scores_gemma":[0.90215725,0.08948645,0.0024788403,0.003205101,0.0022053602,0.0004670562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031008739,0.0018765659,0.002493795,0.0034385729,0.0010963733,0.0028521884,0.0048923185,0.0030778765,0.004763498],"category_scores_gemma":[0.0837349,0.0015662131,0.0034229592,0.0033493713,0.00354799,0.0043700826,0.00416073,0.005720209,0.0013262383],"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":[0.00011287274,0.000072822004,0.0011066885,0.00090694806,0.00034639635,0.00024651203,0.0003276979,0.13737741,0.0010958782,0.749979,0.0040787044,0.10434907],"study_design_scores_gemma":[0.00003823608,0.00006795931,0.00034904093,0.00012382964,0.00004451889,0.00009078761,0.00002457282,0.40026292,0.0004798947,0.5940471,0.004428272,0.000042777196],"about_ca_topic_score_codex":0.0030423123,"about_ca_topic_score_gemma":0.0025900565,"teacher_disagreement_score":0.031008739,"about_ca_system_score_codex":0.00258636,"about_ca_system_score_gemma":0.004014604,"threshold_uncertainty_score":0.16399181},"labels":[],"label_agreement":null},{"id":"W2088485677","doi":"10.1002/cjs.10011","title":"On the incidence–prevalence relation and length‐biased sampling","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Pfizer Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Johns Hopkins University","keywords":"Statistics; Incidence (geometry); Estimator; Confidence interval; Mathematics; Demography; Logistic regression; Odds ratio; Cohort; Population; Medicine; Econometrics","score_opus":0.0911571096396495,"score_gpt":0.3427956837898182,"score_spread":0.25163857415016866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088485677","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":[0.087208904,0.007835483,0.8822178,0.012218833,0.00045136796,0.0005610916,0.0008344575,0.0003156274,0.00835641],"genre_scores_gemma":[0.730108,0.00578184,0.24748717,0.0053695315,0.0011733655,0.0015843101,0.0011385579,0.0002733455,0.0070838253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91443324,0.068493426,0.003345326,0.0066642435,0.0060117957,0.0010518503],"domain_scores_gemma":[0.3708874,0.56906223,0.024622763,0.025781712,0.0088220015,0.0008238721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11896124,0.00088985654,0.0028283857,0.0038802058,0.0015433229,0.0028783535,0.0032378368,0.0027418414,0.006281717],"category_scores_gemma":[0.49536505,0.0014035446,0.0020219858,0.005860314,0.00715749,0.004714886,0.004413267,0.005641622,0.00092694286],"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":[0.0005743421,0.00013424293,0.18592045,0.0010344549,0.0014111558,0.0011879158,0.0027191844,0.035841584,0.0009246699,0.6367197,0.011937696,0.12159466],"study_design_scores_gemma":[0.00038899676,0.00032893993,0.052310634,0.0011317409,0.0010190662,0.0027255025,0.0006156972,0.2190571,0.0015431182,0.7037317,0.017007872,0.00013960301],"about_ca_topic_score_codex":0.012751057,"about_ca_topic_score_gemma":0.007825105,"teacher_disagreement_score":0.11896124,"about_ca_system_score_codex":0.0038286354,"about_ca_system_score_gemma":0.0023216368,"threshold_uncertainty_score":0.62913465},"labels":[],"label_agreement":null},{"id":"W2088581243","doi":"10.1348/000711005x63755","title":"Adaptive robust estimation and testing","year":2006,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"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 Manitoba","funders":"","keywords":"Statistics; Normality; Type I and type II errors; Trimming; Mathematics; Sample size determination; Variance (accounting); Econometrics; Null hypothesis; Population; Computer science; Medicine","score_opus":0.09456399157201978,"score_gpt":0.37824000341710623,"score_spread":0.28367601184508645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088581243","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":[0.0020743543,0.00015783265,0.9956689,0.00025081765,0.000052424733,0.00014711155,0.000066791494,0.0003576487,0.0012240371],"genre_scores_gemma":[0.112626396,0.00027320706,0.88269705,0.00034604408,0.00013562833,0.001291118,0.0003993353,0.00038370586,0.0018475372],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95661885,0.030455384,0.0018414591,0.004582764,0.00546042,0.0010410353],"domain_scores_gemma":[0.7957727,0.16243787,0.0074052303,0.020411657,0.012830295,0.0011423188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055219833,0.0022196146,0.0032505163,0.0025231072,0.0015218947,0.0033483207,0.007867158,0.0031226878,0.010926387],"category_scores_gemma":[0.30601722,0.0011963727,0.002881333,0.0026700546,0.004289446,0.0044745356,0.005531486,0.005048908,0.0026990294],"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":[0.0006781377,0.00020820223,0.008345185,0.00084587734,0.00090274814,0.00035174747,0.00057626783,0.20394628,0.0017531232,0.3829587,0.007746024,0.39168766],"study_design_scores_gemma":[0.00017208674,0.00026785358,0.0015348986,0.00025105698,0.00012124268,0.00021915892,0.00012947763,0.67336315,0.0025870693,0.3144946,0.0067792237,0.00008013186],"about_ca_topic_score_codex":0.0031858692,"about_ca_topic_score_gemma":0.0022779265,"teacher_disagreement_score":0.055219833,"about_ca_system_score_codex":0.0017909033,"about_ca_system_score_gemma":0.0046199546,"threshold_uncertainty_score":0.29203385},"labels":[],"label_agreement":null},{"id":"W2089037674","doi":"10.1080/10705511003659375","title":"Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison","year":2010,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":69,"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 on Drug Abuse","keywords":"Statistics; Statistic; Monte Carlo method; Sample size determination; Missing data; Mathematics; Type I and type II errors; Chi-square test; Econometrics","score_opus":0.4244016015080558,"score_gpt":0.4533223356727255,"score_spread":0.028920734164669726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089037674","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":[0.0021383506,0.00032454033,0.9956678,0.00034153103,0.00008795379,0.00018127727,0.000044358792,0.000106281186,0.0011079106],"genre_scores_gemma":[0.10836015,0.0011218678,0.88477284,0.00052336603,0.00037385718,0.0025889468,0.00022755511,0.00041168358,0.0016198063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90106493,0.08032203,0.002703264,0.004604759,0.010750314,0.000554805],"domain_scores_gemma":[0.42575917,0.52719826,0.01189047,0.02491615,0.009240804,0.0009951855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13642573,0.0019068706,0.004029995,0.0052258116,0.0014593843,0.004181698,0.005253162,0.0034701924,0.009985706],"category_scores_gemma":[0.5066421,0.001291406,0.003139763,0.0062809996,0.007880005,0.013777678,0.0047730426,0.0071674935,0.0013014283],"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":[0.000101438476,0.000082072205,0.002966687,0.00036959467,0.00022615827,0.0002672079,0.0008507798,0.049967263,0.00017867984,0.87592185,0.0030706103,0.06599765],"study_design_scores_gemma":[0.00005459232,0.00020132169,0.0009811907,0.00029120417,0.0000826046,0.00027412432,0.00015307554,0.26232108,0.0003810611,0.7292975,0.0058840904,0.00007813332],"about_ca_topic_score_codex":0.0017916475,"about_ca_topic_score_gemma":0.0015116481,"teacher_disagreement_score":0.13642573,"about_ca_system_score_codex":0.0021467328,"about_ca_system_score_gemma":0.003968487,"threshold_uncertainty_score":0.7214968},"labels":[],"label_agreement":null},{"id":"W2089647864","doi":"10.1016/j.jmva.2011.01.005","title":"The proportional hazards model for survey data from independent and clustered super-populations","year":2011,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","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":"Statistics Canada","funders":"","keywords":"Mathematics; Estimator; Sampling design; Statistics; Sampling (signal processing); Delta method; Asymptotic distribution; Poisson sampling; Variance (accounting); Sample size determination; Survey sampling; Population; Sample (material); Applied mathematics; Importance sampling; Slice sampling; Computer science; Monte Carlo method","score_opus":0.39897563074665,"score_gpt":0.4479242767682306,"score_spread":0.04894864602158061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089647864","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":[0.020270454,0.0007169735,0.975879,0.0012127167,0.00013694658,0.0002963332,0.0005621694,0.00023749852,0.0006879454],"genre_scores_gemma":[0.5896532,0.003661833,0.38197327,0.001551576,0.000994077,0.0051450185,0.0034465315,0.00032408754,0.013250429],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95830435,0.032346085,0.0010237354,0.00473517,0.0022775007,0.0013132642],"domain_scores_gemma":[0.86726725,0.11030389,0.005655607,0.013209598,0.002663387,0.00090014434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07782914,0.002004698,0.0050655925,0.003293449,0.0013967454,0.0035112915,0.010607888,0.0039405692,0.0066724033],"category_scores_gemma":[0.14227347,0.0023320243,0.0045802123,0.004338003,0.005146508,0.0063115824,0.004664631,0.006752693,0.0011976132],"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":[0.0017058889,0.00031498107,0.02937268,0.0011168018,0.0030789003,0.0014047404,0.0024626397,0.19937539,0.0007497935,0.6628299,0.007502498,0.0900858],"study_design_scores_gemma":[0.00040126863,0.0002305314,0.0038081584,0.00014205553,0.0007609652,0.0007610878,0.00025075977,0.48936474,0.0003299984,0.5000115,0.003850765,0.00008819251],"about_ca_topic_score_codex":0.007852355,"about_ca_topic_score_gemma":0.004444578,"teacher_disagreement_score":0.07782914,"about_ca_system_score_codex":0.0024501043,"about_ca_system_score_gemma":0.0039961347,"threshold_uncertainty_score":0.41160476},"labels":[],"label_agreement":null},{"id":"W2089904831","doi":"10.1016/j.jspi.2003.12.021","title":"Computation of distribution functions from likelihood information near observed data","year":2004,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Mathematics; Computation; Distribution (mathematics); Statistics; Applied mathematics; Econometrics; Statistical physics; Algorithm; Mathematical analysis","score_opus":0.1304267517445008,"score_gpt":0.3863378398456862,"score_spread":0.2559110881011854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089904831","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":[0.0075014704,0.00017817756,0.99125856,0.00020080344,0.000014518989,0.000019252006,0.000074934505,0.00035752612,0.0003947478],"genre_scores_gemma":[0.31293947,0.00054704654,0.6831672,0.00012804034,0.00012104721,0.00025774172,0.0008303065,0.00046527406,0.0015438715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99812394,0.0010077462,0.000120775505,0.00024210603,0.0004112791,0.00009411085],"domain_scores_gemma":[0.9698009,0.02737457,0.00070395984,0.0010288834,0.00071805995,0.0003736817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005925559,0.0010985364,0.002171629,0.0028049133,0.00092643464,0.0032894476,0.0029356873,0.0022321788,0.003001812],"category_scores_gemma":[0.045669086,0.002291714,0.0017602467,0.0018767162,0.0021784492,0.0045952396,0.0028662386,0.002687524,0.00069870596],"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":[0.00020716162,0.0000613303,0.0018722641,0.00026449488,0.00012231385,0.00018050493,0.00017084175,0.84142977,0.0009853938,0.06789809,0.0013700475,0.08543779],"study_design_scores_gemma":[0.00001684075,0.000009967685,0.00018096347,0.000018158331,0.000011348616,0.000036679052,0.000010350509,0.9349771,0.0005453888,0.063851535,0.00033049012,0.000011182478],"about_ca_topic_score_codex":0.0045402762,"about_ca_topic_score_gemma":0.0044060536,"teacher_disagreement_score":0.005925559,"about_ca_system_score_codex":0.0018984879,"about_ca_system_score_gemma":0.0027458915,"threshold_uncertainty_score":0.031337738},"labels":[],"label_agreement":null},{"id":"W2089991277","doi":"10.1007/s13571-012-0037-0","title":"Assessing goodness of fit of generalized linear models to sparse data using higher order moment corrections","year":2012,"lang":"en","type":"article","venue":"Sankhya B","topic":"Statistical Methods and Bayesian Inference","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":"University of Regina; University of Windsor","funders":"","keywords":"Mathematics; Statistic; Goodness of fit; Statistics; Edgeworth series; PRESS statistic; Negative binomial distribution; Poisson distribution; Moment (physics); Pearson's chi-squared test; Applied mathematics; Ancillary statistic; Test statistic; Statistical hypothesis testing","score_opus":0.56113278431785,"score_gpt":0.49906243636747344,"score_spread":0.062070347950376525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089991277","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":[0.070362575,0.00022595239,0.92824924,0.00026330366,0.000035052522,0.00003077021,0.00008306729,0.00044623736,0.0003037273],"genre_scores_gemma":[0.77426976,0.0003931413,0.22336927,0.00020868948,0.00009883453,0.00017170206,0.0005615638,0.00038029996,0.00054664986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9795305,0.015541893,0.0008626771,0.0017860838,0.0018452625,0.00043350813],"domain_scores_gemma":[0.7332666,0.2449631,0.004705332,0.012373937,0.00373891,0.0009522266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02336201,0.0011815373,0.0020958818,0.0031144014,0.0010652696,0.0024445283,0.0024840836,0.0030629435,0.0016223491],"category_scores_gemma":[0.2148057,0.0012321871,0.0025273773,0.002079855,0.0030992401,0.0033523904,0.0030296028,0.0033809927,0.00036966839],"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":[0.0007158632,0.00016545177,0.0155495005,0.00043672998,0.0010169587,0.0004842826,0.0006237859,0.84456813,0.0037713933,0.04768441,0.0011553303,0.083828166],"study_design_scores_gemma":[0.000043783966,0.000101827376,0.0025926156,0.000029223655,0.000062365674,0.00016346191,0.000068631954,0.9553109,0.00090348505,0.040395834,0.00028455132,0.00004326677],"about_ca_topic_score_codex":0.0049762265,"about_ca_topic_score_gemma":0.0033783475,"teacher_disagreement_score":0.02336201,"about_ca_system_score_codex":0.00088138145,"about_ca_system_score_gemma":0.0020995822,"threshold_uncertainty_score":0.12355161},"labels":[],"label_agreement":null},{"id":"W2090039778","doi":"10.1017/s1355617709990373","title":"The diagnostic utility of multiple-level likelihood ratios","year":2009,"lang":"en","type":"article","venue":"Journal of the International Neuropsychological Society","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"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":"University of Toronto","keywords":"Sensitivity (control systems); Diagnostic test; Likelihood ratios in diagnostic testing; Neuropsychology; Test (biology); Dementia; Statistics; Medicine; Psychology; Clinical psychology; Diagnostic accuracy; Psychiatry; Pathology; Mathematics; Radiology; Pediatrics; Disease; Cognition","score_opus":0.09362228129241866,"score_gpt":0.3813886196018891,"score_spread":0.2877663383094704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090039778","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":[0.02183369,0.004190662,0.96638864,0.0033723407,0.00022013964,0.0001229669,0.00024883883,0.0004119753,0.0032107458],"genre_scores_gemma":[0.5711237,0.0016558894,0.424294,0.0007362513,0.0006736624,0.00032029572,0.00017772685,0.0001385164,0.00087988423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9269963,0.06211542,0.0020250652,0.003508686,0.0049578366,0.0003967003],"domain_scores_gemma":[0.68834245,0.28833246,0.00953292,0.008936648,0.0040896153,0.0007658557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050137114,0.0010546418,0.0015849513,0.0052119903,0.00056334474,0.003909149,0.0021817663,0.0019653684,0.002084213],"category_scores_gemma":[0.34094095,0.00068614073,0.0013046626,0.003261231,0.00567455,0.00420661,0.0026756248,0.0038688031,0.00058838446],"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":[0.0006622704,0.000112386166,0.08915645,0.00091442966,0.0013305844,0.0010908742,0.0011665162,0.03384299,0.0014633226,0.4635179,0.008935772,0.39780653],"study_design_scores_gemma":[0.00014045721,0.0002308727,0.00846538,0.0002547522,0.00018092153,0.0032929038,0.00025988428,0.17520112,0.0014511013,0.8054543,0.0049370574,0.0001311835],"about_ca_topic_score_codex":0.0008488141,"about_ca_topic_score_gemma":0.0005327549,"teacher_disagreement_score":0.050137114,"about_ca_system_score_codex":0.001184359,"about_ca_system_score_gemma":0.0013070169,"threshold_uncertainty_score":0.2651536},"labels":[],"label_agreement":null},{"id":"W2090906954","doi":"10.1002/sim.2791","title":"A likelihood approach to estimating sensitivity and specificity for binocular data: application in ophthalmology","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"Royal Alexandra Hospital; University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Sensitivity (control systems); Extension (predicate logic); Maximum likelihood; Computer science; Statistics; Binary data; Optometry; Mathematics; Artificial intelligence; Binary number; Medicine","score_opus":0.11626529237192867,"score_gpt":0.4428071937723671,"score_spread":0.32654190140043843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090906954","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":[0.0019115248,0.0004970583,0.9966125,0.00041905756,0.00002244469,0.0000311205,0.000041540705,0.00007350738,0.000391194],"genre_scores_gemma":[0.11488029,0.0015612566,0.88112175,0.00027589028,0.0003158546,0.00040058637,0.00020698491,0.00014065385,0.001096698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97703856,0.019549506,0.000594205,0.0010745912,0.0015601948,0.00018290387],"domain_scores_gemma":[0.8379876,0.15281783,0.0032885142,0.003286108,0.002141108,0.00047890097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030381044,0.0014430323,0.0022706068,0.0049372097,0.00089154165,0.0024815898,0.0027244878,0.0031710577,0.002312196],"category_scores_gemma":[0.18607795,0.0010263489,0.0017753367,0.004081405,0.0035848592,0.002748648,0.003122682,0.003971617,0.00073935237],"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":[0.0002920967,0.00017357529,0.017656177,0.00070507743,0.0007358354,0.001215877,0.0010086311,0.30370763,0.0022177817,0.3546186,0.00506342,0.3126054],"study_design_scores_gemma":[0.000057278336,0.00011961329,0.0034447953,0.00014543039,0.00007847431,0.0011577159,0.00015739343,0.6095952,0.00071610935,0.3808684,0.0035426882,0.00011695382],"about_ca_topic_score_codex":0.0028062994,"about_ca_topic_score_gemma":0.0023556948,"teacher_disagreement_score":0.030381044,"about_ca_system_score_codex":0.0013330461,"about_ca_system_score_gemma":0.0019040139,"threshold_uncertainty_score":0.16067219},"labels":[],"label_agreement":null},{"id":"W2091440408","doi":"10.1002/cjs.10117","title":"The effect of misspecification of random effects distributions in clustered data settings with outcome‐dependent sampling","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Covariate; Random effects model; Statistics; Econometrics; Outcome (game theory); Sampling (signal processing); Sampling bias; Parametric statistics; Conditional probability distribution; Cluster sampling; Mathematics; Sampling distribution; Sample size determination; Computer science; Population; Medicine","score_opus":0.11088145333601152,"score_gpt":0.35098261486333293,"score_spread":0.24010116152732142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091440408","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":[0.07821302,0.0023916627,0.9097789,0.0038358883,0.0002671195,0.0007607911,0.00041208917,0.0007358782,0.0036046726],"genre_scores_gemma":[0.7265054,0.0008247939,0.26548555,0.0033216313,0.00018271655,0.001394412,0.00058560824,0.00032488798,0.0013748393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.5889113,0.3509853,0.015034502,0.024042267,0.018728614,0.0022981432],"domain_scores_gemma":[0.18012862,0.69974744,0.042989355,0.066394165,0.009609175,0.0011311959],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2741288,0.001670208,0.002522186,0.0019341157,0.0023661363,0.0030760907,0.0046052462,0.0046450417,0.0028865025],"category_scores_gemma":[0.66936326,0.002235463,0.002858326,0.0031475394,0.0070346445,0.006719911,0.00432706,0.0071049407,0.00070574816],"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":[0.0036911538,0.00054297596,0.2551138,0.0024698845,0.005996516,0.0042663263,0.016948715,0.16430292,0.0036808767,0.34057263,0.011617572,0.19079661],"study_design_scores_gemma":[0.0010819973,0.0028713173,0.16307454,0.002694363,0.005269205,0.009161056,0.0044769067,0.3877895,0.018270822,0.37604636,0.028274907,0.0009890583],"about_ca_topic_score_codex":0.008380897,"about_ca_topic_score_gemma":0.007337969,"teacher_disagreement_score":0.2741288,"about_ca_system_score_codex":0.0042386376,"about_ca_system_score_gemma":0.002900826,"threshold_uncertainty_score":0.8951284},"labels":[],"label_agreement":null},{"id":"W2091501852","doi":"10.1002/sim.3289","title":"Interval estimation of risk difference for data sampled from clusters","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Statistics; Confidence interval; Mathematics; Cluster (spacecraft); Variance (accounting); Cluster sampling; Sample size determination; Coverage probability; Interval estimation; Sampling (signal processing); Computer science; Econometrics; Population; Demography","score_opus":0.16970249555560965,"score_gpt":0.4414141727607394,"score_spread":0.2717116772051298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091501852","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":[0.009676342,0.00092425075,0.9876386,0.00013378379,0.00009980622,0.00023641226,0.00027063265,0.00033393377,0.0006861515],"genre_scores_gemma":[0.20170195,0.0011637306,0.7912554,0.00033701115,0.00020915682,0.0022946487,0.0018860879,0.00020130974,0.00095062074],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94736177,0.038741317,0.0024445339,0.0057279565,0.005236355,0.0004880214],"domain_scores_gemma":[0.7441729,0.22535656,0.010337118,0.014883391,0.0046536154,0.0005964853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04882107,0.001123504,0.0029259876,0.0039155544,0.00050297106,0.0018345733,0.0040088613,0.0025857917,0.0031050073],"category_scores_gemma":[0.2610552,0.00064689683,0.0027200498,0.0035264937,0.0017829593,0.0023095577,0.0027633207,0.003543293,0.0006845441],"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":[0.0030702525,0.00040009915,0.04407735,0.0024498575,0.0046999934,0.0009063825,0.0019151053,0.18110842,0.00392383,0.22019853,0.0072465497,0.53000367],"study_design_scores_gemma":[0.00044045981,0.0010956716,0.018944684,0.00065494335,0.0008611293,0.0011723951,0.00035108766,0.7141925,0.0055160252,0.2435688,0.012928205,0.0002740718],"about_ca_topic_score_codex":0.0016179511,"about_ca_topic_score_gemma":0.00055938296,"teacher_disagreement_score":0.04882107,"about_ca_system_score_codex":0.001068263,"about_ca_system_score_gemma":0.0011511062,"threshold_uncertainty_score":0.2581936},"labels":[],"label_agreement":null},{"id":"W2091541131","doi":"10.2307/3316047","title":"The behrens‐fisher problem revisited: A bayes‐frequentist synthesis","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Frequentist inference; Credible interval; Mathematics; Coverage probability; Confidence interval; Statistics; Interval (graph theory); Bayes' theorem; Frequentist probability; Binomial proportion confidence interval; Interval estimation; Statistical inference; Inference; Fisher information; Bayesian probability; Confidence distribution; Econometrics; Bayesian inference; Computer science; Combinatorics; Artificial intelligence","score_opus":0.044842594253970657,"score_gpt":0.3057841260161854,"score_spread":0.26094153176221474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091541131","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":[0.007147639,0.005796173,0.9699121,0.008112335,0.00035261302,0.000120873476,0.00027316128,0.00006175362,0.008223243],"genre_scores_gemma":[0.5431922,0.010669481,0.4352909,0.0021767432,0.0028449346,0.0008883179,0.00035638953,0.00009674013,0.004484236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9841649,0.010702795,0.0008468308,0.0014907008,0.0025310102,0.00026379366],"domain_scores_gemma":[0.90519446,0.08694087,0.0026371763,0.0020921454,0.0027051843,0.00043024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03494832,0.0011313899,0.0029191582,0.0053852596,0.0014053241,0.005239209,0.0025726305,0.0037427507,0.006692477],"category_scores_gemma":[0.11605133,0.00086324057,0.0014826476,0.003683862,0.005029008,0.0059071532,0.0021951108,0.0037923094,0.00046756864],"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":[0.000025437246,0.000016822296,0.00029130463,0.00026597985,0.000068486304,0.00006220318,0.00015432053,0.02645888,0.000045120792,0.94124436,0.0015380578,0.029829118],"study_design_scores_gemma":[0.00000962086,0.0000074043173,0.00012843848,0.00008870571,0.00001575542,0.000019141706,0.000033595665,0.03241656,0.000029065532,0.9654847,0.0017563038,0.000010807196],"about_ca_topic_score_codex":0.0035236178,"about_ca_topic_score_gemma":0.0020582823,"teacher_disagreement_score":0.03494832,"about_ca_system_score_codex":0.0030581816,"about_ca_system_score_gemma":0.0028594953,"threshold_uncertainty_score":0.18482661},"labels":[],"label_agreement":null},{"id":"W2091786172","doi":"10.1021/es902382a","title":"Particle and Microorganism Enumeration Data: Enabling Quantitative Rigor and Judicious Interpretation","year":2010,"lang":"en","type":"article","venue":"Environmental Science & Technology","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":"University of Waterloo","funders":"University of Waterloo","keywords":"Enumeration; Replicate; Bayes' theorem; Statistics; Sampling (signal processing); Reduction (mathematics); Variance (accounting); Count data; Variance reduction; Computer science; Sample (material); Data reduction; Probabilistic logic; Sample size determination; Bayesian probability; Mathematics; Algorithm; Poisson distribution; Monte Carlo method; Chemistry","score_opus":0.029621407509267544,"score_gpt":0.3420014934498134,"score_spread":0.31238008594054584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091786172","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":[0.006344029,0.00023531979,0.9910789,0.00046468317,0.000033759974,0.00006847583,0.00064476643,0.0003275315,0.0008025564],"genre_scores_gemma":[0.12941337,0.0007664388,0.86720246,0.00034475455,0.00013006025,0.00039696126,0.0010544972,0.00022017509,0.0004714036],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9791672,0.011756786,0.001322178,0.0018590692,0.0056924126,0.00020233472],"domain_scores_gemma":[0.92707616,0.04829281,0.008473167,0.01072819,0.0050062104,0.00042350846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03238613,0.0012194174,0.0019725703,0.0051298044,0.0007404654,0.0043090214,0.0022564866,0.0024895486,0.001487744],"category_scores_gemma":[0.10642883,0.0011473797,0.0010774651,0.0044075744,0.0032921985,0.0056900117,0.0037160534,0.0029539508,0.00066125643],"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":[0.00037457448,0.00024753495,0.030353993,0.0022848418,0.00052608975,0.00037259236,0.0019860757,0.09818746,0.04036237,0.36310473,0.006356993,0.45584264],"study_design_scores_gemma":[0.000067127614,0.0001830293,0.023106001,0.0005099269,0.00012963853,0.00050828996,0.00048559566,0.3647529,0.024521638,0.56191635,0.02350309,0.0003164305],"about_ca_topic_score_codex":0.0021461598,"about_ca_topic_score_gemma":0.0026631104,"teacher_disagreement_score":0.03238613,"about_ca_system_score_codex":0.0013002012,"about_ca_system_score_gemma":0.0025043397,"threshold_uncertainty_score":0.17127633},"labels":[],"label_agreement":null},{"id":"W2091793380","doi":"10.1002/wics.102","title":"Bayesian inference: an approach to statistical inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Prior probability; Bayes' theorem; Statistical inference; Frequentist inference; Bayesian probability; Inference; Bayes factor; Mathematics; Computer science; Bayesian inference; Artificial intelligence; Statistics","score_opus":0.14517096635581753,"score_gpt":0.485227297478791,"score_spread":0.3400563311229735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091793380","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":[0.0004108771,0.06278247,0.9165899,0.009001987,0.00068407034,0.000058754453,0.00012488251,0.0001413541,0.010205667],"genre_scores_gemma":[0.107015975,0.1697877,0.6974827,0.0071654925,0.007227959,0.00077003066,0.00034301053,0.0003747946,0.009832363],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98263735,0.011525129,0.000670371,0.0012871069,0.0037152006,0.00016482415],"domain_scores_gemma":[0.97422767,0.022083279,0.00069979543,0.0011230878,0.0016518811,0.00021411972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020860488,0.0015782604,0.0027538221,0.0067094383,0.00090175,0.006242874,0.0042050784,0.0046157963,0.0037503752],"category_scores_gemma":[0.038369052,0.0014209726,0.0016393913,0.006415354,0.0117551815,0.006886754,0.0029663933,0.008374342,0.0020736982],"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":[0.000008964122,0.00001181806,0.00014835295,0.0005181504,0.00011237016,0.00005326159,0.00014351748,0.0066152946,0.00009486714,0.92885363,0.0051191896,0.05832047],"study_design_scores_gemma":[0.000006883267,0.0000047225553,0.00008282698,0.00022132085,0.000015342448,0.000055421096,0.000021877167,0.0063626408,0.00005670367,0.97301215,0.02014631,0.000013759833],"about_ca_topic_score_codex":0.0043384605,"about_ca_topic_score_gemma":0.002690863,"teacher_disagreement_score":0.020860488,"about_ca_system_score_codex":0.0046325973,"about_ca_system_score_gemma":0.0036185037,"threshold_uncertainty_score":0.11032218},"labels":[],"label_agreement":null},{"id":"W2092017416","doi":"10.2307/3316145","title":"Score tests for heterogeneity and overdispersion in zero‐inflated Poisson and binomial regression models","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overdispersion; Negative binomial distribution; Count data; Mathematics; Poisson regression; Zero-inflated model; Statistics; Quasi-likelihood; Poisson distribution; Binomial test; Econometrics; Context (archaeology); Null hypothesis; Binomial (polynomial); Regression analysis; Population","score_opus":0.1040603929980713,"score_gpt":0.33514573399779285,"score_spread":0.23108534099972156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092017416","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":[0.2305737,0.0007460005,0.7608278,0.0011935297,0.00019966476,0.00027736888,0.0008030996,0.00045367083,0.0049251793],"genre_scores_gemma":[0.9219261,0.00022976591,0.07417713,0.00034327476,0.0003423649,0.00042411036,0.0009980929,0.00010877102,0.0014503605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9191481,0.0585405,0.0031004266,0.0063718907,0.010506629,0.0023324694],"domain_scores_gemma":[0.65155286,0.30567044,0.014809005,0.018452775,0.0065951603,0.0029198087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057014696,0.0011963882,0.003455771,0.005916143,0.0015156694,0.003039391,0.0055986578,0.0023731627,0.0059655686],"category_scores_gemma":[0.2766481,0.0006294133,0.0033430355,0.0065363077,0.006039828,0.0063164677,0.005522983,0.0038683012,0.0006343642],"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":[0.0022744543,0.00054170296,0.16659158,0.00086856575,0.0073303063,0.002134691,0.0029602603,0.06784215,0.0030741394,0.5351184,0.008779339,0.2024844],"study_design_scores_gemma":[0.0003398377,0.00094257004,0.04202491,0.0001332831,0.00069180096,0.0009456015,0.0009880281,0.2429164,0.0015818201,0.70498466,0.004206023,0.00024500935],"about_ca_topic_score_codex":0.0017458551,"about_ca_topic_score_gemma":0.0011660674,"teacher_disagreement_score":0.057014696,"about_ca_system_score_codex":0.0011806333,"about_ca_system_score_gemma":0.0024403795,"threshold_uncertainty_score":0.30152607},"labels":[],"label_agreement":null},{"id":"W2092227601","doi":"10.1002/cjs.10133","title":"Fully efficient estimation of coefficients of correlation in the presence of imputed survey data","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Estimator; Statistics; Copula (linguistics); Bivariate analysis; Computer science; Missing data; Correlation; Econometrics; Regression; Mathematics","score_opus":0.1936625015074052,"score_gpt":0.3522955306558012,"score_spread":0.15863302914839603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092227601","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":[0.013600862,0.00029006647,0.9852485,0.00021302684,0.000017494764,0.00004674476,0.000116774616,0.00012380465,0.00034271288],"genre_scores_gemma":[0.40153944,0.00042618127,0.595752,0.000245229,0.00007387558,0.0003618608,0.0006724954,0.00008815405,0.00084073836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9400826,0.0512165,0.0017176267,0.003035027,0.00316742,0.000780742],"domain_scores_gemma":[0.8697462,0.0972434,0.008513455,0.019145003,0.004723177,0.0006287724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044661053,0.00075419503,0.0030124206,0.0022610582,0.0005991077,0.0021020973,0.0033466711,0.0014975345,0.0014886098],"category_scores_gemma":[0.1560311,0.0014728913,0.0012275085,0.0040650363,0.0022279487,0.0024559083,0.0032036526,0.001977679,0.00041604013],"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":[0.00039935196,0.00021340046,0.025879284,0.0007533246,0.0022790951,0.0007617126,0.00082536903,0.40174848,0.001990968,0.2815172,0.0052863606,0.27834547],"study_design_scores_gemma":[0.00008873965,0.000089935405,0.008631222,0.00016375058,0.00013681402,0.00016614284,0.00007079856,0.7656728,0.0012276316,0.22076565,0.0029185878,0.00006792391],"about_ca_topic_score_codex":0.004673129,"about_ca_topic_score_gemma":0.0043064095,"teacher_disagreement_score":0.044661053,"about_ca_system_score_codex":0.0010111333,"about_ca_system_score_gemma":0.00315118,"threshold_uncertainty_score":0.23619306},"labels":[],"label_agreement":null},{"id":"W2092407310","doi":"10.1093/biomet/asr063","title":"Combining data from two independent surveys: a model-assisted approach","year":2011,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":80,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Conservation Service; Iowa State University; U.S. Department of Agriculture","keywords":"Library science; Statistics; Mathematics; Computer science","score_opus":0.5148009377854114,"score_gpt":0.42566128156589944,"score_spread":0.08913965621951192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092407310","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":[0.004349355,0.000059672715,0.9948362,0.000132236,0.0000129524515,0.000097169795,0.00006154691,0.0001262415,0.00032458053],"genre_scores_gemma":[0.18073359,0.00021441045,0.81646127,0.00019079664,0.00008667866,0.00068595115,0.0005104868,0.00007728683,0.0010395633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.963267,0.029075176,0.00079552364,0.0027471618,0.0036487433,0.00046640527],"domain_scores_gemma":[0.93784434,0.042444836,0.0035614038,0.0125866225,0.003000815,0.0005619353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03817704,0.0016000965,0.0038385743,0.003831759,0.00092773844,0.0035784997,0.005780564,0.0032314737,0.002708235],"category_scores_gemma":[0.1018723,0.0029015667,0.0035817963,0.005387255,0.0020766638,0.0049822177,0.0062968233,0.0037077987,0.0008397803],"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":[0.00033221606,0.00033441238,0.010431017,0.00045731757,0.0014889011,0.0004899082,0.0006920178,0.65672237,0.0014003816,0.11623958,0.0018467787,0.20956518],"study_design_scores_gemma":[0.00006782994,0.00015280888,0.0011680992,0.000050401977,0.00014026984,0.00019785878,0.00008409503,0.860655,0.0007952218,0.13453887,0.0020801695,0.00006936792],"about_ca_topic_score_codex":0.0025122126,"about_ca_topic_score_gemma":0.0027603433,"teacher_disagreement_score":0.03817704,"about_ca_system_score_codex":0.0013697149,"about_ca_system_score_gemma":0.002371349,"threshold_uncertainty_score":0.20190191},"labels":[],"label_agreement":null},{"id":"W2093789600","doi":"10.2202/1557-4679.1195","title":"Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures","year":2010,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":192,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Statistics; Multilevel model; Logistic regression; Computer science; Variance (accounting); Statistical model; Hierarchical database model; Software; Bayesian probability; Mathematics; Data mining; Econometrics","score_opus":0.08904626714055525,"score_gpt":0.43281489976916027,"score_spread":0.343768632628605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093789600","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":[0.06604794,0.0007301414,0.9288916,0.0007465938,0.00007119952,0.00078869594,0.00043440668,0.00097178307,0.0013176443],"genre_scores_gemma":[0.14045507,0.00047654097,0.8554844,0.00019031369,0.00003247275,0.002012886,0.0004875823,0.00053404097,0.00032676492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8635456,0.11910082,0.0046693757,0.0039606467,0.007822404,0.0009011734],"domain_scores_gemma":[0.5658888,0.39327246,0.010202943,0.019125283,0.010482547,0.0010279373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.103064485,0.0010134199,0.0026317327,0.0037712674,0.0011526704,0.002634489,0.0038749548,0.001745467,0.003987554],"category_scores_gemma":[0.4144878,0.0010793235,0.0036916058,0.005520726,0.0017866071,0.004102206,0.0050717317,0.0032678903,0.00071356754],"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":[0.0046971906,0.0008211776,0.08773332,0.0051037315,0.0070286123,0.0007100552,0.0107267,0.09211794,0.004831906,0.09898749,0.0118589,0.6753829],"study_design_scores_gemma":[0.0016099925,0.0019552875,0.06676786,0.0019595644,0.0029711486,0.0008129569,0.0028027096,0.6859221,0.0080306055,0.20773445,0.018793121,0.0006402346],"about_ca_topic_score_codex":0.0053697126,"about_ca_topic_score_gemma":0.009124969,"teacher_disagreement_score":0.103064485,"about_ca_system_score_codex":0.0019332322,"about_ca_system_score_gemma":0.0038582012,"threshold_uncertainty_score":0.5450636},"labels":[],"label_agreement":null},{"id":"W2094317540","doi":"10.1159/000099829","title":"Imputation of Missing Ages in Pedigree Data","year":2007,"lang":"en","type":"article","venue":"Human Heredity","topic":"Statistical Methods and Bayesian Inference","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":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Regression; Pedigree chart; Regression analysis; Linear regression; Mathematics; Medicine; Computer science; Biology; Genetics","score_opus":0.2951842863507974,"score_gpt":0.4897296952113052,"score_spread":0.1945454088605078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094317540","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":[0.17482498,0.0022714941,0.8199898,0.00049565226,0.00006779863,0.00011839197,0.0010967506,0.00053062185,0.00060447917],"genre_scores_gemma":[0.70127803,0.0011355601,0.2945753,0.00016488135,0.00008349671,0.00021200623,0.002082167,0.000085078434,0.000383474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97247833,0.02316977,0.0009637561,0.0014894803,0.0015819849,0.000316717],"domain_scores_gemma":[0.74369204,0.2295658,0.010663339,0.010623773,0.0047220862,0.0007329578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043550164,0.00079901615,0.0015878063,0.0025832055,0.0007458971,0.0012547666,0.0020241877,0.0011355311,0.0011945695],"category_scores_gemma":[0.16620414,0.0006710028,0.0011382343,0.0030914152,0.0011230182,0.0017855384,0.0011323747,0.0013935467,0.00027877046],"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":[0.0016782631,0.0003192109,0.25690192,0.0014705681,0.0024917207,0.0007252628,0.0015158987,0.4731302,0.0020784806,0.024851441,0.0039508455,0.23088616],"study_design_scores_gemma":[0.00046870508,0.000569392,0.069731586,0.0007307136,0.0008275823,0.0013435934,0.00024438335,0.76034003,0.006412099,0.1532382,0.005931328,0.00016242347],"about_ca_topic_score_codex":0.002619574,"about_ca_topic_score_gemma":0.0021344554,"teacher_disagreement_score":0.043550164,"about_ca_system_score_codex":0.0008223668,"about_ca_system_score_gemma":0.001245552,"threshold_uncertainty_score":0.23031801},"labels":[],"label_agreement":null},{"id":"W2095967715","doi":"","title":"EVALUATION OF INFERENCE METHODS IN GLMMS FOR ECOLOGICAL MODELING","year":2011,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inference; Computer science; Generalized linear mixed model; Consistency (knowledge bases); Statistical inference; Predictive inference; Focus (optics); Count data; Poisson distribution; Econometrics; Data science; Statistics; Machine learning; Artificial intelligence; Frequentist inference; Mathematics; Bayesian inference; Bayesian probability","score_opus":0.10255584807398863,"score_gpt":0.32762801986005113,"score_spread":0.2250721717860625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095967715","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":[0.0036124042,0.000860553,0.99165267,0.0009422374,0.00015297471,0.00034492344,0.0002816041,0.0009215013,0.0012312031],"genre_scores_gemma":[0.031120064,0.0005122297,0.9654717,0.0002859144,0.00009439054,0.0010021403,0.000392654,0.00073991704,0.00038096693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7881089,0.18973283,0.00560415,0.004762923,0.01099957,0.0007916749],"domain_scores_gemma":[0.32806328,0.6342076,0.006382503,0.017373566,0.01309436,0.0008787638],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23560573,0.0029101202,0.0025272246,0.004662889,0.0023249935,0.0052221394,0.006194883,0.0040941723,0.0092520155],"category_scores_gemma":[0.5918299,0.0016257516,0.00492459,0.0054677003,0.0034026497,0.007342357,0.0050419015,0.008690384,0.0019892456],"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":[0.0011138562,0.000333582,0.014887297,0.0032314986,0.0032436403,0.0003664999,0.0018238883,0.18141794,0.0014338723,0.31032735,0.015481814,0.46633875],"study_design_scores_gemma":[0.0003614553,0.00043224992,0.0039728917,0.001272869,0.000446225,0.0002818208,0.00046397356,0.7125242,0.0029164453,0.2621374,0.0150102815,0.0001802183],"about_ca_topic_score_codex":0.011005959,"about_ca_topic_score_gemma":0.012332794,"teacher_disagreement_score":0.23560573,"about_ca_system_score_codex":0.004637737,"about_ca_system_score_gemma":0.0063784583,"threshold_uncertainty_score":0.9426342},"labels":[],"label_agreement":null},{"id":"W2097223884","doi":"10.1002/sim.2813","title":"A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Statistics; Wilcoxon signed-rank test; Statistical power; Mathematics; Generalized estimating equation; Cluster randomised controlled trial; Statistical hypothesis testing; Randomization; Intraclass correlation; Type I and type II errors; Restricted randomization; Randomized controlled trial; Sample size determination; Resampling; Random effects model; Meta-analysis; Medicine; Mann–Whitney U test","score_opus":0.13128335709531094,"score_gpt":0.5424346180488774,"score_spread":0.41115126095356647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097223884","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":[0.078758545,0.014784016,0.8825285,0.0026471382,0.0013731921,0.010467044,0.0012523135,0.00090863224,0.0072806617],"genre_scores_gemma":[0.36240628,0.0034483753,0.6047288,0.0013067516,0.00024614786,0.025523944,0.00084412284,0.00060505985,0.0008904858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.4697768,0.48251575,0.014968529,0.011355676,0.020088788,0.0012944853],"domain_scores_gemma":[0.15701371,0.79582334,0.016003648,0.019888008,0.010511135,0.00076020765],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.40307885,0.0022854668,0.004512257,0.0051955953,0.0013564728,0.0040361737,0.0034853464,0.004551568,0.00626107],"category_scores_gemma":[0.6913324,0.0015252176,0.009927823,0.0048005814,0.004973448,0.005252983,0.0037382944,0.0050061215,0.0007107089],"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":[0.05198953,0.0019183157,0.03597588,0.021739682,0.05326297,0.0006442325,0.005863421,0.11614381,0.004893466,0.14507817,0.01374225,0.5487483],"study_design_scores_gemma":[0.03272177,0.029076185,0.06501802,0.017200468,0.020960914,0.0019223887,0.0018590257,0.4426036,0.016227242,0.32109627,0.049202833,0.0021113933],"about_ca_topic_score_codex":0.0011881974,"about_ca_topic_score_gemma":0.0009100504,"teacher_disagreement_score":0.59692115,"about_ca_system_score_codex":0.0029243103,"about_ca_system_score_gemma":0.0042667673,"threshold_uncertainty_score":0.7361101},"labels":[],"label_agreement":null},{"id":"W2098149189","doi":"10.1007/s00362-006-0039-y","title":"State space mixed models for longitudinal observations with binary and binomial responses","year":2007,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":43,"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 Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Multinomial distribution; State space; Count data; Mathematics; Prior probability; Markov chain; Statistical inference; Negative binomial distribution; Binary data; Covariate; Inference; Applied mathematics; Statistics; Computer science; Binary number; Monte Carlo method; Poisson distribution; Bayesian probability; Artificial intelligence","score_opus":0.12229176094158378,"score_gpt":0.3731342700055466,"score_spread":0.2508425090639628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098149189","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":[0.009296261,0.00090281694,0.9867294,0.0010441481,0.0001740496,0.00013628398,0.0008338504,0.00032852637,0.00055463595],"genre_scores_gemma":[0.35885644,0.0041410937,0.59310234,0.0013463554,0.0014203868,0.0048800944,0.005341742,0.00039692275,0.030514663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9693415,0.021434195,0.00125788,0.004900778,0.0018446487,0.0012209908],"domain_scores_gemma":[0.81970817,0.1574505,0.008267975,0.009554285,0.0036437232,0.001375237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047052424,0.004028854,0.0062147537,0.0045505743,0.0019736597,0.005541289,0.0095652975,0.0069722524,0.012738769],"category_scores_gemma":[0.12504295,0.004706708,0.006291181,0.00495883,0.0055271704,0.010965207,0.0057491018,0.008625931,0.0026061411],"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":[0.0006454909,0.00021134847,0.0056954455,0.00036586446,0.0011746873,0.00036124355,0.00083985605,0.12016146,0.00040976008,0.8309443,0.0031576084,0.036032878],"study_design_scores_gemma":[0.0001499788,0.00012787314,0.0010914505,0.00012480594,0.00033114987,0.00015364867,0.00009918365,0.47169396,0.00029101112,0.5232692,0.0025584726,0.00010924601],"about_ca_topic_score_codex":0.011464122,"about_ca_topic_score_gemma":0.011992386,"teacher_disagreement_score":0.047052424,"about_ca_system_score_codex":0.0038978725,"about_ca_system_score_gemma":0.003992959,"threshold_uncertainty_score":0.24883997},"labels":[],"label_agreement":null},{"id":"W2098373960","doi":"10.1002/sim.1858","title":"Methods for modelling change in cluster randomization trials","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":63,"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 Guelph; Cancer Care Ontario","funders":"National Institute on Drug Abuse","keywords":"Cluster randomised controlled trial; Sample size determination; Cluster (spacecraft); Randomization; Analysis of covariance; Intervention (counseling); Computer science; Statistics; Covariance; Statistical power; Outcome (game theory); Random effects model; Econometrics; Randomized controlled trial; Medicine; Mathematics; Meta-analysis","score_opus":0.3197844407598542,"score_gpt":0.5497557916040277,"score_spread":0.22997135084417347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098373960","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":[0.0004779922,0.001107817,0.9942971,0.0007431931,0.00023545255,0.0018118384,0.00028854492,0.0003252156,0.0007129252],"genre_scores_gemma":[0.028218852,0.001921655,0.9378795,0.0009509808,0.00031667075,0.028285025,0.0005359174,0.00021287374,0.0016785705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7100932,0.26211658,0.007395058,0.007888422,0.011345756,0.0011609243],"domain_scores_gemma":[0.57672364,0.3758335,0.018754242,0.019644257,0.008085459,0.0009589265],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.24552618,0.003362334,0.006151138,0.0066169035,0.00118443,0.0045064525,0.008615368,0.0077457125,0.011185549],"category_scores_gemma":[0.45565516,0.0025321709,0.006336267,0.007738261,0.00511469,0.004994622,0.0041475887,0.00956134,0.003088278],"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":[0.0012674235,0.00020085114,0.0031528107,0.00414362,0.0030184584,0.00025606004,0.0011168729,0.15660752,0.00030557273,0.6419577,0.01413231,0.17384084],"study_design_scores_gemma":[0.0011950537,0.0005373169,0.0007693913,0.0012026241,0.0007620887,0.0001603134,0.0000907893,0.2707694,0.00045670886,0.7021411,0.021760702,0.00015454329],"about_ca_topic_score_codex":0.0052103098,"about_ca_topic_score_gemma":0.0039540473,"teacher_disagreement_score":0.7544738,"about_ca_system_score_codex":0.0047178203,"about_ca_system_score_gemma":0.0061156936,"threshold_uncertainty_score":0.93040055},"labels":[],"label_agreement":null},{"id":"W2098775098","doi":"10.1177/0962280210371561","title":"On Gaussian Markov random fields and Bayesian disease mapping","year":2010,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Child and Family Research Institute; University of British Columbia","funders":"Indian Council of Agricultural Research; Canadian Institutes of Health Research; Gallipoli Medical Research Foundation","keywords":"Bayesian probability; Univariate; Prior probability; Multivariate statistics; Computer science; Bayesian linear regression; Artificial intelligence; Autoregressive model; Bayesian inference; Bayes' theorem; Mathematics; Statistics; Machine learning","score_opus":0.12325957161327475,"score_gpt":0.5478868605346664,"score_spread":0.4246272889213917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098775098","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":[0.0026142858,0.0072226953,0.9776642,0.0038977042,0.00020685168,0.00002441311,0.00021122234,0.00007919705,0.008079447],"genre_scores_gemma":[0.33025157,0.056227457,0.57627773,0.0059809205,0.005534416,0.00076036266,0.0010411952,0.0002632634,0.023663105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974631,0.0015650812,0.00008746213,0.00037302033,0.0003872219,0.00012396],"domain_scores_gemma":[0.9880591,0.010150602,0.0006580633,0.00048170245,0.0005051553,0.00014528361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073012533,0.0012194361,0.001264284,0.0020698002,0.0006341258,0.0017709808,0.0016516399,0.0029566712,0.0040137134],"category_scores_gemma":[0.016683506,0.0006629785,0.001339778,0.0030409081,0.0041102828,0.003525396,0.0018257448,0.0033032778,0.0009862192],"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":[0.000006254236,0.000008974084,0.00022795865,0.00007453062,0.000020408752,0.00007043561,0.000081586135,0.03081463,0.0001512707,0.95406705,0.0019775908,0.012499302],"study_design_scores_gemma":[0.000004101277,0.000011256748,0.00019448869,0.000057411187,0.000009799152,0.000059183865,0.000012809031,0.053279858,0.000058614864,0.9399167,0.006380862,0.000015041088],"about_ca_topic_score_codex":0.0054089515,"about_ca_topic_score_gemma":0.0030954857,"teacher_disagreement_score":0.0073012533,"about_ca_system_score_codex":0.001953585,"about_ca_system_score_gemma":0.0014343719,"threshold_uncertainty_score":0.0386132},"labels":[],"label_agreement":null},{"id":"W2099449275","doi":"10.1093/aje/152.12.1192","title":"Application of a Generalized Random Effects Regression Model for Cluster-correlated Longitudinal Data to a School-based Smoking Prevention Trial","year":2000,"lang":"en","type":"review","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random effects model; Covariate; Cluster (spacecraft); Generalized estimating equation; Statistics; Cluster randomised controlled trial; Longitudinal data; Longitudinal study; Correlation; Mixed model; Generalized linear mixed model; Demography; Randomized controlled trial; Psychology; Medicine; Mathematics; Computer science; Meta-analysis","score_opus":0.285895185704728,"score_gpt":0.5229638256230786,"score_spread":0.23706863991835064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099449275","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":[0.001654266,0.4813168,0.50709695,0.00483331,0.0012022882,0.0008282436,0.0002953843,0.0005205218,0.002252249],"genre_scores_gemma":[0.058261342,0.5560193,0.37261894,0.003697786,0.0015997966,0.004392065,0.00057106244,0.00023914191,0.0026005304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96509427,0.030336224,0.0012005059,0.0011295178,0.0020902397,0.00014932707],"domain_scores_gemma":[0.964664,0.031252112,0.0013929995,0.0013449189,0.001193446,0.00015260847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03792933,0.001307612,0.004763265,0.0030143831,0.00030391102,0.0013827349,0.0041772504,0.002672935,0.0019226603],"category_scores_gemma":[0.058257718,0.0008785848,0.0038529343,0.0043409476,0.0013400626,0.0014651213,0.0010061532,0.0044834996,0.0010758276],"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":[0.0008157887,0.00020653632,0.0012471519,0.016405098,0.0078079016,0.00029676134,0.00021791835,0.048562735,0.00030100334,0.09255722,0.022028456,0.80955344],"study_design_scores_gemma":[0.0044945944,0.0020824946,0.0065727285,0.01212619,0.010108666,0.002606928,0.00020948054,0.1995478,0.0012485455,0.54695857,0.21358356,0.00046045805],"about_ca_topic_score_codex":0.0050328076,"about_ca_topic_score_gemma":0.0053343936,"teacher_disagreement_score":0.03792933,"about_ca_system_score_codex":0.002588309,"about_ca_system_score_gemma":0.0033192856,"threshold_uncertainty_score":0.20059186},"labels":[],"label_agreement":null},{"id":"W2100697007","doi":"10.1177/0962280214558972","title":"Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models","year":2014,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":484,"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; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; National Institute of Neurological Disorders and Stroke; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Statistics; Sample size determination; Logistic regression; Sample (material); Mathematics; Regression analysis; Econometrics; Mean squared error; Regression; Linear regression; Variance (accounting); Variables; Economics","score_opus":0.39344469527150544,"score_gpt":0.579674810655022,"score_spread":0.18623011538351658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100697007","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":[0.6910125,0.004195445,0.29925233,0.0012287676,0.00017317066,0.00051297375,0.0006399172,0.00051064306,0.0024741846],"genre_scores_gemma":[0.94567585,0.00036827844,0.052514207,0.00019828707,0.000050437524,0.00027109557,0.0005884896,0.00011474148,0.00021873863],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.803992,0.16309655,0.0092961,0.0109505365,0.011089396,0.0015754199],"domain_scores_gemma":[0.21982309,0.727664,0.019056832,0.025299702,0.0068700938,0.0012863079],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.31584197,0.0027932287,0.0023816389,0.004318582,0.0008581803,0.0033181848,0.0029183656,0.0028855777,0.0006402535],"category_scores_gemma":[0.50718516,0.0009556768,0.004145395,0.0020871884,0.004441849,0.004880811,0.0043879473,0.0038611253,0.00031621012],"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":[0.007734605,0.0005596726,0.5463855,0.0012331876,0.010977417,0.00055626524,0.0026398252,0.26949397,0.003111103,0.007467972,0.001422545,0.14841793],"study_design_scores_gemma":[0.00054808194,0.0042386185,0.20892851,0.0008154054,0.0013882881,0.0010016295,0.0009436856,0.74789757,0.010935039,0.021287028,0.0015344798,0.0004816649],"about_ca_topic_score_codex":0.0017529065,"about_ca_topic_score_gemma":0.0014414089,"teacher_disagreement_score":0.684158,"about_ca_system_score_codex":0.0013466759,"about_ca_system_score_gemma":0.001551827,"threshold_uncertainty_score":0.84368867},"labels":[],"label_agreement":null},{"id":"W2101434317","doi":"10.1186/1471-2288-12-135","title":"Estimation methods with ordered exposure subject to measurement error and missingness in semi-ecological design","year":2012,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Calgary","funders":"University of Birmingham","keywords":"Missing data; Statistics; Estimation; Subject (documents); Research design; Computer science; Observational error; Econometrics; Mathematics","score_opus":0.7773759964735921,"score_gpt":0.6186229033009765,"score_spread":0.15875309317261566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101434317","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":[0.0064210887,0.0002423891,0.99256355,0.000093576404,0.000027035052,0.00031124707,0.00010961567,0.000076242526,0.00015538766],"genre_scores_gemma":[0.1679963,0.0005518362,0.8267884,0.00023503842,0.0000728463,0.0027481294,0.00066879374,0.000054823173,0.00088382134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92542475,0.06561477,0.0022953865,0.0036573913,0.0025074296,0.0005001875],"domain_scores_gemma":[0.83889896,0.13677703,0.008092149,0.010577652,0.005038779,0.00061534945],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05993408,0.0017303375,0.00268626,0.0017905019,0.00075770076,0.0014743045,0.0038366583,0.0018172943,0.0043031517],"category_scores_gemma":[0.13136423,0.001163003,0.0030197203,0.0020518086,0.0026092874,0.0021841615,0.0029619276,0.0018178758,0.00047387937],"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":[0.0014563367,0.00047482408,0.035310883,0.0030697451,0.0027946536,0.0007828115,0.0017689143,0.45147648,0.0016185432,0.23809409,0.003108037,0.26004466],"study_design_scores_gemma":[0.00039626338,0.0006269937,0.0067252405,0.00034104454,0.00034084427,0.00028601984,0.00020453776,0.7872726,0.00079681654,0.19878173,0.0041141417,0.000113867594],"about_ca_topic_score_codex":0.0052331216,"about_ca_topic_score_gemma":0.0031131078,"teacher_disagreement_score":0.9400659,"about_ca_system_score_codex":0.0012188932,"about_ca_system_score_gemma":0.0027918983,"threshold_uncertainty_score":0.31696552},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W2102082048","doi":"10.1093/jssam/smu011","title":"Small Area Prediction of Proportions with Applications to the Canadian Labour Force Survey","year":2014,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"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":"Estimator; Statistics; Small area estimation; Econometrics; Mathematics; Mean squared error; Multinomial distribution; Census; Estimation; Current Population Survey; Standard error; Table (database); Population; Benchmarking; Computer science; Demography; Economics; Data mining","score_opus":0.3842642301409165,"score_gpt":0.4131436403482028,"score_spread":0.028879410207286293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102082048","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":[0.0068053612,0.00009573777,0.99025655,0.00012950184,0.00006391813,0.00024461746,0.00056329474,0.0005937765,0.0012471796],"genre_scores_gemma":[0.15310803,0.00031841066,0.8375044,0.00009362943,0.00007464361,0.0011850485,0.0018722772,0.0002551051,0.00558846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99365777,0.0036477188,0.00019261986,0.00088144874,0.001407348,0.00021310299],"domain_scores_gemma":[0.97692966,0.016163155,0.0009167995,0.0026482115,0.0031169474,0.00022531724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011066184,0.0007956003,0.0010958591,0.0023906184,0.0011851235,0.0012029593,0.002148845,0.0006350814,0.008678866],"category_scores_gemma":[0.070549406,0.0005132234,0.0011451293,0.0049161683,0.001052401,0.0010132655,0.0017500486,0.0021434145,0.0013245959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024301649,0.0001296868,0.03525268,0.0002495291,0.00023494368,0.00030856972,0.0008919213,0.29180664,0.0010837727,0.18474744,0.013419494,0.4716323],"study_design_scores_gemma":[0.000049261056,0.00007527692,0.012299835,0.00007297634,0.0000392161,0.00009110229,0.00020067184,0.9040748,0.0009538796,0.06959777,0.012478753,0.000066429624],"about_ca_topic_score_codex":0.3221852,"about_ca_topic_score_gemma":0.30559093,"teacher_disagreement_score":0.67781484,"about_ca_system_score_codex":0.0032582406,"about_ca_system_score_gemma":0.007476464,"threshold_uncertainty_score":0.64061975},"labels":[],"label_agreement":null},{"id":"W2102353599","doi":"10.1186/1756-0500-5-330","title":"Do the methods used to analyse missing data really matter? An examination of data from an observational study of Intermediate Care patients","year":2012,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Statistical Methods and Bayesian 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 British Columbia; Centre for Advancing Health Outcomes","funders":"Medical Research Council; National Institute for Health and Care Research; Cancer Research UK","keywords":"Observational study; Missing data; Medicine; Data science; Family medicine; Computer science; Statistics; Mathematics; Pathology","score_opus":0.8515865959146718,"score_gpt":0.6496585373619265,"score_spread":0.20192805855274532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102353599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.754551,0.020596124,0.1845227,0.02936957,0.0009499418,0.0025336745,0.003687496,0.00013239724,0.0036570886],"genre_scores_gemma":[0.9236365,0.0041919956,0.06321674,0.0044152453,0.0004640499,0.0019936687,0.0014799478,0.000152012,0.00044979958],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.4111925,0.5436476,0.018199546,0.0051949318,0.0205199,0.001245578],"domain_scores_gemma":[0.101773806,0.8190387,0.04445964,0.017536215,0.01627404,0.0009176607],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5174847,0.0008186588,0.0022892295,0.0036964915,0.0013566964,0.0032679061,0.003084198,0.0027190812,0.0020235635],"category_scores_gemma":[0.69496423,0.0010255987,0.005867001,0.0085241655,0.003827826,0.0036869943,0.002253501,0.00326842,0.00032738916],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018624278,0.00032726582,0.8441542,0.0051310365,0.0076209814,0.00083126,0.026505271,0.00516605,0.00047311693,0.009117394,0.0054240613,0.09338699],"study_design_scores_gemma":[0.00071013725,0.002883647,0.8971804,0.011651616,0.004078195,0.0014718989,0.019416053,0.024986835,0.00090746547,0.014770159,0.021480097,0.00046350132],"about_ca_topic_score_codex":0.007441579,"about_ca_topic_score_gemma":0.009461148,"teacher_disagreement_score":0.48251528,"about_ca_system_score_codex":0.002381896,"about_ca_system_score_gemma":0.005130883,"threshold_uncertainty_score":0.59502727},"labels":[],"label_agreement":null},{"id":"W2102720558","doi":"10.1109/tsmca.2007.902631","title":"A Novel Framework for Imputation of Missing Values in Databases","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":250,"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":"Imputation (statistics); Computer science; Missing data; Data mining; Machine learning","score_opus":0.11245947814794505,"score_gpt":0.38434346439805134,"score_spread":0.2718839862501063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102720558","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":[0.00013887914,0.00015909289,0.9992507,0.00016386464,0.000029070141,0.000020752286,0.000049247938,0.00004785193,0.00014057933],"genre_scores_gemma":[0.022286888,0.0009805412,0.9737959,0.00038654302,0.00040563333,0.0005107899,0.00048051588,0.00006557256,0.0010875526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9714612,0.018965274,0.0014447662,0.0029086813,0.004522231,0.0006977536],"domain_scores_gemma":[0.9693247,0.02117826,0.001934026,0.0039723036,0.0029597203,0.0006309581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034816325,0.001743562,0.0035781171,0.003817525,0.0016630001,0.0050381906,0.008950355,0.0038040585,0.00270811],"category_scores_gemma":[0.05284298,0.001767661,0.0047988365,0.00748033,0.0029008288,0.005708248,0.0059209485,0.006057364,0.0015813329],"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":[0.00012806877,0.000098138495,0.0017485379,0.000592289,0.0005328715,0.0004334393,0.00044526462,0.16547702,0.0008132108,0.6942994,0.008686902,0.12674479],"study_design_scores_gemma":[0.00007274931,0.00011534276,0.00045627303,0.00015981626,0.00012520101,0.0004725505,0.00006207156,0.50840956,0.0005305226,0.4723631,0.017158514,0.00007418685],"about_ca_topic_score_codex":0.0034315507,"about_ca_topic_score_gemma":0.003747884,"teacher_disagreement_score":0.034816325,"about_ca_system_score_codex":0.0018793053,"about_ca_system_score_gemma":0.005159907,"threshold_uncertainty_score":0.18412852},"labels":[],"label_agreement":null},{"id":"W2103338349","doi":"10.1002/sim.5683","title":"Ties between event times and jump times in the Cox model","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Event (particle physics); Computer science; Proportional hazards model; Software; Jump; Statistics; Binary data; Event data; Econometrics; Binary number; Mathematics; Machine learning","score_opus":0.09058773425645432,"score_gpt":0.42891557197893215,"score_spread":0.3383278377224778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103338349","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":[0.015110779,0.00056548894,0.9814626,0.00076427293,0.00010197149,0.000101884005,0.00018801812,0.00027882814,0.0014262575],"genre_scores_gemma":[0.6340096,0.0022964196,0.3451422,0.00085805985,0.001049791,0.0015602628,0.0012278077,0.0005701337,0.01328562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98027724,0.011467171,0.0012186333,0.0035760878,0.0026841897,0.0007766686],"domain_scores_gemma":[0.82515496,0.1547672,0.006912835,0.010549956,0.0014068125,0.0012081364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048803847,0.0009537321,0.0018385015,0.0026630256,0.0019962604,0.0041880626,0.0041463245,0.0032881282,0.010254857],"category_scores_gemma":[0.2082643,0.001792928,0.0029116066,0.002993182,0.0040033115,0.011025404,0.00506032,0.007200295,0.0012168335],"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":[0.00074920245,0.00011017083,0.019909957,0.00040954826,0.00041288603,0.001071753,0.0020933107,0.10003149,0.0007827768,0.7603968,0.003405917,0.11062633],"study_design_scores_gemma":[0.00012653926,0.0001123918,0.002791739,0.00012782286,0.000235329,0.00048309178,0.00014754375,0.3151777,0.0007078845,0.6743975,0.0055960743,0.00009636612],"about_ca_topic_score_codex":0.0022610785,"about_ca_topic_score_gemma":0.0018936448,"teacher_disagreement_score":0.048803847,"about_ca_system_score_codex":0.0016373285,"about_ca_system_score_gemma":0.0015703457,"threshold_uncertainty_score":0.25810254},"labels":[],"label_agreement":null},{"id":"W2104550218","doi":"10.1007/s10463-010-0319-0","title":"Instrumental variable approach to covariate measurement error in generalized linear models","year":2010,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Covariate; Mathematics; Instrumental variable; Nonparametric statistics; Applied mathematics; Parametric statistics; Observational error; Statistics; Variable (mathematics); Errors-in-variables models; Asymptotic distribution; Generalized linear model; Econometrics; Mathematical analysis","score_opus":0.26574496962159494,"score_gpt":0.4000895641791143,"score_spread":0.13434459455751935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104550218","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":[0.0016353127,0.0004429061,0.9966478,0.00061864464,0.00006469403,0.00002146634,0.00010778283,0.000078428784,0.0003829246],"genre_scores_gemma":[0.25892714,0.0032804422,0.72523445,0.001268942,0.0012586516,0.0011556669,0.0010558827,0.00034344822,0.007475329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9417255,0.04858476,0.0015422554,0.004383006,0.0028712621,0.00089329924],"domain_scores_gemma":[0.820649,0.1613951,0.0047289277,0.009752671,0.0029257454,0.0005484057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045592003,0.0022510707,0.004285393,0.0026481308,0.0011994696,0.0038307416,0.008243053,0.004443958,0.004372345],"category_scores_gemma":[0.1558109,0.0024013917,0.0029996433,0.004311317,0.0055944514,0.0050070975,0.0049361577,0.0075070276,0.00086817826],"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":[0.00012894852,0.00007885842,0.0017140374,0.00026428234,0.0006332776,0.00015389922,0.00034717435,0.057539705,0.00021446492,0.9089336,0.001539241,0.028452551],"study_design_scores_gemma":[0.000102816,0.000054403645,0.0005357736,0.00009187303,0.00019051263,0.00006978208,0.000041180974,0.18535417,0.0002642016,0.8105534,0.0026835706,0.00005836832],"about_ca_topic_score_codex":0.0061999788,"about_ca_topic_score_gemma":0.004570581,"teacher_disagreement_score":0.045592003,"about_ca_system_score_codex":0.0023757876,"about_ca_system_score_gemma":0.00505683,"threshold_uncertainty_score":0.2411164},"labels":[],"label_agreement":null},{"id":"W2105004777","doi":"10.1093/biostatistics/kxr005","title":"Weighted scores method for regression models with dependent data","year":2011,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Univariate; Copula (linguistics); Statistics; Mathematics; Weighting; Multivariate statistics; Negative binomial distribution; Regression; Regression analysis; Inference; Binomial regression; Econometrics; Computer science; Poisson distribution; Artificial intelligence; Medicine","score_opus":0.2664853473612795,"score_gpt":0.4255545416501072,"score_spread":0.1590691942888277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105004777","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":[0.0007203348,0.00008242907,0.99866366,0.000051645213,0.000020094074,0.000027916778,0.00004730683,0.00009548016,0.00029111514],"genre_scores_gemma":[0.05664639,0.000710716,0.93555164,0.0002120485,0.00023910064,0.0009061335,0.0007743999,0.00044322826,0.004516315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99044377,0.0063949646,0.00031523095,0.0008094515,0.0018248762,0.00021163846],"domain_scores_gemma":[0.978182,0.016342882,0.00125613,0.002118903,0.001723302,0.00037682016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013171795,0.0018425155,0.001865671,0.004126997,0.00076610106,0.0019518296,0.0043984856,0.0017947819,0.00897602],"category_scores_gemma":[0.049360428,0.00084976776,0.0021019282,0.0039767437,0.002276632,0.0041642543,0.0028990214,0.003992062,0.0023152805],"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":[0.00008675212,0.000101367616,0.0018169084,0.0002702397,0.0002800278,0.00018612594,0.00020933549,0.11292373,0.0012983292,0.6932161,0.004654196,0.18495688],"study_design_scores_gemma":[0.00002879053,0.00006028924,0.0005424573,0.000049978822,0.000047143458,0.00011470057,0.0000387043,0.6116886,0.0007387042,0.37668297,0.0099661,0.000041555817],"about_ca_topic_score_codex":0.0037940913,"about_ca_topic_score_gemma":0.0033727905,"teacher_disagreement_score":0.013171795,"about_ca_system_score_codex":0.0011754245,"about_ca_system_score_gemma":0.0023938266,"threshold_uncertainty_score":0.06965995},"labels":[],"label_agreement":null},{"id":"W2105005539","doi":"10.1002/sim.2341","title":"Curious phenomena in Bayesian adjustment for exposure misclassification","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Intuition; Bayesian probability; Confounding; Econometrics; Bayes' theorem; Computer science; Statistics; Psychology; Artificial intelligence; Mathematics","score_opus":0.06258520134647907,"score_gpt":0.3989627422616326,"score_spread":0.3363775409151535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105005539","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":[0.0033293383,0.0031752652,0.96646434,0.02121236,0.0006724476,0.00012307789,0.0000887749,0.00025263627,0.004681724],"genre_scores_gemma":[0.2963923,0.005924355,0.6668184,0.017117579,0.006371098,0.0017824138,0.00022152215,0.0006895207,0.0046827816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8680648,0.100589916,0.005375737,0.010747364,0.013655375,0.0015666757],"domain_scores_gemma":[0.4749541,0.4708757,0.015241773,0.029658092,0.008316566,0.0009537268],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2595974,0.0018116082,0.0040250695,0.0037422387,0.0031734942,0.0058316337,0.0059406585,0.007617914,0.003956963],"category_scores_gemma":[0.60774523,0.0019480758,0.0032309531,0.0040618484,0.020874297,0.018987106,0.007434063,0.02078804,0.000928116],"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":[0.000060184873,0.00001687137,0.0012910748,0.00022775352,0.0001697386,0.00018646546,0.00089297007,0.006865543,0.000112918555,0.9575711,0.0046805963,0.027924834],"study_design_scores_gemma":[0.000030436093,0.000016233294,0.00030830994,0.0000977642,0.000025443358,0.000104921586,0.000035516066,0.01261108,0.00012344628,0.98347175,0.0031490289,0.000026176893],"about_ca_topic_score_codex":0.0039798347,"about_ca_topic_score_gemma":0.0018028239,"teacher_disagreement_score":0.2595974,"about_ca_system_score_codex":0.0037487298,"about_ca_system_score_gemma":0.0025479815,"threshold_uncertainty_score":0.91304827},"labels":[],"label_agreement":null},{"id":"W2105049581","doi":"10.1002/cjs.10085","title":"The pseudo‐GEE approach to the analysis of longitudinal surveys","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia; University of Waterloo; Statistics Canada","funders":"","keywords":"Gee; Estimator; Generalized estimating equation; Statistics; Mathematics; Sampling (signal processing); Population; Stratified sampling; Variance (accounting); Econometrics; Estimating equations; Consistency (knowledge bases); Computer science; Demography","score_opus":0.06578449412445457,"score_gpt":0.33328736644395224,"score_spread":0.26750287231949765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105049581","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":[0.002548583,0.00023419922,0.99633265,0.00020422993,0.0000376915,0.000059210208,0.00010050531,0.000116525174,0.000366348],"genre_scores_gemma":[0.16437612,0.001439185,0.8286561,0.0003968837,0.00018920896,0.0011383392,0.00068772683,0.00021874649,0.0028977324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9748189,0.021072974,0.0005350677,0.0014078615,0.0018346406,0.00033052202],"domain_scores_gemma":[0.94602424,0.04177734,0.0032964125,0.0044212244,0.004063618,0.0004171117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024397755,0.0010979098,0.0015445799,0.0030399356,0.00061366544,0.0019311171,0.0027951526,0.0013353843,0.0047098654],"category_scores_gemma":[0.09256378,0.000862669,0.002485671,0.0030811718,0.0023444958,0.0025527864,0.0026824754,0.0029388657,0.0010165195],"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":[0.00017653743,0.00016393697,0.014715979,0.00072489626,0.0012877259,0.00048772863,0.0007939833,0.22641335,0.0011018732,0.5109009,0.007570771,0.23566228],"study_design_scores_gemma":[0.000063873274,0.00016065981,0.003920618,0.00009751287,0.000111778696,0.00026730107,0.000121183366,0.54058945,0.00045707505,0.4439733,0.010141516,0.00009577849],"about_ca_topic_score_codex":0.0090148635,"about_ca_topic_score_gemma":0.007320367,"teacher_disagreement_score":0.024397755,"about_ca_system_score_codex":0.0011370226,"about_ca_system_score_gemma":0.003397066,"threshold_uncertainty_score":0.12902921},"labels":[],"label_agreement":null},{"id":"W2105640027","doi":"10.1214/08-sts273","title":"Accurate Parametric Inference for Small Samples","year":2008,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Neuchâtel; University of Toronto; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Inference; Parametric statistics; Statistical inference; Logistic regression; Focus (optics); Computer science; Sampling (signal processing); Parametric model; Mathematics; Variety (cybernetics); Econometrics; Asymptotic analysis; Applied mathematics; Statistics; Machine learning; Artificial intelligence","score_opus":0.28638891724177035,"score_gpt":0.4476443073628183,"score_spread":0.16125539012104795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105640027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014237254,0.00024388937,0.9967385,0.0002784446,0.00003519133,0.000016559174,0.000033978613,0.00013216051,0.0010975174],"genre_scores_gemma":[0.21314351,0.0014141476,0.7806784,0.0007280765,0.00045482005,0.00053132995,0.00028964624,0.00026076342,0.002499313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9808553,0.011926457,0.000715684,0.0017772538,0.004237567,0.000487763],"domain_scores_gemma":[0.8898234,0.091359116,0.0033138816,0.011365111,0.0035641587,0.00057435996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02517937,0.0010278225,0.0022223261,0.0023656343,0.0011536996,0.0028792815,0.0025688855,0.001925095,0.00408793],"category_scores_gemma":[0.20021924,0.0009436457,0.0010531016,0.0021446785,0.0042666295,0.006110736,0.0042562257,0.004953767,0.0012921133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006371853,0.00003824952,0.0015693919,0.00021725698,0.00008952617,0.00026632313,0.0003796978,0.05723727,0.0009618601,0.8476161,0.0035544378,0.08800606],"study_design_scores_gemma":[0.000018111268,0.000019597373,0.00035384978,0.000051070736,0.00001721675,0.00011164112,0.00003824055,0.135696,0.00058665103,0.85866165,0.004429387,0.000016722239],"about_ca_topic_score_codex":0.0016750045,"about_ca_topic_score_gemma":0.0017172048,"teacher_disagreement_score":0.02517937,"about_ca_system_score_codex":0.0013783948,"about_ca_system_score_gemma":0.0017974826,"threshold_uncertainty_score":0.13316286},"labels":[],"label_agreement":null},{"id":"W2106268411","doi":"10.1016/j.annepidem.2013.10.007","title":"Missing data in longitudinal studies: cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood","year":2013,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research; Government of Canada","keywords":"Missing data; Imputation (statistics); Categorical variable; Statistics; Data set; Latent variable; Estimating equations; Econometrics; Maximum likelihood; Mathematics","score_opus":0.41620941739993456,"score_gpt":0.525378220302224,"score_spread":0.10916880290228947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106268411","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":[0.00362963,0.0019937786,0.9928311,0.0005929448,0.00007967546,0.00008749011,0.00024936735,0.00030646598,0.00022959677],"genre_scores_gemma":[0.096049726,0.0030769943,0.89669186,0.0005864297,0.00041643585,0.00059227046,0.0011145618,0.00031640037,0.0011553088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9445262,0.04902239,0.0019614,0.0025797414,0.0016716605,0.00023862999],"domain_scores_gemma":[0.7739902,0.19382437,0.0050912322,0.022744631,0.0037209087,0.00062863436],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08079751,0.0014896891,0.0057591572,0.0037502225,0.0009265385,0.0043015745,0.0050682537,0.00398185,0.0035730088],"category_scores_gemma":[0.28061906,0.0024677834,0.004402528,0.0067070834,0.001507058,0.005737252,0.0033959087,0.0043053366,0.0010829062],"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":[0.0020285835,0.000756417,0.034146216,0.003847584,0.017734954,0.0007436906,0.0014494514,0.17480223,0.0016594883,0.14850569,0.016246282,0.5980794],"study_design_scores_gemma":[0.0007098767,0.00035307303,0.0068040006,0.0006456431,0.0029436958,0.00067077484,0.00024440826,0.5550961,0.00093139673,0.4235862,0.0077875014,0.00022733683],"about_ca_topic_score_codex":0.002943829,"about_ca_topic_score_gemma":0.0038289898,"teacher_disagreement_score":0.9192025,"about_ca_system_score_codex":0.0006399259,"about_ca_system_score_gemma":0.0027130768,"threshold_uncertainty_score":0.4273032},"labels":[],"label_agreement":null},{"id":"W2106891714","doi":"10.1002/cjs.5550350301","title":"Robust likelihood inference for public policy","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistic; Statistics; Jackknife resampling; Mathematics; Inference; Variance (accounting); Statistical inference; Econometrics; Likelihood-ratio test; Computer science; Artificial intelligence","score_opus":0.11656568902416875,"score_gpt":0.36354187455468,"score_spread":0.24697618553051126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106891714","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":[0.0026796616,0.00084734964,0.9904676,0.001281661,0.000080954545,0.000052511703,0.00019191064,0.0002641934,0.0041341465],"genre_scores_gemma":[0.46693322,0.0030192256,0.51594603,0.0008225037,0.0010029434,0.0010550809,0.0012031763,0.00052858895,0.009489151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97304046,0.021120505,0.00066850986,0.0020693084,0.0024987215,0.00060252676],"domain_scores_gemma":[0.84764916,0.13716951,0.0059117964,0.0051253266,0.003490382,0.0006538537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03157094,0.0016845873,0.0038138707,0.0049894513,0.0011447738,0.0045993747,0.003936912,0.003661831,0.008754223],"category_scores_gemma":[0.21041779,0.0014486726,0.00209814,0.0052679717,0.0052194395,0.004371859,0.0036221654,0.0063793133,0.001660898],"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":[0.0000704938,0.000037645274,0.0007753162,0.00019800778,0.00022767465,0.00011108025,0.00010508727,0.16373627,0.000105855936,0.8008767,0.0036950454,0.030060984],"study_design_scores_gemma":[0.000028858829,0.000014322553,0.00017205554,0.00003886952,0.000022090253,0.000019372816,0.000018735116,0.28669733,0.00011157911,0.71128845,0.0015728119,0.00001553726],"about_ca_topic_score_codex":0.008136625,"about_ca_topic_score_gemma":0.004080761,"teacher_disagreement_score":0.03157094,"about_ca_system_score_codex":0.005585421,"about_ca_system_score_gemma":0.0036048342,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W2107318672","doi":"10.1016/j.cmpb.2006.04.006","title":"Creating non-parametric bootstrap samples using Poisson frequencies","year":2006,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; McGill University; Group for Research in Decision Analysis; McGill University Health Centre","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Poisson distribution; Estimator; Variance (accounting); Multinomial distribution; Statistic; Parametric statistics; Statistics; Computer science; Sampling (signal processing); Sampling distribution; Mathematics; Standard error; Sample (material); Algorithm","score_opus":0.21016494684582568,"score_gpt":0.4523462914305649,"score_spread":0.2421813445847392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107318672","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":[0.016659042,0.000036417947,0.9807637,0.000047306825,0.00005038096,0.00024231823,0.00011237947,0.00090009446,0.001188451],"genre_scores_gemma":[0.11280033,0.00004390254,0.8848992,0.00006378784,0.000035931513,0.0006196106,0.00051360345,0.0004190652,0.0006045521],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99377394,0.0034276827,0.00031501325,0.00075417216,0.0015283708,0.00020083859],"domain_scores_gemma":[0.96071887,0.027194915,0.001055974,0.006856423,0.003671521,0.0005023517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012757674,0.0006609934,0.0013118845,0.002286292,0.0012084967,0.0017598079,0.002847595,0.0016319397,0.008174159],"category_scores_gemma":[0.086109415,0.00090423436,0.0011565683,0.0019485404,0.0009450539,0.0021250208,0.002375113,0.0025463558,0.0029005737],"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":[0.0014919585,0.0007957426,0.011318799,0.00060818053,0.00028638198,0.0006993669,0.002417941,0.07933976,0.0129802115,0.12927414,0.01183951,0.748948],"study_design_scores_gemma":[0.00046513323,0.00025977907,0.0037089156,0.00013523904,0.000115343624,0.000464728,0.0005217185,0.6600461,0.014982202,0.30432582,0.014873381,0.00010162163],"about_ca_topic_score_codex":0.0006038043,"about_ca_topic_score_gemma":0.0010979119,"teacher_disagreement_score":0.012757674,"about_ca_system_score_codex":0.00048806364,"about_ca_system_score_gemma":0.0009803977,"threshold_uncertainty_score":0.067469835},"labels":[],"label_agreement":null},{"id":"W2107915265","doi":"","title":"On the uniqueness of probability matching priors","year":2007,"lang":"en","type":"article","venue":"Explore Bristol Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Mathematics; Frequentist inference; Matching (statistics); Quantile; Bayesian probability; Posterior probability; Orthogonality; Bayesian inference; Applied mathematics; Statistics","score_opus":0.4404868570541292,"score_gpt":0.5208279661793662,"score_spread":0.08034110912523701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107915265","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":[0.0105067305,0.0007922361,0.9768991,0.0017730539,0.00006958489,0.000042649885,0.00012142384,0.00009187807,0.009703316],"genre_scores_gemma":[0.5286938,0.004284654,0.45205843,0.002144866,0.0010928966,0.0006114007,0.00053755206,0.00043505276,0.010141345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923373,0.0043188855,0.00028949924,0.0014138949,0.001266431,0.0003739998],"domain_scores_gemma":[0.9313659,0.05712554,0.0029857398,0.004417956,0.0032812897,0.00082352833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016613474,0.000892921,0.0019193671,0.002670889,0.0020630842,0.0033288307,0.0023669659,0.0034697806,0.005517125],"category_scores_gemma":[0.11027104,0.0010833172,0.001775996,0.002408575,0.008517474,0.009783553,0.0044779144,0.005647318,0.0014285308],"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":[0.000014621969,0.000010184384,0.0003686806,0.00003325478,0.00001578866,0.000044689106,0.00012415559,0.006624316,0.00013430823,0.9834011,0.0006428021,0.008586203],"study_design_scores_gemma":[0.000009086343,0.000010781906,0.00017769636,0.000038049726,0.000007240783,0.00007940959,0.000028312093,0.030954253,0.0001777418,0.96676654,0.0017367218,0.000014157512],"about_ca_topic_score_codex":0.0013277789,"about_ca_topic_score_gemma":0.00066467153,"teacher_disagreement_score":0.016613474,"about_ca_system_score_codex":0.0015552184,"about_ca_system_score_gemma":0.0017441167,"threshold_uncertainty_score":0.08786148},"labels":[],"label_agreement":null},{"id":"W2108227051","doi":"10.1093/biomet/asq078","title":"Assessing the validity of weighted generalized estimating equations","year":2011,"lang":"en","type":"article","venue":"Biometrika","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Biostatistics; Library science; Statistics; Mathematics; History; Medicine; Computer science; Public health","score_opus":0.40022121795792187,"score_gpt":0.4594524092906369,"score_spread":0.05923119133271504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108227051","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":[0.12439131,0.0009994509,0.8697027,0.001020706,0.00013995539,0.0005678934,0.00033412967,0.00018305662,0.0026607795],"genre_scores_gemma":[0.6361628,0.00052331935,0.3599743,0.0003976764,0.00013567613,0.0013143229,0.00086963,0.00010761697,0.00051460596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7245671,0.22631031,0.013647886,0.01274898,0.021129956,0.0015957312],"domain_scores_gemma":[0.27983537,0.65824705,0.021909773,0.02266659,0.016470693,0.0008704456],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21663302,0.0015478329,0.0030915167,0.005650081,0.0015442196,0.002876627,0.0039808024,0.003866813,0.0032149237],"category_scores_gemma":[0.6460598,0.0009855321,0.0033716497,0.0052097933,0.006477784,0.0066311485,0.0048896098,0.0030651677,0.00044539012],"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":[0.0022011595,0.0005099968,0.20163298,0.0022676706,0.0073944908,0.0012932608,0.003744568,0.14369054,0.0023915975,0.36488733,0.0028699168,0.2671165],"study_design_scores_gemma":[0.0006621714,0.0017619313,0.03883613,0.00073120976,0.001194126,0.00086740893,0.0013974647,0.5918029,0.0032070957,0.35315314,0.0060913116,0.00029512326],"about_ca_topic_score_codex":0.0026748793,"about_ca_topic_score_gemma":0.0012654924,"teacher_disagreement_score":0.21663302,"about_ca_system_score_codex":0.0012126161,"about_ca_system_score_gemma":0.0037943765,"threshold_uncertainty_score":0.96603096},"labels":[],"label_agreement":null},{"id":"W2108875528","doi":"10.1111/j.1741-3737.2005.00192.x","title":"Generalized Linear Models in Family Studies","year":2005,"lang":"en","type":"article","venue":"Journal of Marriage and the Family","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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 Victoria","funders":"","keywords":"Generalized linear model; Categorical variable; Exponential family; Probit; Econometrics; Linear model; Mathematics; Log-linear model; Probit model; Poisson distribution; Generalized linear mixed model; Statistics","score_opus":0.12690634595743472,"score_gpt":0.3929834678071112,"score_spread":0.26607712184967647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108875528","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":[0.0085255755,0.0536232,0.90809095,0.008972318,0.0012958725,0.00042700057,0.0019538586,0.00047110388,0.016640078],"genre_scores_gemma":[0.31867793,0.0954767,0.5550463,0.0034855553,0.004722505,0.005273306,0.0031599882,0.00035482342,0.013802793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9737872,0.02212082,0.0005433666,0.0015322632,0.00162729,0.00038904516],"domain_scores_gemma":[0.9556271,0.03854195,0.002282418,0.0018338923,0.0013965521,0.0003181813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020825833,0.001956406,0.002771912,0.004189871,0.0011955708,0.003266437,0.003330057,0.0030973225,0.011687699],"category_scores_gemma":[0.053748425,0.00089213304,0.0018343654,0.010032913,0.002977714,0.0034582743,0.0027208237,0.0049614166,0.002249208],"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":[0.000042383537,0.000096574615,0.0052109575,0.0007648673,0.00044544888,0.0003242257,0.0013577464,0.026893705,0.00007015012,0.834558,0.017153775,0.11308218],"study_design_scores_gemma":[0.000038344984,0.00005949293,0.0016304036,0.0004504572,0.0000920081,0.00016102128,0.00035179674,0.031222085,0.00003915105,0.9386604,0.027248474,0.000046407393],"about_ca_topic_score_codex":0.010877017,"about_ca_topic_score_gemma":0.0073107323,"teacher_disagreement_score":0.020825833,"about_ca_system_score_codex":0.0030058632,"about_ca_system_score_gemma":0.0037684757,"threshold_uncertainty_score":0.11013883},"labels":[],"label_agreement":null},{"id":"W2109264950","doi":"10.1002/env.982","title":"On spatial skew‐Gaussian processes and applications","year":2009,"lang":"en","type":"article","venue":"Environmetrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"National Science Foundation","keywords":"Skew; Covariance function; Covariance; Inference; Monte Carlo method; Marginal distribution; Gaussian process; Marginal likelihood; Mathematics; Statistics; Applied mathematics; Likelihood function; Statistical physics; Computer science; Gaussian; Econometrics; Maximum likelihood; Random variable; Artificial intelligence; Physics","score_opus":0.033079505225748894,"score_gpt":0.32651730683799846,"score_spread":0.2934378016122496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109264950","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":[0.018565191,0.0019065647,0.97247475,0.0011249591,0.000111509005,0.00003442581,0.00009948803,0.000106946085,0.005576158],"genre_scores_gemma":[0.7472375,0.009720524,0.22667679,0.00093953166,0.0010562603,0.0003462875,0.00041311336,0.0001951237,0.013414827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772257,0.0012118218,0.00009897371,0.00034058525,0.00048285254,0.00014315653],"domain_scores_gemma":[0.98466563,0.011124858,0.0013781331,0.0008256439,0.001596744,0.00040898306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065827146,0.0012228255,0.0014693346,0.0022789899,0.0011759995,0.0019257913,0.0017803845,0.002499388,0.005276331],"category_scores_gemma":[0.027008863,0.0006287151,0.0013983212,0.0033591462,0.004124664,0.0036067788,0.0036215135,0.0031907032,0.00075613876],"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":[0.000018437966,0.000018165481,0.00088233955,0.0000711478,0.000023215001,0.00014222485,0.0001678675,0.09744261,0.0002595229,0.8880722,0.0011333278,0.011768959],"study_design_scores_gemma":[0.000007911755,0.000009377888,0.00022774495,0.000031279757,0.0000073253614,0.0000716108,0.000037373145,0.3771214,0.00009724015,0.6204105,0.0019652464,0.0000129581085],"about_ca_topic_score_codex":0.0058617247,"about_ca_topic_score_gemma":0.0030901104,"teacher_disagreement_score":0.0065827146,"about_ca_system_score_codex":0.0014372556,"about_ca_system_score_gemma":0.0010961781,"threshold_uncertainty_score":0.034813166},"labels":[],"label_agreement":null},{"id":"W2109573291","doi":"10.1002/cjs.10123","title":"A special issue of CJS in honour of Jack Kalbfleisch and Jerry Lawless","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Honour; Citation; Associate editor; Art history; Sociology; Library science; Art; Computer science; Law; Political science","score_opus":0.0786945819417947,"score_gpt":0.31431787633975306,"score_spread":0.23562329439795837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109573291","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001622525,0.005468556,0.0008149415,0.0810138,0.9059593,0.000034580455,0.00022187017,0.00012530047,0.006199464],"genre_scores_gemma":[0.0023322327,0.0053011305,0.0006505675,0.02701025,0.8947579,0.000054634555,0.00032707534,0.00044505243,0.069121055],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994293,0.0010057364,0.0005207352,0.00093804556,0.002702036,0.0005405848],"domain_scores_gemma":[0.9639831,0.008067917,0.0016458747,0.0018887004,0.016660064,0.0077543794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062571224,0.0021664114,0.0026940284,0.0039792866,0.0037111857,0.009698987,0.00235729,0.006289255,0.066274844],"category_scores_gemma":[0.04001912,0.0008181444,0.0014519459,0.002065091,0.0019581618,0.005733812,0.003610966,0.008468779,0.03427201],"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":[0.000017411585,0.000006354238,0.00003751943,0.00003203867,0.0000040680698,0.00003977589,0.000008814619,0.0000178122,0.00003985669,0.00045054304,0.99522614,0.0041197715],"study_design_scores_gemma":[0.000013823994,0.0000146289785,0.0002940434,0.000085377,0.000009663108,0.00013638225,0.000036507096,0.0001501444,0.00008569416,0.0016362616,0.99752,0.000017377528],"about_ca_topic_score_codex":0.0041206013,"about_ca_topic_score_gemma":0.010916941,"teacher_disagreement_score":0.066274844,"about_ca_system_score_codex":0.0032479295,"about_ca_system_score_gemma":0.003987346,"threshold_uncertainty_score":0.22171146},"labels":[],"label_agreement":null},{"id":"W2109847120","doi":"10.1002/cjs.10059","title":"On quasi‐likelihood estimation for branching processes with immigration","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overdispersion; Statistics; Mathematics; Econometrics; Conditional variance; Quasi-likelihood; Negative binomial distribution; Ordinary least squares; Context (archaeology); Variance (accounting); Conditional probability distribution; Poisson distribution; Geography; Economics; Autoregressive conditional heteroskedasticity; Volatility (finance)","score_opus":0.024129267535347925,"score_gpt":0.3123631100470355,"score_spread":0.28823384251168754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109847120","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":[0.0054579535,0.000120414996,0.9939229,0.00016044763,0.000015831989,0.000013051797,0.000027159587,0.00004599603,0.00023624164],"genre_scores_gemma":[0.34090617,0.0008331906,0.6532294,0.00031307765,0.00026153453,0.00030940393,0.00056658173,0.00022688627,0.0033536511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907733,0.0072359014,0.00021868479,0.000716164,0.00082894415,0.00022697392],"domain_scores_gemma":[0.90808386,0.08325981,0.0030730288,0.002897168,0.0022347777,0.00045138338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022655053,0.0006072691,0.0013128645,0.0015809813,0.0006667452,0.0016189411,0.0026711633,0.0015012616,0.002986125],"category_scores_gemma":[0.088740766,0.0009329595,0.0011250515,0.0020456247,0.0036042538,0.0029547096,0.0023392164,0.0030142502,0.0004904465],"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":[0.00014231526,0.000053556716,0.004266678,0.00019065727,0.00016263069,0.00019373173,0.00036585063,0.3767475,0.0014344151,0.55784535,0.002047893,0.05654948],"study_design_scores_gemma":[0.000015655265,0.000015802492,0.00058579317,0.000021073676,0.000010047213,0.00003598941,0.000013151938,0.86250275,0.00023337906,0.13576391,0.0007847643,0.000017607666],"about_ca_topic_score_codex":0.00715928,"about_ca_topic_score_gemma":0.004874247,"teacher_disagreement_score":0.022655053,"about_ca_system_score_codex":0.0016382999,"about_ca_system_score_gemma":0.0018895189,"threshold_uncertainty_score":0.11981279},"labels":[],"label_agreement":null},{"id":"W2110182275","doi":"10.1002/sim.4087","title":"Copula‐based regression models for a bivariate mixed discrete and continuous outcome","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":132,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Bivariate analysis; Covariate; Econometrics; Joint probability distribution; Marginal distribution; Marginal model; Regression; Mathematics; Statistics; Regression analysis; Computer science; Random variable","score_opus":0.08217114540050523,"score_gpt":0.43601078137216187,"score_spread":0.35383963597165663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110182275","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":[0.0037896873,0.0004864786,0.99407554,0.0003290734,0.00002791634,0.000044175056,0.00022147835,0.00016166101,0.00086402806],"genre_scores_gemma":[0.32430902,0.004573247,0.6531082,0.0006582709,0.00037651038,0.001664007,0.00182839,0.0005433173,0.012939053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9925689,0.005068508,0.000233339,0.0011166921,0.0006553766,0.00035718112],"domain_scores_gemma":[0.98192364,0.013881222,0.0019859183,0.001149313,0.00083858665,0.0002212942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013864906,0.0023888848,0.0030661311,0.0025644177,0.00075591396,0.0032552397,0.004616938,0.0025088857,0.00645716],"category_scores_gemma":[0.034969468,0.0014956689,0.0032981858,0.0038141408,0.0020368476,0.0041129086,0.0022050028,0.004532021,0.002203673],"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":[0.00006676352,0.00008600857,0.0028465292,0.00026780416,0.00038281942,0.00028804265,0.00042000084,0.35383767,0.0005931672,0.607613,0.0038178235,0.029780392],"study_design_scores_gemma":[0.00001827028,0.000037096936,0.0007433318,0.000048804268,0.00006897795,0.000105897045,0.000038115413,0.8478377,0.00014108683,0.14816687,0.0027577977,0.000036097306],"about_ca_topic_score_codex":0.008963105,"about_ca_topic_score_gemma":0.0063107396,"teacher_disagreement_score":0.013864906,"about_ca_system_score_codex":0.002066891,"about_ca_system_score_gemma":0.0017589587,"threshold_uncertainty_score":0.073325515},"labels":[],"label_agreement":null},{"id":"W2111418884","doi":"10.1111/j.1461-0248.2007.01047.x","title":"Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods","year":2007,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":269,"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; University of Alberta","funders":"","keywords":"Markov chain Monte Carlo; Frequentist inference; Computer science; Bayesian probability; Algorithm; Likelihood function; Data mining; Bayesian inference; Machine learning; Statistics; Mathematics; Estimation theory; Artificial intelligence","score_opus":0.19714551899715488,"score_gpt":0.44422467780748054,"score_spread":0.24707915881032566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111418884","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":[0.00024031842,0.00002677237,0.99940026,0.000031892763,0.000011564945,0.000011652272,0.000027108214,0.00017119884,0.000079199184],"genre_scores_gemma":[0.011146794,0.000097097574,0.98765683,0.00007231965,0.000045115055,0.0002578906,0.00015799198,0.00030348537,0.0002624897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99304444,0.004070893,0.00040629556,0.00074028724,0.0015794345,0.00015863415],"domain_scores_gemma":[0.9641103,0.027472394,0.001748147,0.0042717876,0.0020097017,0.000387799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012022117,0.0016272019,0.0019620818,0.0032590602,0.001073106,0.0022941686,0.0041623525,0.0020312325,0.0047279],"category_scores_gemma":[0.06542285,0.0016644812,0.0021751784,0.003400324,0.0021488972,0.003827284,0.0046102065,0.004929513,0.0020340513],"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":[0.00008555675,0.00014235917,0.0033182385,0.00051641033,0.0003062117,0.0002818876,0.0004995012,0.23432685,0.003818395,0.44724324,0.008548285,0.30091304],"study_design_scores_gemma":[0.00003907825,0.00002366236,0.00034391534,0.000055935758,0.000025971642,0.0001007271,0.000018153725,0.7383747,0.0019786772,0.25017583,0.00879467,0.0000687272],"about_ca_topic_score_codex":0.0026335595,"about_ca_topic_score_gemma":0.0024675932,"teacher_disagreement_score":0.012022117,"about_ca_system_score_codex":0.0011053439,"about_ca_system_score_gemma":0.0023845956,"threshold_uncertainty_score":0.0635798},"labels":[],"label_agreement":null},{"id":"W2112200626","doi":"10.1186/1471-2288-12-82","title":"Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable","year":2012,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":271,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Statistics; Mathematics; Statistic; Logistic regression; Sample size determination; Normal distribution; Standard deviation; Sampling distribution; PRESS statistic; Monte Carlo method; Variable (mathematics); Cumulative distribution function; Regression analysis; Econometrics; Ancillary statistic; F-test; Probability density function","score_opus":0.5031041758697814,"score_gpt":0.5613378965208847,"score_spread":0.05823372065110333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112200626","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":[0.4865938,0.001796046,0.4994492,0.0016890483,0.00026959076,0.00033084137,0.00069313706,0.000685051,0.008493198],"genre_scores_gemma":[0.9730249,0.00013441859,0.02581284,0.00013994929,0.000078572375,0.0001300775,0.00023731486,0.00009507892,0.00034697822],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94376576,0.039323457,0.0023462127,0.006124461,0.007392852,0.0010472593],"domain_scores_gemma":[0.42653665,0.52315915,0.024588179,0.0156142665,0.008780859,0.0013208726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.063414074,0.0010493002,0.002015884,0.004334131,0.0010211233,0.0038239986,0.0029705437,0.0022748811,0.0018643314],"category_scores_gemma":[0.41640103,0.0007895849,0.0018365363,0.0037095486,0.0045585237,0.0025451535,0.0027849704,0.0028440806,0.0004232976],"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":[0.0011873535,0.00017966364,0.6331563,0.00066919986,0.0016315879,0.0026024107,0.0016958389,0.25305614,0.0014413433,0.04699138,0.0033198227,0.054068964],"study_design_scores_gemma":[0.00011199988,0.00036614446,0.08314411,0.0002630237,0.00042647586,0.0033686978,0.00059811445,0.80020887,0.0026688916,0.10611293,0.0024935473,0.00023711413],"about_ca_topic_score_codex":0.007884999,"about_ca_topic_score_gemma":0.0033878363,"teacher_disagreement_score":0.063414074,"about_ca_system_score_codex":0.0029474532,"about_ca_system_score_gemma":0.0032995215,"threshold_uncertainty_score":0.3353697},"labels":[],"label_agreement":null},{"id":"W2112669742","doi":"10.1093/aje/kwh090","title":"A Modified Poisson Regression Approach to Prospective Studies with Binary Data","year":2004,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9468,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Poisson regression; Statistics; Poisson distribution; Mathematics; Regression analysis; Binary data; Regression; Variance (accounting); Linear regression; Binary number; Econometrics; Medicine; Population","score_opus":0.27622504196053843,"score_gpt":0.4846306984267521,"score_spread":0.20840565646621367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112669742","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":[0.0011799,0.0010071971,0.9954668,0.0008195982,0.00017858314,0.00028169926,0.00019513891,0.00009801062,0.00077304535],"genre_scores_gemma":[0.050310243,0.003298863,0.9361517,0.0016102808,0.00083401275,0.003390508,0.00049173686,0.00014668185,0.003766046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9421471,0.046578683,0.00238375,0.0039490126,0.0046110353,0.0003305547],"domain_scores_gemma":[0.9477255,0.04088442,0.0035145623,0.005411173,0.0021494785,0.0003148924],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05157736,0.0013514111,0.0025983206,0.0046134684,0.0007827951,0.002535432,0.007217813,0.0023971926,0.005157342],"category_scores_gemma":[0.1324003,0.0013385975,0.0039343103,0.005078729,0.0012560047,0.0028733406,0.0029518944,0.004768555,0.0017582129],"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":[0.00037775026,0.00020052008,0.009394142,0.0016918955,0.0025229203,0.0011562962,0.0009801928,0.055540897,0.0014625193,0.5737464,0.013215615,0.3397108],"study_design_scores_gemma":[0.00043525666,0.0005696715,0.0043163886,0.0006372277,0.0008777379,0.0018473858,0.00016156262,0.21877249,0.0011924424,0.7181151,0.052866887,0.00020779938],"about_ca_topic_score_codex":0.0021826895,"about_ca_topic_score_gemma":0.0021607382,"teacher_disagreement_score":0.9484227,"about_ca_system_score_codex":0.001153203,"about_ca_system_score_gemma":0.0018849829,"threshold_uncertainty_score":0.2727704},"labels":[],"label_agreement":null},{"id":"W2112925229","doi":"10.1093/biomet/asp003","title":"Jackknife estimation of mean squared error of small area predictors in nonlinear mixed models","year":2009,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Jackknife resampling; Mathematics; Mean squared error; Statistics; Estimator; Small area estimation; Bias of an estimator; Minimum-variance unbiased estimator","score_opus":0.12281142003469807,"score_gpt":0.36263652267336266,"score_spread":0.23982510263866458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112925229","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":[0.0077065956,0.00018710077,0.9916883,0.00005870897,0.000012919818,0.000029651703,0.00004131685,0.00008797515,0.00018752091],"genre_scores_gemma":[0.3047672,0.00062501105,0.69078165,0.00022221725,0.00006572658,0.000608688,0.0004944947,0.00019196044,0.0022430308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9815564,0.013664232,0.00080796186,0.0023633284,0.0012438073,0.00036426797],"domain_scores_gemma":[0.9195319,0.068862624,0.0039726,0.0043640053,0.0028770356,0.0003919141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032199375,0.0015876581,0.0036348223,0.0020062088,0.0011306007,0.0017926866,0.0033112406,0.002319302,0.0016527042],"category_scores_gemma":[0.1412925,0.0013617352,0.0017793154,0.0017370699,0.0023717866,0.003094513,0.0025773176,0.0023842517,0.00052103575],"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":[0.00062359654,0.00011107103,0.016783275,0.0004894065,0.0014157031,0.0003932518,0.0007914022,0.6980608,0.0024620772,0.13271342,0.0017399976,0.14441592],"study_design_scores_gemma":[0.000044107583,0.000095380194,0.003013824,0.00012927507,0.000133153,0.00020021407,0.00009519953,0.86819834,0.0020784005,0.12433635,0.0016031353,0.00007252165],"about_ca_topic_score_codex":0.0064017186,"about_ca_topic_score_gemma":0.006445021,"teacher_disagreement_score":0.032199375,"about_ca_system_score_codex":0.0011804596,"about_ca_system_score_gemma":0.0016849274,"threshold_uncertainty_score":0.17028856},"labels":[],"label_agreement":null},{"id":"W2112995480","doi":"10.1007/s11222-015-9577-2","title":"Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC","year":2015,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saskatchewan Health; University of Saskatchewan","funders":"","keywords":"Markov chain Monte Carlo; Bayesian probability; Latent variable; Mathematics; Posterior probability; Computer science; Logistic regression; Statistics; Artificial intelligence; Data mining","score_opus":0.09200036493050348,"score_gpt":0.36984197461487,"score_spread":0.27784160968436655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112995480","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":[0.029114066,0.0007752534,0.9658916,0.00062605675,0.00006867398,0.00006616447,0.00011491726,0.00028159094,0.0030617479],"genre_scores_gemma":[0.7679375,0.0011173044,0.21777157,0.00047845297,0.0002824476,0.00036611024,0.0009997512,0.0005120532,0.010534808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99222624,0.0044301525,0.00037022153,0.0011258409,0.0012586068,0.00058893027],"domain_scores_gemma":[0.94179094,0.047243513,0.0021029147,0.0037077365,0.00410203,0.0010529291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028405868,0.0018357473,0.0035178445,0.0032668996,0.0012959661,0.0051509463,0.005755156,0.003318125,0.005597038],"category_scores_gemma":[0.100943774,0.0020138524,0.001946331,0.0031969626,0.004572638,0.0076455954,0.0057693804,0.0050389287,0.0007937095],"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":[0.000185508,0.00012886248,0.0026683859,0.00015618361,0.00013133204,0.000106243395,0.00024538767,0.76154715,0.00027936228,0.19215406,0.0020916397,0.04030585],"study_design_scores_gemma":[0.0000053002695,0.0000117105365,0.00015190132,0.00002887898,0.000013853987,0.000010481721,0.000014339895,0.96482307,0.000098734796,0.0346466,0.00018697283,0.0000081388025],"about_ca_topic_score_codex":0.017451648,"about_ca_topic_score_gemma":0.015736762,"teacher_disagreement_score":0.028405868,"about_ca_system_score_codex":0.0044625504,"about_ca_system_score_gemma":0.0044139503,"threshold_uncertainty_score":0.15022635},"labels":[],"label_agreement":null},{"id":"W2114018246","doi":"10.3390/ijerph7041520","title":"Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies","year":2010,"lang":"en","type":"review","venue":"International Journal of Environmental Research and Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Statistics; Bayesian probability; Probabilistic logic; Statistical model; Sampling (signal processing); Econometrics; Computer science; Confidence interval; Variation (astronomy); Statistical power; Data mining; Mathematics","score_opus":0.8410853560864656,"score_gpt":0.5946231165331897,"score_spread":0.24646223955327595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114018246","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079039525,0.40426752,0.5731675,0.010636146,0.0011033003,0.00019571773,0.00040764478,0.00018402749,0.009247741],"genre_scores_gemma":[0.039394137,0.55795115,0.3906549,0.0032691185,0.0046454216,0.00087386416,0.00044754858,0.0001486634,0.0026152588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9660573,0.023330795,0.0020733026,0.0021014735,0.006166648,0.00027053824],"domain_scores_gemma":[0.88038725,0.10689535,0.004660371,0.0036982298,0.004003584,0.00035536216],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.047908075,0.0033166143,0.004099641,0.01298683,0.0008367072,0.0061077224,0.0058699837,0.0047761514,0.0054988028],"category_scores_gemma":[0.11069153,0.0016437593,0.0023302764,0.010569137,0.008639704,0.009878894,0.0050131776,0.00817126,0.0016187592],"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":[0.00003958874,0.00007268981,0.0009457633,0.007533235,0.00054558343,0.00013363073,0.00041499923,0.018264808,0.00035530954,0.73819596,0.009563639,0.22393483],"study_design_scores_gemma":[0.000016390173,0.000045659177,0.0006304504,0.0019159998,0.00012275644,0.00022410242,0.00011127485,0.0068086144,0.00022162255,0.9340538,0.0557894,0.000059950133],"about_ca_topic_score_codex":0.0022595474,"about_ca_topic_score_gemma":0.0022718203,"teacher_disagreement_score":0.95209193,"about_ca_system_score_codex":0.0037633607,"about_ca_system_score_gemma":0.004186528,"threshold_uncertainty_score":0.25336516},"labels":[],"label_agreement":null},{"id":"W2114205141","doi":"10.1111/1467-9892.00182","title":"Bayesian Prediction Mean Squared Error for State Space Models with Estimated Parameters","year":2000,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Statistical Methods and Bayesian 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":"Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mean squared error; Frequentist inference; Bayesian probability; Statistics; State space; Applied mathematics; Series (stratigraphy); Approximation error; Algorithm; Bayesian inference","score_opus":0.041437079782133145,"score_gpt":0.32354605941304404,"score_spread":0.2821089796309109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114205141","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":[0.01895484,0.00080882537,0.9776225,0.00064194977,0.00008146604,0.00002891442,0.00009834415,0.00021275281,0.0015503978],"genre_scores_gemma":[0.5507825,0.0024971978,0.43840787,0.0005521917,0.00029661987,0.00031979955,0.0009061277,0.0002737943,0.005963886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951061,0.0024842168,0.0002479838,0.0007592774,0.0011554317,0.00024708622],"domain_scores_gemma":[0.9720929,0.023264544,0.0013151166,0.001545173,0.0016240855,0.00015811219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011238153,0.0007928839,0.0013089766,0.0012570304,0.0005975754,0.0013874123,0.0024924988,0.0016805952,0.0019724972],"category_scores_gemma":[0.057094518,0.0009147248,0.0010593311,0.0014350271,0.0014817796,0.004042867,0.0018100016,0.002546655,0.00047276902],"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":[0.00009870881,0.00003760087,0.0020712782,0.000096784555,0.000086499946,0.00004979786,0.00013274795,0.82809114,0.00028632348,0.110206,0.0027659228,0.05607728],"study_design_scores_gemma":[0.000008616556,0.00001089904,0.00048658627,0.000013427352,0.000010109538,0.00001347961,0.000010364085,0.9533755,0.00015523765,0.045328427,0.00057194254,0.000015361173],"about_ca_topic_score_codex":0.023242986,"about_ca_topic_score_gemma":0.017319016,"teacher_disagreement_score":0.023242986,"about_ca_system_score_codex":0.0023671305,"about_ca_system_score_gemma":0.002480038,"threshold_uncertainty_score":0.05943376},"labels":[],"label_agreement":null},{"id":"W2114697353","doi":"10.1002/cjs.10014","title":"Likelihood analysis of joint marginal and conditional models for longitudinal categorical data","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Categorical variable; Covariate; Econometrics; Consistency (knowledge bases); Marginal model; Estimator; Computer science; Statistics; Inference; Statistical inference; Random effects model; Marginal likelihood; Missing data; Mathematics; Maximum likelihood; Artificial intelligence; Regression analysis","score_opus":0.21053002640667715,"score_gpt":0.3643773558150982,"score_spread":0.15384732940842108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114697353","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":[0.0069247894,0.00020086051,0.99188644,0.00033229621,0.000015585576,0.000034967696,0.00018281095,0.00014521682,0.00027697926],"genre_scores_gemma":[0.39687836,0.0009645797,0.5940307,0.00033372774,0.00022542827,0.0011390202,0.002014095,0.0003323482,0.0040817447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9804921,0.014687953,0.00064330746,0.0014838324,0.0021210713,0.0005717542],"domain_scores_gemma":[0.87330884,0.1123094,0.005026215,0.005418271,0.0027935805,0.0011436018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03388032,0.0008652135,0.002391235,0.0031964823,0.00091624993,0.002814071,0.004105357,0.0017773701,0.0049357666],"category_scores_gemma":[0.12569708,0.0012639319,0.003236888,0.003343371,0.0031413515,0.0035449434,0.004581482,0.0040637758,0.0008493521],"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":[0.0004092617,0.000153243,0.012435265,0.00033987715,0.0006736188,0.0004429954,0.00075685483,0.31340456,0.0010411881,0.5923877,0.003903568,0.07405184],"study_design_scores_gemma":[0.000048950922,0.000045934867,0.0013628078,0.000051037354,0.000055904296,0.00012598097,0.000048212463,0.7005715,0.00027483414,0.29610288,0.0012674975,0.000044491084],"about_ca_topic_score_codex":0.005878053,"about_ca_topic_score_gemma":0.0047469926,"teacher_disagreement_score":0.03388032,"about_ca_system_score_codex":0.0022436203,"about_ca_system_score_gemma":0.0039142566,"threshold_uncertainty_score":0.17917842},"labels":[],"label_agreement":null},{"id":"W2115955076","doi":"10.1002/sim.6223","title":"A multiple imputation strategy for sequential multiple assignment randomized trials","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":62,"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 Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Drug Abuse; National Institutes of Natural Sciences; Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; University of North Carolina at Chapel Hill; National Institutes of Health; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Imputation (statistics); Computer science; Inference; Randomized controlled trial; Machine learning; Data mining; Artificial intelligence; Medicine","score_opus":0.17336014046715387,"score_gpt":0.4659763089343173,"score_spread":0.29261616846716343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115955076","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":[0.00013609625,0.00019498979,0.99828404,0.00038941187,0.00007398931,0.00027024446,0.00009479906,0.00011970909,0.00043657274],"genre_scores_gemma":[0.016055582,0.0006376914,0.9761874,0.0006075831,0.00026693038,0.0038385622,0.00031682535,0.00013427684,0.0019551609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9035568,0.08406589,0.0031001463,0.003146007,0.0055070263,0.00062403374],"domain_scores_gemma":[0.91819376,0.06745299,0.004002636,0.005524675,0.0041188253,0.0007070861],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09010335,0.0020960653,0.004434889,0.003669938,0.0011361787,0.0031885188,0.006103229,0.0041231755,0.01366069],"category_scores_gemma":[0.15185446,0.0017105516,0.0045341705,0.0066143903,0.0019796442,0.003346652,0.0036406089,0.006836162,0.0045978706],"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":[0.00048761498,0.00016335394,0.0012277329,0.0013583759,0.0018236339,0.0004752923,0.00052206544,0.08268206,0.0007401021,0.5813754,0.023429029,0.3057153],"study_design_scores_gemma":[0.00052775483,0.00033292195,0.00035233045,0.00044614906,0.0003792343,0.00031970532,0.000066286855,0.36472347,0.0006187421,0.6038574,0.028275426,0.00010055476],"about_ca_topic_score_codex":0.0019178148,"about_ca_topic_score_gemma":0.0023829762,"teacher_disagreement_score":0.9098967,"about_ca_system_score_codex":0.0020955577,"about_ca_system_score_gemma":0.006548816,"threshold_uncertainty_score":0.47651774},"labels":[],"label_agreement":null},{"id":"W2116692750","doi":"10.1002/cjs.5550340105","title":"Empirical likelihood inference for a common mean in the presence of heteroscedasticity","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Confidence interval; Mathematics; Estimator; Heteroscedasticity; Coverage probability; Maximum likelihood; Point estimation; Econometrics","score_opus":0.08220632404899891,"score_gpt":0.37735835536553575,"score_spread":0.29515203131653683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116692750","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":[0.005923767,0.00022298156,0.99310607,0.0001723303,0.000014572402,0.000015331256,0.000028440942,0.000043617823,0.00047289906],"genre_scores_gemma":[0.44108143,0.0010465895,0.5542357,0.0003891764,0.00017477167,0.00030448064,0.0003817998,0.000097653196,0.0022884414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9886811,0.006083801,0.0007366557,0.0019542912,0.0021399532,0.000404197],"domain_scores_gemma":[0.8984059,0.08669277,0.0054500964,0.005379683,0.0036263803,0.00044522074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022920903,0.00084144564,0.0020506792,0.0025234986,0.0006726916,0.0026404962,0.0035101355,0.0019937514,0.002132418],"category_scores_gemma":[0.15625647,0.0007379945,0.001461677,0.0027423147,0.0032077436,0.0046891943,0.0035993985,0.002824718,0.0004304752],"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":[0.00012095901,0.00009384779,0.009933675,0.00032597288,0.0005587692,0.0005292622,0.0005737911,0.19940427,0.001184357,0.65742713,0.0014087892,0.12843922],"study_design_scores_gemma":[0.0000470954,0.00005594392,0.0025945103,0.00008630933,0.00008664012,0.0002656806,0.000100573234,0.50965905,0.0012060049,0.48353764,0.0023048955,0.000055609657],"about_ca_topic_score_codex":0.0027886822,"about_ca_topic_score_gemma":0.0017030321,"teacher_disagreement_score":0.022920903,"about_ca_system_score_codex":0.0014965992,"about_ca_system_score_gemma":0.0018430207,"threshold_uncertainty_score":0.1212188},"labels":[],"label_agreement":null},{"id":"W2117179243","doi":"10.1080/02664760903521476","title":"Bayesian parametric accelerated failure time spatial model and its application to prostate cancer","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":56,"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; McGill University","keywords":"Prostate cancer; Bayesian probability; Parametric statistics; Accelerated failure time model; Semiparametric model; Computer science; Parametric model; Statistics; Econometrics; Mathematics; Cancer; Proportional hazards model; Medicine; Internal medicine","score_opus":0.026308004376340482,"score_gpt":0.3414047602150342,"score_spread":0.3150967558386937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117179243","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":[0.047344532,0.00085744134,0.9483656,0.0007705794,0.00006234081,0.00007682903,0.000243587,0.00020971414,0.0020692388],"genre_scores_gemma":[0.7675201,0.0021334004,0.22208431,0.00028671307,0.00027356212,0.00045153472,0.0008133719,0.00014529939,0.0062917103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614656,0.0027475383,0.00011068097,0.00032917046,0.0004565131,0.000209534],"domain_scores_gemma":[0.97693163,0.01879735,0.0014573606,0.0009262055,0.0015214016,0.0003660369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012237096,0.0007299759,0.0020425091,0.0019760327,0.00086138723,0.0013174854,0.0022896337,0.0014740314,0.0026357989],"category_scores_gemma":[0.038040478,0.0005638156,0.0018219451,0.002549839,0.001551008,0.001396137,0.0017480813,0.0022598957,0.00036313303],"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":[0.00012535798,0.00007586463,0.0073089474,0.0001238008,0.00020772235,0.0003073139,0.00041629202,0.7559317,0.00041830217,0.19637288,0.0017398997,0.03697197],"study_design_scores_gemma":[0.000017995866,0.000038005765,0.001155282,0.000017633629,0.000033525706,0.000067868365,0.000039309325,0.9446416,0.00006608813,0.052840803,0.0010584593,0.000023450384],"about_ca_topic_score_codex":0.02216971,"about_ca_topic_score_gemma":0.013033464,"teacher_disagreement_score":0.02216971,"about_ca_system_score_codex":0.0013754574,"about_ca_system_score_gemma":0.0021781733,"threshold_uncertainty_score":0.06471676},"labels":[],"label_agreement":null},{"id":"W2117370753","doi":"10.1111/j.1467-9531.2006.00180.x","title":"Effect Displays for Multinomial and Proportional-Odds Logit Models","year":2006,"lang":"en","type":"article","venue":"Sociological Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":66,"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","funders":"","keywords":"Multinomial logistic regression; Odds; Categorical variable; Polytomous Rasch model; Logit; Econometrics; Multinomial distribution; Statistics; Multinomial probit; Generalized linear model; Linear model; Mixed logit; Logistic regression; Computer science; Mathematics; Item response theory","score_opus":0.2904778191804896,"score_gpt":0.47268293729448524,"score_spread":0.18220511811399565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117370753","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":[0.0033915495,0.0006364115,0.93577987,0.0013991789,0.0005898965,0.00036817868,0.012647498,0.022259595,0.022927802],"genre_scores_gemma":[0.13975966,0.0009548587,0.8073525,0.0019120219,0.0006558881,0.0041574677,0.010771502,0.01590769,0.018528422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903196,0.006442559,0.00070853886,0.00074100087,0.0015446171,0.00024368279],"domain_scores_gemma":[0.91777134,0.06947521,0.0024824527,0.005723909,0.0040141745,0.00053295994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010587424,0.0020530312,0.0014437992,0.0037424255,0.0006661326,0.0028434547,0.0021598008,0.0020963077,0.18224007],"category_scores_gemma":[0.11099747,0.0009823669,0.0021945594,0.0024669818,0.0008962085,0.0056240405,0.003529508,0.003230357,0.021263283],"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":[0.0011025151,0.00027917078,0.0043746983,0.0032111148,0.00036233728,0.0005150104,0.0021600346,0.012684937,0.0044378005,0.40408334,0.37769306,0.18909594],"study_design_scores_gemma":[0.00052453484,0.00039715343,0.005258396,0.00094861136,0.00033260003,0.0010538956,0.00048610094,0.08162,0.007911109,0.3952143,0.5059201,0.00033321403],"about_ca_topic_score_codex":0.0012044756,"about_ca_topic_score_gemma":0.0014995356,"teacher_disagreement_score":0.18224007,"about_ca_system_score_codex":0.0007523065,"about_ca_system_score_gemma":0.0008711818,"threshold_uncertainty_score":0.6096538},"labels":[],"label_agreement":null},{"id":"W2117746627","doi":"10.1002/sim.5598","title":"Predictive accuracy of risk factors and markers: a simulation study of the effect of novel markers on different performance measures for logistic regression models","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Logistic regression; Statistics; Brier score; Statistic; Context (archaeology); Econometrics; Risk factor; Mathematics; Computer science; Medicine; Internal medicine; Biology","score_opus":0.1122183754170676,"score_gpt":0.4199947875634422,"score_spread":0.30777641214637463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117746627","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":[0.92960465,0.0018170706,0.06551755,0.0009093871,0.00006527399,0.00022015837,0.00039126203,0.00017840364,0.001296214],"genre_scores_gemma":[0.9811172,0.0002711119,0.01786615,0.000096868,0.000017026588,0.00012750461,0.0002774136,0.000028106146,0.00019862609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97934777,0.017432796,0.00057707797,0.0011647616,0.00096965587,0.00050791894],"domain_scores_gemma":[0.54427904,0.42974538,0.009193383,0.01019159,0.0052778632,0.0013127621],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05825346,0.0013680416,0.0017189435,0.0015331156,0.00077028695,0.0016372871,0.0016075539,0.0022618596,0.0007587098],"category_scores_gemma":[0.17630641,0.00062836317,0.002448845,0.0014867159,0.0021114864,0.002306999,0.0017881979,0.0032698307,0.00013710443],"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":[0.0032074118,0.00046234127,0.06366641,0.00020265067,0.000844394,0.00026266923,0.00033188894,0.91017926,0.000973915,0.006056252,0.0006000989,0.013212688],"study_design_scores_gemma":[0.0002131875,0.0009781847,0.0076921084,0.000085693995,0.00024661154,0.00012273202,0.00008366531,0.9833059,0.001239713,0.0056892415,0.00028225788,0.000060689516],"about_ca_topic_score_codex":0.007983718,"about_ca_topic_score_gemma":0.0051717875,"teacher_disagreement_score":0.94174653,"about_ca_system_score_codex":0.002131586,"about_ca_system_score_gemma":0.0017223574,"threshold_uncertainty_score":0.3080774},"labels":[],"label_agreement":null},{"id":"W2118358821","doi":"10.1186/1472-6963-11-s2-s15","title":"Clustering and meso-level variables in cross-sectional surveys: an example of food aid during the Bosnian crisis","year":2011,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"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":"Bosnian; Odds; Cluster analysis; Econometrics; Odds ratio; Statistics; Generalized estimating equation; Gee; Medicine; Demography; Mathematics; Sociology; Logistic regression","score_opus":0.4088475486946951,"score_gpt":0.4918715610213394,"score_spread":0.0830240123266443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118358821","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":[0.81344455,0.007418467,0.15163444,0.011726302,0.00026443743,0.0006725095,0.00129929,0.00020279664,0.013337224],"genre_scores_gemma":[0.9466974,0.0008508737,0.050551552,0.0007509578,0.00006402913,0.00035208583,0.00021657348,0.000024704315,0.00049195386],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95767164,0.0396348,0.00049351715,0.0010073233,0.0008842581,0.00030834012],"domain_scores_gemma":[0.9226425,0.065338634,0.006119914,0.003311562,0.002308624,0.00027871615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033192024,0.00039528566,0.00045443783,0.0014392849,0.0014433119,0.00091985165,0.0008645988,0.000958995,0.0018060353],"category_scores_gemma":[0.05805966,0.00027519933,0.00055012037,0.0037145265,0.002232181,0.0011600527,0.0014562731,0.0011694434,0.000105591054],"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":[0.0010097348,0.00036760428,0.5439416,0.0027417329,0.0011555776,0.0025168743,0.049612157,0.024419082,0.0011761037,0.07724023,0.013383829,0.28243545],"study_design_scores_gemma":[0.00009865652,0.0004553186,0.81544745,0.0018397667,0.00042503627,0.0011724833,0.027600056,0.056172736,0.0014160096,0.06323325,0.03198575,0.00015355086],"about_ca_topic_score_codex":0.021897843,"about_ca_topic_score_gemma":0.028283289,"teacher_disagreement_score":0.033192024,"about_ca_system_score_codex":0.0021639399,"about_ca_system_score_gemma":0.001311508,"threshold_uncertainty_score":0.1755383},"labels":[],"label_agreement":null},{"id":"W2118670046","doi":"10.1002/sim.2518","title":"Confidence intervals for multinomial logistic regression in sparse data","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Multinomial logistic regression; Statistics; Logistic regression; Mathematics; Covariate; Confidence interval; Likelihood function; Binary data; Multinomial distribution; Econometrics; Wald test; Maximum likelihood; Binary number; Statistical hypothesis testing","score_opus":0.2664078337446776,"score_gpt":0.4945271978202856,"score_spread":0.228119364075608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118670046","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":[0.018239489,0.004447138,0.9726778,0.0008716058,0.00012252462,0.00011278503,0.00047325136,0.0006581369,0.0023972362],"genre_scores_gemma":[0.5197155,0.0035464722,0.47092944,0.0005690887,0.00040964442,0.0011762455,0.0023317933,0.0003409926,0.0009807787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9628756,0.02480062,0.0022109668,0.0033976194,0.0061724246,0.0005428099],"domain_scores_gemma":[0.47336295,0.47689793,0.01956654,0.016864043,0.011684511,0.0016239858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057452593,0.001018041,0.0022484825,0.0058980705,0.0007125,0.0030829709,0.0037267066,0.0029183533,0.0042086397],"category_scores_gemma":[0.46963584,0.0006826512,0.0017408783,0.0046881037,0.0025459828,0.00452929,0.0035822222,0.0041006105,0.00076435605],"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":[0.0010933193,0.00015078217,0.031006448,0.0021112482,0.0011860316,0.00080170785,0.0011301462,0.157902,0.0013835436,0.45765257,0.009322716,0.33625942],"study_design_scores_gemma":[0.00019454618,0.00025399379,0.0110844765,0.0016094589,0.0002884482,0.0012946568,0.00031656027,0.55163705,0.0021600004,0.42037934,0.010593848,0.00018764751],"about_ca_topic_score_codex":0.0017823677,"about_ca_topic_score_gemma":0.00080830423,"teacher_disagreement_score":0.057452593,"about_ca_system_score_codex":0.0013941514,"about_ca_system_score_gemma":0.0009957701,"threshold_uncertainty_score":0.30384195},"labels":[],"label_agreement":null},{"id":"W2119509409","doi":"10.1023/a:1007525900030","title":"Elicited Data and Incorporation of Expert Opinion for Statistical Inference in Spatial Studies","year":2000,"lang":"en","type":"article","venue":"Mathematical Geology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"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":"Computer science; Inference; Prior probability; Bayesian inference; Machine learning; Artificial intelligence; Bayesian probability; Realization (probability); Statistical inference; Expert elicitation; Field (mathematics); Feature (linguistics); Construct (python library); Data mining; Data science; Statistics; Mathematics","score_opus":0.21485939415848318,"score_gpt":0.48061142121970524,"score_spread":0.26575202706122203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119509409","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":[0.023105519,0.0006541415,0.9692543,0.002230485,0.000111250236,0.00040919258,0.00043070872,0.00008107037,0.003723276],"genre_scores_gemma":[0.5111876,0.00096836366,0.4824024,0.0013601248,0.0002741493,0.0021497498,0.00075898465,0.000089715606,0.00080880494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.67385274,0.29629666,0.009708143,0.0068544736,0.012366718,0.00092129817],"domain_scores_gemma":[0.14474817,0.81360954,0.010999068,0.021629166,0.008272365,0.0007417693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18513943,0.0012652604,0.0018514293,0.004746776,0.0016949842,0.0050620106,0.0046753897,0.0066990196,0.0058205915],"category_scores_gemma":[0.6943839,0.0016621328,0.0019101159,0.00377066,0.005549362,0.008754512,0.0052961367,0.0056313192,0.0006172751],"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":[0.0019639211,0.00084751105,0.019148955,0.0054438664,0.0020074297,0.0015876783,0.025621245,0.054176636,0.003002996,0.5697045,0.0073099392,0.30918533],"study_design_scores_gemma":[0.0002931548,0.00024463033,0.004905502,0.0012742227,0.00043242518,0.00032670752,0.0014834275,0.13584723,0.0020420437,0.8474259,0.005580753,0.00014393892],"about_ca_topic_score_codex":0.0035968171,"about_ca_topic_score_gemma":0.0050382954,"teacher_disagreement_score":0.18513943,"about_ca_system_score_codex":0.0020842587,"about_ca_system_score_gemma":0.0035118388,"threshold_uncertainty_score":0.9791226},"labels":[],"label_agreement":null},{"id":"W2120911483","doi":"10.5705/ss.2012.187","title":"Multiple-Inflation Poisson Model with L1 Regularization","year":2012,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","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":"McGill University","funders":"","keywords":"Poisson distribution; Regularization (linguistics); Inflation (cosmology); Mathematics; Econometrics; Applied mathematics; Computer science; Statistics; Artificial intelligence; Physics","score_opus":0.07571112703351526,"score_gpt":0.37693916781252407,"score_spread":0.3012280407790088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120911483","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":[0.0055384403,0.00018434144,0.9915177,0.0008047128,0.000057054924,0.0000675674,0.00029019403,0.00018969997,0.0013502531],"genre_scores_gemma":[0.35853046,0.0010692655,0.60963196,0.001283629,0.0007721878,0.0017085918,0.0018345712,0.0004105525,0.024758764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939422,0.003915845,0.00021717454,0.0007753303,0.000793878,0.00035549855],"domain_scores_gemma":[0.9846126,0.011159424,0.0014232357,0.0013071897,0.0011707023,0.00032677964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016168537,0.0011252773,0.0024911133,0.0018769295,0.0010224875,0.0023755515,0.0066699963,0.0031914571,0.005342876],"category_scores_gemma":[0.030613013,0.0009884759,0.002480537,0.0025331383,0.002223827,0.0034126977,0.0028924746,0.00473625,0.0016506589],"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":[0.00011251402,0.00007786294,0.0027951857,0.00017422093,0.000119047785,0.00037028003,0.00032114543,0.22874129,0.0006449075,0.7319935,0.0062069315,0.02844327],"study_design_scores_gemma":[0.000028239378,0.000026437972,0.00037052357,0.000019603836,0.000023401692,0.000098494645,0.000027607899,0.8552714,0.00017585667,0.1414043,0.0025217356,0.000032350188],"about_ca_topic_score_codex":0.008205591,"about_ca_topic_score_gemma":0.0047712126,"teacher_disagreement_score":0.016168537,"about_ca_system_score_codex":0.0021209044,"about_ca_system_score_gemma":0.0022282687,"threshold_uncertainty_score":0.085508406},"labels":[],"label_agreement":null},{"id":"W2122717603","doi":"10.1503/cmaj.110977","title":"Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis","year":2012,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":542,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Cancer Research UK","keywords":"Missing data; Covariate; Computer science; Data mining; Data science; Statistics; Machine learning; Mathematics","score_opus":0.44233275461564664,"score_gpt":0.5402522117396086,"score_spread":0.09791945712396194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122717603","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":[0.008813719,0.07668388,0.7858771,0.11960302,0.004717936,0.00080013723,0.0010138662,0.00050353835,0.0019868056],"genre_scores_gemma":[0.116799,0.031146206,0.8205231,0.019556295,0.008198577,0.0018744317,0.0006429588,0.0005034902,0.0007559678],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6588071,0.30561638,0.012930965,0.0065476573,0.015296243,0.0008017818],"domain_scores_gemma":[0.41746643,0.53142357,0.020485114,0.018172843,0.009264999,0.0031869935],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27761528,0.0012226636,0.005366456,0.0036115854,0.0018481005,0.0054161134,0.0070340214,0.009180841,0.0039152587],"category_scores_gemma":[0.57803273,0.0013923588,0.0025628654,0.007936428,0.007829897,0.011228185,0.004372687,0.01586245,0.0012516243],"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":[0.0044893776,0.00052808423,0.035709444,0.010337958,0.002600931,0.00071981177,0.0048262044,0.0064438856,0.00091620156,0.15220666,0.09777578,0.6834457],"study_design_scores_gemma":[0.0011949948,0.0011244119,0.011719586,0.011879658,0.0008360408,0.001483362,0.0016076679,0.033838898,0.0014753987,0.8872056,0.047087494,0.00054700417],"about_ca_topic_score_codex":0.0020789346,"about_ca_topic_score_gemma":0.0029435416,"teacher_disagreement_score":0.7223847,"about_ca_system_score_codex":0.0019163004,"about_ca_system_score_gemma":0.0069777146,"threshold_uncertainty_score":0.89082897},"labels":[],"label_agreement":null},{"id":"W2123482702","doi":"10.1111/1467-9876.00179","title":"Designing and Integrating Composite Networks for Monitoring Multivariate Gaussian Pollution Fields","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"BC Cancer Agency; Statistics Canada; University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Research Councils UK","keywords":"Pollution; Multivariate statistics; Environmental science; Gaussian; Multivariate normal distribution; Computer science; Maximization; Pollutant; Hyperparameter; Air pollution; Statistics; Mathematics; Mathematical optimization; Machine learning; Ecology","score_opus":0.0254967679320331,"score_gpt":0.3160598567177564,"score_spread":0.2905630887857233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123482702","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":[0.04352883,0.0000892709,0.9553599,0.000091294954,0.000008091043,0.000047465932,0.000057839818,0.00027874566,0.0005386143],"genre_scores_gemma":[0.4695794,0.00016219346,0.5284497,0.000077620854,0.00004876028,0.00025615445,0.0002773249,0.00007196962,0.0010769003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975804,0.0011581932,0.00008626901,0.00060843385,0.00040805564,0.00015864817],"domain_scores_gemma":[0.9920632,0.0051786597,0.001103031,0.0005163793,0.00087442074,0.0002641891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071423934,0.00073578383,0.0011789427,0.0017542121,0.0006687119,0.0014533928,0.0019285016,0.0011279339,0.0009681217],"category_scores_gemma":[0.014926697,0.0010623555,0.0009610641,0.0014100141,0.0013392097,0.0022385994,0.0028314914,0.0011258089,0.00018579586],"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":[0.00025136003,0.00009910976,0.0071680453,0.000054753506,0.00009141711,0.00007006066,0.00015498548,0.9160232,0.0034724062,0.013130049,0.00032925935,0.05915542],"study_design_scores_gemma":[0.000007735001,0.00003140869,0.00069986423,0.000003885436,0.0000148636345,0.000009332185,0.000011527646,0.99155915,0.0008309374,0.0065807924,0.00024320863,0.0000073087563],"about_ca_topic_score_codex":0.008046822,"about_ca_topic_score_gemma":0.012278767,"teacher_disagreement_score":0.008046822,"about_ca_system_score_codex":0.0019093453,"about_ca_system_score_gemma":0.001226,"threshold_uncertainty_score":0.037773073},"labels":[],"label_agreement":null},{"id":"W2123602065","doi":"10.1177/0962280214527386","title":"Bayesian hierarchical modelling of noisy spatial rates on a modestly large and discontinuous irregular lattice","year":2014,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian 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":"Child and Family Research Institute; University of British Columbia","funders":"Health Technology Assessment Programme; Natural Sciences and Engineering Research Council of Canada; National Institute for Health and Care Research","keywords":"Markov chain Monte Carlo; Overdispersion; Bayesian probability; Bayesian inference; Computer science; Statistics; Autoregressive model; Random effects model; Negative binomial distribution; Spatial analysis; Spatial dependence; Bayesian hierarchical modeling; Hierarchical database model; Econometrics; Mathematics; Poisson distribution; Data mining","score_opus":0.1687164579898777,"score_gpt":0.5362347401415055,"score_spread":0.36751828215162785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123602065","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":[0.06079733,0.0002511891,0.9362634,0.0006263994,0.000026887932,0.00006179299,0.0004133566,0.00020549457,0.0013541278],"genre_scores_gemma":[0.76074195,0.000559349,0.2315857,0.00019209734,0.00008536267,0.0003927998,0.00094606756,0.000122058285,0.0053746686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955642,0.0028588912,0.00018182662,0.00064916146,0.0004482399,0.00029767354],"domain_scores_gemma":[0.9664904,0.027408905,0.002679826,0.0016581266,0.0012284656,0.0005343325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010194576,0.00061734597,0.002094219,0.001386213,0.0006774412,0.0021693697,0.004304204,0.0018352877,0.0028008523],"category_scores_gemma":[0.038335875,0.0012206406,0.0015918033,0.0018494619,0.0031606727,0.0022844598,0.0026471221,0.0028321028,0.00042345322],"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":[0.000063441206,0.000021550002,0.0028745946,0.000055019103,0.000042439475,0.00010274757,0.0002004453,0.8434305,0.00024242808,0.14571406,0.00043160122,0.0068211555],"study_design_scores_gemma":[0.000008466569,0.000009293379,0.00037030107,0.000008240398,0.000006311537,0.000013588335,0.000019059229,0.9639807,0.000048442034,0.035241965,0.00028373953,0.000010075733],"about_ca_topic_score_codex":0.025999323,"about_ca_topic_score_gemma":0.016541414,"teacher_disagreement_score":0.025999323,"about_ca_system_score_codex":0.002306505,"about_ca_system_score_gemma":0.0016059038,"threshold_uncertainty_score":0.053914666},"labels":[],"label_agreement":null},{"id":"W2124951422","doi":"10.1111/j.1467-9469.2008.00623.x","title":"Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates","year":2008,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University; University of Alberta","funders":"","keywords":"Jackknife resampling; Mathematics; Statistics; Covariate; Estimator; Bayes' theorem; Small area estimation; Linear regression; Mean squared error; Linear model; Regression analysis; Observational error; Regression; Econometrics; Bayesian probability","score_opus":0.21522161548573832,"score_gpt":0.38722596317826175,"score_spread":0.17200434769252343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124951422","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":[0.03753935,0.00021830303,0.9615082,0.00016101291,0.000020889993,0.000037343005,0.000065708744,0.00012723815,0.00032202076],"genre_scores_gemma":[0.54029083,0.00052032346,0.4552518,0.00016925269,0.00011678864,0.00032485777,0.0005493963,0.000100360594,0.0026764958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9897085,0.0069296355,0.00034664402,0.0017220873,0.0009777193,0.00031543462],"domain_scores_gemma":[0.9408234,0.04840585,0.0046030753,0.0031860818,0.002479168,0.00050245854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022052057,0.00095223525,0.0029400955,0.0011410807,0.00055888173,0.0013607979,0.0022948834,0.0013578688,0.002038428],"category_scores_gemma":[0.08258546,0.0008759167,0.0011674995,0.0012351872,0.0017704918,0.0024151122,0.0019635174,0.0020217155,0.0004505648],"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":[0.0005231184,0.00021921148,0.03553259,0.0003138335,0.0007867983,0.00042043973,0.00062249537,0.6598239,0.0021167374,0.15754196,0.0022239785,0.13987492],"study_design_scores_gemma":[0.000036923746,0.00007594585,0.0037184365,0.000057062447,0.000052445208,0.00008806253,0.00004056201,0.9243356,0.0006483179,0.0701633,0.0007538007,0.000029540324],"about_ca_topic_score_codex":0.004926347,"about_ca_topic_score_gemma":0.004583951,"teacher_disagreement_score":0.022052057,"about_ca_system_score_codex":0.00073105184,"about_ca_system_score_gemma":0.001820002,"threshold_uncertainty_score":0.11662388},"labels":[],"label_agreement":null},{"id":"W2125245433","doi":"10.1007/s12561-012-9074-5","title":"On Method of Moments Estimation in Linear Mixed Effects Models with Measurement Error on Covariates and Response with Application to a Longitudinal Study of Gene-Environment Interaction","year":2012,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Manitoba; Memorial University of Newfoundland","funders":"","keywords":"Covariate; Observational error; Estimator; Errors-in-variables models; Identifiability; Instrumental variable; Statistics; Random effects model; Mathematics; Moment (physics); Econometrics; Mixed model; Computer science","score_opus":0.10426120790213093,"score_gpt":0.41350311985067895,"score_spread":0.309241911948548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125245433","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":[0.00049342745,0.00057944393,0.9983754,0.00017454718,0.0000648578,0.00003493808,0.00003392126,0.000086615604,0.00015679093],"genre_scores_gemma":[0.017115192,0.0023567786,0.9770981,0.0002819803,0.0006145709,0.0006998184,0.00021271476,0.00034365026,0.0012772395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97986335,0.017213691,0.0006125395,0.00096272206,0.0010865057,0.00026115033],"domain_scores_gemma":[0.8550345,0.13754636,0.0017798307,0.0030814838,0.0021218208,0.00043597075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033099223,0.0030769065,0.0039195623,0.0038326236,0.0012310364,0.0019129389,0.0055408217,0.0039588767,0.004351051],"category_scores_gemma":[0.10896009,0.0023325912,0.004082275,0.0043616407,0.0039159916,0.0033316065,0.0051789144,0.007562826,0.0013156271],"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":[0.00028619636,0.00027615254,0.002838346,0.00096747116,0.0008562676,0.0008562541,0.001234114,0.1656452,0.0029014961,0.63297683,0.009307439,0.18185422],"study_design_scores_gemma":[0.00009185237,0.00012332771,0.00092638,0.00017604526,0.00017954141,0.00032511368,0.000094240124,0.56515217,0.001161094,0.42236143,0.009211023,0.00019783073],"about_ca_topic_score_codex":0.007261128,"about_ca_topic_score_gemma":0.0074910424,"teacher_disagreement_score":0.033099223,"about_ca_system_score_codex":0.0018828292,"about_ca_system_score_gemma":0.0035785662,"threshold_uncertainty_score":0.17504752},"labels":[],"label_agreement":null},{"id":"W2125308298","doi":"10.1186/1471-2288-13-9","title":"Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study","year":2013,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Hamilton Health Sciences; Population Health Research Institute; McMaster University; St. Joseph’s Healthcare Hamilton","funders":"Canadian Institutes of Health Research","keywords":"Cluster (spacecraft); Population; Missing data; Statistics; Randomized controlled trial; Psychology; Medicine; Computer science; Mathematics; Environmental health; Internal medicine","score_opus":0.7779863805310022,"score_gpt":0.6482697854431466,"score_spread":0.1297165950878556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125308298","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":[0.29042038,0.0055190385,0.6902064,0.0020434044,0.00030265222,0.004941569,0.0015608758,0.0007868648,0.0042187823],"genre_scores_gemma":[0.79377294,0.0013874776,0.19553514,0.000747883,0.00006869786,0.006720825,0.00088145764,0.0001127215,0.00077279715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.88574255,0.10791375,0.0015341921,0.0023892864,0.0016391651,0.00078111235],"domain_scores_gemma":[0.45629117,0.50962704,0.014272471,0.010741086,0.0072604246,0.0018078454],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.13562527,0.0016926071,0.0035122493,0.0021661546,0.00064976787,0.0019608205,0.0036363895,0.0032060412,0.0037732024],"category_scores_gemma":[0.26674107,0.0009493885,0.0052569536,0.002452356,0.0014324649,0.002363052,0.0019053383,0.0040278593,0.00034410128],"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":[0.005868522,0.0004332179,0.009583408,0.00077350845,0.002892413,0.00021072944,0.00031015568,0.94495326,0.00011864003,0.017956514,0.0015810113,0.015318763],"study_design_scores_gemma":[0.0013391706,0.0012773783,0.0013322924,0.00028660294,0.0006425956,0.00012875895,0.00007990297,0.98037845,0.00015061762,0.01347483,0.0008441485,0.00006529724],"about_ca_topic_score_codex":0.006895062,"about_ca_topic_score_gemma":0.004224677,"teacher_disagreement_score":0.86437476,"about_ca_system_score_codex":0.0034802528,"about_ca_system_score_gemma":0.004553958,"threshold_uncertainty_score":0.7172636},"labels":[],"label_agreement":null},{"id":"W2125703774","doi":"10.1016/j.jclinepi.2003.08.002","title":"Selection bias found in interpreting analyses with missing data for the prehospital index for trauma","year":2004,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal General Hospital","funders":"","keywords":"Index (typography); Missing data; Selection bias; Selection (genetic algorithm); Statistics; Medicine; Computer science; Mathematics; Artificial intelligence","score_opus":0.7710354620548242,"score_gpt":0.6536011011170065,"score_spread":0.11743436093781767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125703774","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":[0.38289756,0.029002625,0.5493978,0.02104969,0.003850009,0.0016003168,0.0016064845,0.0010744081,0.00952107],"genre_scores_gemma":[0.92853755,0.0011383048,0.06419805,0.0034465296,0.0010837399,0.0003834122,0.0002140108,0.00032875774,0.0006696272],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.5338215,0.41362378,0.02498217,0.011465899,0.014241491,0.001865164],"domain_scores_gemma":[0.08117589,0.8674963,0.02375499,0.021811925,0.0049834764,0.0007774228],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3616073,0.0013995017,0.002452546,0.0055309483,0.0016116749,0.003814648,0.003706906,0.0037642028,0.0030503874],"category_scores_gemma":[0.8253674,0.0012775693,0.004464625,0.005511103,0.004225257,0.003131373,0.0020795767,0.0039850324,0.0002977293],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010421764,0.00028106055,0.5811999,0.007727136,0.03930313,0.009492686,0.018799324,0.01303204,0.0036457283,0.03350961,0.017948657,0.26463896],"study_design_scores_gemma":[0.0037380334,0.0034600052,0.4568131,0.0066983947,0.05488502,0.019866941,0.0077106943,0.1163481,0.016212666,0.29506847,0.018404577,0.0007939651],"about_ca_topic_score_codex":0.0035672702,"about_ca_topic_score_gemma":0.0042387885,"teacher_disagreement_score":0.6383927,"about_ca_system_score_codex":0.0014073182,"about_ca_system_score_gemma":0.0027475096,"threshold_uncertainty_score":0.78725183},"labels":[],"label_agreement":null},{"id":"W2125726371","doi":"10.1080/00949650701282507","title":"Assessing the performance of variational methods for mixed logistic regression models","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"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":"Vlaamse regering; University of Windsor","keywords":"Mathematics; Logistic regression; Laplace's method; Statistics; Applied mathematics; Logistic model tree; Logistic distribution; Multinomial logistic regression; Mathematical optimization; Bayesian probability","score_opus":0.23984592411533384,"score_gpt":0.509925401499834,"score_spread":0.27007947738450017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125726371","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":[0.035341624,0.00078955764,0.9621957,0.00050356955,0.000036799327,0.0000638089,0.000054543776,0.00012668753,0.0008875292],"genre_scores_gemma":[0.31175226,0.00071208476,0.68566436,0.00016125939,0.00007498872,0.0002749062,0.00028323397,0.00017532107,0.0009016114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98952055,0.008445735,0.0003137362,0.0004667181,0.0010709398,0.00018223547],"domain_scores_gemma":[0.8585413,0.13304718,0.0021266218,0.0026466248,0.002854224,0.0007840202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030705238,0.0011923386,0.0015482641,0.0019333648,0.0008987606,0.0016400926,0.0026289932,0.003097243,0.0014473998],"category_scores_gemma":[0.11706486,0.0011018446,0.0013772964,0.0012994646,0.0017083489,0.0027260857,0.0030967807,0.0021639192,0.00028210157],"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":[0.00026831563,0.00010431648,0.005516745,0.00024818996,0.00038216275,0.00009553868,0.0003074767,0.85024625,0.0011846614,0.08943403,0.00092894427,0.051283397],"study_design_scores_gemma":[0.000013621282,0.000035008896,0.00022022222,0.000013714113,0.00000911126,0.000023442366,0.000013400797,0.98572284,0.00022991309,0.013470456,0.00023541918,0.000012842038],"about_ca_topic_score_codex":0.007666687,"about_ca_topic_score_gemma":0.004756994,"teacher_disagreement_score":0.030705238,"about_ca_system_score_codex":0.0011882195,"about_ca_system_score_gemma":0.0020604557,"threshold_uncertainty_score":0.16238678},"labels":[],"label_agreement":null},{"id":"W2125748810","doi":"10.82308/16725","title":"Stationarity in a prevalent cohort study with follow-up","year":2005,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"","keywords":"Estimator; Statistics; Incidence (geometry); Mathematics; Nonparametric statistics; Econometrics; Confidence interval; Cohort; Asymptotic distribution; Constant (computer programming); Computer science","score_opus":0.044499353426129276,"score_gpt":0.32364462850841347,"score_spread":0.2791452750822842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125748810","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":[0.25785646,0.0006363716,0.7377153,0.000662223,0.000089017434,0.00031848074,0.00067480747,0.00014467997,0.0019026877],"genre_scores_gemma":[0.8973623,0.00037957533,0.09845189,0.00038527013,0.00012958345,0.00048427435,0.0010409836,0.000048373426,0.0017177811],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9854536,0.008054343,0.0011106405,0.0029819338,0.0018088921,0.0005906423],"domain_scores_gemma":[0.9050541,0.06571949,0.008663302,0.017540906,0.002283224,0.00073903194],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.044176295,0.00029809258,0.0010732291,0.0016503738,0.00083499355,0.0016226851,0.0022531697,0.001132618,0.0017272885],"category_scores_gemma":[0.13790299,0.0005829948,0.0012856206,0.002118149,0.0018257118,0.0025718745,0.0023650853,0.0018121722,0.00025484286],"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":[0.0013483966,0.00035561924,0.547165,0.0004892064,0.0014414891,0.0029957537,0.0035718442,0.0407819,0.005172054,0.2852508,0.0019087835,0.109519154],"study_design_scores_gemma":[0.00036654097,0.0011448509,0.28621364,0.00034387197,0.001185439,0.0022049004,0.000879154,0.30646494,0.004812972,0.38592544,0.010268751,0.00018951687],"about_ca_topic_score_codex":0.003902926,"about_ca_topic_score_gemma":0.003263225,"teacher_disagreement_score":0.9558237,"about_ca_system_score_codex":0.00069693534,"about_ca_system_score_gemma":0.0013914527,"threshold_uncertainty_score":0.23362935},"labels":[],"label_agreement":null},{"id":"W2126367292","doi":"10.1027/1614-2241/a000032","title":"Two-Part Modeling of Semicontinuous Longitudinal Variables","year":2011,"lang":"en","type":"article","venue":"Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"","keywords":"Covariate; Outcome (game theory); Zero (linguistics); Mathematics; Statistics; Variable (mathematics); Interpretation (philosophy); Econometrics; Range (aeronautics); Latent variable; Growth curve (statistics); Maximum likelihood; Continuous variable; Computer science; Mathematical economics; Engineering; Mathematical analysis","score_opus":0.48186026290138945,"score_gpt":0.43865334274944784,"score_spread":0.04320692015194161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126367292","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":[0.050532587,0.00051102455,0.9443005,0.0009936449,0.00009550725,0.00016673509,0.0006459872,0.00025983754,0.0024942253],"genre_scores_gemma":[0.7434474,0.0013608787,0.23080376,0.00050634315,0.00022739291,0.0014491723,0.0017921993,0.00017020339,0.020242615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99060947,0.00564895,0.00036101305,0.0016940936,0.0010266289,0.00065975054],"domain_scores_gemma":[0.96462715,0.026442429,0.0040886304,0.0025102727,0.0016264627,0.00070506847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01918726,0.0017371658,0.0025725425,0.0021722545,0.0009791277,0.0034456241,0.0061830757,0.0031041529,0.007653124],"category_scores_gemma":[0.03943049,0.0019643651,0.003295989,0.0026964429,0.0034508677,0.0039307987,0.004169484,0.0037021982,0.0010677151],"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":[0.00041192054,0.00022106261,0.025096457,0.00019078555,0.00053723773,0.0009061423,0.0015483378,0.40706092,0.0011355052,0.5276424,0.0014496568,0.033799488],"study_design_scores_gemma":[0.00003756948,0.000110248904,0.003150979,0.000055788423,0.00007850145,0.00013121669,0.0001034669,0.8392617,0.00026396688,0.15511139,0.001638621,0.000056577865],"about_ca_topic_score_codex":0.009595095,"about_ca_topic_score_gemma":0.009593559,"teacher_disagreement_score":0.01918726,"about_ca_system_score_codex":0.0017202359,"about_ca_system_score_gemma":0.0024364383,"threshold_uncertainty_score":0.10147315},"labels":[],"label_agreement":null},{"id":"W2126786143","doi":"10.1111/j.1467-9868.2010.00747.x","title":"Bayesian Pseudo-Empirical-Likelihood Intervals for Complex Surveys","year":2010,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":66,"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; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Statistics; Mathematics; Empirical likelihood; Likelihood function; Bayesian probability; Bayesian average; Likelihood principle; Population; Bayesian inference; Inference; Posterior probability; Marginal likelihood; Bayesian linear regression; Computer science; Confidence interval; Estimation theory; Artificial intelligence","score_opus":0.1658229595234719,"score_gpt":0.4315150260649659,"score_spread":0.265692066541494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126786143","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":[0.002282827,0.0005199163,0.99610245,0.00012409905,0.000021128459,0.000027709975,0.0000588819,0.00010165306,0.0007612907],"genre_scores_gemma":[0.23461743,0.0016990728,0.76014894,0.00027788573,0.00024596334,0.0008805154,0.0005547801,0.00019188668,0.00138353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97590476,0.019172108,0.00067051925,0.0011165814,0.0029000363,0.0002358827],"domain_scores_gemma":[0.85696715,0.1261889,0.005277225,0.0071094534,0.0039235745,0.00053369463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03044413,0.00072785513,0.0013436063,0.0030654154,0.0004932299,0.0022414245,0.0025092987,0.0014080079,0.0039784797],"category_scores_gemma":[0.17626795,0.00060431357,0.0011183455,0.0028129457,0.0030465096,0.0037374382,0.0026476434,0.0027255595,0.00091174635],"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":[0.000081557315,0.000032239706,0.0011100202,0.0002686518,0.00008321604,0.000101296755,0.0002755391,0.09340237,0.00036277107,0.8225432,0.0018047915,0.07993432],"study_design_scores_gemma":[0.00003323059,0.000029074821,0.0007054234,0.00009593708,0.000016020376,0.000084760475,0.000033652104,0.26438138,0.00019508216,0.731038,0.0033583783,0.000029034643],"about_ca_topic_score_codex":0.00080297294,"about_ca_topic_score_gemma":0.00053227117,"teacher_disagreement_score":0.03044413,"about_ca_system_score_codex":0.0011820169,"about_ca_system_score_gemma":0.0011796667,"threshold_uncertainty_score":0.16100585},"labels":[],"label_agreement":null},{"id":"W2127084139","doi":"10.2307/3316037","title":"Modeling of rates over a hierarchical health administrative structure","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pooling; Hierarchy; Inference; Random effects model; Statistics; Hierarchical database model; Econometrics; Distribution (mathematics); Computer science; Mathematics; Data mining; Artificial intelligence; Medicine; Political science","score_opus":0.10198601413972176,"score_gpt":0.3986732542494921,"score_spread":0.2966872401097704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127084139","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":[0.22819158,0.0004527703,0.75931907,0.0025916786,0.00006813524,0.00027993534,0.0025250237,0.0005299677,0.006041853],"genre_scores_gemma":[0.883585,0.0003202622,0.106668785,0.00020163212,0.00010073221,0.00037261573,0.0015388639,0.00008134563,0.0071308655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921555,0.0038809197,0.00033898588,0.0016818268,0.0010318647,0.00091093447],"domain_scores_gemma":[0.9753956,0.014621879,0.004641467,0.0024425501,0.002122345,0.0007760106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011385354,0.00070298294,0.0012495838,0.0020004543,0.0011469333,0.0024458042,0.0044077,0.001524729,0.005062601],"category_scores_gemma":[0.033663135,0.0014778572,0.0022478793,0.0029142601,0.0023077608,0.0029445286,0.0028293275,0.0029751288,0.000710103],"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":[0.00015166387,0.00014879853,0.041005407,0.0000976514,0.0002691758,0.0002381903,0.00097521895,0.70495903,0.0007084747,0.22990875,0.001905715,0.019631885],"study_design_scores_gemma":[0.00003787086,0.000046296605,0.0077220057,0.00002849057,0.00005987433,0.000045075125,0.000119866185,0.92402035,0.00016406749,0.06648228,0.0012443434,0.000029409004],"about_ca_topic_score_codex":0.10098243,"about_ca_topic_score_gemma":0.0720901,"teacher_disagreement_score":0.10098243,"about_ca_system_score_codex":0.004537641,"about_ca_system_score_gemma":0.0026939695,"threshold_uncertainty_score":0.20078927},"labels":[],"label_agreement":null},{"id":"W2127269683","doi":"10.5539/ijsp.v2n4p29","title":"Bayesian Estimation with Flexible Prior for the Covariance Structure of Linear Mixed Effects Models","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wishart distribution; Mathematics; Markov chain Monte Carlo; Estimation of covariance matrices; Covariance; Random effects model; Covariance matrix; Applied mathematics; Inverse-Wishart distribution; Multivariate normal distribution; Generalized linear mixed model; Bayesian probability; Statistics; Multivariate statistics","score_opus":0.032534184220727576,"score_gpt":0.33875649797843405,"score_spread":0.3062223137577065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127269683","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":[0.00078032626,0.0000881577,0.99861395,0.00008155354,0.000010875287,0.000049182538,0.0000562958,0.00009291944,0.00022669754],"genre_scores_gemma":[0.0455972,0.00054646266,0.950924,0.00016220695,0.00008432306,0.0009777892,0.00053094525,0.00014707772,0.0010299834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97707593,0.017400453,0.0007551877,0.0021165113,0.002181515,0.0004703643],"domain_scores_gemma":[0.9597941,0.033566058,0.0015301969,0.003270231,0.0015369068,0.00030248973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032527216,0.0018013897,0.0030655013,0.0032705148,0.0015474949,0.0032412012,0.004852911,0.0028791819,0.0057937005],"category_scores_gemma":[0.11006438,0.0021112433,0.0033468509,0.004365571,0.0025474534,0.004644098,0.00402342,0.0086463485,0.0016479157],"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":[0.00016662199,0.00016778734,0.0027973864,0.0005283017,0.00047964856,0.0003035748,0.0006009678,0.1955358,0.0016135958,0.6625113,0.004033907,0.13126117],"study_design_scores_gemma":[0.000071134426,0.00007038827,0.0011090313,0.00018934542,0.00014315835,0.00013453973,0.00006953665,0.49258843,0.00085008005,0.5002275,0.0044674585,0.00007937026],"about_ca_topic_score_codex":0.006504257,"about_ca_topic_score_gemma":0.010719512,"teacher_disagreement_score":0.032527216,"about_ca_system_score_codex":0.0021169651,"about_ca_system_score_gemma":0.004359769,"threshold_uncertainty_score":0.17202246},"labels":[],"label_agreement":null},{"id":"W2127403744","doi":"10.1111/1467-9876.00182","title":"Bayesian Sample Size Determination for Estimating Binomial Parameters from Data Subject to Misclassification","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","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":true,"ca_institutions":"Montreal General Hospital; McGill University","funders":"","keywords":"Sample size determination; Statistics; Sample (material); Bayesian probability; Degree (music); Mathematics; Binomial (polynomial); Binomial distribution; Negative binomial distribution; Econometrics; Computer science; Poisson distribution","score_opus":0.05710207231426012,"score_gpt":0.3451959507327966,"score_spread":0.28809387841853645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127403744","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":[0.014480783,0.00036965334,0.98354924,0.00053362915,0.000033323955,0.00019922158,0.0000660827,0.00009252797,0.00067551195],"genre_scores_gemma":[0.3212196,0.0006712553,0.67453164,0.00046741017,0.000181364,0.0015812318,0.00042930007,0.00012950492,0.00078871136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93352944,0.055373177,0.002017765,0.0034573146,0.0050095906,0.0006127482],"domain_scores_gemma":[0.44794574,0.5225195,0.011482048,0.010435569,0.006648188,0.0009690152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12573767,0.0009214055,0.0030650876,0.0031127674,0.0011558083,0.0024796862,0.0037700296,0.0028832091,0.0021788357],"category_scores_gemma":[0.47040942,0.0014654276,0.001330175,0.0018579625,0.003937011,0.003553144,0.0033862623,0.00451145,0.00038503757],"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":[0.00089195697,0.0002322715,0.030146843,0.0012418656,0.000829546,0.0008731597,0.0018521751,0.37330323,0.0033286589,0.36531368,0.0040033623,0.21798328],"study_design_scores_gemma":[0.00014618777,0.00013478691,0.0036917091,0.00033836855,0.000076128395,0.00024740092,0.00014056863,0.65443546,0.0015532969,0.33711085,0.0020591416,0.00006609459],"about_ca_topic_score_codex":0.0033780697,"about_ca_topic_score_gemma":0.0024473008,"teacher_disagreement_score":0.12573767,"about_ca_system_score_codex":0.0018680709,"about_ca_system_score_gemma":0.0022108636,"threshold_uncertainty_score":0.6649723},"labels":[],"label_agreement":null},{"id":"W2127648054","doi":"10.1002/cjs.11206","title":"Bootstrap methods for imputed data from regression, ratio and hot‐deck imputation","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Statistics; Imputation (statistics); Estimator; Econometrics; Mathematics; Simple random sample; Stratified sampling; Regression; Survey sampling; Sampling (signal processing); Sample size determination; Context (archaeology); Missing data; Computer science; Geography; Demography; Population","score_opus":0.1554713517242148,"score_gpt":0.4400998114586056,"score_spread":0.28462845973439077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127648054","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":[0.0016071625,0.00039725186,0.99671555,0.000118441916,0.0000582964,0.00010855157,0.00018126392,0.00032579,0.0004876229],"genre_scores_gemma":[0.078299284,0.00078890275,0.9156693,0.00019815817,0.00023695131,0.0011832074,0.0013617569,0.0005219197,0.0017404135],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9589772,0.033705264,0.0013186168,0.0019656199,0.0036076459,0.0004256007],"domain_scores_gemma":[0.89007473,0.084248975,0.004584257,0.0136706,0.006694683,0.00072677474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042931054,0.0011297254,0.002657488,0.0057394775,0.0009897051,0.0023085908,0.006570409,0.0019124681,0.009556054],"category_scores_gemma":[0.21507262,0.0011984588,0.0030666133,0.00717047,0.0022267671,0.0038596126,0.004421996,0.0040341634,0.0039743115],"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":[0.0005188225,0.00019278782,0.006163227,0.0009883952,0.000996852,0.00042883062,0.000649792,0.08457654,0.00064919057,0.35976923,0.01570713,0.5293593],"study_design_scores_gemma":[0.00012763313,0.000090407535,0.0023642103,0.00026484768,0.00010128485,0.0002326185,0.000108750704,0.56220084,0.0006727035,0.422063,0.0116894655,0.00008425069],"about_ca_topic_score_codex":0.0032541782,"about_ca_topic_score_gemma":0.002580439,"teacher_disagreement_score":0.042931054,"about_ca_system_score_codex":0.0011503701,"about_ca_system_score_gemma":0.0017744615,"threshold_uncertainty_score":0.22704381},"labels":[],"label_agreement":null},{"id":"W2128031689","doi":"10.1002/bimj.200510167","title":"An ‘Unconditional-like’ Structure for the Conditional Estimator of Odds Ratio from 2 × 2 Tables","year":2006,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian 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":"McGill University; Royal Victoria Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Statistics; Odds ratio; Applied mathematics; Maximum likelihood; Population; Odds; Econometrics; Combinatorics; Demography","score_opus":0.04819641955252056,"score_gpt":0.37759214135154445,"score_spread":0.3293957217990239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128031689","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":[0.0017925403,0.00006483819,0.9962069,0.00021482346,0.00007909458,0.00005885983,0.00030029923,0.00037302982,0.00090954866],"genre_scores_gemma":[0.07414296,0.00018306627,0.91984737,0.00050530356,0.00027630848,0.0007933554,0.001290063,0.00031863537,0.002642806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9866166,0.00786156,0.0009288503,0.0019449318,0.002302029,0.00034603683],"domain_scores_gemma":[0.9627691,0.025347186,0.0019254046,0.0074359667,0.0022869152,0.00023546586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022779007,0.00072641275,0.001049409,0.0019905383,0.0006878882,0.002711034,0.003834514,0.0017531477,0.023793304],"category_scores_gemma":[0.10694485,0.0008062615,0.0015551738,0.002409652,0.0020901423,0.0044729384,0.0027003183,0.0035333887,0.0060028275],"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":[0.0002502109,0.00010794051,0.0049027503,0.00038871574,0.00016610403,0.00030842697,0.0005179633,0.010115699,0.004102919,0.79506755,0.012151435,0.17192036],"study_design_scores_gemma":[0.00009406661,0.0003765267,0.005421935,0.00019537655,0.00011048338,0.0026384667,0.00011946375,0.14050172,0.0067467033,0.80959994,0.03397277,0.00022257572],"about_ca_topic_score_codex":0.00064783986,"about_ca_topic_score_gemma":0.0008621151,"teacher_disagreement_score":0.023793304,"about_ca_system_score_codex":0.0006423533,"about_ca_system_score_gemma":0.0011661712,"threshold_uncertainty_score":0.12046832},"labels":[],"label_agreement":null},{"id":"W2128514707","doi":"10.1093/biostatistics/kxm029","title":"Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data","year":2007,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":46,"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 British Columbia","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Covariate; Inference; Missing data; Event (particle physics); Computer science; Econometrics; Proportional hazards model; Mixed model; Process (computing); Statistical inference; Longitudinal data; Counting process; Event data; Statistics; Data mining; Artificial intelligence; Mathematics; Machine learning","score_opus":0.13065622976442884,"score_gpt":0.41384063250208264,"score_spread":0.2831844027376538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128514707","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":[0.0043180953,0.0003645289,0.99414814,0.00050852704,0.000039709925,0.000041362968,0.000110450506,0.0000993818,0.00036983375],"genre_scores_gemma":[0.2453944,0.002150562,0.7459981,0.00056788453,0.0003932406,0.0010816155,0.000946606,0.00016596903,0.003301729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9766816,0.018662682,0.0006525983,0.0023678197,0.0012833981,0.0003519361],"domain_scores_gemma":[0.8692023,0.12017671,0.0045844247,0.0038946823,0.001546921,0.000594946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046703573,0.0017588012,0.0030420606,0.0030935083,0.0014145593,0.002556992,0.004258338,0.0027006073,0.004510897],"category_scores_gemma":[0.16242544,0.0017779472,0.0031286757,0.0030422779,0.004237436,0.0050378176,0.0033672494,0.0043574288,0.00062546524],"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":[0.00024877704,0.00013813896,0.0092963185,0.00050961925,0.0009873325,0.0006431381,0.0009013179,0.12370744,0.00047979466,0.80171126,0.001857472,0.059519533],"study_design_scores_gemma":[0.00008925718,0.000062840656,0.0009924211,0.00008508017,0.00015373448,0.00016143796,0.00008723628,0.3402221,0.00026294673,0.6557756,0.0020686043,0.000038729777],"about_ca_topic_score_codex":0.009327344,"about_ca_topic_score_gemma":0.0076434934,"teacher_disagreement_score":0.046703573,"about_ca_system_score_codex":0.002465844,"about_ca_system_score_gemma":0.0038152442,"threshold_uncertainty_score":0.24699509},"labels":[],"label_agreement":null},{"id":"W2129323029","doi":"10.1053/j.ajkd.2011.08.003","title":"In Reply to ‘Missing Data and Multiple Imputation When Predicting Mortality in Incident Dialysis Patients’","year":2011,"lang":"en","type":"article","venue":"American Journal of Kidney Diseases","topic":"Statistical Methods and Bayesian Inference","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":"University of Manitoba","funders":"National Health and Medical Research Council; Medical Research Council; FibroGen","keywords":"Missing data; Medicine; Imputation (statistics); Statistics","score_opus":0.06837940857258862,"score_gpt":0.36453144701039925,"score_spread":0.2961520384378106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129323029","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":[0.00028663114,0.0017734914,0.0007670625,0.9061617,0.090481035,0.000017999831,0.00026790236,0.00005352802,0.00019061855],"genre_scores_gemma":[0.0016861991,0.0009852913,0.000684065,0.9152778,0.079899445,0.00007605537,0.000090051195,0.000055862143,0.001245304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98245203,0.0059011825,0.004656423,0.0025344922,0.0030458546,0.0014099303],"domain_scores_gemma":[0.88123834,0.08978689,0.007194816,0.0026660457,0.01503672,0.0040772744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024542658,0.0020403818,0.0038164512,0.0025747423,0.0051983804,0.007383205,0.0056678304,0.06230349,0.0064207884],"category_scores_gemma":[0.19270039,0.0026336685,0.0033257564,0.0038057214,0.005783442,0.0054736286,0.0048357095,0.078052394,0.0064971065],"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":[0.00010496952,0.000021621006,0.00095752475,0.00019346054,0.00009760424,0.00029101112,0.00032421353,0.00011220046,0.00008926558,0.00087133615,0.9929137,0.004022955],"study_design_scores_gemma":[0.00041629784,0.00012172982,0.008710501,0.0018314461,0.0005198144,0.0018325808,0.0023013195,0.0023357195,0.0007241068,0.010898567,0.9699033,0.00040463769],"about_ca_topic_score_codex":0.008795635,"about_ca_topic_score_gemma":0.012335211,"teacher_disagreement_score":0.06230349,"about_ca_system_score_codex":0.003714954,"about_ca_system_score_gemma":0.005372344,"threshold_uncertainty_score":0.12979555},"labels":[],"label_agreement":null},{"id":"W2129637723","doi":"10.1002/cjs.10058","title":"Estimating functions for evaluating treatment effects in cluster‐randomized longitudinal studies in the presence of drop‐out and non‐compliance","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Missing data; Generalized estimating equation; Statistics; Estimating equations; Parametric statistics; Mathematics; Variance (accounting); Average treatment effect; Randomized experiment; Econometrics; Randomized controlled trial; Computer science; Maximum likelihood; Medicine; Propensity score matching","score_opus":0.1971334219149425,"score_gpt":0.4516207380821436,"score_spread":0.2544873161672011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129637723","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":[0.009643171,0.0013029253,0.986997,0.00042657458,0.000046420915,0.0007300385,0.00025785743,0.00029403597,0.00030186895],"genre_scores_gemma":[0.22461502,0.0016906072,0.76235527,0.00045934567,0.00013146637,0.008487472,0.00093557575,0.0002661938,0.0010590196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7553676,0.2321649,0.0038160968,0.003559452,0.004251423,0.00084058754],"domain_scores_gemma":[0.3585062,0.6086932,0.012953276,0.014244445,0.0048192316,0.0007836639],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.28971207,0.0025758296,0.0069734007,0.008505338,0.0010512567,0.0029806374,0.004730405,0.004603446,0.004425169],"category_scores_gemma":[0.47463167,0.0021103634,0.0065670735,0.005650609,0.004066884,0.0038815534,0.0040665865,0.0053352234,0.000674249],"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":[0.0036941166,0.000441627,0.029326145,0.0028694866,0.012508023,0.0005175635,0.0011131902,0.49150747,0.0010853632,0.20090675,0.004811839,0.25121838],"study_design_scores_gemma":[0.0006959797,0.0008583153,0.0054764245,0.00072489074,0.0016995632,0.00015789129,0.00015676212,0.8105203,0.0010594418,0.17463398,0.0038753808,0.00014113657],"about_ca_topic_score_codex":0.0051583215,"about_ca_topic_score_gemma":0.0023528826,"teacher_disagreement_score":0.28971207,"about_ca_system_score_codex":0.0039137434,"about_ca_system_score_gemma":0.0048450967,"threshold_uncertainty_score":0.8759115},"labels":[],"label_agreement":null},{"id":"W2129664941","doi":"10.1093/biomet/asr035","title":"Marginal methods for correlated binary data with misclassified responses","year":2011,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":28,"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; Mitacs","keywords":"Statistics; Library science; Chen; Mathematics; History; Demography; Sociology; Computer science; Biology","score_opus":0.43889636353023403,"score_gpt":0.4884611359971358,"score_spread":0.049564772466901774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129664941","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":[0.0009295753,0.00043025365,0.9980634,0.0001469612,0.000036754493,0.00004038713,0.00004050972,0.000084031955,0.00022823115],"genre_scores_gemma":[0.07517906,0.0015437839,0.91759086,0.00044018903,0.00035484313,0.0009932858,0.00042948595,0.00025104763,0.0032175141],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9778516,0.017444894,0.00061721506,0.0020576632,0.0017324288,0.00029613188],"domain_scores_gemma":[0.92071474,0.06545717,0.0036188914,0.0073015485,0.0023716346,0.00053598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041131783,0.0017703132,0.0025303422,0.0031726977,0.00094213156,0.0023792393,0.005642663,0.0022691588,0.0060405694],"category_scores_gemma":[0.10706659,0.001240481,0.0028017664,0.0027810852,0.0040129493,0.0034875982,0.0042593996,0.005330582,0.0013782765],"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":[0.00024821164,0.000110762085,0.0044602933,0.000744543,0.0008619422,0.00026791202,0.00097396265,0.08201924,0.0008531979,0.7032389,0.0051989695,0.20102203],"study_design_scores_gemma":[0.000069739355,0.00007454278,0.0013287117,0.00016306348,0.00013196409,0.00018925693,0.00009249958,0.32674697,0.0006681861,0.6619196,0.0085591255,0.000056261408],"about_ca_topic_score_codex":0.0025665394,"about_ca_topic_score_gemma":0.0031834943,"teacher_disagreement_score":0.041131783,"about_ca_system_score_codex":0.001731791,"about_ca_system_score_gemma":0.002621138,"threshold_uncertainty_score":0.21752822},"labels":[],"label_agreement":null},{"id":"W2130717440","doi":"","title":"On AR(1) versus MA(1) models for non-stationary time series of Poisson counts: part II (application)","year":2005,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Count data; Poisson distribution; Series (stratigraphy); Goodness of fit; Statistics; Poisson regression; Mathematics; Data set; Time series; Econometrics; Demography; Population","score_opus":0.057191676465836165,"score_gpt":0.3574960394714494,"score_spread":0.30030436300561325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130717440","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":[0.031532753,0.0016250852,0.9613444,0.0014594285,0.00011450472,0.0000877082,0.00016073712,0.0001974517,0.0034778689],"genre_scores_gemma":[0.7303299,0.0042363745,0.24966513,0.0010513755,0.0009529361,0.0008084541,0.00095907523,0.00036792646,0.01162894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9850668,0.012551271,0.00026976274,0.0009607006,0.00081738905,0.0003341013],"domain_scores_gemma":[0.8337654,0.15719911,0.003850015,0.002583324,0.0022017325,0.0004004627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033318713,0.001385594,0.002140513,0.0019561397,0.0009118832,0.0027260454,0.0037462339,0.0034429072,0.008597572],"category_scores_gemma":[0.11313425,0.00088149944,0.0020983098,0.0029645409,0.0038471883,0.0039985934,0.0026330936,0.0041862554,0.0013587867],"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":[0.00012649978,0.00009023313,0.0055596805,0.00040891743,0.00024200468,0.0004837913,0.00096360635,0.3250778,0.0008828675,0.6247699,0.004117873,0.037276775],"study_design_scores_gemma":[0.000030486266,0.00007413601,0.001563873,0.00007938635,0.000054006534,0.00010585498,0.00012751925,0.8217724,0.00020809117,0.1735871,0.0023528098,0.000044297478],"about_ca_topic_score_codex":0.008082823,"about_ca_topic_score_gemma":0.004539649,"teacher_disagreement_score":0.033318713,"about_ca_system_score_codex":0.001863793,"about_ca_system_score_gemma":0.0012977893,"threshold_uncertainty_score":0.17620832},"labels":[],"label_agreement":null},{"id":"W2130744536","doi":"","title":"CONFIDENCE INTERVALS FOR PROPORTIONS AND QUANTILES UNDER TWO-STAGE SAMPLING DESIGNS: AN EMPIRICAL STUDY","year":2008,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"","keywords":"Quantile; Confidence interval; Statistics; Sampling design; Sampling (signal processing); Independent and identically distributed random variables; National Health and Nutrition Examination Survey; Sample size determination; CDF-based nonparametric confidence interval; Mathematics; Multistage sampling; Population; Stratified sampling; Robust confidence intervals; Coverage probability; Econometrics; Sample (material); Computer science; Demography; Random variable","score_opus":0.5587614730044075,"score_gpt":0.5481278381675964,"score_spread":0.010633634836811123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130744536","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":[0.22636731,0.007996647,0.7585243,0.00084525964,0.000086195105,0.00041120086,0.00046406977,0.00028217633,0.005022835],"genre_scores_gemma":[0.81896824,0.0024046926,0.17691208,0.000121159894,0.00011188203,0.00032080995,0.00043919703,0.00009283895,0.0006290823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.932343,0.052674815,0.0018535352,0.0029220043,0.009296305,0.00091030874],"domain_scores_gemma":[0.22886741,0.73268765,0.013503405,0.013776629,0.010263064,0.0009018551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11677149,0.00083492516,0.0015651003,0.0027849665,0.00060293655,0.0029947332,0.003358024,0.0026871674,0.004045252],"category_scores_gemma":[0.53737515,0.0005977144,0.0016952051,0.005998911,0.0033482602,0.004966089,0.0023356038,0.003419459,0.00036096238],"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":[0.002509414,0.00068882684,0.07445763,0.0018934269,0.0008678624,0.0008001004,0.004430995,0.2535506,0.0021901887,0.38825303,0.0032105262,0.26714742],"study_design_scores_gemma":[0.0005710499,0.0019866757,0.050722193,0.0009644633,0.00045174244,0.0014932835,0.001240559,0.7117254,0.0025894498,0.22144563,0.0065196864,0.00028984688],"about_ca_topic_score_codex":0.00260321,"about_ca_topic_score_gemma":0.00088089326,"teacher_disagreement_score":0.11677149,"about_ca_system_score_codex":0.0016221275,"about_ca_system_score_gemma":0.0014673343,"threshold_uncertainty_score":0.61755407},"labels":[],"label_agreement":null},{"id":"W2130845266","doi":"10.1177/0962280211427759","title":"Extension of the modified Poisson regression model to prospective studies with correlated binary data","year":2011,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":745,"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":"Covariate; Statistics; Binary data; Estimator; Poisson distribution; Mathematics; Poisson regression; Logistic regression; Econometrics; Variance (accounting); Regression analysis; Cluster (spacecraft); Binary number; Computer science; Population; Medicine","score_opus":0.6529093814670139,"score_gpt":0.6393137010129344,"score_spread":0.01359568045407955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130845266","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":[0.0057355408,0.0009492314,0.99040097,0.0011272181,0.00014349933,0.00034114323,0.00022067585,0.0000909618,0.0009908227],"genre_scores_gemma":[0.1954444,0.0045732586,0.78959626,0.001557808,0.0006776008,0.0031256664,0.00064266205,0.00012006673,0.0042622723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97606236,0.018827543,0.0011123266,0.0017530281,0.0019646806,0.0002799997],"domain_scores_gemma":[0.9494762,0.038223617,0.0046662055,0.0043165265,0.0029662808,0.00035117852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04815462,0.00087831734,0.0018022251,0.0020660236,0.0004912283,0.0014649705,0.0045171804,0.0014946779,0.0030369428],"category_scores_gemma":[0.09785425,0.0008428098,0.0030670026,0.0031765078,0.0012440011,0.0018997896,0.0021091548,0.0032473153,0.00080223876],"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":[0.0004315352,0.00021248941,0.023983173,0.0017829294,0.0018920164,0.0017579078,0.0014466913,0.124557935,0.0014924812,0.5624952,0.008449356,0.27149832],"study_design_scores_gemma":[0.00033504804,0.0005410733,0.006931272,0.0004537994,0.0007020995,0.0013147355,0.00021458455,0.3826031,0.00086548453,0.5879287,0.017959505,0.00015056555],"about_ca_topic_score_codex":0.0035432982,"about_ca_topic_score_gemma":0.0031038828,"teacher_disagreement_score":0.04815462,"about_ca_system_score_codex":0.001112441,"about_ca_system_score_gemma":0.0034932918,"threshold_uncertainty_score":0.254669},"labels":[],"label_agreement":null},{"id":"W2131468492","doi":"10.1002/jrsm.1055","title":"Evidence‐based sample size estimation based upon an updated meta‐regression analysis","year":2012,"lang":"en","type":"article","venue":"Research Synthesis Methods","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":"Western University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Meta-regression; Sample size determination; Statistics; Meta-analysis; Econometrics; Regression analysis; Computer science; Set (abstract data type); Regression; Sample (material); Contrast (vision); Statistical power; Estimation; Mathematics; Medicine; Artificial intelligence","score_opus":0.6002511614464304,"score_gpt":0.6067797145749805,"score_spread":0.006528553128550052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131468492","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":[0.0047259927,0.010697881,0.97296745,0.0009946589,0.00046141888,0.00797649,0.0007072438,0.000590195,0.000878698],"genre_scores_gemma":[0.063503854,0.0034509778,0.9161951,0.0006310396,0.00018289304,0.014873234,0.0005565568,0.00011529663,0.00049106183],"study_design_codex":"design_other","study_design_gemma":"meta_analysis","domain_scores_codex":[0.8363253,0.13727495,0.011345432,0.006216552,0.008481916,0.00035589864],"domain_scores_gemma":[0.79837155,0.17693616,0.006189916,0.012603853,0.0055517755,0.0003467848],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.18987915,0.0026015034,0.00957676,0.0073647588,0.0005530079,0.0031769774,0.0053604273,0.002885763,0.0036157263],"category_scores_gemma":[0.41646832,0.0016872935,0.012220473,0.005110991,0.0009771936,0.0034224144,0.0027211986,0.005013856,0.0005558676],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007923817,0.0004107673,0.0071041947,0.04674378,0.095870234,0.00054731406,0.00080731907,0.11328027,0.0044363146,0.050756153,0.010627397,0.6614924],"study_design_scores_gemma":[0.016985543,0.004261034,0.009413592,0.013400853,0.18682288,0.0009298039,0.00018042019,0.39955646,0.00777266,0.31476268,0.045366373,0.00054771785],"about_ca_topic_score_codex":0.0015166396,"about_ca_topic_score_gemma":0.0017979823,"teacher_disagreement_score":0.8101208,"about_ca_system_score_codex":0.001982947,"about_ca_system_score_gemma":0.0029061974,"threshold_uncertainty_score":0.99902326},"labels":[],"label_agreement":null},{"id":"W2131502529","doi":"10.1002/cjs.11159","title":"Likelihood inference in complex settings","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Statistical inference; Mathematics; Econometrics; Humanities; Statistics; Philosophy; Epistemology","score_opus":0.08120352152605469,"score_gpt":0.3545217685082666,"score_spread":0.2733182469822119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131502529","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":[0.00444691,0.0013475274,0.98894346,0.0023563863,0.00007460722,0.000026316133,0.00017723125,0.000101875565,0.002525767],"genre_scores_gemma":[0.50115305,0.0066858702,0.48090494,0.0016635219,0.0013912083,0.00056690205,0.0010698641,0.0002812231,0.0062834867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9786225,0.01635322,0.0007500013,0.0018389663,0.001986183,0.00044916556],"domain_scores_gemma":[0.7843129,0.19942595,0.005505094,0.006854272,0.0028128857,0.0010888539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030154357,0.0011934055,0.0031232492,0.0041810866,0.0012735849,0.0048817885,0.0031253297,0.0029282894,0.007486935],"category_scores_gemma":[0.16719739,0.0015516431,0.0020708158,0.0046461285,0.006306426,0.007573463,0.005440289,0.006642862,0.0010469053],"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":[0.000022466762,0.00002055876,0.0014814426,0.0001630521,0.00016650763,0.00014112357,0.00018323089,0.05778606,0.00008493929,0.9201146,0.002414717,0.017421355],"study_design_scores_gemma":[0.00001019877,0.000004745488,0.00020052084,0.000036198697,0.000009490075,0.000032472417,0.000017665743,0.087852016,0.000036435496,0.9106049,0.0011843718,0.000010884815],"about_ca_topic_score_codex":0.006349021,"about_ca_topic_score_gemma":0.004230854,"teacher_disagreement_score":0.030154357,"about_ca_system_score_codex":0.0032079755,"about_ca_system_score_gemma":0.0025479137,"threshold_uncertainty_score":0.15947342},"labels":[],"label_agreement":null},{"id":"W2132094063","doi":"10.1920/wp.cem.2015.5415","title":"Inference for functions of partially identified parameters in moment inequality models","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"Economic and Social Research Council","keywords":"Inference; Moment (physics); Inequality; Mathematics; Statistical inference; Econometrics; Statistics; Applied mathematics; Statistical physics; Computer science; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.353626042998698,"score_gpt":0.4561308772583564,"score_spread":0.1025048342596584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132094063","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":[0.004150439,0.00008824992,0.99500567,0.00012503275,0.000006714541,0.00001272485,0.000075021126,0.0000853944,0.00045087902],"genre_scores_gemma":[0.46786857,0.00094341557,0.5253028,0.000344395,0.00028376913,0.00052322506,0.0011871213,0.00034738987,0.0031992972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99317026,0.004566796,0.00020954508,0.0007950022,0.0009716379,0.00028675498],"domain_scores_gemma":[0.96460783,0.029663771,0.0021376451,0.0022136562,0.0010570572,0.0003198919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009771268,0.0011612097,0.0020488726,0.0020328155,0.00075070735,0.0020284662,0.0029243007,0.0016158936,0.0039455607],"category_scores_gemma":[0.07974813,0.0010510819,0.0015206061,0.0015479892,0.002883793,0.0046827337,0.0029588495,0.0032706514,0.0006890056],"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":[0.00019313829,0.00008438698,0.0041754334,0.00028151053,0.00018607367,0.00021335717,0.0002913624,0.28753603,0.0019097711,0.63177145,0.0018506792,0.07150686],"study_design_scores_gemma":[0.000017428332,0.000029523857,0.0004945922,0.00003895251,0.000022559014,0.000042054406,0.000023110244,0.700676,0.0009287181,0.29652548,0.0011824144,0.00001908368],"about_ca_topic_score_codex":0.0027637081,"about_ca_topic_score_gemma":0.0018865964,"teacher_disagreement_score":0.009771268,"about_ca_system_score_codex":0.0015424532,"about_ca_system_score_gemma":0.0015853823,"threshold_uncertainty_score":0.051676035},"labels":[],"label_agreement":null},{"id":"W2133331977","doi":"10.31274/etd-180810-1709","title":"A Small Area Procedure for Estimating Population Counts","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"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":"Estimator; Contingency table; Statistics; Small area estimation; Mathematics; Econometrics; Table (database); Mean squared error; Sample (material); Sample size determination; Population; Geography; Computer science; Demography; Data mining","score_opus":0.09662603949932043,"score_gpt":0.4030580495697122,"score_spread":0.3064320100703918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133331977","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":[0.0005952392,0.00012718998,0.99391794,0.00008665236,0.00019631715,0.00072109554,0.0008232582,0.00063098315,0.0029013464],"genre_scores_gemma":[0.007880129,0.00027763567,0.97753453,0.00014191066,0.0001554918,0.004014206,0.0018176449,0.00041887586,0.0077595683],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99166226,0.0045851655,0.00060947094,0.0013738419,0.0016029346,0.000166291],"domain_scores_gemma":[0.99017686,0.0053148745,0.0004574345,0.0025351653,0.0014064319,0.000109272754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00894932,0.0013898985,0.0015299851,0.0052093263,0.0013748189,0.0022173643,0.0026931008,0.0011754491,0.044497114],"category_scores_gemma":[0.03361857,0.0010000021,0.0022504004,0.005762544,0.0013768688,0.0019214684,0.0029902218,0.0038112067,0.019081086],"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":[0.00008433435,0.00013636633,0.003699996,0.0005817941,0.00028526734,0.00020222677,0.00059680344,0.017652372,0.0016918238,0.24880832,0.060583238,0.6656774],"study_design_scores_gemma":[0.00017563721,0.0005071026,0.008069441,0.000616839,0.0001930698,0.0009913188,0.0005455487,0.18185788,0.0042858496,0.25723502,0.5453269,0.00019527656],"about_ca_topic_score_codex":0.0054756016,"about_ca_topic_score_gemma":0.007278351,"teacher_disagreement_score":0.044497114,"about_ca_system_score_codex":0.00081212434,"about_ca_system_score_gemma":0.0027604287,"threshold_uncertainty_score":0.14885765},"labels":[],"label_agreement":null},{"id":"W2133470535","doi":"","title":"A multivariate technique for multiply imputing missing values using a sequence of regression models","year":2001,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1995,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Imputation (statistics); Statistics; Mathematics; Logistic regression; Multivariate statistics; Regression analysis; Regression; Computer science","score_opus":0.6952655713588801,"score_gpt":0.5554953450934195,"score_spread":0.13977022626546065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133470535","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":[0.00066209206,0.00005976053,0.9986212,0.000080272526,0.00002395703,0.00005247639,0.00003467766,0.00018881891,0.00027675924],"genre_scores_gemma":[0.02410957,0.00038578862,0.97337294,0.00012502978,0.000115166804,0.0006550699,0.000180771,0.00013351253,0.0009221462],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96953267,0.023092497,0.00090837094,0.001695352,0.0043579293,0.00041315248],"domain_scores_gemma":[0.95091546,0.035783976,0.003559864,0.0068874913,0.0024208308,0.00043235996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034174934,0.002251467,0.00306186,0.0054897796,0.0015395824,0.0018672753,0.0036516625,0.0017278396,0.008163091],"category_scores_gemma":[0.104843855,0.0012618154,0.004720956,0.0074894372,0.0019010566,0.003973859,0.004206848,0.004753977,0.0023246473],"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":[0.00028760798,0.0003329614,0.0067252708,0.0007024316,0.0012280805,0.00037305022,0.0011829109,0.108698435,0.0022997297,0.32841164,0.007120108,0.54263777],"study_design_scores_gemma":[0.00016265604,0.00074465986,0.0035192643,0.00045879968,0.00047985732,0.001215081,0.00026890717,0.5545548,0.004808016,0.39930543,0.034202367,0.0002801122],"about_ca_topic_score_codex":0.0031021042,"about_ca_topic_score_gemma":0.0035504238,"teacher_disagreement_score":0.034174934,"about_ca_system_score_codex":0.001145862,"about_ca_system_score_gemma":0.003655971,"threshold_uncertainty_score":0.18073648},"labels":[],"label_agreement":null},{"id":"W2133959349","doi":"10.1093/aje/kwf215","title":"Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation","year":2003,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2200,"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 Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Gee; Generalized estimating equation; Binary data; Binary number; Multivariate statistics; Simple (philosophy); Orientation (vector space); Set (abstract data type); Longitudinal data; Data set; Statistics; Estimating equations; Computer science; Mathematics; Multivariate analysis; Applied mathematics; Algorithm; Data mining; Maximum likelihood; Arithmetic","score_opus":0.30749711667691604,"score_gpt":0.5219258977512772,"score_spread":0.21442878107436114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133959349","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":[0.00062316394,0.0038576333,0.99116653,0.0026247597,0.0002995399,0.000069301546,0.000064764296,0.000103308645,0.0011909927],"genre_scores_gemma":[0.024306424,0.013072199,0.95697844,0.0016574224,0.0017839858,0.0005880321,0.00014999976,0.00021556708,0.0012479343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9467785,0.04296141,0.0016810831,0.003254037,0.005036261,0.0002886353],"domain_scores_gemma":[0.94284457,0.047480542,0.0022300615,0.004034436,0.0030867998,0.00032358282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04706145,0.0019873846,0.0028262387,0.0059851455,0.0010032419,0.0042610783,0.0025370438,0.0026907248,0.0023924182],"category_scores_gemma":[0.07611102,0.0014236552,0.003368501,0.006418401,0.0068511325,0.0053223385,0.004675454,0.006998913,0.0012062616],"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":[0.000049692913,0.00007550833,0.0021079907,0.00082430255,0.00045286687,0.00019894156,0.00063545146,0.0107822595,0.00054047245,0.8223648,0.009429604,0.15253814],"study_design_scores_gemma":[0.00007908304,0.00013351535,0.0012589672,0.0004349601,0.00015081343,0.00034556814,0.00012779635,0.05844649,0.0006169514,0.89043355,0.04785554,0.000116665244],"about_ca_topic_score_codex":0.0028602174,"about_ca_topic_score_gemma":0.0017933719,"teacher_disagreement_score":0.04706145,"about_ca_system_score_codex":0.0018989207,"about_ca_system_score_gemma":0.0027060772,"threshold_uncertainty_score":0.24888778},"labels":[],"label_agreement":null},{"id":"W2134201249","doi":"10.1198/016214505000001023","title":"Bayesian Sample Size Determination for Case-Control Studies","year":2006,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":31,"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":"Sample size determination; Bayesian probability; Statistics; Monte Carlo method; Range (aeronautics); Computer science; Sample (material); Confidence interval; Interval estimation; Interval (graph theory); Mathematics; Engineering","score_opus":0.03259294827984577,"score_gpt":0.38508688999481944,"score_spread":0.35249394171497367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134201249","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":[0.00075366534,0.00075250585,0.99711007,0.00030009143,0.00008333024,0.00032595874,0.000054896904,0.00009129172,0.00052814686],"genre_scores_gemma":[0.037026323,0.001426236,0.9552381,0.0005314436,0.00033354913,0.004479273,0.000321908,0.00012422334,0.00051893725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8515161,0.124622285,0.004984791,0.006512339,0.011707944,0.00065657665],"domain_scores_gemma":[0.69415873,0.2709385,0.010167172,0.016124787,0.0078187,0.0007920573],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15820593,0.0018780929,0.004754013,0.0058672577,0.001733297,0.0026425563,0.0060189324,0.0046769143,0.0047242083],"category_scores_gemma":[0.47934696,0.0017293617,0.0024854064,0.0043456713,0.0047185705,0.0049673356,0.0044244276,0.0061844145,0.0011029153],"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":[0.0005764482,0.00020146946,0.0067258473,0.0019651784,0.00079878565,0.00038784015,0.0009099753,0.03497207,0.0013610745,0.69422716,0.009190385,0.24868383],"study_design_scores_gemma":[0.0004431287,0.00026702287,0.0017317379,0.00074934924,0.0002860331,0.00045998607,0.00009823082,0.15425064,0.0016021716,0.8236286,0.016390279,0.00009275753],"about_ca_topic_score_codex":0.0019916971,"about_ca_topic_score_gemma":0.0013816173,"teacher_disagreement_score":0.8417941,"about_ca_system_score_codex":0.0023317703,"about_ca_system_score_gemma":0.003859308,"threshold_uncertainty_score":0.8366829},"labels":[],"label_agreement":null},{"id":"W2134699177","doi":"10.1093/swr/30.1.19","title":"Imputing Missing Data: A Comparison of Methods for Social Work Researchers","year":2006,"lang":"en","type":"article","venue":"Social Work Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":233,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Missing data; Imputation (statistics); Computer science; Multivariate statistics; Data science; Data mining; Statistics; Mathematics; Machine learning","score_opus":0.709767389951839,"score_gpt":0.6728735764746552,"score_spread":0.03689381347718379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134699177","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":[0.020078095,0.053117827,0.9044411,0.007259806,0.0013878546,0.0038200598,0.0005429319,0.0013190353,0.008033213],"genre_scores_gemma":[0.06699725,0.032219876,0.8885595,0.0011242029,0.0004535299,0.008377556,0.0004063199,0.0007207909,0.0011410145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.61359483,0.33276144,0.014066992,0.005351578,0.033061005,0.0011641723],"domain_scores_gemma":[0.2817849,0.675548,0.007648977,0.015143838,0.018294338,0.0015800128],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.31644157,0.0025964032,0.0045881476,0.011379597,0.0027462335,0.008799257,0.005496863,0.005248734,0.0037872973],"category_scores_gemma":[0.5273373,0.0023631498,0.005075891,0.012573234,0.005515456,0.0102361115,0.007699716,0.005898484,0.001200679],"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":[0.0030599537,0.00054302975,0.008992409,0.008778079,0.003459417,0.00019501772,0.010771227,0.010996741,0.0005069512,0.122454494,0.009605006,0.8206378],"study_design_scores_gemma":[0.0036087555,0.005127315,0.03517591,0.026520517,0.0033138755,0.002720368,0.016431741,0.18024395,0.0044576502,0.6060012,0.11432172,0.0020770438],"about_ca_topic_score_codex":0.0028743383,"about_ca_topic_score_gemma":0.0035771125,"teacher_disagreement_score":0.68355846,"about_ca_system_score_codex":0.0045944303,"about_ca_system_score_gemma":0.007968366,"threshold_uncertainty_score":0.8429493},"labels":[],"label_agreement":null},{"id":"W2135236603","doi":"10.1139/f07-036","title":"Consequences of assuming an incorrect error structure in von Bertalanffy growth models: a simulation study","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Growth curve (statistics); Econometrics; Model selection; Mathematics; Selection (genetic algorithm); Population; Computer science","score_opus":0.09833630944378784,"score_gpt":0.3634395621756643,"score_spread":0.26510325273187646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135236603","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":[0.97401166,0.00035389897,0.02274585,0.00051340455,0.000025543302,0.000063729734,0.00030381262,0.00007283108,0.0019092202],"genre_scores_gemma":[0.98634934,0.00019150497,0.012414286,0.00005861826,0.000012053769,0.000073483185,0.0003491458,0.000026483654,0.0005251318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965919,0.0024837905,0.00014922368,0.00024412877,0.000247454,0.0002835397],"domain_scores_gemma":[0.8771723,0.11338523,0.0032308837,0.0026923902,0.002716131,0.00080308586],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014244665,0.0009730226,0.001304419,0.0014729103,0.0010839966,0.0011133103,0.0014365762,0.0020529483,0.0010216229],"category_scores_gemma":[0.038058676,0.0005115416,0.0015880262,0.0016995677,0.0014525019,0.001604957,0.0011324611,0.0022217236,0.00011925614],"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":[0.00026537446,0.00018219624,0.019333992,0.00005381023,0.00011222248,0.00021596241,0.00019812024,0.96906203,0.00033169313,0.0068177856,0.00044854777,0.002978233],"study_design_scores_gemma":[0.00006289449,0.00010593833,0.0023473743,0.000023085904,0.000037464328,0.00005438789,0.00008399703,0.9927739,0.00028749113,0.0040128175,0.00018227212,0.00002824719],"about_ca_topic_score_codex":0.036103044,"about_ca_topic_score_gemma":0.023900446,"teacher_disagreement_score":0.9857553,"about_ca_system_score_codex":0.0025516138,"about_ca_system_score_gemma":0.0011505827,"threshold_uncertainty_score":0.07533389},"labels":[],"label_agreement":null},{"id":"W2135592375","doi":"10.1002/cjs.5550360408","title":"On probability matching priors","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Frequentist inference; Mathematics; Matching (statistics); Posterior probability; Bayesian probability; Orthogonality; Quantile; Bayesian inference; Inference; Applied mathematics; Statistics; Computer science; Artificial intelligence","score_opus":0.0815094091292638,"score_gpt":0.32173115835464905,"score_spread":0.24022174922538525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135592375","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":[0.0036428212,0.0008772721,0.97155905,0.001952452,0.0001409512,0.00004348255,0.00019560926,0.00010807475,0.02148031],"genre_scores_gemma":[0.43788084,0.006393867,0.51180905,0.0040274537,0.002558464,0.0008069431,0.0012466789,0.00054592744,0.034730826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943743,0.0032796094,0.00017584205,0.00079280493,0.0010607616,0.00031669688],"domain_scores_gemma":[0.9694613,0.024247073,0.001238705,0.0025894206,0.0020083853,0.00045520594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010725621,0.0011093941,0.0014198046,0.002691339,0.0013905652,0.0035955256,0.0025627557,0.003833339,0.012172899],"category_scores_gemma":[0.065369055,0.0011153144,0.0014107897,0.0035723958,0.0053259875,0.007535788,0.0037266286,0.006187282,0.0031008227],"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":[0.000010415087,0.000006715706,0.00012239999,0.000021206493,0.000008087858,0.000026535205,0.000053734944,0.009618095,0.00008208586,0.98024565,0.0018800352,0.007925093],"study_design_scores_gemma":[0.000005813625,0.0000041435887,0.00007936836,0.00002632591,0.0000042339216,0.000021643003,0.00000748688,0.02564318,0.00006577215,0.9705757,0.0035591945,0.000007060713],"about_ca_topic_score_codex":0.0054588835,"about_ca_topic_score_gemma":0.0021054307,"teacher_disagreement_score":0.012172899,"about_ca_system_score_codex":0.002690579,"about_ca_system_score_gemma":0.0017243179,"threshold_uncertainty_score":0.056723177},"labels":[],"label_agreement":null},{"id":"W2135596283","doi":"10.1177/096228020101000502","title":"Statistical methods for the meta-analysis of cluster randomization trials","year":2001,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":159,"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":"Meta-analysis; Estimator; Randomization; Computer science; Context (archaeology); Cluster randomised controlled trial; Cluster (spacecraft); Statistics; Restricted randomization; Statistical power; Variance (accounting); Sample size determination; Econometrics; Statistical hypothesis testing; Clinical trial; Randomized controlled trial; Medicine; Mathematics","score_opus":0.6678900408750544,"score_gpt":0.7118649138188959,"score_spread":0.0439748729438415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135596283","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":[0.0002722455,0.005594969,0.9868623,0.0010871517,0.0010678337,0.002853023,0.0007718751,0.000935476,0.00055515044],"genre_scores_gemma":[0.0076113464,0.0031927936,0.95392513,0.0007645492,0.00066492683,0.032352637,0.00060359115,0.00038547657,0.0004994855],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.672075,0.2973038,0.012439779,0.0065793027,0.0109371655,0.00066496397],"domain_scores_gemma":[0.5323329,0.409231,0.01720605,0.03176193,0.008625786,0.0008423403],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23621407,0.0046102284,0.011450534,0.01438809,0.001507178,0.0042340537,0.007713521,0.0056074406,0.016946293],"category_scores_gemma":[0.51843303,0.0025134531,0.0116425045,0.015321518,0.0033916284,0.003915724,0.0045723645,0.013666396,0.004084189],"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":[0.0027640471,0.0003191891,0.0022708317,0.03643893,0.03204782,0.000623534,0.0009871364,0.04896517,0.0010216701,0.34519973,0.08472772,0.44463423],"study_design_scores_gemma":[0.0038661303,0.0010814008,0.0018293158,0.005858192,0.008740105,0.0005106332,0.00013966461,0.10844385,0.0013368165,0.7884951,0.07932209,0.00037660496],"about_ca_topic_score_codex":0.0019714874,"about_ca_topic_score_gemma":0.001743577,"teacher_disagreement_score":0.76378596,"about_ca_system_score_codex":0.0035614073,"about_ca_system_score_gemma":0.0085278135,"threshold_uncertainty_score":0.94188404},"labels":[],"label_agreement":null},{"id":"W2135760566","doi":"10.1016/j.jclinepi.2014.12.014","title":"The number of subjects per variable required in linear regression analyses","year":2015,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1043,"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; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"","keywords":"Statistics; Linear regression; Confidence interval; Mathematics; Regression analysis; Regression dilution; Standard error; Regression; Population; Segmented regression; Statistic; Linear model; Proper linear model; Standard deviation; Polynomial regression; Medicine","score_opus":0.6415066932237838,"score_gpt":0.6428740076196767,"score_spread":0.0013673143958928824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135760566","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":[0.15792271,0.0017444252,0.8230364,0.0032484126,0.00046045225,0.0065860986,0.0019435413,0.00077540503,0.004282537],"genre_scores_gemma":[0.52688783,0.00064146693,0.45436502,0.0010663349,0.00017785361,0.014527541,0.0010093127,0.00018814346,0.0011364934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.74938536,0.2232286,0.009245711,0.008540376,0.008455928,0.0011439534],"domain_scores_gemma":[0.3827098,0.57796293,0.008066538,0.024071775,0.0057407636,0.0014482767],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17392156,0.0011487029,0.0041248105,0.0010505255,0.0017383951,0.0031993126,0.0046686335,0.006394171,0.0052479072],"category_scores_gemma":[0.49992973,0.0023111766,0.0041861343,0.0017758345,0.002660561,0.0046286983,0.003126089,0.004934966,0.0010754581],"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":[0.024639552,0.0037803235,0.21311882,0.003763521,0.00496623,0.0029982908,0.003114931,0.38272753,0.008784002,0.050586395,0.010481926,0.29103854],"study_design_scores_gemma":[0.00662131,0.009107693,0.076148376,0.0018786034,0.0025091711,0.0022860125,0.0010117772,0.7003396,0.012989473,0.17142697,0.015253537,0.0004273932],"about_ca_topic_score_codex":0.0024291743,"about_ca_topic_score_gemma":0.0023303053,"teacher_disagreement_score":0.8260784,"about_ca_system_score_codex":0.0015963194,"about_ca_system_score_gemma":0.004139085,"threshold_uncertainty_score":0.9197961},"labels":[],"label_agreement":null},{"id":"W2135893344","doi":"10.1002/env.849","title":"The generalized linear model and extensions: a review and some biological and environmental applications","year":2007,"lang":"en","type":"review","venue":"Environmetrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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 Windsor","funders":"","keywords":"Generalized linear model; Count data; Poisson distribution; Econometrics; Computer science; Dispersion (optics); Linear model; Negative binomial distribution; Applied mathematics; Mathematics; Statistics","score_opus":0.2211672329283388,"score_gpt":0.42984377886969266,"score_spread":0.20867654594135385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135893344","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":[0.000326864,0.97538984,0.02011953,0.00094935263,0.00033478808,0.000015365878,0.00006368481,0.000052092317,0.0027485157],"genre_scores_gemma":[0.0056165583,0.97470933,0.016326973,0.0004976592,0.0009673079,0.000053134805,0.000117127165,0.000031915664,0.0016799533],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984498,0.00064921763,0.00014452182,0.00025854184,0.00045504252,0.000042924235],"domain_scores_gemma":[0.99630827,0.0027799737,0.00025087202,0.00011602223,0.00048018707,0.0000646847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033942885,0.0015276122,0.0026728967,0.0042864694,0.0003929495,0.0014903182,0.0020882902,0.0021020512,0.0042596282],"category_scores_gemma":[0.006009905,0.00073531875,0.0012127605,0.0085145105,0.0016120033,0.002273282,0.0010378718,0.0021916516,0.002843794],"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":[0.000046913497,0.00008886848,0.00067880505,0.009632624,0.0001910845,0.00041426317,0.00019077516,0.009908566,0.00046764457,0.08309927,0.032345552,0.86293566],"study_design_scores_gemma":[0.00003348161,0.00019563253,0.0024742526,0.0054790317,0.00022277236,0.0031646653,0.00020466401,0.011415803,0.00053188624,0.16537912,0.8107333,0.00016533719],"about_ca_topic_score_codex":0.0032687213,"about_ca_topic_score_gemma":0.0026489962,"teacher_disagreement_score":0.0042864694,"about_ca_system_score_codex":0.0011493613,"about_ca_system_score_gemma":0.0014488389,"threshold_uncertainty_score":0.017950952},"labels":[],"label_agreement":null},{"id":"W2136037152","doi":"10.1111/rssc.12036","title":"Statistical Inference and Computational Efficiency for Spatial Infectious Disease Models with Plantation Data","year":2013,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","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":"Cancer Care Ontario; University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Inference; Computer science; Gibbs sampling; Monte Carlo method; Markov chain; Bayesian inference; Algorithm; Data mining; Machine learning; Statistics; Artificial intelligence; Mathematics; Bayesian probability","score_opus":0.035369886723969755,"score_gpt":0.3173897390464121,"score_spread":0.28201985232244237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136037152","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":[0.09283314,0.0004753169,0.90228826,0.0014714636,0.000029090666,0.00005532503,0.0002506454,0.00058185,0.0020149942],"genre_scores_gemma":[0.6052289,0.00039739237,0.39220485,0.00017797554,0.00006763867,0.00019748947,0.000483162,0.00019249655,0.0010502225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99434483,0.0044658603,0.00020461433,0.00045065949,0.00040550673,0.0001286174],"domain_scores_gemma":[0.868965,0.124554746,0.0016626404,0.0033624268,0.0010920606,0.00036313967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017163545,0.00042882096,0.0010876972,0.0015507385,0.0006075722,0.0016902568,0.0018970718,0.0010100699,0.0023088239],"category_scores_gemma":[0.088878796,0.00066382164,0.0009884674,0.0015538002,0.0014434638,0.0027279116,0.0017415788,0.0018962612,0.00039872705],"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":[0.00015693739,0.000067199006,0.008956861,0.00011965908,0.00014359732,0.00015474761,0.00017872971,0.8874654,0.0007538945,0.0684714,0.0008016567,0.032729853],"study_design_scores_gemma":[0.0000095909045,0.0000074398504,0.0003727765,0.000009174688,0.0000063048105,0.000020722737,0.000021156642,0.9697231,0.00013946084,0.029503448,0.00018136913,0.000005399728],"about_ca_topic_score_codex":0.0120871905,"about_ca_topic_score_gemma":0.010672647,"teacher_disagreement_score":0.017163545,"about_ca_system_score_codex":0.0013622353,"about_ca_system_score_gemma":0.0021117437,"threshold_uncertainty_score":0.0907706},"labels":[],"label_agreement":null},{"id":"W2136825758","doi":"10.1002/sim.1601","title":"Estimating linear regression models in the presence of a censored independent variable","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"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; Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Health and Long-Term Care","keywords":"Statistics; Ordinary least squares; Censored regression model; Variables; Mathematics; Linear regression; Regression analysis; Econometrics; Monte Carlo method; Censoring (clinical trials); Local regression; Segmented regression; Polynomial regression","score_opus":0.07086286344301371,"score_gpt":0.41269204990285613,"score_spread":0.3418291864598424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136825758","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":[0.09841992,0.0006290716,0.8994127,0.00038847097,0.000041624022,0.000102273145,0.00007726154,0.00025917793,0.00066941656],"genre_scores_gemma":[0.5240985,0.0008087063,0.47304258,0.00029373978,0.00008732345,0.00030169848,0.00032689553,0.000071939794,0.0009685023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92618495,0.06687321,0.00125626,0.0024301868,0.002668935,0.000586453],"domain_scores_gemma":[0.6724223,0.30720246,0.008972724,0.008295174,0.0027378197,0.00036945855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06096867,0.0012705748,0.002256812,0.0014703927,0.000592213,0.0021310498,0.0017753671,0.0017609458,0.00096394954],"category_scores_gemma":[0.25164092,0.0011750036,0.0014187531,0.0018020844,0.001607602,0.003234693,0.0022970636,0.0019517664,0.0003147175],"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":[0.00089562684,0.00035828835,0.05338308,0.0007385308,0.0016064958,0.00071186456,0.0014073038,0.6497557,0.0015498905,0.116550244,0.0010609225,0.171982],"study_design_scores_gemma":[0.00014751308,0.00031481555,0.006781556,0.00014874409,0.00023577854,0.00020392676,0.00016055639,0.8560592,0.0015036653,0.13266827,0.0016723151,0.00010367328],"about_ca_topic_score_codex":0.006317836,"about_ca_topic_score_gemma":0.007409611,"teacher_disagreement_score":0.06096867,"about_ca_system_score_codex":0.0014240185,"about_ca_system_score_gemma":0.002465418,"threshold_uncertainty_score":0.322437},"labels":[],"label_agreement":null},{"id":"W2136946349","doi":"","title":"HIGHER ORDER ASYMPTOTICS: AN INTRINSIC DIFFERENCE BETWEEN UNIVARIATE AND MULTIVARIATE MODELS","year":2007,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian 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":"","keywords":"Univariate; Mathematics; Exponential family; Multivariate statistics; Exponential function; Applied mathematics; Statistic; Statistical inference; Natural exponential family; Term (time); Statistics; Inference; Econometrics; Statistical physics; Mathematical analysis; Computer science; Artificial intelligence","score_opus":0.10600588027139346,"score_gpt":0.3795687587408808,"score_spread":0.27356287846948735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136946349","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":[0.020676939,0.0013134291,0.96356046,0.0038567726,0.00012835473,0.000019958112,0.00015602881,0.00032294702,0.009965148],"genre_scores_gemma":[0.77194136,0.0020456729,0.2127067,0.0018957608,0.0012104466,0.0002026066,0.0004888958,0.000562457,0.008946045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9863621,0.0067401263,0.00057834043,0.0016666524,0.0041579558,0.0004947912],"domain_scores_gemma":[0.88167983,0.09259536,0.0048020743,0.015856111,0.0041509015,0.0009157021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021631267,0.00057767035,0.0015713525,0.0016787698,0.00064501207,0.0034448549,0.0026308119,0.0020991366,0.0046713264],"category_scores_gemma":[0.099080324,0.0005584081,0.0015838818,0.0014323395,0.0037000757,0.0060847793,0.0034438006,0.0069811447,0.00081658905],"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":[0.000023931503,0.000026062711,0.0016295342,0.00006968819,0.000036164693,0.00009936696,0.00021534285,0.012166971,0.00034564434,0.96922976,0.00169368,0.014463807],"study_design_scores_gemma":[0.0000058732217,0.000012932178,0.0008790065,0.00002833729,0.000008391079,0.00011791238,0.000024775247,0.06319654,0.00013857691,0.9329934,0.002577855,0.000016408703],"about_ca_topic_score_codex":0.0015631826,"about_ca_topic_score_gemma":0.0011215237,"teacher_disagreement_score":0.021631267,"about_ca_system_score_codex":0.0019724108,"about_ca_system_score_gemma":0.0013130208,"threshold_uncertainty_score":0.11439848},"labels":[],"label_agreement":null},{"id":"W2136972950","doi":"10.1002/sim.4453","title":"Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of British Columbia; Simon Fraser University","funders":"","keywords":"Prior probability; Covariate; Bayesian probability; Econometrics; Confounding; Computer science; Statistics; Mathematics","score_opus":0.15468768461269244,"score_gpt":0.44139871257239166,"score_spread":0.2867110279596992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136972950","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":[0.002432843,0.0004823196,0.9956163,0.00045274358,0.000030416571,0.00012355833,0.00008140525,0.00011940536,0.0006608916],"genre_scores_gemma":[0.2201408,0.001362805,0.7745187,0.00075785763,0.00020835598,0.0014619487,0.00028444428,0.0002327376,0.001032268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91438794,0.0756698,0.0020256431,0.0033026726,0.0039402954,0.0006736633],"domain_scores_gemma":[0.67382586,0.30313691,0.0066042743,0.012908725,0.0030136816,0.00051048544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12276398,0.001968331,0.0030352785,0.0043070535,0.0014541335,0.0034314676,0.003828986,0.004168617,0.004457063],"category_scores_gemma":[0.3234968,0.0019722783,0.0039150617,0.0034419748,0.0050126216,0.0053542224,0.0047348225,0.007831172,0.00047454092],"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":[0.0002713239,0.00007632637,0.0037665968,0.0009528971,0.0011110522,0.0003393453,0.0007983941,0.25864905,0.0009130624,0.64700437,0.002477358,0.08364014],"study_design_scores_gemma":[0.0001325626,0.00006126328,0.0008642519,0.00031472242,0.00026083714,0.000112568385,0.000054635584,0.22887005,0.0006555423,0.76600444,0.0025947334,0.000074399686],"about_ca_topic_score_codex":0.004327987,"about_ca_topic_score_gemma":0.0036236336,"teacher_disagreement_score":0.12276398,"about_ca_system_score_codex":0.0035217062,"about_ca_system_score_gemma":0.0031311156,"threshold_uncertainty_score":0.64924574},"labels":[],"label_agreement":null},{"id":"W2137873676","doi":"10.2307/3316148","title":"On the simultaneous effects of model misspecification and errors in variables","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Econometrics; Inference; Context (archaeology); Statistics; Specification; Sample (material); Mathematics; Computer science; Artificial intelligence","score_opus":0.05873710564939252,"score_gpt":0.2917615853982581,"score_spread":0.23302447974886556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137873676","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":[0.12602426,0.0018768493,0.86312443,0.0037822826,0.000120424535,0.00011227542,0.00017597107,0.00027893548,0.0045045484],"genre_scores_gemma":[0.936674,0.00085334055,0.060191564,0.0005017492,0.00015101334,0.000118986565,0.00014811818,0.00014090537,0.0012204279],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94503343,0.040314253,0.0021464238,0.0038034446,0.0074546803,0.001247673],"domain_scores_gemma":[0.15486458,0.8118363,0.017082563,0.010988983,0.0046947,0.00053288427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08969659,0.0011441878,0.001960676,0.0024412794,0.0007333212,0.003204315,0.0020207055,0.0027785446,0.0026451403],"category_scores_gemma":[0.48111138,0.0011088046,0.001291954,0.002981176,0.006725187,0.0048148157,0.0044981493,0.003935708,0.00020747873],"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":[0.0015305725,0.00019989375,0.06328514,0.0008633504,0.00122804,0.0020367068,0.0025394454,0.35533637,0.0022498765,0.42231703,0.0038705412,0.14454305],"study_design_scores_gemma":[0.00013943312,0.0003671647,0.018379325,0.00042433268,0.0007040341,0.0007445804,0.000513685,0.55013484,0.005378821,0.42027304,0.0027739308,0.00016681325],"about_ca_topic_score_codex":0.005373193,"about_ca_topic_score_gemma":0.003073236,"teacher_disagreement_score":0.08969659,"about_ca_system_score_codex":0.0018929158,"about_ca_system_score_gemma":0.0014455575,"threshold_uncertainty_score":0.4743666},"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":"W213901707","doi":"10.1023/a:1025818432525","title":"Imputing a Binary Variable from Donor to Recipient Dataset when Recipient Dataset Holds Restricted Information on the Variable","year":2002,"lang":"en","type":"article","venue":"Health Services and Outcomes Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Statistics Canada; Health Canada","funders":"Health Canada","keywords":"Variable (mathematics); Imputation (statistics); Logistic regression; Computer science; Data mining; Population; Regression; Statistics; Econometrics; Mathematics; Missing data; Machine learning; Medicine","score_opus":0.3935089949768288,"score_gpt":0.5154611245270454,"score_spread":0.12195212955021661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W213901707","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":[0.31870112,0.0011660606,0.63173443,0.006843711,0.0016408561,0.0016806091,0.031073414,0.0013123844,0.0058475425],"genre_scores_gemma":[0.8089625,0.000505293,0.15270968,0.0031589805,0.00083974615,0.00266249,0.023062518,0.00023639823,0.007862439],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94823784,0.037915822,0.0029587557,0.0057947664,0.0031740514,0.0019187042],"domain_scores_gemma":[0.83766687,0.09575703,0.012520988,0.048982978,0.0037716867,0.0013004794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08936755,0.0010295131,0.002935961,0.0028151616,0.0018881273,0.003375674,0.0056771846,0.0036177938,0.0064091813],"category_scores_gemma":[0.20221353,0.0011701771,0.004174964,0.0052300594,0.002299621,0.0028460652,0.004277953,0.0043423325,0.0012482431],"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":[0.009914925,0.001636893,0.6142774,0.0015239604,0.0076347655,0.0015138786,0.0021570362,0.03590084,0.0026202889,0.074414514,0.040478095,0.20792736],"study_design_scores_gemma":[0.0040661814,0.0026010743,0.33815023,0.00088204345,0.014191095,0.0029521338,0.0019305078,0.27138606,0.020834439,0.29449254,0.047949884,0.0005638],"about_ca_topic_score_codex":0.00555571,"about_ca_topic_score_gemma":0.00544097,"teacher_disagreement_score":0.08936755,"about_ca_system_score_codex":0.0013277599,"about_ca_system_score_gemma":0.00278932,"threshold_uncertainty_score":0.47262645},"labels":[],"label_agreement":null},{"id":"W2139159843","doi":"10.1093/fampra/20.1.77","title":"Randomizing patients by family practice: sample size estimation, intracluster correlation and data analysis","year":2002,"lang":"en","type":"article","venue":"Family Practice","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McMaster University; Hamilton Health Sciences; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Medicine; Sample size determination; Cluster (spacecraft); Psychological intervention; Sample (material); Statistics; Randomized controlled trial; Nursing; Internal medicine","score_opus":0.08093317421367884,"score_gpt":0.3882804673656616,"score_spread":0.3073472931519827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139159843","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":[0.019360123,0.003667421,0.8514858,0.0019654557,0.0014246305,0.11809732,0.0011254208,0.00087547157,0.001998453],"genre_scores_gemma":[0.10281283,0.0007656374,0.6841543,0.0009204505,0.0002946679,0.20994619,0.00045129936,0.00011916678,0.0005354332],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.24002966,0.70689976,0.019147577,0.012247157,0.020733913,0.00094185176],"domain_scores_gemma":[0.31454062,0.61076355,0.029122131,0.031878687,0.0128967045,0.000798272],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.49883685,0.0031961664,0.008196453,0.0062386394,0.0016339452,0.0024282397,0.004368998,0.0048365123,0.0041295104],"category_scores_gemma":[0.67191,0.0022789564,0.007649522,0.0065066954,0.00772336,0.003386193,0.003051832,0.0044100066,0.0009374494],"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":[0.03118784,0.0040524825,0.054408357,0.028108595,0.026814854,0.0008962459,0.0061226967,0.07839348,0.0027898054,0.1122425,0.042385653,0.61259747],"study_design_scores_gemma":[0.040123887,0.036136523,0.04082052,0.013712415,0.014093848,0.0010499966,0.00139998,0.5467075,0.009751822,0.24219555,0.053071097,0.000936826],"about_ca_topic_score_codex":0.0021389236,"about_ca_topic_score_gemma":0.0015006484,"teacher_disagreement_score":0.5011631,"about_ca_system_score_codex":0.0046475646,"about_ca_system_score_gemma":0.0072967205,"threshold_uncertainty_score":0.6180234},"labels":[],"label_agreement":null},{"id":"W2139939331","doi":"10.1002/cjs.5550350203","title":"A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Covariate; Errors-in-variables models; Mathematics; Nonparametric statistics; Monte Carlo method; Statistics; Parametric statistics; Observational error; Random variable; Random effects model; Applied mathematics; Econometrics; Computer science","score_opus":0.09944275655723335,"score_gpt":0.328300467265542,"score_spread":0.22885771070830868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139939331","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":[0.000515061,0.00023113943,0.998553,0.00011083984,0.000024249264,0.000020845067,0.000021833319,0.000029612998,0.00049348857],"genre_scores_gemma":[0.046413828,0.0012050315,0.94774956,0.00022327159,0.00014006374,0.00050795835,0.00017056201,0.00007505885,0.0035146582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97529334,0.019473996,0.00094905915,0.0015702322,0.0023650134,0.00034836557],"domain_scores_gemma":[0.98529124,0.010976438,0.0010812769,0.0008576721,0.0016266322,0.00016672254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021182513,0.0016245014,0.0030520875,0.0030379754,0.00090983586,0.0034430707,0.0038737361,0.0021059928,0.0031365247],"category_scores_gemma":[0.05009551,0.00176553,0.0022673823,0.0031785893,0.0018307405,0.0031665044,0.0040153987,0.0028939368,0.00086788967],"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":[0.000057826404,0.00007422254,0.0012210247,0.0003692381,0.00038613396,0.00018149223,0.0005429339,0.110932015,0.0007456674,0.77758163,0.0017501434,0.10615767],"study_design_scores_gemma":[0.000049955805,0.00009603464,0.0010873328,0.00020636094,0.00017488615,0.00014141199,0.00012079147,0.51709473,0.00080763415,0.46516246,0.014975856,0.0000825022],"about_ca_topic_score_codex":0.007886259,"about_ca_topic_score_gemma":0.007872635,"teacher_disagreement_score":0.021182513,"about_ca_system_score_codex":0.0026028834,"about_ca_system_score_gemma":0.0037860165,"threshold_uncertainty_score":0.11202514},"labels":[],"label_agreement":null},{"id":"W2140085139","doi":"10.1002/sim.3552","title":"Bayesian adjustment for covariate measurement errors: A flexible parametric approach","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Covariate; Bayesian probability; Statistics; Parametric statistics; Computer science; Econometrics; Semiparametric model; Observational error; Mathematics","score_opus":0.17226786948241857,"score_gpt":0.41952464059558425,"score_spread":0.24725677111316569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140085139","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":[0.0014331925,0.00017263474,0.99786216,0.00012250569,0.000022534858,0.000048398302,0.00003645458,0.00012986777,0.00017225501],"genre_scores_gemma":[0.1937344,0.001015952,0.80018353,0.0005204269,0.00032450847,0.0008575795,0.0005960272,0.00031055015,0.002457056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.978308,0.015333068,0.0007529999,0.0025131328,0.002453308,0.0006394629],"domain_scores_gemma":[0.93676114,0.050195593,0.0032928123,0.0060697887,0.0032148047,0.0004658164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03519347,0.0017815374,0.0031560638,0.0024290415,0.0013834039,0.0022128737,0.006269305,0.0028679303,0.0031684344],"category_scores_gemma":[0.11739619,0.0015766008,0.0035882418,0.0037404026,0.0025979003,0.003884283,0.00466786,0.0053171967,0.00079391815],"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":[0.00048942247,0.00020024827,0.008535261,0.0006004284,0.00131316,0.00065908505,0.0009989714,0.4717046,0.0018590906,0.18474224,0.004917161,0.32398036],"study_design_scores_gemma":[0.00009586139,0.000107341955,0.0016650102,0.00011058987,0.00021555816,0.00021699784,0.00007143379,0.8465583,0.0007231845,0.14583407,0.0043132934,0.00008841176],"about_ca_topic_score_codex":0.008193914,"about_ca_topic_score_gemma":0.0062805354,"teacher_disagreement_score":0.03519347,"about_ca_system_score_codex":0.0013890319,"about_ca_system_score_gemma":0.0041013625,"threshold_uncertainty_score":0.18612307},"labels":[],"label_agreement":null},{"id":"W2140588557","doi":"10.1177/0013164414548894","title":"A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes","year":2014,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of British Columbia","funders":"","keywords":"Kurtosis; Skewness; Statistics; Univariate; Mathematics; Monte Carlo method; Population; Variable (mathematics); Multivariate statistics; Sample (material); Econometrics; Algorithm","score_opus":0.6134090036945616,"score_gpt":0.49396079300045054,"score_spread":0.1194482106941111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140588557","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":[0.021744736,0.00885955,0.48707992,0.3831297,0.07107913,0.0014994668,0.0021790727,0.0063988445,0.018029658],"genre_scores_gemma":[0.1306675,0.0035281489,0.646181,0.1690458,0.017459925,0.0034785038,0.00046229197,0.0031296443,0.026047276],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.82531196,0.13747689,0.0117634805,0.005738563,0.018772487,0.00093660696],"domain_scores_gemma":[0.43019906,0.4683334,0.009434226,0.029766437,0.060473353,0.0017935312],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14919408,0.0023046196,0.0031139245,0.0035005365,0.004273951,0.007271471,0.009923424,0.006520729,0.005656888],"category_scores_gemma":[0.5352716,0.0015044083,0.0036328267,0.0041737785,0.010081617,0.0060670953,0.00423345,0.02753326,0.0056310636],"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":[0.0014864459,0.0002861936,0.016561719,0.0021969774,0.0009433393,0.0022386294,0.011766899,0.009386298,0.0030300657,0.1334917,0.6966916,0.12192017],"study_design_scores_gemma":[0.00073431584,0.0007249418,0.016098844,0.006065868,0.00052819314,0.0031198487,0.004953667,0.07260272,0.01192517,0.23964675,0.6422994,0.0013003106],"about_ca_topic_score_codex":0.011273417,"about_ca_topic_score_gemma":0.023079634,"teacher_disagreement_score":0.85080594,"about_ca_system_score_codex":0.0031587037,"about_ca_system_score_gemma":0.0045493604,"threshold_uncertainty_score":0.7890231},"labels":[],"label_agreement":null},{"id":"W2140621159","doi":"10.1002/cjs.10008","title":"Three enigmatic examples and inference from likelihood","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Inference; Context (archaeology); Likelihood function; Generality; Statistical inference; Bayes' theorem; Bayesian probability; Computer science; Econometrics; Bayesian inference; Bayes factor; Simple (philosophy); Statistics; Maximum likelihood; Artificial intelligence; Mathematics; Epistemology; Psychology","score_opus":0.059463970298833914,"score_gpt":0.3178455296177027,"score_spread":0.25838155931886875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140621159","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":[0.011275757,0.009096359,0.87488276,0.07607468,0.0005698806,0.00006151014,0.0002388672,0.00032936898,0.027470859],"genre_scores_gemma":[0.42797956,0.008475868,0.5427107,0.009636547,0.0020519134,0.00032882267,0.00028872758,0.00042007872,0.0081077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9751449,0.018364426,0.0009139637,0.0013926144,0.0037708324,0.00041327553],"domain_scores_gemma":[0.8980425,0.08846251,0.002517965,0.006286733,0.003988018,0.0007022831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027314637,0.0009761049,0.0013712941,0.0033830742,0.003642846,0.0047652004,0.003456868,0.006647058,0.0040312563],"category_scores_gemma":[0.13222228,0.0010389229,0.0009890194,0.003963599,0.025279244,0.011888486,0.0041024922,0.010346714,0.0009718965],"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":[0.000018098086,0.0000058422984,0.00029830722,0.00006277314,0.000011292168,0.00012309552,0.00053328375,0.002385832,0.000025645364,0.9807277,0.00528623,0.010522001],"study_design_scores_gemma":[0.000006145382,0.0000019531462,0.00008197954,0.00003693022,0.0000023542352,0.00011937102,0.000066099085,0.0050071483,0.000044123204,0.9884266,0.0061957934,0.000011430175],"about_ca_topic_score_codex":0.0051804692,"about_ca_topic_score_gemma":0.0040756715,"teacher_disagreement_score":0.027314637,"about_ca_system_score_codex":0.0039832024,"about_ca_system_score_gemma":0.0018536688,"threshold_uncertainty_score":0.14445531},"labels":[],"label_agreement":null},{"id":"W2141260650","doi":"10.1002/sim.2572","title":"Longitudinal variable selection by cross‐validation in the case of many covariates","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Covariate; Computer science; Markov chain Monte Carlo; Variable (mathematics); Feature selection; Selection (genetic algorithm); Model selection; Markov chain; Scale (ratio); Variety (cybernetics); Econometrics; Marginal model; Statistics; Data mining; Machine learning; Regression analysis; Artificial intelligence; Mathematics; Bayesian probability","score_opus":0.04109148622515056,"score_gpt":0.40199404916491205,"score_spread":0.3609025629397615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141260650","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":[0.00555255,0.00037435218,0.992955,0.00024473027,0.00006846029,0.00007395513,0.000064218046,0.00025461422,0.00041209813],"genre_scores_gemma":[0.16368327,0.00053318706,0.8317836,0.00042367404,0.00019051592,0.00078354817,0.0005794117,0.00022267344,0.0018001683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9507992,0.044145886,0.0009315206,0.002111357,0.0015856357,0.0004262584],"domain_scores_gemma":[0.90756375,0.07755186,0.002615385,0.009177942,0.0026023888,0.0004887296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.070951335,0.0013439064,0.0020978726,0.0023598627,0.0015263392,0.0016067657,0.002647209,0.0029671665,0.0033371544],"category_scores_gemma":[0.13582636,0.00090889534,0.0019189818,0.0028002106,0.002258853,0.0023314862,0.0028010001,0.004087268,0.0009988962],"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":[0.00060742506,0.00035948283,0.02613168,0.0005163834,0.0021589869,0.0012739215,0.0009064885,0.4964702,0.0017412539,0.16269481,0.010839459,0.29629993],"study_design_scores_gemma":[0.0000910461,0.00019103156,0.0027604073,0.0001555345,0.00013136145,0.00029449188,0.00008790833,0.85672045,0.0013174637,0.1313119,0.0068754614,0.000063019135],"about_ca_topic_score_codex":0.004980221,"about_ca_topic_score_gemma":0.0055903993,"teacher_disagreement_score":0.070951335,"about_ca_system_score_codex":0.00069393637,"about_ca_system_score_gemma":0.0023267693,"threshold_uncertainty_score":0.37523103},"labels":[],"label_agreement":null},{"id":"W2141523535","doi":"10.1080/10691898.2009.11889537","title":"Teaching Bayesian Statistics in a Health Research Methodology Program","year":2009,"lang":"en","type":"article","venue":"Journal of Statistics Education","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":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Bayesian probability; Bayesian statistics; Computer science; Data science; Statistics; Bayesian inference; Artificial intelligence; Mathematics","score_opus":0.32150312114316804,"score_gpt":0.5846462743249152,"score_spread":0.2631431531817472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141523535","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":[0.018868532,0.0023509606,0.81523544,0.070443325,0.001724018,0.0011778801,0.0004313055,0.0019871504,0.08778145],"genre_scores_gemma":[0.09829634,0.0032066626,0.8431441,0.010183099,0.0008106028,0.0016239437,0.00038024192,0.0004769739,0.041877955],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99117285,0.0052793794,0.0005117653,0.00053716335,0.0020854548,0.00041341974],"domain_scores_gemma":[0.95603687,0.031241737,0.0015834044,0.001787733,0.0050010565,0.004349189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023499724,0.00065170065,0.0007384504,0.0014671114,0.002389518,0.003254427,0.0016746322,0.0025590044,0.02680466],"category_scores_gemma":[0.047975086,0.00051489624,0.0008358298,0.001708326,0.002382167,0.0036123542,0.004131211,0.0057013044,0.008990117],"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":[0.00025250847,0.0021456517,0.0044764685,0.0009082155,0.000036325986,0.00084466534,0.010238864,0.0055877627,0.0033709274,0.24559496,0.15544733,0.57109636],"study_design_scores_gemma":[0.00016571539,0.00043406108,0.0037283849,0.0009779879,0.00004190456,0.0011982332,0.00249264,0.013359473,0.003450551,0.4063181,0.56776154,0.00007149939],"about_ca_topic_score_codex":0.0014591771,"about_ca_topic_score_gemma":0.00290583,"teacher_disagreement_score":0.02680466,"about_ca_system_score_codex":0.0031101007,"about_ca_system_score_gemma":0.009161456,"threshold_uncertainty_score":0.12427992},"labels":[],"label_agreement":null},{"id":"W2141588553","doi":"10.1002/sim.2662","title":"The merits of breaking the matches: a cautionary tale","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Matching (statistics); Outcome (game theory); Computer science; Econometrics; Cluster (spacecraft); Research design; Test (biology); Randomization; Statistics; Intervention (counseling); Restricted randomization; Scale (ratio); Mathematics; Clinical trial; Psychology; Medicine","score_opus":0.037239099333314964,"score_gpt":0.3812329515603517,"score_spread":0.3439938522270367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141588553","genre_codex":"commentary","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":[0.0023431175,0.023717966,0.068168715,0.8571701,0.041634277,0.00055937667,0.00030627864,0.00042027613,0.005679865],"genre_scores_gemma":[0.039018795,0.012710153,0.14731094,0.7347204,0.057214536,0.002249961,0.00009674308,0.00040124063,0.00627722],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8576683,0.09465544,0.012265663,0.009536369,0.024868187,0.0010060187],"domain_scores_gemma":[0.52129185,0.41689757,0.007182166,0.027735664,0.023477776,0.0034150034],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.22706033,0.0025119246,0.0054848874,0.0033289902,0.004554937,0.007749958,0.011595438,0.0170384,0.00447186],"category_scores_gemma":[0.48748514,0.0011298772,0.0036875359,0.0028322,0.037434608,0.017572988,0.006592188,0.059356842,0.0028964407],"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":[0.0020204606,0.00021160668,0.0029389467,0.0029047043,0.0012221583,0.0014568336,0.004455269,0.0021022202,0.00084966864,0.3504765,0.48893848,0.1424231],"study_design_scores_gemma":[0.00069680373,0.00041652084,0.001858395,0.0033693542,0.00035295764,0.0010896669,0.0018659275,0.0042555723,0.0010544134,0.723199,0.26148984,0.00035162637],"about_ca_topic_score_codex":0.0053323545,"about_ca_topic_score_gemma":0.006323943,"teacher_disagreement_score":0.7729397,"about_ca_system_score_codex":0.0034743042,"about_ca_system_score_gemma":0.0055215796,"threshold_uncertainty_score":0.9531722},"labels":[],"label_agreement":null},{"id":"W2143838957","doi":"10.1177/0962280212447152","title":"On identification in Bayesian disease mapping and ecological–spatial regression models","year":2012,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Child and Family Research Institute; University of British Columbia","funders":"Canadian Institutes of Health Research; University of British Columbia; Ministry of Health, British Columbia; Indian Council of Agricultural Research","keywords":"Bayesian probability; Prior probability; Bayesian linear regression; Bayesian inference; Poisson distribution; Statistics; Econometrics; Computer science; Random effects model; Univariate; Gaussian; Bayesian multivariate linear regression; Identification (biology); Mathematics; Regression analysis; Multivariate statistics; Ecology; Biology","score_opus":0.28231828112242724,"score_gpt":0.5858811142094335,"score_spread":0.3035628330870062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143838957","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":[0.0023927558,0.00021762158,0.9961696,0.00038727207,0.000014672957,0.000015276286,0.00003840121,0.000034844335,0.0007294835],"genre_scores_gemma":[0.23721273,0.0029488544,0.7503612,0.0006495165,0.00051418564,0.00072976976,0.0006145867,0.00025999238,0.0067092027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98860323,0.008200464,0.00036676344,0.0012192824,0.0012623069,0.00034795242],"domain_scores_gemma":[0.9338138,0.058452453,0.0033200171,0.002052966,0.0018151173,0.0005456118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028048653,0.0016488039,0.002106031,0.0027439687,0.0010191594,0.002362618,0.0030593171,0.0026648436,0.00354987],"category_scores_gemma":[0.09174851,0.0012233194,0.0025726662,0.0028382365,0.0040809126,0.0051815608,0.0042880573,0.004454349,0.00067371584],"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":[0.000022139142,0.00002533617,0.0010853397,0.000104298095,0.000058231104,0.0000859428,0.0002390916,0.15052141,0.00031593168,0.8262959,0.0007161535,0.020530296],"study_design_scores_gemma":[0.0000069131092,0.000015870724,0.00027159843,0.000045484892,0.000018314327,0.000054529897,0.000024494613,0.4000559,0.00018852965,0.59807026,0.0012245153,0.000023526985],"about_ca_topic_score_codex":0.004669256,"about_ca_topic_score_gemma":0.004044943,"teacher_disagreement_score":0.028048653,"about_ca_system_score_codex":0.0022560444,"about_ca_system_score_gemma":0.0020785301,"threshold_uncertainty_score":0.14833719},"labels":[],"label_agreement":null},{"id":"W2144443379","doi":"10.1002/sim.1613","title":"A Bayesian analysis of the 4‐year follow‐up data of the Wisconsin epidemiologic study of diabetic retinopathy","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Eye Institute","keywords":"Covariate; Bayesian probability; Baseline (sea); Diabetic retinopathy; Medicine; Population; Data set; Statistics; Computer science; Demography; Mathematics; Diabetes mellitus; Environmental health","score_opus":0.1301802120599601,"score_gpt":0.4246668987784154,"score_spread":0.2944866867184553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144443379","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":[0.45351487,0.0027674478,0.5369327,0.001818972,0.00006316984,0.00023596584,0.0027217597,0.00017645817,0.0017686212],"genre_scores_gemma":[0.8272296,0.0022163275,0.16281311,0.00025578993,0.0001316189,0.00036914807,0.0048479126,0.000061890714,0.002074545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99300283,0.005432715,0.0001861809,0.0005582487,0.00058556465,0.00023440763],"domain_scores_gemma":[0.96226186,0.03177569,0.0024843304,0.0016989205,0.001446091,0.00033322826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026186204,0.000609116,0.0011627271,0.0026330673,0.00075509545,0.0012352742,0.0011762975,0.0009736759,0.0010537133],"category_scores_gemma":[0.07750899,0.0006015317,0.0014871471,0.0020914318,0.0007847411,0.0011708395,0.0011679166,0.0015896887,0.00018408167],"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":[0.0019031406,0.00041099233,0.1329163,0.00062087976,0.0017436368,0.0009678887,0.0015302377,0.4427278,0.004109716,0.16065343,0.0070429174,0.24537307],"study_design_scores_gemma":[0.00018093872,0.0003695531,0.073044725,0.00024907608,0.00073521124,0.00033180404,0.0003276875,0.79417294,0.0009759744,0.12276889,0.0066648056,0.00017837237],"about_ca_topic_score_codex":0.020730782,"about_ca_topic_score_gemma":0.020076806,"teacher_disagreement_score":0.026186204,"about_ca_system_score_codex":0.0010653767,"about_ca_system_score_gemma":0.0018390389,"threshold_uncertainty_score":0.13848752},"labels":[],"label_agreement":null},{"id":"W2144504198","doi":"10.1002/bimj.200410143","title":"Strategies for Analyzing Missing Item Response Data with an Application to Lung Cancer","year":2005,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Missing data; Computer science; Estimator; Data quality; Data mining; Sample (material); Statistics; Machine learning; Mathematics; Engineering","score_opus":0.14678267627514058,"score_gpt":0.4822111086364818,"score_spread":0.33542843236134123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144504198","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":[0.002688026,0.0004558075,0.996176,0.00031297954,0.00001784543,0.00012507172,0.000029950153,0.000086920736,0.00010741214],"genre_scores_gemma":[0.05410605,0.0008435975,0.9430751,0.00021474308,0.00006042636,0.0012064809,0.00010354096,0.000054865726,0.00033514146],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9183882,0.075403936,0.0012852979,0.001654465,0.00292164,0.00034656824],"domain_scores_gemma":[0.6531434,0.32321694,0.007736168,0.009346829,0.005679415,0.0008772486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11168457,0.0020707382,0.0029403176,0.0058974614,0.0016585239,0.0018941135,0.0057706796,0.004085015,0.0033882922],"category_scores_gemma":[0.2812124,0.0016307716,0.003051539,0.0056946483,0.003418811,0.0036353858,0.004128769,0.0047957157,0.0007162907],"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":[0.000618893,0.00056659116,0.013192332,0.0018316798,0.0028765877,0.0011757269,0.0035562231,0.22520356,0.002487743,0.28613916,0.0038282345,0.45852333],"study_design_scores_gemma":[0.00052679074,0.00077786425,0.0028958824,0.00043177066,0.00042259425,0.0007983706,0.0006235787,0.63201064,0.0025718096,0.35392237,0.0047744573,0.00024379784],"about_ca_topic_score_codex":0.0030546072,"about_ca_topic_score_gemma":0.0036866372,"teacher_disagreement_score":0.11168457,"about_ca_system_score_codex":0.0010983418,"about_ca_system_score_gemma":0.0027659158,"threshold_uncertainty_score":0.5906515},"labels":[],"label_agreement":null},{"id":"W2145504411","doi":"10.1007/978-3-030-44246-0_8","title":"Empirical Likelihood Methods","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"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 Waterloo","funders":"","keywords":"Empirical likelihood; Statistics; Point estimation; Mathematics; Maximum likelihood; Econometrics; Population; Confidence interval; Sample (material); Likelihood function; Sample size determination; Empirical research; Estimation; Focus (optics); Confidence region; Demography; Economics","score_opus":0.10775381738103199,"score_gpt":0.43515669790708383,"score_spread":0.3274028805260518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145504411","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":[0.00052939454,0.014307866,0.7969533,0.00242746,0.0011967752,0.000068673355,0.0008431366,0.0019232966,0.18175003],"genre_scores_gemma":[0.029569194,0.017601192,0.46116316,0.0025602703,0.0026122115,0.00047400137,0.0031059529,0.003260877,0.4796531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845934,0.00064318324,0.000056373734,0.00020355069,0.0005959755,0.000041609943],"domain_scores_gemma":[0.9977387,0.0013831338,0.00005758036,0.0004377829,0.00033642544,0.00004632213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018972586,0.0013882497,0.0013770774,0.0019559127,0.00062731095,0.002800148,0.0017206258,0.0019270639,0.07891292],"category_scores_gemma":[0.007831479,0.000936924,0.000957092,0.0020406672,0.0011834347,0.0025091185,0.0018074336,0.003429811,0.05328278],"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":[0.000014810212,0.000056021763,0.00021788533,0.00027812258,0.0000549599,0.0000766624,0.000117258955,0.0049347323,0.00047203765,0.42222494,0.21762805,0.3539245],"study_design_scores_gemma":[0.000009305848,0.000012307945,0.00028409908,0.00017150394,0.000022528497,0.00026487667,0.000038194197,0.020101389,0.00055724336,0.5332163,0.44529757,0.000024690431],"about_ca_topic_score_codex":0.0010191643,"about_ca_topic_score_gemma":0.0017981551,"teacher_disagreement_score":0.07891292,"about_ca_system_score_codex":0.00080822647,"about_ca_system_score_gemma":0.0010028435,"threshold_uncertainty_score":0.26398998},"labels":[],"label_agreement":null},{"id":"W2145677862","doi":"10.1111/1467-9868.00289","title":"Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":103,"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 Victoria","funders":"Division of Mathematical Sciences","keywords":"Markov chain Monte Carlo; Metropolis–Hastings algorithm; Monte Carlo method; Gibbs sampling; Algorithm; Markov chain; Computer science; Stochastic approximation; Forward algorithm; Hybrid Monte Carlo; Monte Carlo algorithm; Mathematical optimization; Markov model; Mathematics; Variable-order Markov model; Statistics; Artificial intelligence; Machine learning; Bayesian probability","score_opus":0.07012751073827549,"score_gpt":0.3516130782871342,"score_spread":0.2814855675488587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145677862","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":[0.0022554144,0.00005214,0.9973617,0.000046528618,0.00000442108,0.000011804048,0.000014092905,0.00009908777,0.00015464964],"genre_scores_gemma":[0.124816135,0.00019515186,0.8731496,0.000066560264,0.000040929386,0.0002315206,0.00022713939,0.000131963,0.0011410671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99722517,0.0017405743,0.00010498717,0.00033144536,0.00047941582,0.00011845852],"domain_scores_gemma":[0.9879578,0.01032076,0.00047652036,0.00056163245,0.00056624686,0.000117095056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055919746,0.0006750536,0.0014817809,0.0016858942,0.0005533324,0.0012671666,0.0021931033,0.0013869823,0.0025598248],"category_scores_gemma":[0.020287301,0.00089462503,0.0012660639,0.0014979801,0.0013259407,0.0019554566,0.00165676,0.0018220189,0.0007862801],"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":[0.00007379883,0.000055817214,0.0013748999,0.00012260905,0.00012446495,0.00008427191,0.000114038696,0.80568814,0.0011920466,0.10943579,0.0012579089,0.08047622],"study_design_scores_gemma":[0.00000972365,0.00000485062,0.00012371718,0.0000070804504,0.0000039267848,0.000013265568,0.0000041143676,0.96327895,0.00022140045,0.035906337,0.0004181412,0.000008545761],"about_ca_topic_score_codex":0.0044148536,"about_ca_topic_score_gemma":0.004318621,"teacher_disagreement_score":0.0055919746,"about_ca_system_score_codex":0.0010878232,"about_ca_system_score_gemma":0.0017449055,"threshold_uncertainty_score":0.02957356},"labels":[],"label_agreement":null},{"id":"W2148112176","doi":"10.1186/1471-2288-10-49","title":"Testing for heterogeneity among the components of a binary composite outcome in a clinical trial","year":2010,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"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","funders":"","keywords":"Logistic regression; Statistics; Outcome (game theory); Generalized estimating equation; Gee; Population; Regression analysis; Regression; Mathematics; Random effects model; Statistical power; Econometrics; Medicine; Meta-analysis; Internal medicine","score_opus":0.8556687575528438,"score_gpt":0.6744447666117647,"score_spread":0.1812239909410791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148112176","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":[0.11086574,0.019009229,0.82725877,0.010896584,0.0020140016,0.014307766,0.0023284752,0.0009690695,0.012350421],"genre_scores_gemma":[0.7963409,0.0027160319,0.17548363,0.0031769092,0.00059028604,0.019736601,0.0010592914,0.0002058874,0.0006904659],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.49036652,0.43826786,0.025018701,0.016696634,0.028053325,0.0015969978],"domain_scores_gemma":[0.15895459,0.7818875,0.034960613,0.018287148,0.005081413,0.0008287421],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.4233748,0.0011605998,0.0033559343,0.003914619,0.0009778785,0.005143667,0.004183988,0.0040442073,0.0070781037],"category_scores_gemma":[0.6490687,0.0006194055,0.01005171,0.0048594764,0.005549357,0.004165809,0.0026633486,0.0040619755,0.00059881055],"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":[0.021761062,0.0013547969,0.17653409,0.043440577,0.08443376,0.0021507351,0.0060762353,0.06980622,0.0032161255,0.18607304,0.020427857,0.38472554],"study_design_scores_gemma":[0.01197771,0.010570523,0.069778584,0.020270273,0.037602607,0.0025623394,0.0011177284,0.15846239,0.008256627,0.65274894,0.025971733,0.00068051333],"about_ca_topic_score_codex":0.0007527697,"about_ca_topic_score_gemma":0.0005324074,"teacher_disagreement_score":0.4233748,"about_ca_system_score_codex":0.0027271772,"about_ca_system_score_gemma":0.006640361,"threshold_uncertainty_score":0.7110815},"labels":[],"label_agreement":null},{"id":"W2148129708","doi":"10.1198/016214502388618889","title":"Marginal Methods for Incomplete Longitudinal Data Arising in Clusters","year":2002,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":78,"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":"Missing data; Generalized estimating equation; Statistics; Cluster analysis; Marginal model; Multivariate statistics; Mathematics; Estimating equations; Econometrics; Logistic regression; Random effects model; Computer science; Regression analysis; Data mining; Estimator","score_opus":0.183358493295378,"score_gpt":0.4736633770486534,"score_spread":0.2903048837532754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148129708","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":[0.0006348634,0.00018948648,0.99875236,0.00010733477,0.000019693585,0.000048461254,0.00004652427,0.00006633439,0.00013492408],"genre_scores_gemma":[0.040862765,0.0010611964,0.9533871,0.00027178664,0.00019883728,0.0015375223,0.00042671151,0.00016866268,0.0020853577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97923684,0.016282918,0.0007768657,0.0017949163,0.00160537,0.00030314008],"domain_scores_gemma":[0.9063527,0.07860484,0.0042455913,0.00783869,0.0024571645,0.0005010639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04498631,0.0016684906,0.0027160444,0.0036955713,0.0010514305,0.0021105262,0.0058076535,0.0026799773,0.005981787],"category_scores_gemma":[0.12472366,0.0018042866,0.0031938553,0.0039269337,0.0034224356,0.0046963547,0.0051091877,0.0053336876,0.001278481],"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":[0.00012084503,0.000068319714,0.002840002,0.00053675694,0.0005614923,0.00028043264,0.00062869577,0.08518861,0.00044088298,0.8128488,0.0028639915,0.09362117],"study_design_scores_gemma":[0.00005893998,0.000055839562,0.00065777363,0.00009590078,0.00008204127,0.0001415768,0.00007371426,0.37517115,0.00034099992,0.6171041,0.006176219,0.000041671487],"about_ca_topic_score_codex":0.004298702,"about_ca_topic_score_gemma":0.004839766,"teacher_disagreement_score":0.04498631,"about_ca_system_score_codex":0.0017449113,"about_ca_system_score_gemma":0.0034657693,"threshold_uncertainty_score":0.23791319},"labels":[],"label_agreement":null},{"id":"W2148216078","doi":"10.1002/sim.3619","title":"Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Poisson distribution; Random effects model; Overdispersion; Poisson regression; Statistics; Zero-inflated model; Quasi-likelihood; Variance (accounting); Multilevel model; Computer science; Hierarchical database model; Compound Poisson distribution; Mathematics; Econometrics; Data mining; Medicine; Population; Meta-analysis","score_opus":0.15086653236908357,"score_gpt":0.43161931460224834,"score_spread":0.2807527822331648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148216078","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":[0.06757997,0.00025400892,0.9308188,0.0003326416,0.000034980876,0.00011583848,0.00021413514,0.00011894297,0.0005306268],"genre_scores_gemma":[0.6897313,0.0005644977,0.3057801,0.00025178946,0.0001013552,0.0005806767,0.0006015274,0.00009662808,0.0022920775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9855921,0.010676825,0.0005553001,0.0016690328,0.00108193,0.00042482174],"domain_scores_gemma":[0.9152073,0.07378791,0.0056885527,0.0036356028,0.0012624975,0.0004180925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03485744,0.00083725475,0.0022203224,0.0020479544,0.0006104387,0.0018044172,0.0035726829,0.0017778202,0.002129288],"category_scores_gemma":[0.065637454,0.0008360541,0.0021735635,0.0028352311,0.001993457,0.0025606041,0.0021480424,0.0017810109,0.0003140012],"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":[0.00058372127,0.00018007355,0.032642197,0.00048256177,0.0008643468,0.0018021846,0.001668493,0.47808126,0.001685037,0.4262696,0.0015810417,0.054159414],"study_design_scores_gemma":[0.00010962147,0.00016187149,0.004157715,0.00006423072,0.0001856535,0.00026174998,0.00017124762,0.7809782,0.0006313236,0.21197127,0.001226911,0.000080177306],"about_ca_topic_score_codex":0.004216321,"about_ca_topic_score_gemma":0.0036229333,"teacher_disagreement_score":0.03485744,"about_ca_system_score_codex":0.0013360432,"about_ca_system_score_gemma":0.0015305637,"threshold_uncertainty_score":0.18434596},"labels":[],"label_agreement":null},{"id":"W2148441587","doi":"10.1002/sim.2757","title":"A residuals‐based transition model for longitudinal analysis with estimation in the presence of missing data","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"McMaster University","funders":"National Institute on Drug Abuse; National Institute on Alcohol Abuse and Alcoholism; Rural Development Administration","keywords":"Imputation (statistics); Missing data; Covariate; Econometrics; Computer science; Longitudinal data; Statistics; Regression; Autoregressive model; Data mining; Mathematics","score_opus":0.14013846414275608,"score_gpt":0.4426742623897916,"score_spread":0.30253579824703547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148441587","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":[0.0038431017,0.00015103439,0.99504304,0.00027362307,0.000039156646,0.0000323277,0.0001558632,0.00016003824,0.00030182043],"genre_scores_gemma":[0.32558334,0.0011168489,0.6620296,0.0005085667,0.0003170779,0.0013330528,0.0016398864,0.00035950248,0.007112131],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914226,0.005823921,0.0003973439,0.0011273542,0.00079245627,0.0004363819],"domain_scores_gemma":[0.9656384,0.027172774,0.0023148127,0.0027127904,0.0016734683,0.00048770654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020706033,0.0008213784,0.0017195825,0.001680073,0.00052346004,0.0017085474,0.005441597,0.002272735,0.0044694026],"category_scores_gemma":[0.053435497,0.00093038747,0.0022707724,0.0024177318,0.0022305134,0.0037812789,0.0025764094,0.0037140923,0.0012836551],"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":[0.00029736586,0.00012239105,0.007834814,0.00027129415,0.00033420516,0.0004236926,0.0008343906,0.3039754,0.0009620102,0.60934263,0.004136541,0.071465276],"study_design_scores_gemma":[0.000051672916,0.00011357807,0.0012306493,0.000053051026,0.00008808463,0.00011371429,0.000048011236,0.7929044,0.000279871,0.20082234,0.004236847,0.000057789683],"about_ca_topic_score_codex":0.00549545,"about_ca_topic_score_gemma":0.003565171,"teacher_disagreement_score":0.020706033,"about_ca_system_score_codex":0.0011105689,"about_ca_system_score_gemma":0.0019464829,"threshold_uncertainty_score":0.109505236},"labels":[],"label_agreement":null},{"id":"W2148476761","doi":"10.1002/cjs.10077","title":"Confidence intervals for the mean of a population containing many zero values under unequal‐probability sampling","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"National Science Council","keywords":"Statistics; Mathematics; Estimator; Confidence interval; Likelihood function; Population; Sampling (signal processing); Coverage probability; Econometrics; Maximum likelihood; Computer science; Demography","score_opus":0.15661812411320303,"score_gpt":0.38943970866371497,"score_spread":0.23282158455051194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148476761","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":[0.1289081,0.0015154497,0.8649189,0.0005969294,0.00009832343,0.00011332748,0.00043326785,0.00035148975,0.003064199],"genre_scores_gemma":[0.8468013,0.00047678174,0.15083884,0.00018561746,0.00010922924,0.000331396,0.00071584026,0.00007969539,0.0004613861],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9574021,0.030895552,0.0013821434,0.0032287783,0.006437907,0.00065354427],"domain_scores_gemma":[0.4730056,0.4860581,0.01406153,0.014016384,0.011662328,0.0011961012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06028157,0.00060010987,0.0015114761,0.004133854,0.0009010896,0.0032157856,0.0033001003,0.0020520594,0.0035789264],"category_scores_gemma":[0.37864816,0.0004834065,0.0013693906,0.0029819596,0.0043499176,0.0036630263,0.0030291926,0.0023805324,0.00037961322],"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":[0.0024441497,0.00019637872,0.06648602,0.00086488377,0.0012459982,0.00077486143,0.0012943937,0.27024835,0.0018634141,0.44303197,0.004529228,0.20702046],"study_design_scores_gemma":[0.000274463,0.00040540783,0.022513993,0.00047404756,0.0003176867,0.000771706,0.00033435802,0.7093886,0.0035747446,0.25781447,0.0039263377,0.00020417736],"about_ca_topic_score_codex":0.0029275431,"about_ca_topic_score_gemma":0.0011976087,"teacher_disagreement_score":0.06028157,"about_ca_system_score_codex":0.0015492006,"about_ca_system_score_gemma":0.0011500234,"threshold_uncertainty_score":0.31880325},"labels":[],"label_agreement":null},{"id":"W2149096153","doi":"10.1007/s11538-012-9739-8","title":"Linearized Forms of Individual-Level Models for Large-Scale Spatial Infectious Disease Systems","year":2012,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":23,"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":"Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Covariate; Approximate Bayesian computation; Kernel (algebra); Computation; Bayesian probability; Computer science; Population; Scale (ratio); Spatial heterogeneity; Mathematics; Statistics; Artificial intelligence; Algorithm; Geography; Ecology; Cartography; Biology; Inference; Medicine","score_opus":0.08418910378551181,"score_gpt":0.3486003080767462,"score_spread":0.2644112042912344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149096153","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":[0.03657386,0.00066991354,0.95107704,0.0022803792,0.000057693815,0.00007578915,0.0005329358,0.00037434237,0.008357983],"genre_scores_gemma":[0.8482312,0.0028286679,0.09213122,0.0011110004,0.00040041338,0.00080075144,0.0015802153,0.00049897446,0.0524176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837095,0.00082131254,0.00007790327,0.0002766685,0.00024915425,0.00020404109],"domain_scores_gemma":[0.99043053,0.0063278154,0.0014435153,0.00051759486,0.0008091204,0.00047145528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034875714,0.0011718146,0.001900542,0.0018186034,0.00073485787,0.0032214432,0.003595864,0.0027584212,0.009460464],"category_scores_gemma":[0.019814337,0.0013112762,0.0019834435,0.0014561185,0.00285719,0.0056898394,0.003175602,0.003577293,0.0016714535],"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":[0.000016501279,0.00003509021,0.0009267093,0.00008555777,0.00007781955,0.000119646524,0.00028726587,0.43661085,0.00043096396,0.55421567,0.0024667182,0.004727245],"study_design_scores_gemma":[0.000008239662,0.000010332625,0.00033232087,0.000012059457,0.000018998733,0.000057512323,0.000044570686,0.74425,0.000057030156,0.25454825,0.00063324947,0.000027427664],"about_ca_topic_score_codex":0.009580986,"about_ca_topic_score_gemma":0.007747548,"teacher_disagreement_score":0.009580986,"about_ca_system_score_codex":0.0026470618,"about_ca_system_score_gemma":0.0022687928,"threshold_uncertainty_score":0.031648457},"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":"W2150227555","doi":"10.1002/cjs.11268","title":"Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Institute on Drug Abuse","keywords":"Covariate; Missing data; Inference; Model selection; Statistical inference; Statistics; Computer science; Selection (genetic algorithm); Econometrics; Marginal distribution; Observational error; Contrast (vision); Estimating equations; Marginal model; Data mining; Mathematics; Regression analysis; Machine learning; Artificial intelligence; Maximum likelihood; Random variable","score_opus":0.3525414427975988,"score_gpt":0.3873494002220161,"score_spread":0.03480795742441728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150227555","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":[0.0004225962,0.00013956451,0.99902594,0.00008846245,0.000019455836,0.000038891067,0.00003786916,0.000080224534,0.00014703085],"genre_scores_gemma":[0.030137612,0.00076446304,0.9659542,0.00019076177,0.00022859649,0.0010661386,0.00032760744,0.00019539827,0.0011352104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9835422,0.01317638,0.00050778204,0.0010845641,0.0014175781,0.00027147093],"domain_scores_gemma":[0.9597494,0.03316577,0.0015817034,0.002882597,0.0022084338,0.00041206423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02869594,0.0016527143,0.0019751978,0.0035499444,0.0011951819,0.0014872362,0.003552857,0.0014109731,0.00903767],"category_scores_gemma":[0.083202526,0.0010860225,0.0032636032,0.0036257056,0.0030561744,0.0024542524,0.00379801,0.0045428975,0.001637988],"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":[0.00017496821,0.00012751104,0.0028511847,0.0005055144,0.00045080186,0.0002861189,0.0006247445,0.05264784,0.0013884522,0.7081394,0.0064260955,0.22637746],"study_design_scores_gemma":[0.00008664121,0.00009006803,0.0010154283,0.000146472,0.00011577732,0.00020353239,0.00007665027,0.32179803,0.0012296648,0.666928,0.008248851,0.000060820792],"about_ca_topic_score_codex":0.0036379409,"about_ca_topic_score_gemma":0.0046369424,"teacher_disagreement_score":0.02869594,"about_ca_system_score_codex":0.0014465007,"about_ca_system_score_gemma":0.0047552334,"threshold_uncertainty_score":0.15176046},"labels":[],"label_agreement":null},{"id":"W2150394428","doi":"","title":"A Note on Sampling and Estimation in the Presence of Cut-Off Sampling","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian Inference","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":"Université de Montréal","funders":"","keywords":"Estimator; Sampling (signal processing); Selection (genetic algorithm); Sampling bias; Statistics; Sample (material); Selection bias; Econometrics; Set (abstract data type); Sampling design; Population; Estimation; Computer science; Sample size determination; Mathematics; Artificial intelligence; Economics","score_opus":0.16190939068409155,"score_gpt":0.44765892392015827,"score_spread":0.2857495332360667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150394428","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":[0.0019402448,0.001971145,0.9899502,0.0029912908,0.00040164232,0.00012627177,0.000076493314,0.000100835496,0.0024419702],"genre_scores_gemma":[0.1323786,0.0045569884,0.8524195,0.002806063,0.0024050816,0.0011751344,0.00035087464,0.00019114921,0.0037166039],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8179064,0.15775259,0.0038891041,0.0064112237,0.01269874,0.0013419827],"domain_scores_gemma":[0.5614096,0.3852843,0.008858852,0.03599836,0.007206022,0.0012427928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14398766,0.0022330498,0.0048848307,0.002581323,0.0023240843,0.006222398,0.005798979,0.007392751,0.004876456],"category_scores_gemma":[0.39845017,0.002227512,0.003633027,0.0048202127,0.0111069195,0.011966087,0.008078286,0.0144923935,0.00088700803],"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":[0.0002083571,0.00006706228,0.0037825836,0.000529913,0.0002646354,0.00058053236,0.0005331432,0.015214294,0.00040236025,0.9303657,0.0040665036,0.043984927],"study_design_scores_gemma":[0.00008262482,0.000113744,0.00077891466,0.00029815186,0.00009121354,0.00028244013,0.00007279331,0.09828464,0.0006099599,0.8894642,0.00986483,0.00005642342],"about_ca_topic_score_codex":0.004272555,"about_ca_topic_score_gemma":0.002518786,"teacher_disagreement_score":0.14398766,"about_ca_system_score_codex":0.0030471527,"about_ca_system_score_gemma":0.0030347218,"threshold_uncertainty_score":0.7614886},"labels":[],"label_agreement":null},{"id":"W2151923367","doi":"10.1002/sim.2868","title":"Regression B‐spline smoothing in Bayesian disease mapping: with an application to patient safety surveillance","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Child and Family Research Institute; University of British Columbia; Providence Health Care","funders":"","keywords":"Smoothing; Computer science; Prior probability; Bayesian probability; Deviance information criterion; Bayes' theorem; Marginal likelihood; Smoothing spline; Random effects model; Context (archaeology); Statistics; Bayesian inference; Econometrics; Mathematics; Artificial intelligence; Geography","score_opus":0.030658900740926413,"score_gpt":0.37988102876256025,"score_spread":0.3492221280216338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151923367","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":[0.008611183,0.0015649649,0.9877423,0.0007743814,0.00006206023,0.000045343262,0.00006935676,0.00020698208,0.0009234274],"genre_scores_gemma":[0.24232133,0.0048972997,0.7487208,0.00031611187,0.00030591575,0.0003602187,0.0003538749,0.00020286316,0.0025215955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99416286,0.0045810076,0.00016965035,0.00042973002,0.000533815,0.00012306466],"domain_scores_gemma":[0.98101693,0.01598328,0.00093939586,0.00076991087,0.001034574,0.00025583484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015514388,0.00084805896,0.0014721727,0.0023142959,0.0008391576,0.0017400151,0.0016174581,0.0020985673,0.0016568777],"category_scores_gemma":[0.05140092,0.00070296484,0.0019855506,0.0036504588,0.0014573281,0.0013441036,0.0025471698,0.00232434,0.0003820733],"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":[0.00013560665,0.000100683144,0.007108402,0.00030520637,0.00019688359,0.00030262856,0.00068165234,0.6248103,0.0010868285,0.18101566,0.0030742227,0.18118194],"study_design_scores_gemma":[0.000020466912,0.00003672342,0.0010584934,0.00005459077,0.000025805452,0.000093213035,0.000058195757,0.9242647,0.00024913816,0.0704307,0.0036689905,0.00003899801],"about_ca_topic_score_codex":0.025123307,"about_ca_topic_score_gemma":0.013674807,"teacher_disagreement_score":0.025123307,"about_ca_system_score_codex":0.0011906504,"about_ca_system_score_gemma":0.0022162874,"threshold_uncertainty_score":0.08204889},"labels":[],"label_agreement":null},{"id":"W2152325169","doi":"10.1016/j.envres.2014.05.016","title":"Indirect adjustment for multiple missing variables applicable to environmental epidemiology","year":2014,"lang":"en","type":"article","venue":"Environmental Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":74,"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; Statistics Canada; McGill University; Institute of Population and Public Health; University of Toronto; Queen's University; University of Ottawa; Health Canada","funders":"","keywords":"Missing data; Statistics; Proportional hazards model; Environmental epidemiology; Censoring (clinical trials); Weibull distribution; Epidemiology; Confounding; Regression analysis; Cohort study; Hazard ratio; Cohort; Environmental health; Econometrics; Medicine; Demography; Confidence interval; Mathematics; Internal medicine","score_opus":0.18870077085740045,"score_gpt":0.4465999730553326,"score_spread":0.25789920219793216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152325169","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":[0.0016367083,0.00008228881,0.9976539,0.0000960607,0.000016121918,0.00008287088,0.00006807236,0.00013728981,0.00022678413],"genre_scores_gemma":[0.07358139,0.0005093096,0.9216582,0.00017269028,0.00010381808,0.0016071695,0.00041842237,0.00020359323,0.0017454089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923344,0.005472474,0.00023850854,0.00053595763,0.0012837357,0.0001348922],"domain_scores_gemma":[0.973345,0.019703459,0.002315065,0.0024669345,0.0019521386,0.00021748613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014782857,0.0009799869,0.0009822451,0.0014224572,0.00031846983,0.0007316194,0.0022093116,0.0007107808,0.0055522597],"category_scores_gemma":[0.09152476,0.00053833774,0.0018577927,0.0011546018,0.00090943417,0.00089714443,0.0023637828,0.0021626707,0.0007890133],"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":[0.00028753935,0.000200126,0.034831755,0.0012027319,0.0017186828,0.00033261755,0.0007411215,0.21325327,0.004454539,0.19201533,0.0070055276,0.5439567],"study_design_scores_gemma":[0.00022350112,0.00033730286,0.015672307,0.00050670037,0.00040006,0.00041727285,0.00011165091,0.7068892,0.004956764,0.24485841,0.025532676,0.000094201954],"about_ca_topic_score_codex":0.0028310404,"about_ca_topic_score_gemma":0.0044784816,"teacher_disagreement_score":0.014782857,"about_ca_system_score_codex":0.0008181051,"about_ca_system_score_gemma":0.0030308191,"threshold_uncertainty_score":0.078180194},"labels":[],"label_agreement":null},{"id":"W2152863386","doi":"10.22237/jmasm/1177993500","title":"A Comparison of One-High-Threshold and Two-High-Threshold Multinomial Models of Source Monitoring","year":2007,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Statistical Methods and Bayesian Inference","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":"Riverview Hospital","funders":"","keywords":"Multinomial distribution; Mathematics; Deflation; Threshold model; Statistics; Econometrics; High dimensional; Inflation (cosmology); Computer science; Economics; Artificial intelligence; Monetary policy; Physics","score_opus":0.14924307725687444,"score_gpt":0.4658634981135635,"score_spread":0.31662042085668907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152863386","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":[0.8397159,0.00063631876,0.15134591,0.0020835972,0.00009592334,0.0002572232,0.00069498626,0.00024123977,0.004928969],"genre_scores_gemma":[0.9863528,0.00016244472,0.011446912,0.000097088654,0.000025552474,0.00008690978,0.00024929494,0.000039003316,0.0015400917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935557,0.0040595075,0.00027854537,0.0010319118,0.0005163413,0.0005580402],"domain_scores_gemma":[0.90734285,0.07926315,0.004385397,0.0043654474,0.0032229705,0.0014201654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023295218,0.00086333096,0.001763463,0.0015038606,0.0009599691,0.0028405369,0.0043656207,0.0030017747,0.0032408426],"category_scores_gemma":[0.070582055,0.00080514053,0.0021415122,0.0014505176,0.0019020431,0.00582161,0.0019491563,0.002819769,0.0003961135],"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":[0.0023792859,0.00036625066,0.042168878,0.00028188416,0.000479876,0.0005456812,0.0009326945,0.82774484,0.0011295186,0.10122198,0.0019664618,0.020782635],"study_design_scores_gemma":[0.00013910634,0.00015972488,0.0072346106,0.000024133693,0.00012424933,0.0001738113,0.00017274263,0.9609866,0.0002723162,0.030358862,0.00028173026,0.00007204776],"about_ca_topic_score_codex":0.016487123,"about_ca_topic_score_gemma":0.011698532,"teacher_disagreement_score":0.023295218,"about_ca_system_score_codex":0.0038355633,"about_ca_system_score_gemma":0.0016700554,"threshold_uncertainty_score":0.12319839},"labels":[],"label_agreement":null},{"id":"W2154413108","doi":"10.1177/0013164406299132","title":"Correction for Attenuation With Biased Reliability Estimates and Correlated Errors in Populations and Samples","year":2007,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Statistics; Reliability (semiconductor); Monte Carlo method; Correction for attenuation; Attenuation; Sample size determination; Observational error; Mathematics; Population; Sampling error; Sample (material); Sampling (signal processing); Random variable; Econometrics; Computer science; Physics; Demography; Optics","score_opus":0.34262566925646354,"score_gpt":0.449982568421525,"score_spread":0.10735689916506147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154413108","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":[0.017196974,0.00041832848,0.9802776,0.00045241127,0.000107773354,0.00024694446,0.00006198874,0.0002934617,0.00094451796],"genre_scores_gemma":[0.47753626,0.0006049871,0.5177509,0.00042651105,0.00019080988,0.0014355,0.00017325427,0.00031252505,0.0015692135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81852823,0.14861983,0.006939017,0.009681499,0.014721603,0.0015097918],"domain_scores_gemma":[0.22824623,0.68512684,0.024867635,0.04816501,0.012981579,0.00061274774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17967556,0.0019829746,0.0024213803,0.003913106,0.0014095932,0.002722097,0.0034448367,0.0028610292,0.0027901067],"category_scores_gemma":[0.728368,0.0015981139,0.002294332,0.004668631,0.0065981527,0.0047782506,0.0053975405,0.0045482013,0.0005412932],"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":[0.0010470311,0.00020480664,0.085673496,0.0012737586,0.0021587738,0.0018296053,0.0072504464,0.16651462,0.0023324331,0.4726632,0.004436365,0.2546155],"study_design_scores_gemma":[0.00018975473,0.0005734818,0.032553867,0.0010535277,0.00082450075,0.0018588927,0.00067197275,0.44082317,0.0045623975,0.5090791,0.0075457245,0.00026358257],"about_ca_topic_score_codex":0.004496467,"about_ca_topic_score_gemma":0.004130697,"teacher_disagreement_score":0.17967556,"about_ca_system_score_codex":0.0024684938,"about_ca_system_score_gemma":0.004260959,"threshold_uncertainty_score":0.95022655},"labels":[],"label_agreement":null},{"id":"W2154943371","doi":"10.1177/1525822x04271006","title":"Methodological Issues in the Effects of Attrition: Simple Solutions for Social Scientists","year":2004,"lang":"en","type":"article","venue":"Field Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":92,"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":"Attrition; Data collection; Psychology; Quarter (Canadian coin); Longitudinal data; Simple (philosophy); Sample (material); Computer science; Social science; Sociology; Epistemology; Medicine; Geography; Data mining","score_opus":0.4577670085167675,"score_gpt":0.5760908758708554,"score_spread":0.1183238673540879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154943371","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":[0.007168371,0.012368778,0.8198335,0.14555147,0.0048692715,0.003397232,0.00027161516,0.0005051842,0.006034566],"genre_scores_gemma":[0.15730473,0.0059180297,0.7729447,0.040060766,0.0065097734,0.014288808,0.00012833219,0.00033667506,0.0025081048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.28884587,0.6133267,0.036554918,0.015439951,0.043633413,0.0021990973],"domain_scores_gemma":[0.14310798,0.73979163,0.03835773,0.052107528,0.02465465,0.0019804777],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5969493,0.0029861717,0.005076002,0.0074918442,0.006691552,0.0075191776,0.0060288943,0.014058775,0.0042347414],"category_scores_gemma":[0.8169136,0.0028151711,0.0050457013,0.0071143825,0.034359276,0.017363766,0.012426518,0.021015631,0.0009715474],"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":[0.00091578456,0.00028035478,0.012976677,0.006479135,0.0027275716,0.00081230985,0.020604812,0.0038231947,0.0007546668,0.6091521,0.032699987,0.3087734],"study_design_scores_gemma":[0.0006804016,0.0005214482,0.004111426,0.006353476,0.0009786072,0.00065338484,0.00201491,0.009931151,0.0012602577,0.937541,0.0356533,0.0003005368],"about_ca_topic_score_codex":0.0026055016,"about_ca_topic_score_gemma":0.0030591544,"teacher_disagreement_score":0.40305072,"about_ca_system_score_codex":0.007215494,"about_ca_system_score_gemma":0.010624541,"threshold_uncertainty_score":0.49703336},"labels":[],"label_agreement":null},{"id":"W2155013980","doi":"10.1002/nur.20100","title":"Handling missing data in self-report measures","year":2005,"lang":"en","type":"review","venue":"Research in Nursing & Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":408,"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 Windsor","funders":"Wayne State University","keywords":"Missing data; Imputation (statistics); Computer science; Data mining; Statistics; Mathematics; Machine learning","score_opus":0.6966503124980666,"score_gpt":0.6780807088257569,"score_spread":0.018569603672309754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155013980","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067184586,0.3361848,0.6360849,0.009816859,0.001312348,0.0007090083,0.00054619304,0.0005387396,0.00808867],"genre_scores_gemma":[0.083261125,0.39509806,0.50947016,0.003429121,0.0022957462,0.0024777881,0.0011354552,0.00023195836,0.0026005358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.916511,0.06081069,0.004668026,0.0027047824,0.014789668,0.0005158617],"domain_scores_gemma":[0.78567964,0.18344302,0.012897805,0.008477728,0.0089549525,0.00054688944],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.073297895,0.0010907691,0.0034103245,0.0058035115,0.0009321341,0.0027806633,0.005230169,0.003095527,0.0030143275],"category_scores_gemma":[0.2120925,0.0012058716,0.0019409142,0.009671646,0.0034475208,0.005929439,0.003390442,0.0037417002,0.0022300878],"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":[0.00011508998,0.00013022662,0.0052035362,0.011246946,0.00044596728,0.00024779412,0.0013656453,0.0029937632,0.00033178728,0.04417952,0.0102478415,0.9234919],"study_design_scores_gemma":[0.00025003133,0.001158673,0.026642416,0.03499038,0.0009828185,0.006371766,0.002271921,0.025677769,0.0058104186,0.62136024,0.2740475,0.00043602163],"about_ca_topic_score_codex":0.0012667545,"about_ca_topic_score_gemma":0.0014118555,"teacher_disagreement_score":0.9267021,"about_ca_system_score_codex":0.0012236055,"about_ca_system_score_gemma":0.004482804,"threshold_uncertainty_score":0.38764095},"labels":[],"label_agreement":null},{"id":"W2155925908","doi":"10.1016/j.cct.2008.01.005","title":"Modelling overdispersion in longitudinal count data in clinical trials with application to epileptic data","year":2008,"lang":"en","type":"article","venue":"Contemporary Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"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 the Fraser Valley","funders":"","keywords":"Overdispersion; Count data; Statistics; Quasi-likelihood; Deviance information criterion; Econometrics; Outlier; Heteroscedasticity; Deviance (statistics); Markov chain Monte Carlo; Bayesian probability; Goodness of fit; Random effects model; Mathematics; Medicine; Poisson distribution","score_opus":0.8488626793025281,"score_gpt":0.6125816052196825,"score_spread":0.2362810740828456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155925908","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":[0.02980332,0.0040783775,0.96110773,0.0032193256,0.00019468769,0.00027446382,0.00039825216,0.00032779205,0.0005960449],"genre_scores_gemma":[0.6053378,0.0059333458,0.376071,0.0020827514,0.0010265848,0.0023834575,0.0010635351,0.0003357448,0.0057658534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8965069,0.08999212,0.0037510735,0.005487076,0.0033082373,0.0009545208],"domain_scores_gemma":[0.26514694,0.7066915,0.016023872,0.008713593,0.0025636612,0.00086039986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1796174,0.002661424,0.0068065166,0.00347753,0.0012041901,0.00511956,0.006298658,0.0069183097,0.003935686],"category_scores_gemma":[0.4346707,0.002647253,0.0041580503,0.005479919,0.006085832,0.0060028955,0.0039699804,0.007318797,0.00050605077],"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":[0.0013782022,0.00026969804,0.018555062,0.0015747807,0.0029669418,0.0012741251,0.0014636774,0.64407897,0.0005067488,0.23109752,0.0037591958,0.09307518],"study_design_scores_gemma":[0.00023414302,0.00022131397,0.0026202751,0.00020671013,0.00053700065,0.00035420147,0.00009101567,0.7865281,0.00022954817,0.20711969,0.0017871818,0.00007073073],"about_ca_topic_score_codex":0.0075491737,"about_ca_topic_score_gemma":0.0054828953,"teacher_disagreement_score":0.1796174,"about_ca_system_score_codex":0.0024785814,"about_ca_system_score_gemma":0.0038850708,"threshold_uncertainty_score":0.949919},"labels":[],"label_agreement":null},{"id":"W2156025750","doi":"10.1080/03610920600672278","title":"On the Intervened Generalized Poisson Distribution","year":2006,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"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 Calgary","funders":"","keywords":"Poisson distribution; Poisson process; Event (particle physics); Mathematics; Distribution (mathematics); Zero-inflated model; Bayesian probability; Statistics; Applied mathematics; Poisson regression; Computer science; Medicine; Mathematical analysis; Physics","score_opus":0.070545233024318,"score_gpt":0.4548389701233897,"score_spread":0.38429373709907166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156025750","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":[0.008544202,0.0014081715,0.97340864,0.0024003384,0.00028083305,0.000083558014,0.00026246862,0.00016353765,0.013448238],"genre_scores_gemma":[0.5404336,0.008456963,0.41229936,0.0030393938,0.002166077,0.0011750672,0.00089861016,0.00036323306,0.031167727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99248546,0.003999803,0.00028406084,0.0009802907,0.0017761196,0.0004742918],"domain_scores_gemma":[0.9770633,0.01743377,0.0013376699,0.0015511677,0.0021896616,0.00042451886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011891427,0.0008031062,0.0012519426,0.0022947327,0.0010706042,0.0028323964,0.0031567898,0.0026573236,0.0074884053],"category_scores_gemma":[0.039247762,0.0006307828,0.0015368499,0.0027704714,0.0047212075,0.00492996,0.0026657283,0.0047116065,0.0018619454],"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":[0.000015064734,0.000006415335,0.0004053768,0.000033601773,0.00000861714,0.0001483816,0.00014437622,0.012143197,0.00011509591,0.97833633,0.001756489,0.006887145],"study_design_scores_gemma":[0.000015475716,0.000018041394,0.00024110726,0.000055898716,0.000010825429,0.0002504662,0.000072503106,0.10799208,0.00010780937,0.8825443,0.008663149,0.000028365828],"about_ca_topic_score_codex":0.005437318,"about_ca_topic_score_gemma":0.0021628684,"teacher_disagreement_score":0.011891427,"about_ca_system_score_codex":0.0026078136,"about_ca_system_score_gemma":0.0019694723,"threshold_uncertainty_score":0.06288862},"labels":[],"label_agreement":null},{"id":"W2157235443","doi":"10.1177/0962280214521348","title":"Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model","year":2014,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":491,"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 on Aging; Economic and Social Research Council; University of California, San Diego; National Institutes of Health; Genentech; IXICO; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Covariate; Imputation (statistics); Missing data; Econometrics; Computer science; Statistics; Specification; Mathematics","score_opus":0.2276033325385271,"score_gpt":0.5725945814759557,"score_spread":0.34499124893742855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157235443","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":[0.0017032668,0.000072411756,0.9974125,0.00023554493,0.00001952022,0.000045243087,0.000103302635,0.00014245977,0.000265662],"genre_scores_gemma":[0.100503735,0.00032854045,0.89642215,0.0003933157,0.00012249166,0.00042343672,0.0008739007,0.00019278088,0.00073963637],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9532902,0.03901273,0.0016276606,0.0022468637,0.0030311462,0.0007913705],"domain_scores_gemma":[0.8854293,0.08484989,0.005221821,0.01898801,0.004720888,0.0007900204],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04854816,0.0010404018,0.0023072723,0.0016694147,0.00080463476,0.0026678538,0.0051223338,0.0023419913,0.003995533],"category_scores_gemma":[0.15986452,0.0012082309,0.0027686513,0.0049327887,0.0015308217,0.0030462763,0.004445557,0.0041473936,0.0012100985],"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":[0.00034120693,0.0001782241,0.015641112,0.00079627737,0.001005512,0.0005977505,0.0012284546,0.23910853,0.0014411875,0.46896172,0.012662876,0.25803712],"study_design_scores_gemma":[0.00014212415,0.00012789473,0.0016979208,0.00020509058,0.0001958123,0.0003290573,0.00009379063,0.6288177,0.0010267907,0.36094224,0.006345931,0.000075663476],"about_ca_topic_score_codex":0.003062418,"about_ca_topic_score_gemma":0.003861716,"teacher_disagreement_score":0.95145184,"about_ca_system_score_codex":0.00088957004,"about_ca_system_score_gemma":0.004958197,"threshold_uncertainty_score":0.2567503},"labels":[],"label_agreement":null},{"id":"W2157401555","doi":"10.1111/1469-7610.00706","title":"Multilevel Modelling of Hierarchical Data in Developmental Studies","year":2001,"lang":"en","type":"article","venue":"Journal of Child Psychology and Psychiatry","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":110,"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 New Brunswick; McMaster University; Hamilton Health Sciences","funders":"","keywords":"Psychology; Multilevel model; Developmental psychology; Computer science; Machine learning","score_opus":0.2706704858668096,"score_gpt":0.45948932987827007,"score_spread":0.18881884401146048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157401555","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":[0.0036556676,0.0008961338,0.9925075,0.0010065064,0.00011166575,0.00021773204,0.0003121379,0.00020660668,0.0010861129],"genre_scores_gemma":[0.12728669,0.0015995848,0.86605215,0.00041699022,0.0002500671,0.00255952,0.00068210624,0.0001740671,0.0009788292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8810877,0.1050231,0.0032768438,0.003940001,0.0054961964,0.0011761107],"domain_scores_gemma":[0.8173628,0.15731464,0.008082697,0.010823299,0.005310984,0.0011055426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07473765,0.0011580278,0.0030817841,0.005346734,0.0023092357,0.0054304963,0.0049732714,0.0025840276,0.0044526854],"category_scores_gemma":[0.2088084,0.0014971402,0.0042949324,0.0100125875,0.004075642,0.004769157,0.0065073427,0.0072449567,0.0007076555],"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":[0.00013038893,0.000076731274,0.009859695,0.00093785627,0.0009949001,0.00034104625,0.0050564874,0.056037296,0.0004850764,0.85242474,0.0046278853,0.06902793],"study_design_scores_gemma":[0.000074821764,0.00014251136,0.003322285,0.0004279326,0.00022977113,0.00014327571,0.0007151052,0.1853943,0.0003448152,0.7957631,0.013351576,0.00009063126],"about_ca_topic_score_codex":0.013229394,"about_ca_topic_score_gemma":0.01901017,"teacher_disagreement_score":0.07473765,"about_ca_system_score_codex":0.003757996,"about_ca_system_score_gemma":0.004269722,"threshold_uncertainty_score":0.3952552},"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":"W2157860153","doi":"10.1002/sim.3523","title":"Confidence interval construction for a difference between two dependent intraclass correlation coefficients","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":36,"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":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Confidence interval; Intraclass correlation; Statistics; Point estimation; Interval estimation; Inference; Mathematics; Sample size determination; Confidence distribution; Nominal level; Variance (accounting); Correlation; Coverage probability; CDF-based nonparametric confidence interval; Standard error; Computer science; Reproducibility; Artificial intelligence","score_opus":0.06250443287661349,"score_gpt":0.41815188558518207,"score_spread":0.35564745270856857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157860153","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":[0.0017815572,0.00021094512,0.99685836,0.00012437637,0.000052535994,0.000071586655,0.00009180895,0.00020522648,0.0006035748],"genre_scores_gemma":[0.07435401,0.00044879332,0.9222498,0.00017390665,0.00016944754,0.0012404907,0.0006264285,0.0002302011,0.0005069672],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9565833,0.023786318,0.0033382445,0.005943328,0.0094810175,0.0008678584],"domain_scores_gemma":[0.66959554,0.27996078,0.013062696,0.01678945,0.01935102,0.0012404706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07093043,0.0016792174,0.0021772915,0.009154476,0.0014450282,0.0037846898,0.005302833,0.0039354633,0.0069103865],"category_scores_gemma":[0.35109308,0.0009980729,0.0026069356,0.005185453,0.003920084,0.0048035686,0.0049988087,0.006986889,0.0015741879],"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":[0.00079917663,0.00022501692,0.008873642,0.0011804102,0.000612643,0.00080013025,0.0016563928,0.060002625,0.004139573,0.55874836,0.008797389,0.35416454],"study_design_scores_gemma":[0.00033880657,0.0007007974,0.006879782,0.00114794,0.00040007228,0.0018638325,0.00043853858,0.36700562,0.011137004,0.5873063,0.02242679,0.00035448108],"about_ca_topic_score_codex":0.0014899189,"about_ca_topic_score_gemma":0.0006358287,"teacher_disagreement_score":0.07093043,"about_ca_system_score_codex":0.001338727,"about_ca_system_score_gemma":0.0021513342,"threshold_uncertainty_score":0.37512046},"labels":[],"label_agreement":null},{"id":"W2158449790","doi":"10.1016/j.spl.2006.01.010","title":"Fitting MA(q) models in the closed invertible region","year":2006,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","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":"Western University; Acadia University","funders":"","keywords":"Invertible matrix; Maximization; Boundary (topology); Mathematics; Function (biology); Applied mathematics; Expectation–maximization algorithm; Maximum likelihood; Boundary value problem; Mathematical analysis; Statistics; Pure mathematics; Mathematical optimization","score_opus":0.08141926124053472,"score_gpt":0.32377361446724284,"score_spread":0.24235435322670812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158449790","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":[0.02133763,0.00019753644,0.9767785,0.00035480174,0.00001874168,0.000023343935,0.000084409294,0.00025662433,0.00094836793],"genre_scores_gemma":[0.7403474,0.0004821054,0.25165638,0.00041063438,0.00018108514,0.0002442914,0.00062373374,0.00040019053,0.0056542093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99495846,0.0029635124,0.00017935861,0.0012340669,0.00038370877,0.0002809026],"domain_scores_gemma":[0.96476203,0.030839037,0.0013505775,0.0018065176,0.00088609866,0.00035572017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008101681,0.0012357067,0.0019816821,0.0013074981,0.0007822344,0.0032093907,0.0032592164,0.0034462153,0.0046251034],"category_scores_gemma":[0.055275533,0.0014456951,0.0017012011,0.0012802734,0.0021237673,0.0038514063,0.0028708405,0.0041875914,0.0015771143],"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":[0.00024568412,0.000103439976,0.0022196546,0.00021374534,0.00024051113,0.0003648566,0.00044261533,0.7652134,0.0024169043,0.19197989,0.0016964593,0.034862928],"study_design_scores_gemma":[0.000021776403,0.000030942007,0.00021508962,0.000017278264,0.000018751014,0.000063764244,0.000039167742,0.8653292,0.00037334973,0.13326828,0.000604346,0.000018045594],"about_ca_topic_score_codex":0.006253657,"about_ca_topic_score_gemma":0.0035399843,"teacher_disagreement_score":0.008101681,"about_ca_system_score_codex":0.00086831854,"about_ca_system_score_gemma":0.0013453318,"threshold_uncertainty_score":0.042846322},"labels":[],"label_agreement":null},{"id":"W2158487021","doi":"10.1016/j.jmva.2012.02.011","title":"Hierarchical likelihood methods for nonlinear and generalized linear mixed models with missing data and measurement errors in covariates","year":2012,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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 British Columbia","funders":"","keywords":"Covariate; Laplace's method; Generalized linear mixed model; Mathematics; Missing data; Mixed model; Quasi-likelihood; Inference; Generalized linear model; Marginal likelihood; Nonlinear system; Applied mathematics; Convergence (economics); Algorithm; Statistics; Count data; Computer science; Maximum likelihood; Bayesian probability; Artificial intelligence","score_opus":0.18130570812435626,"score_gpt":0.44388168507602355,"score_spread":0.2625759769516673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158487021","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":[0.0006048416,0.00022743411,0.9986877,0.00010427638,0.00001912704,0.000041412946,0.000088852976,0.00010340561,0.00012293817],"genre_scores_gemma":[0.036985375,0.0008773405,0.95736134,0.0002096864,0.00022517296,0.0011297993,0.0008814088,0.000316566,0.0020132689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9705372,0.023601584,0.0012503363,0.0021508846,0.0019152898,0.00054478185],"domain_scores_gemma":[0.8812886,0.10383672,0.0040875226,0.0071451794,0.0027315589,0.0009104467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037828956,0.0026925823,0.004906889,0.0040369886,0.0020397652,0.0037857166,0.010839426,0.003688885,0.008118467],"category_scores_gemma":[0.118214756,0.0035345107,0.0053084143,0.0060459427,0.00434036,0.0057587284,0.00676061,0.0067437883,0.0014791173],"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":[0.0002921915,0.00016398361,0.0021176338,0.0008657043,0.0012601912,0.00036005402,0.0009170858,0.17323723,0.00067036273,0.69408363,0.0042509437,0.12178093],"study_design_scores_gemma":[0.000111086156,0.00005131457,0.00046285326,0.00009869536,0.0001623109,0.000102126476,0.00006842461,0.44479853,0.00022496545,0.55096,0.0028889647,0.00007077124],"about_ca_topic_score_codex":0.012264555,"about_ca_topic_score_gemma":0.018461479,"teacher_disagreement_score":0.037828956,"about_ca_system_score_codex":0.0031278087,"about_ca_system_score_gemma":0.0073115807,"threshold_uncertainty_score":0.20006102},"labels":[],"label_agreement":null},{"id":"W2160558412","doi":"10.1016/s0167-9473(01)00048-2","title":"A modified score function estimator for multinomial logistic regression in small samples","year":2002,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"","keywords":"Mathematics; Statistics; Multinomial logistic regression; Logistic regression; Covariate; Binomial regression; Estimator; Econometrics; Multinomial distribution","score_opus":0.4420553852036066,"score_gpt":0.4284358865853111,"score_spread":0.013619498618295478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160558412","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":[0.0034408139,0.00019484095,0.9954437,0.00016945341,0.00007050079,0.000049319777,0.0000750017,0.0001963309,0.00036007134],"genre_scores_gemma":[0.09522243,0.0004601012,0.89759004,0.00035678453,0.0004316424,0.0005892157,0.0006716985,0.000376419,0.0043017496],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899832,0.006919423,0.00038978993,0.00096353213,0.0015049054,0.00023906614],"domain_scores_gemma":[0.9692248,0.02180794,0.0012159322,0.0036451106,0.003549937,0.0005561186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015521722,0.0010205159,0.0022066631,0.0023571027,0.00069615565,0.0023848654,0.0044297045,0.0026653304,0.006324461],"category_scores_gemma":[0.07944721,0.00093121396,0.0015548895,0.0029998787,0.0017736988,0.0036300938,0.0033043833,0.002766151,0.0023885963],"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":[0.0006264541,0.00025103596,0.00877787,0.0005787921,0.00073715,0.00031996326,0.00031083112,0.09501627,0.0060437736,0.2780012,0.013409316,0.5959274],"study_design_scores_gemma":[0.00021528588,0.00017684732,0.0034305674,0.000098679244,0.00019611594,0.00041318746,0.000046622154,0.82845753,0.0015283646,0.1563603,0.008985551,0.00009089214],"about_ca_topic_score_codex":0.0028550497,"about_ca_topic_score_gemma":0.0035877577,"teacher_disagreement_score":0.015521722,"about_ca_system_score_codex":0.0010985039,"about_ca_system_score_gemma":0.0026522256,"threshold_uncertainty_score":0.082087696},"labels":[],"label_agreement":null},{"id":"W2162490486","doi":"10.1002/cjs.5550340301","title":"Pseudo‐empirical likelihood ratio confidence intervals for complex surveys","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Empirical likelihood; Mathematics; Confidence interval; Stratified sampling; CDF-based nonparametric confidence interval; Confidence distribution; Statistic; Coverage probability; Sampling (signal processing); Population; Sampling distribution; Likelihood function; Confidence region; Empirical distribution function; Maximum likelihood; Computer science; Demography","score_opus":0.12818388577127693,"score_gpt":0.3805694175399234,"score_spread":0.2523855317686465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162490486","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":[0.013831946,0.0007682571,0.9828414,0.00023846047,0.00006224108,0.000059191705,0.00008720821,0.00028012533,0.0018313483],"genre_scores_gemma":[0.4795269,0.0008224041,0.5171722,0.00029914317,0.00013451216,0.0005596977,0.00043558548,0.00022010587,0.0008295162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96250784,0.028818388,0.0012198822,0.001598032,0.005469821,0.00038602494],"domain_scores_gemma":[0.6045481,0.35779542,0.013185426,0.013250309,0.010366806,0.0008538846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03807738,0.000864759,0.0012131834,0.0039135986,0.0004501422,0.0028129378,0.002753605,0.002118769,0.0033411407],"category_scores_gemma":[0.3986364,0.00066278427,0.0010768006,0.003185674,0.003213034,0.0040213703,0.0028356723,0.002593925,0.0006966813],"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":[0.00062117906,0.0001241394,0.008673616,0.00074430386,0.00038314448,0.00033496326,0.0007531213,0.2502141,0.0016663247,0.58907235,0.003172303,0.14424053],"study_design_scores_gemma":[0.00017331267,0.00019593589,0.004304865,0.0002890974,0.00006861448,0.000423073,0.00012009393,0.6704639,0.0017735179,0.3170709,0.0050105443,0.000106163396],"about_ca_topic_score_codex":0.0012610687,"about_ca_topic_score_gemma":0.0005308712,"teacher_disagreement_score":0.03807738,"about_ca_system_score_codex":0.0012182408,"about_ca_system_score_gemma":0.0010264731,"threshold_uncertainty_score":0.20137489},"labels":[],"label_agreement":null},{"id":"W2162923601","doi":"10.1002/cjs.10044","title":"Nonparametric covariate adjustment for receiver operating characteristic curves","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Covariate; Nonparametric statistics; Receiver operating characteristic; Estimator; Statistics; Mathematics; Consistency (knowledge bases); Asymptotic distribution; Econometrics","score_opus":0.08102564128087644,"score_gpt":0.34876839508765656,"score_spread":0.26774275380678014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162923601","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":[0.007554647,0.00044359034,0.9900899,0.0002587311,0.00008857645,0.00015057423,0.0001994832,0.00053098967,0.00068351894],"genre_scores_gemma":[0.4630575,0.0003973794,0.5314293,0.0004906356,0.00020522525,0.0010473722,0.0010098333,0.00042885306,0.0019339364],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9523132,0.038540248,0.0011299095,0.0033257953,0.004098747,0.00059211184],"domain_scores_gemma":[0.9060497,0.06683349,0.007867681,0.012397393,0.0064273416,0.00042442873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029312922,0.00075817347,0.0013893778,0.0025683432,0.00056405476,0.0013491535,0.0026484318,0.0015110025,0.0025887156],"category_scores_gemma":[0.21177924,0.00060040166,0.0017938414,0.0035740563,0.0014343993,0.0015055471,0.0017962663,0.0027820864,0.0007123798],"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":[0.001420086,0.00029617958,0.048442263,0.000814885,0.0020172945,0.0006785892,0.0006968559,0.23325479,0.005775065,0.12668756,0.017001871,0.5629146],"study_design_scores_gemma":[0.00018471885,0.0006151954,0.039214198,0.00019219579,0.0002783918,0.000660053,0.00011413892,0.7674145,0.0046769115,0.16163415,0.024814202,0.00020125335],"about_ca_topic_score_codex":0.0047039767,"about_ca_topic_score_gemma":0.0026296636,"teacher_disagreement_score":0.029312922,"about_ca_system_score_codex":0.0018606607,"about_ca_system_score_gemma":0.0019567804,"threshold_uncertainty_score":0.1550234},"labels":[],"label_agreement":null},{"id":"W2163538316","doi":"","title":"On AR(1) versus MA(1) models for Non-stationary time series of Poisson counts: part I (theory)","year":2005,"lang":"en","type":"article","venue":"","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":"Memorial University of Newfoundland","funders":"","keywords":"Count data; Series (stratigraphy); Poisson distribution; Time series; Econometrics; Gaussian; Inference; Autoregressive model; Statistics; Computer science; Poisson regression; Mathematics; Artificial intelligence; Demography","score_opus":0.06314326095121194,"score_gpt":0.3553275614115586,"score_spread":0.29218430046034666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163538316","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":[0.018509272,0.0020371194,0.97453576,0.0012167791,0.00014779095,0.000059012254,0.00014532423,0.00013542647,0.0032134398],"genre_scores_gemma":[0.75221235,0.010971381,0.2124076,0.0014303902,0.002036282,0.0008511261,0.0011586265,0.00036228838,0.018570025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99329937,0.0044908645,0.00023343612,0.00082673325,0.0007741294,0.00037542422],"domain_scores_gemma":[0.89127254,0.09823346,0.004797894,0.0017238833,0.0033923043,0.0005799175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027471807,0.0018000853,0.0027927614,0.0027771404,0.0010219753,0.0030762998,0.0043757213,0.003970135,0.006277278],"category_scores_gemma":[0.059197262,0.0013864592,0.0027662683,0.003163079,0.0043264604,0.0053363345,0.0023485527,0.0042259195,0.0013621905],"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":[0.00006589658,0.00008688011,0.0021455549,0.00024488918,0.00013742896,0.00028378225,0.0004167911,0.37023625,0.00043721424,0.609991,0.0025755635,0.013378724],"study_design_scores_gemma":[0.000015158724,0.000034402707,0.0004146046,0.00004172601,0.000038791604,0.000047918875,0.00004521926,0.88660425,0.00008242044,0.111767784,0.0008779825,0.00002972344],"about_ca_topic_score_codex":0.011142201,"about_ca_topic_score_gemma":0.0062120864,"teacher_disagreement_score":0.027471807,"about_ca_system_score_codex":0.002412291,"about_ca_system_score_gemma":0.0015852745,"threshold_uncertainty_score":0.14528656},"labels":[],"label_agreement":null},{"id":"W2164186633","doi":"10.1177/0962280211414620","title":"A likelihood-based two-part marginal model for longitudinal semicontinuous data","year":2011,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":25,"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":"Medical Research Council","keywords":"Marginal model; Marginal structural model; Marginal distribution; Marginal likelihood; Covariate; Random effects model; Mixed model; Robustness (evolution); Statistics; Computer science; Statistical model; Population; Econometrics; Mathematics; Maximum likelihood; Regression analysis; Random variable; Medicine; Observational study","score_opus":0.6006715937666185,"score_gpt":0.6232256022617889,"score_spread":0.02255400849517042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164186633","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":[0.0046050055,0.00008599828,0.9943152,0.00021673062,0.000023350765,0.00006884162,0.00021531305,0.00012989492,0.00033959615],"genre_scores_gemma":[0.28093728,0.0005249022,0.706816,0.0006638588,0.00014448143,0.0020027976,0.0016182996,0.0002654259,0.0070269126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99009377,0.0069257645,0.0003620883,0.0012935362,0.0010021186,0.0003227167],"domain_scores_gemma":[0.9735839,0.020467104,0.0015761079,0.002806026,0.0011908109,0.0003760911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022840636,0.0012064741,0.0024096083,0.0016492679,0.0007128766,0.0022304624,0.004618871,0.0024842434,0.0071110516],"category_scores_gemma":[0.05048248,0.0011643986,0.0030962008,0.0022771247,0.0030488654,0.0034413224,0.0029791244,0.003971402,0.0014826505],"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":[0.00053085905,0.00019265921,0.010977355,0.00040719032,0.00043903204,0.0005888483,0.0016304305,0.19867137,0.0023319228,0.6805704,0.0039034449,0.09975646],"study_design_scores_gemma":[0.00007204228,0.0002184397,0.002474806,0.00009072367,0.00009483571,0.00027229163,0.00011836333,0.64301634,0.0006156767,0.34810466,0.004829026,0.00009278942],"about_ca_topic_score_codex":0.00388133,"about_ca_topic_score_gemma":0.0041478327,"teacher_disagreement_score":0.022840636,"about_ca_system_score_codex":0.0014905574,"about_ca_system_score_gemma":0.0021521018,"threshold_uncertainty_score":0.12079424},"labels":[],"label_agreement":null},{"id":"W2164627526","doi":"10.1002/sim.6395","title":"Sample size and robust marginal methods for cluster‐randomized trials with censored event times","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Waterloo","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Censoring (clinical trials); Statistics; Sample size determination; Estimator; Copula (linguistics); Marginal model; Mathematics; Econometrics; Proportional hazards model; Censored regression model; Regression analysis; Computer science","score_opus":0.09310038489766441,"score_gpt":0.46693814710278747,"score_spread":0.37383776220512305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164627526","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":[0.0005795897,0.00069732044,0.9971705,0.0003961043,0.00009723362,0.00034860222,0.000063709434,0.00015370111,0.000493258],"genre_scores_gemma":[0.058867924,0.0012408167,0.92955536,0.0007553278,0.00043601313,0.007247296,0.00030312783,0.00030837688,0.0012856562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9012337,0.088400565,0.0020587873,0.0033063937,0.004491565,0.0005090695],"domain_scores_gemma":[0.70727915,0.2632852,0.007347321,0.015062398,0.006037995,0.0009879564],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.119006455,0.001966274,0.0043015177,0.0039214375,0.00082665135,0.0025976163,0.0060066357,0.0029188497,0.006255871],"category_scores_gemma":[0.31938922,0.0013865194,0.0030646406,0.0031474526,0.0038704665,0.0035052486,0.0042563532,0.0062433057,0.0010388833],"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":[0.00086033397,0.00015055959,0.001568548,0.001394114,0.0010956153,0.00019439163,0.0005446932,0.06869783,0.0006910928,0.7559228,0.005921375,0.16295873],"study_design_scores_gemma":[0.0006367421,0.00040569998,0.0006786133,0.000436999,0.00033919635,0.00013199286,0.000070106486,0.28552154,0.0007383274,0.7003754,0.0106035825,0.00006188274],"about_ca_topic_score_codex":0.0015746609,"about_ca_topic_score_gemma":0.0013135918,"teacher_disagreement_score":0.88099355,"about_ca_system_score_codex":0.002530425,"about_ca_system_score_gemma":0.004919265,"threshold_uncertainty_score":0.6293738},"labels":[],"label_agreement":null},{"id":"W2166148588","doi":"10.1002/sim.3828","title":"A nonstationary Markov transition model for computing the relative risk of dementia before death","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"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 on Aging; Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation","keywords":"Dementia; Markov model; Transition (genetics); Markov chain; Computer science; Econometrics; Medicine; Mathematics; Disease; Machine learning; Internal medicine","score_opus":0.043449792847671864,"score_gpt":0.3868699731316014,"score_spread":0.3434201802839295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166148588","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":[0.016849129,0.00016790834,0.9822594,0.0001751484,0.000027509113,0.000028317567,0.00008931272,0.00013309499,0.0002701977],"genre_scores_gemma":[0.5882517,0.0008847199,0.40610167,0.00022853147,0.00020076812,0.0005101846,0.00081584975,0.000119088065,0.002887395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983962,0.00087682693,0.00009211208,0.0003075939,0.00021846357,0.000108706925],"domain_scores_gemma":[0.98096126,0.016858991,0.0009888986,0.0005349564,0.00045643188,0.00019950132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063902307,0.00068415113,0.0013218807,0.00166256,0.0006197093,0.0012375071,0.0020571512,0.0016086517,0.0025964787],"category_scores_gemma":[0.030165477,0.00076357706,0.0013409378,0.0012936982,0.0012785784,0.0021002935,0.0012197567,0.0020899563,0.00047763844],"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":[0.00012174545,0.000058388097,0.0033765635,0.00008956978,0.00011941811,0.000117074975,0.00012933338,0.88083655,0.0007175242,0.092290424,0.00067924015,0.021464111],"study_design_scores_gemma":[0.000009772597,0.00002786241,0.00032782683,0.000008689964,0.000016406495,0.00002659895,0.000007880132,0.9710407,0.000108680666,0.028219648,0.00019490249,0.0000109939265],"about_ca_topic_score_codex":0.009126819,"about_ca_topic_score_gemma":0.006442997,"teacher_disagreement_score":0.009126819,"about_ca_system_score_codex":0.0014175214,"about_ca_system_score_gemma":0.0015839692,"threshold_uncertainty_score":0.033795178},"labels":[],"label_agreement":null},{"id":"W2167092443","doi":"10.2307/3316088","title":"Local influence for generalized linear mixed models","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chinese University of Hong Kong","keywords":"Generalized linear mixed model; Likelihood function; Generalized linear model; Random effects model; Maximum likelihood; Mathematics; Mixed model; Applied mathematics; Linear model; Basis (linear algebra); Expectation–maximization algorithm; Function (biology); Algorithm; Computer science; Statistics","score_opus":0.08039790854700812,"score_gpt":0.3379807777922667,"score_spread":0.2575828692452586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167092443","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":[0.0030119778,0.00026763167,0.99551153,0.00013259864,0.000024375286,0.000038930615,0.000018804549,0.000113918875,0.00088039093],"genre_scores_gemma":[0.33245656,0.00092153833,0.6621856,0.00038533896,0.0003363333,0.0007459682,0.00021122971,0.00023714201,0.0025202902],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9735518,0.021564694,0.000489904,0.0014032666,0.002645135,0.00034517623],"domain_scores_gemma":[0.90117776,0.089289606,0.0031835237,0.002929948,0.0027154787,0.0007036591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022936665,0.0014152102,0.0020699813,0.003197356,0.0012656172,0.0021457216,0.0025239799,0.0021399527,0.0028008122],"category_scores_gemma":[0.10697114,0.0010935831,0.0026793377,0.0017883435,0.004238191,0.002139963,0.0036930093,0.0029174723,0.00051520823],"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":[0.00024443967,0.00006699956,0.003395515,0.00042337153,0.00055848603,0.0006239894,0.0007332474,0.21459739,0.001484547,0.7013284,0.0018072847,0.074736275],"study_design_scores_gemma":[0.000025023039,0.000059656086,0.0004934905,0.000058019286,0.00007538144,0.0001278284,0.00004527411,0.7291367,0.00075600337,0.2666298,0.0025634882,0.00002924],"about_ca_topic_score_codex":0.0038159,"about_ca_topic_score_gemma":0.002853146,"teacher_disagreement_score":0.022936665,"about_ca_system_score_codex":0.0022460266,"about_ca_system_score_gemma":0.0014207994,"threshold_uncertainty_score":0.12130207},"labels":[],"label_agreement":null},{"id":"W2167824481","doi":"10.1371/journal.pone.0058327","title":"The Mantel-Haenszel Procedure Revisited: Models and Generalizations","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"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 Manitoba","funders":"","keywords":"Statistics; Mathematics; Logistic regression; Confounding; Homogeneity (statistics); Estimator; Multinomial logistic regression; Odds ratio; Statistical hypothesis testing; Odds; Logarithm; Binary data; Binary number; Econometrics; Applied mathematics","score_opus":0.14223480287542686,"score_gpt":0.32353953801789664,"score_spread":0.18130473514246978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167824481","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":[0.0018532866,0.0061053564,0.98709756,0.002267672,0.00020239885,0.00008231877,0.0001521855,0.00025734887,0.001981827],"genre_scores_gemma":[0.13768668,0.019025855,0.8309689,0.0021335324,0.001964069,0.0013738787,0.0004295863,0.0004296867,0.0059878444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9580199,0.030995773,0.00150285,0.0036690223,0.005173539,0.000638858],"domain_scores_gemma":[0.9096268,0.077820376,0.0039155213,0.00561465,0.0026113484,0.00041133634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.069002815,0.0016505214,0.0036318537,0.0065561975,0.0010535226,0.003823432,0.0058386903,0.0033682594,0.004074609],"category_scores_gemma":[0.14171252,0.0017159146,0.0036184674,0.00942957,0.0074470933,0.005194358,0.0029395449,0.008251345,0.001543215],"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":[0.000039542654,0.000025515028,0.0017512651,0.00045312813,0.00034920624,0.00016162531,0.0006061264,0.011215586,0.00012129952,0.90869355,0.00381117,0.07277189],"study_design_scores_gemma":[0.00003133304,0.000047192014,0.00085840374,0.00017713933,0.00009453019,0.00032588647,0.000068579684,0.039884064,0.00015834646,0.94546616,0.012831596,0.00005677913],"about_ca_topic_score_codex":0.008961834,"about_ca_topic_score_gemma":0.005008842,"teacher_disagreement_score":0.069002815,"about_ca_system_score_codex":0.0023696835,"about_ca_system_score_gemma":0.0033160564,"threshold_uncertainty_score":0.3649261},"labels":[],"label_agreement":null},{"id":"W2168126634","doi":"10.1093/biostatistics/kxs028","title":"Testing multiple variance components in linear mixed-effects models","year":2012,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Polytechnique Montréal","funders":"","keywords":"Mathematics; Statistics; Test statistic; Null distribution; Estimator; Variance (accounting); Statistical hypothesis testing; Variance-based sensitivity analysis; One-way analysis of variance; Context (archaeology); Analysis of variance","score_opus":0.17829970945503099,"score_gpt":0.36776649057259386,"score_spread":0.18946678111756288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168126634","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":[0.012332291,0.00044123194,0.98586535,0.0003430524,0.000076329525,0.00017614312,0.00012677001,0.00023919552,0.00039962053],"genre_scores_gemma":[0.2936103,0.00085367047,0.70157206,0.0004033473,0.00022150793,0.0019359646,0.00050754426,0.00016881595,0.0007267484],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.87572604,0.1085251,0.003123088,0.006652115,0.005241186,0.00073246745],"domain_scores_gemma":[0.6706559,0.3139336,0.005418079,0.0069587994,0.0025503992,0.0004831409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0712146,0.002110912,0.0035250196,0.003275611,0.0013038571,0.0031324166,0.0035817898,0.0033146557,0.0035246727],"category_scores_gemma":[0.24208568,0.0012747573,0.0041902727,0.0033740797,0.0041395766,0.003040592,0.0031754572,0.0035354446,0.000556425],"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":[0.00200984,0.0005518119,0.05064761,0.0030583316,0.007380927,0.0017884949,0.0021218779,0.15573122,0.0038479208,0.34512445,0.003571144,0.42416644],"study_design_scores_gemma":[0.00034967324,0.0010553253,0.008988221,0.00039160126,0.00085972936,0.0005009492,0.0003449037,0.46510977,0.0031880557,0.515569,0.0034387684,0.0002039942],"about_ca_topic_score_codex":0.0019806826,"about_ca_topic_score_gemma":0.0017094845,"teacher_disagreement_score":0.0712146,"about_ca_system_score_codex":0.001140539,"about_ca_system_score_gemma":0.0024818492,"threshold_uncertainty_score":0.37662333},"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":"W2168884061","doi":"10.1016/s0167-6687(02)00130-0","title":"Estimators of the regression parameters of the zeta distribution","year":2002,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Estimator; Likelihood function; Restricted maximum likelihood; M-estimator; Variance function; Estimation theory; Applied mathematics","score_opus":0.0554313715532154,"score_gpt":0.2871325012176768,"score_spread":0.2317011296644614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168884061","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":[0.04300895,0.00058574945,0.9525111,0.0008150906,0.000081472106,0.000033545224,0.00019372621,0.0003210423,0.0024493667],"genre_scores_gemma":[0.7908346,0.0029209326,0.19337328,0.00037487262,0.00053816655,0.00036716083,0.0013015554,0.0005167473,0.009772718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981621,0.0011040603,0.000066616194,0.00027554613,0.00026952583,0.00012213906],"domain_scores_gemma":[0.96255696,0.028607965,0.0032890348,0.0025072778,0.0022606566,0.00077806064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010571082,0.0009281186,0.0010559487,0.0039445315,0.00048468067,0.0027367584,0.0020525106,0.0017597316,0.0055256947],"category_scores_gemma":[0.08969504,0.0008332712,0.00077672437,0.0014456194,0.0026546498,0.005661846,0.0020567118,0.0037269525,0.00151713],"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":[0.0001933191,0.00008692311,0.0065524154,0.00017621859,0.00013556502,0.000055308887,0.00022314451,0.0906046,0.0022253916,0.83760095,0.0033336433,0.05881243],"study_design_scores_gemma":[0.00007246238,0.000032002572,0.0027869148,0.000107984175,0.000041887124,0.000082525454,0.00008797104,0.44403225,0.0013100512,0.5494504,0.0019311317,0.00006438499],"about_ca_topic_score_codex":0.0010763023,"about_ca_topic_score_gemma":0.0008116474,"teacher_disagreement_score":0.010571082,"about_ca_system_score_codex":0.0011351109,"about_ca_system_score_gemma":0.0012074773,"threshold_uncertainty_score":0.05590588},"labels":[],"label_agreement":null},{"id":"W2169168582","doi":"10.1191/1740774505cn126oa","title":"Group sequential methods for cluster randomization trials with binary outcomes","year":2005,"lang":"en","type":"article","venue":"Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Robarts Clinical Trials; Western University","funders":"World Health Organization","keywords":"Randomization; Sample size determination; Cluster randomised controlled trial; Statistics; Early stopping; Interim; Interim analysis; Cluster (spacecraft); Computer science; Statistical hypothesis testing; Clinical trial; Randomized controlled trial; Data mining; Medicine; Mathematics; Machine learning","score_opus":0.6068582697390743,"score_gpt":0.654639154337866,"score_spread":0.047780884598791706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169168582","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":[0.0006867978,0.0004218889,0.99683845,0.00022851913,0.00010528369,0.0009952015,0.000082646155,0.0001431884,0.0004981169],"genre_scores_gemma":[0.033058174,0.00091262313,0.9560854,0.0003032165,0.00016325575,0.008452847,0.00020704507,0.0001082201,0.00070916046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8463785,0.14018218,0.0028080707,0.0027303598,0.007387751,0.0005132303],"domain_scores_gemma":[0.7883066,0.18756762,0.008123796,0.008988647,0.0062363325,0.00077694916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12266474,0.002321131,0.0033641371,0.0046220706,0.0008510152,0.0020615042,0.003693804,0.0025348426,0.011547015],"category_scores_gemma":[0.25293797,0.0010254034,0.0025720971,0.0052470784,0.0038725256,0.0024539148,0.0027165066,0.004554678,0.0017484906],"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":[0.0011661374,0.00020668999,0.0020603728,0.0032760962,0.0013526875,0.00027965553,0.00086768315,0.09241082,0.00068005413,0.63347983,0.009403516,0.2548165],"study_design_scores_gemma":[0.0011687455,0.0006515174,0.0005180552,0.0006692541,0.0002881108,0.00016384589,0.000088506495,0.29106003,0.00081242603,0.69309545,0.011409744,0.00007441964],"about_ca_topic_score_codex":0.0015011965,"about_ca_topic_score_gemma":0.0016129176,"teacher_disagreement_score":0.12266474,"about_ca_system_score_codex":0.0026981696,"about_ca_system_score_gemma":0.006059469,"threshold_uncertainty_score":0.6487209},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2169649310","doi":"10.1002/sim.2892","title":"Accelerated failure time models with covariates subject to measurement error","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":49,"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; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Observational error; Estimator; Econometrics; Statistics; Proportional hazards model; Extrapolation; Computer science; Errors-in-variables models; Accelerated failure time model; Data set; Mathematics","score_opus":0.12878516186870306,"score_gpt":0.39882542307849034,"score_spread":0.2700402612097873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169649310","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":[0.022906125,0.0010088586,0.97330666,0.00086717267,0.00017027443,0.00013678477,0.00029451327,0.00024082215,0.0010688789],"genre_scores_gemma":[0.6486276,0.0055387057,0.31793147,0.0007227941,0.0011022319,0.001980622,0.0018238872,0.00023076312,0.022041878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.979752,0.0140390955,0.0007908915,0.0020921524,0.0023437275,0.0009821171],"domain_scores_gemma":[0.8790699,0.09782225,0.011216368,0.0061582364,0.004793033,0.00094025646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043434177,0.0020950036,0.00411573,0.0028185775,0.0011332256,0.002878809,0.005720396,0.0036450087,0.004576277],"category_scores_gemma":[0.10392434,0.0015991871,0.003259308,0.0041212356,0.0032906279,0.0037494188,0.0039590723,0.0052000675,0.0011999826],"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":[0.00025917374,0.00011440573,0.009864296,0.0004099413,0.00042203293,0.0007396145,0.0008000602,0.5062264,0.00055144454,0.43965313,0.00227849,0.038680978],"study_design_scores_gemma":[0.000132908,0.00016639233,0.001707091,0.00010068422,0.00012589262,0.00020676367,0.000056337034,0.7587271,0.00022519368,0.23535354,0.0031280671,0.0000700841],"about_ca_topic_score_codex":0.007541768,"about_ca_topic_score_gemma":0.0041784346,"teacher_disagreement_score":0.043434177,"about_ca_system_score_codex":0.0016518004,"about_ca_system_score_gemma":0.0030210442,"threshold_uncertainty_score":0.22970462},"labels":[],"label_agreement":null},{"id":"W2169943518","doi":"10.6000/1929-6029.2012.01.02.08","title":"Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Ordinal data; Ordinal regression; Random effects model; Statistics; Econometrics; Hyperparameter; Variable-order Bayesian network; Longitudinal data; Computer science; Mathematics; Logistic regression; Bayesian hierarchical modeling; Bayes' theorem; Bayesian inference; Data mining; Machine learning","score_opus":0.3674978022759443,"score_gpt":0.5906975653593451,"score_spread":0.22319976308340084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169943518","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":[0.006453795,0.00025758013,0.99241626,0.00026505545,0.000017532928,0.00005953585,0.00016511843,0.0001390121,0.00022605354],"genre_scores_gemma":[0.27018335,0.0015316495,0.7217033,0.00042796438,0.00023119157,0.0013023039,0.0016825328,0.0002686573,0.002669011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98947585,0.007745145,0.00035465063,0.0010390419,0.0010799681,0.00030530474],"domain_scores_gemma":[0.92525554,0.06751563,0.0022723821,0.0022209974,0.0022229478,0.0005124811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024882885,0.0008532863,0.0023605095,0.002494545,0.001077009,0.0016742375,0.0024332637,0.0018062012,0.0038616408],"category_scores_gemma":[0.078983344,0.00077198667,0.0022033846,0.0027499397,0.0014825813,0.0028461274,0.0022762995,0.004072158,0.0005959007],"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":[0.00052262103,0.00024593782,0.012951988,0.00068894366,0.0006905905,0.00068830437,0.0012451363,0.42972574,0.002285813,0.38388193,0.005004797,0.16206817],"study_design_scores_gemma":[0.000042654752,0.00006953512,0.0019216381,0.00005754612,0.000067069515,0.0001230439,0.00007352908,0.8476604,0.00028533416,0.1478706,0.0017831929,0.000045442568],"about_ca_topic_score_codex":0.008454608,"about_ca_topic_score_gemma":0.0063199494,"teacher_disagreement_score":0.024882885,"about_ca_system_score_codex":0.0013110584,"about_ca_system_score_gemma":0.0025184148,"threshold_uncertainty_score":0.13159484},"labels":[],"label_agreement":null},{"id":"W2170016983","doi":"10.1093/biostatistics/kxm054","title":"A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates","year":2008,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian 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":"National Heart, Lung, and Blood Institute","keywords":"Covariate; Missing data; Estimator; Observational error; Statistics; Computer science; Econometrics; Dropout (neural networks); Framingham Heart Study; Data set; Inference; Mathematics; Artificial intelligence; Machine learning","score_opus":0.18878984038607685,"score_gpt":0.42635502145611714,"score_spread":0.2375651810700403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170016983","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":[0.00063042965,0.0000930977,0.9988136,0.00008190043,0.000020849651,0.00004097453,0.0000282453,0.00011061499,0.00018031236],"genre_scores_gemma":[0.06857351,0.0006542673,0.9265342,0.00021552156,0.0001532502,0.0010087965,0.00037527992,0.00021224194,0.002272933],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99415994,0.0046777674,0.00013794434,0.00040074877,0.0005237775,0.00009975978],"domain_scores_gemma":[0.9816066,0.014845171,0.00067266234,0.0014336089,0.0010920404,0.00034999388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0152488155,0.0008880935,0.0016418002,0.0020924879,0.00082368276,0.0011531695,0.0033432848,0.0012980989,0.00720063],"category_scores_gemma":[0.04656116,0.0008074898,0.001963782,0.0018927185,0.0017789953,0.0021588483,0.002607444,0.0028233458,0.0012568077],"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":[0.00028310574,0.00015560165,0.0043838494,0.00038375173,0.00048470998,0.00026847658,0.000478392,0.3177242,0.001300318,0.46842295,0.0056600906,0.20045461],"study_design_scores_gemma":[0.00007898371,0.00008011314,0.00055015786,0.00006766105,0.000058104953,0.00013899707,0.000029919396,0.85754746,0.00036242494,0.13461256,0.006429839,0.00004373987],"about_ca_topic_score_codex":0.0043039587,"about_ca_topic_score_gemma":0.004867422,"teacher_disagreement_score":0.0152488155,"about_ca_system_score_codex":0.001137883,"about_ca_system_score_gemma":0.003688657,"threshold_uncertainty_score":0.08064443},"labels":[],"label_agreement":null},{"id":"W2170395705","doi":"10.2307/3315496","title":"A uniform saddlepoint expansion for the null‐distribution of the wilcoxon‐mann‐whitney statistic","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Mathematics; Statistic; Mann–Whitney U test; Wilcoxon signed-rank test; Statistics; Null distribution; Null (SQL); Null hypothesis; Test statistic; Statistical hypothesis testing; Econometrics; Computer science; Data mining","score_opus":0.04556840740202034,"score_gpt":0.3061026123567303,"score_spread":0.26053420495470997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170395705","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":[0.0058423444,0.00018186096,0.99080276,0.00024294107,0.000077275276,0.000030765164,0.000058560523,0.00010133416,0.002662129],"genre_scores_gemma":[0.49188212,0.0019088897,0.48301902,0.0008496175,0.0005220762,0.00070137886,0.0005717013,0.0007614111,0.019783784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986436,0.0006630042,0.000055532964,0.00016246925,0.0003895001,0.00008596718],"domain_scores_gemma":[0.9884165,0.009180965,0.00040893536,0.00053616683,0.0012241537,0.0002331875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064347084,0.0008301843,0.00090181746,0.0018692341,0.00049111526,0.0011734002,0.0014473473,0.00086612336,0.0073182015],"category_scores_gemma":[0.030251963,0.00047232534,0.0009845463,0.001074294,0.001940396,0.0021918423,0.0015550257,0.00257395,0.001874363],"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":[0.00008278331,0.000033295735,0.00086667057,0.00015097608,0.000049958227,0.00034042264,0.00015976984,0.1169034,0.0040812297,0.83792067,0.005277094,0.034133818],"study_design_scores_gemma":[0.000015957847,0.00003912599,0.00044683905,0.000064293505,0.000018920182,0.00018635941,0.000033191074,0.7595935,0.001621855,0.23417103,0.00377757,0.000031462852],"about_ca_topic_score_codex":0.0016171132,"about_ca_topic_score_gemma":0.0013087223,"teacher_disagreement_score":0.0073182015,"about_ca_system_score_codex":0.00096048473,"about_ca_system_score_gemma":0.001038874,"threshold_uncertainty_score":0.034030378},"labels":[],"label_agreement":null},{"id":"W2171484293","doi":"10.1177/1740774508089511","title":"Profile-specific survival estimates: Making reports of clinical trials more patient-relevant","year":2008,"lang":"en","type":"article","venue":"Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Clinical trial; Medicine; Intensive care medicine; Internal medicine","score_opus":0.7632904334094643,"score_gpt":0.6297236592842288,"score_spread":0.13356677412523554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171484293","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060786386,0.03826037,0.8019325,0.10287145,0.01673626,0.0026551133,0.009628903,0.004996641,0.016840126],"genre_scores_gemma":[0.1443082,0.027705751,0.7648553,0.02104167,0.02142451,0.010107674,0.0056223166,0.002766133,0.0021684933],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.30807683,0.57284,0.07136264,0.012155305,0.034622863,0.00094244373],"domain_scores_gemma":[0.035258878,0.8031256,0.066433355,0.05815681,0.03547275,0.001552498],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.58686495,0.002674953,0.00435722,0.021837153,0.001274736,0.024087733,0.0053797252,0.006726063,0.009548652],"category_scores_gemma":[0.92268926,0.0032043455,0.004329968,0.014157258,0.0066932915,0.03227301,0.010049669,0.011572595,0.003638458],"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":[0.0014598488,0.00014849872,0.00972322,0.013673608,0.0029221396,0.00026956887,0.0071519027,0.009762654,0.0005793815,0.12077179,0.11323719,0.72030014],"study_design_scores_gemma":[0.0015597421,0.0010155303,0.010240104,0.038552515,0.0035157357,0.0008616063,0.0025830215,0.01821691,0.0034474775,0.44339827,0.47578824,0.000820812],"about_ca_topic_score_codex":0.0012056339,"about_ca_topic_score_gemma":0.00083575497,"teacher_disagreement_score":0.41313505,"about_ca_system_score_codex":0.0057703434,"about_ca_system_score_gemma":0.009535825,"threshold_uncertainty_score":0.50946915},"labels":[],"label_agreement":null},{"id":"W2176987742","doi":"10.1002/bimj.201700101","title":"Bivariate random‐effects meta‐analysis models for diagnostic test accuracy studies using arcsine‐based transformations","year":2018,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bivariate analysis; Statistics; Bivariate data; Mathematics; Random effects model; Sensitivity (control systems); Gold standard (test); Meta-analysis; Computer science; Medicine","score_opus":0.3701916153503816,"score_gpt":0.48661353815634933,"score_spread":0.11642192280596775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176987742","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":[0.006595049,0.013232792,0.974345,0.0010436694,0.00036751054,0.0014981308,0.0015357356,0.00085245364,0.000529613],"genre_scores_gemma":[0.29000363,0.009839745,0.6767507,0.0014055192,0.0004078978,0.015073931,0.0028413476,0.00042718163,0.003250056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9013399,0.08513376,0.0038111988,0.006270889,0.002980532,0.00046383764],"domain_scores_gemma":[0.8393709,0.14386387,0.0051742964,0.008928292,0.002392786,0.00026983613],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12409342,0.0034081114,0.005748993,0.005486222,0.0006605926,0.0029371951,0.0053199665,0.002776372,0.0072751204],"category_scores_gemma":[0.19880262,0.0014114946,0.018522007,0.00595862,0.0017679586,0.002836549,0.0024404887,0.0050765965,0.00095191825],"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":[0.005966694,0.00034650098,0.024676377,0.018675305,0.11738148,0.0008892349,0.00092490274,0.4100337,0.0019784244,0.115466714,0.013093956,0.2905667],"study_design_scores_gemma":[0.0028723464,0.0014566001,0.0085348375,0.002357116,0.07126351,0.0005100184,0.00016148755,0.69302696,0.002869785,0.19419073,0.022468584,0.00028801887],"about_ca_topic_score_codex":0.0039556553,"about_ca_topic_score_gemma":0.0034468032,"teacher_disagreement_score":0.8759066,"about_ca_system_score_codex":0.0018857996,"about_ca_system_score_gemma":0.0024467958,"threshold_uncertainty_score":0.6562766},"labels":[],"label_agreement":null},{"id":"W2182010196","doi":"","title":"NEIGHBOURHOOD FACTORS AND CHILDREN: SMALL AREA STATISTICS","year":2003,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian 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":"","keywords":"Small area estimation; Estimator; Statistics; Mean squared error; Mathematics; Neighbourhood (mathematics); Metropolitan area; Econometrics; Geography","score_opus":0.060749306209135386,"score_gpt":0.3190212886509765,"score_spread":0.25827198244184113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182010196","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":[0.6442812,0.0060414434,0.3394703,0.0013368905,0.00027557742,0.00022397972,0.0006682986,0.00017192237,0.007530405],"genre_scores_gemma":[0.96950746,0.00060290145,0.027860187,0.00017338508,0.00013258494,0.0001367326,0.00050699693,0.000043077293,0.0010366788],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9797672,0.015108424,0.00056537736,0.0019149979,0.0024253305,0.00021864758],"domain_scores_gemma":[0.7105975,0.25876406,0.014930468,0.011919622,0.0027199525,0.001068367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028768342,0.0003794484,0.0014656201,0.0016533966,0.000611361,0.0013796778,0.0015481345,0.0009862422,0.0034258119],"category_scores_gemma":[0.1593209,0.00029099028,0.00092736067,0.0026456497,0.0027332548,0.0025899864,0.001578758,0.0014936251,0.00020485448],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012975682,0.00019537147,0.7423575,0.00055439724,0.003613306,0.0005854103,0.0016483283,0.042230174,0.0004942471,0.06377679,0.0026002247,0.14064665],"study_design_scores_gemma":[0.00026094328,0.0020295633,0.62230396,0.00033328668,0.0014740317,0.0013421031,0.002023821,0.18949069,0.001317649,0.16395818,0.0153335715,0.00013226668],"about_ca_topic_score_codex":0.0074931025,"about_ca_topic_score_gemma":0.007864684,"teacher_disagreement_score":0.028768342,"about_ca_system_score_codex":0.000694739,"about_ca_system_score_gemma":0.00091461063,"threshold_uncertainty_score":0.15214336},"labels":[],"label_agreement":null},{"id":"W2182669574","doi":"10.1007/s10985-015-9352-x","title":"A case-base sampling method for estimating recurrent event intensities","year":2015,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Sampling (signal processing); Context (archaeology); Event (particle physics); Proportional hazards model; Outcome (game theory); Logistic regression; Mathematics; Hazard; Econometrics; Computer science","score_opus":0.32579478472646684,"score_gpt":0.49139728238729197,"score_spread":0.16560249766082513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182669574","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":[0.0022214998,0.0001157779,0.99702066,0.00007546388,0.00002992585,0.000100638325,0.00005881035,0.00010159706,0.00027549945],"genre_scores_gemma":[0.11163932,0.00055996835,0.8824634,0.0002483981,0.00028242404,0.0011489153,0.0008465513,0.00014376015,0.002667343],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98958987,0.0069396896,0.000397076,0.0012551249,0.0015895503,0.00022871434],"domain_scores_gemma":[0.94215214,0.047886163,0.0015956734,0.0051344153,0.0025599077,0.0006717357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032305636,0.0013102501,0.002626676,0.0035755849,0.0013062241,0.0019196476,0.0057275905,0.0023657312,0.0068568382],"category_scores_gemma":[0.07936241,0.001409122,0.0021360258,0.002835869,0.001994799,0.002978497,0.002232714,0.0038424907,0.0010532732],"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":[0.0008289636,0.0007674864,0.010161916,0.00042353672,0.0010246248,0.00076101464,0.00067475485,0.26300192,0.0032555861,0.34337854,0.008354173,0.3673675],"study_design_scores_gemma":[0.00011176893,0.000103706356,0.00095152564,0.0000645083,0.00018233818,0.00036194042,0.00004746262,0.8901264,0.00073270185,0.10413916,0.003136672,0.000041906715],"about_ca_topic_score_codex":0.0051937564,"about_ca_topic_score_gemma":0.004858346,"teacher_disagreement_score":0.032305636,"about_ca_system_score_codex":0.001148559,"about_ca_system_score_gemma":0.0020075545,"threshold_uncertainty_score":0.17085063},"labels":[],"label_agreement":null},{"id":"W2184159343","doi":"","title":"Confidence interval estimation of small area parameters shrinking both means and variances","year":2012,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Small area estimation; Statistics; Confidence interval; Mathematics; Mean squared error; Likelihood function; Maximum likelihood; Efficiency; Sampling (signal processing); Estimation; Applied mathematics; Computer science","score_opus":0.4756757551178532,"score_gpt":0.45104081200118146,"score_spread":0.024634943116671748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184159343","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":[0.0021950158,0.00032346023,0.9971259,0.000036654823,0.000013613731,0.0000071868058,0.000013599716,0.00005707208,0.00022751154],"genre_scores_gemma":[0.3217999,0.0022872707,0.67326885,0.00020287317,0.00033555512,0.0002802941,0.00037145545,0.00019258325,0.001261281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928738,0.0035408163,0.00036361022,0.001239087,0.0017368408,0.0002458478],"domain_scores_gemma":[0.9402081,0.048792664,0.0034008066,0.00375045,0.003433779,0.00041427164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012731809,0.0010896662,0.0019380104,0.0028531596,0.00043200614,0.0021805272,0.00317015,0.0018295071,0.0017017679],"category_scores_gemma":[0.07595586,0.0007822131,0.0013678891,0.0030017644,0.0016661555,0.0038780607,0.0031156777,0.0028117944,0.0004620681],"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":[0.0002600689,0.00009354193,0.005472392,0.00058413163,0.0004252912,0.00022727536,0.00039721042,0.44185922,0.0065578516,0.2975064,0.0020389052,0.2445777],"study_design_scores_gemma":[0.000024661818,0.00007676169,0.0011995919,0.00007094361,0.00006633566,0.00013481002,0.000030260584,0.9149357,0.0024094079,0.078677244,0.0023231246,0.000051050545],"about_ca_topic_score_codex":0.0011794936,"about_ca_topic_score_gemma":0.0005510524,"teacher_disagreement_score":0.012731809,"about_ca_system_score_codex":0.00065457594,"about_ca_system_score_gemma":0.0008918377,"threshold_uncertainty_score":0.06733304},"labels":[],"label_agreement":null},{"id":"W2186127712","doi":"10.1002/cjs.11273","title":"A semivarying joint model for longitudinal binary and continuous outcomes","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Institute on Drug Abuse; National Institutes of Health; National Science Foundation","keywords":"Covariate; Estimator; Binary number; Statistics; Multivariate statistics; Asymptotic distribution; Binary data; Latent variable; Mathematics; Marginal model; Computer science; Latent variable model; Multivariate normal distribution; Marginal distribution; Constant (computer programming); Econometrics; Regression analysis; Random variable","score_opus":0.1988227043769351,"score_gpt":0.3623722910237851,"score_spread":0.16354958664685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186127712","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":[0.016076686,0.00031306324,0.9814964,0.00038949162,0.0000486654,0.00007156469,0.0006058832,0.00015636384,0.0008418536],"genre_scores_gemma":[0.6918581,0.0014529055,0.28694555,0.00064066914,0.00033442542,0.0011758759,0.0031238447,0.0001890566,0.014279551],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99099183,0.0057382155,0.00030297606,0.0018191345,0.00067932275,0.00046857068],"domain_scores_gemma":[0.9727069,0.019020615,0.0032561158,0.0033425735,0.0012029306,0.0004708325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017014522,0.0011835091,0.0017360622,0.0015293085,0.0005253099,0.001714636,0.004778348,0.002027312,0.0063774036],"category_scores_gemma":[0.032125764,0.0008776877,0.0027938345,0.00202951,0.0025635848,0.0033379255,0.0019828603,0.00299729,0.0012348167],"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":[0.00036356738,0.0001386333,0.015076953,0.000295418,0.00045777202,0.00064972375,0.00094667403,0.2174184,0.0020400595,0.7042446,0.0026302321,0.055738013],"study_design_scores_gemma":[0.00007162135,0.00029618185,0.006033415,0.00009225914,0.00019301487,0.0003643908,0.00015794799,0.68804586,0.0006894227,0.2970279,0.006927551,0.00010040321],"about_ca_topic_score_codex":0.00577597,"about_ca_topic_score_gemma":0.0045606443,"teacher_disagreement_score":0.017014522,"about_ca_system_score_codex":0.0013093612,"about_ca_system_score_gemma":0.001217394,"threshold_uncertainty_score":0.08998245},"labels":[],"label_agreement":null},{"id":"W2188701520","doi":"","title":"Model-Based Unemployment Rate Estimation for the Canadian Labour Force Survey: A Hierarchical Bayes Approach","year":2003,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":35,"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":"Unemployment; Small area estimation; Econometrics; Statistics; Estimator; Bayes' theorem; Gibbs sampling; Estimation; Bayesian probability; Bayes estimator; Posterior probability; Economics; Covariate; Bayesian hierarchical modeling; Mathematics","score_opus":0.12086902713100069,"score_gpt":0.3685073281617664,"score_spread":0.24763830103076573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188701520","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":[0.0065671788,0.0013176115,0.98825127,0.00052491954,0.00003888474,0.00030124394,0.000792,0.00037927512,0.0018276607],"genre_scores_gemma":[0.17496039,0.0025207116,0.8125799,0.000334052,0.00014929302,0.0011576064,0.00292708,0.00022642029,0.005144635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99597067,0.0021662933,0.00018107283,0.0005313986,0.0008879545,0.00026260104],"domain_scores_gemma":[0.9934977,0.0048576524,0.00028987593,0.00030175058,0.00094770687,0.00010526361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008859234,0.00093234587,0.0022118725,0.0036576719,0.0021822366,0.0019468322,0.0034439627,0.0011738557,0.0044287606],"category_scores_gemma":[0.025516715,0.0013387181,0.0020400602,0.0029669632,0.0010831914,0.0011978887,0.0011698757,0.0016467394,0.00075091235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011446558,0.00010629827,0.010423324,0.0005592161,0.0005882366,0.00029854936,0.0006303905,0.5446486,0.00071028067,0.19209284,0.015017819,0.23480998],"study_design_scores_gemma":[0.000044814096,0.000012460101,0.0028831153,0.000097459924,0.000103508166,0.00006598419,0.00007119598,0.9270878,0.00013746528,0.064686865,0.004757892,0.00005141079],"about_ca_topic_score_codex":0.68664753,"about_ca_topic_score_gemma":0.7488869,"teacher_disagreement_score":0.31335247,"about_ca_system_score_codex":0.008987097,"about_ca_system_score_gemma":0.019006435,"threshold_uncertainty_score":0.6303957},"labels":[],"label_agreement":null},{"id":"W2197240700","doi":"10.1108/s0731-905320190000039004","title":"Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013 Methods-of-Payment Survey Questionnaire","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"Bank of Canada","funders":"","keywords":"Resampling; Statistics; Stratified sampling; Variance (accounting); Weighting; Consistency (knowledge bases); Econometrics; Sampling (signal processing); Sampling design; Survey sampling; Computer science; Mathematics; Population; Artificial intelligence; Medicine","score_opus":0.46279660571577197,"score_gpt":0.5243988977165219,"score_spread":0.06160229200074996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2197240700","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":[0.003157543,0.0002704675,0.99263555,0.00023310176,0.00010528957,0.00028569216,0.00031652776,0.0002067721,0.0027891311],"genre_scores_gemma":[0.057632938,0.00052781176,0.9345042,0.00026767055,0.00014264957,0.002412546,0.0011516524,0.00034458164,0.0030159384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9604542,0.033147763,0.00077525893,0.0014194618,0.003933599,0.00026969396],"domain_scores_gemma":[0.92170006,0.062447157,0.0030790502,0.0068853754,0.005707336,0.00018098178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0397978,0.00084674725,0.0009819804,0.0023869607,0.00061777537,0.0014076378,0.0020680635,0.00088834774,0.009178898],"category_scores_gemma":[0.16852584,0.0005854978,0.0013779418,0.004033158,0.0011802837,0.0017189606,0.001514767,0.0021595336,0.0023774598],"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":[0.00012189535,0.00016387942,0.012144547,0.00076658104,0.00040843402,0.00015811692,0.0014745942,0.02218582,0.0011471465,0.35387546,0.050062064,0.5574915],"study_design_scores_gemma":[0.00013950236,0.00031555656,0.027953645,0.0013652164,0.0002326156,0.00045637877,0.00088449783,0.2775747,0.004389722,0.576103,0.11039382,0.0001913595],"about_ca_topic_score_codex":0.0035940122,"about_ca_topic_score_gemma":0.0046899067,"teacher_disagreement_score":0.0397978,"about_ca_system_score_codex":0.0012773924,"about_ca_system_score_gemma":0.0016475619,"threshold_uncertainty_score":0.21047336},"labels":[],"label_agreement":null},{"id":"W2224720476","doi":"10.1177/0049124115610345","title":"Obtaining Predictions from Models Fit to Multiply Imputed Data","year":2015,"lang":"en","type":"article","venue":"Sociological Methods & Research","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Computer science; Imputation (statistics); Set (abstract data type); Data set; Data mining; Missing data; Machine learning; Artificial intelligence","score_opus":0.8988619494689873,"score_gpt":0.6764142898064175,"score_spread":0.22244765966256985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2224720476","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":[0.0142172035,0.00018502594,0.98059857,0.00053476525,0.00006842508,0.00015517768,0.0006534717,0.001054041,0.0025333283],"genre_scores_gemma":[0.24654344,0.0005693018,0.74460226,0.0004855439,0.000105280684,0.0010281131,0.0027356003,0.0013125291,0.0026179468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9782995,0.01529642,0.0009910653,0.0022620424,0.0026447973,0.0005062238],"domain_scores_gemma":[0.8692594,0.11006575,0.003871038,0.01173598,0.0046265353,0.00044133913],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031939212,0.0020530527,0.0019992809,0.0030535546,0.0009397486,0.0037557937,0.0030478043,0.0017742105,0.0073140725],"category_scores_gemma":[0.17646106,0.0017756096,0.004048218,0.002877913,0.0016935654,0.0042051063,0.0031197888,0.005975431,0.003614196],"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":[0.000404,0.0002579637,0.027694864,0.0012935756,0.0016558171,0.001248776,0.002179503,0.52522945,0.002462534,0.17594035,0.021030283,0.24060291],"study_design_scores_gemma":[0.000048155616,0.00012587945,0.0039834464,0.00036685908,0.00018793135,0.00028497417,0.00036533488,0.7202096,0.0032822369,0.26353315,0.0074841245,0.000128278],"about_ca_topic_score_codex":0.00673378,"about_ca_topic_score_gemma":0.0061300425,"teacher_disagreement_score":0.9680608,"about_ca_system_score_codex":0.0019169305,"about_ca_system_score_gemma":0.002378729,"threshold_uncertainty_score":0.16891271},"labels":[],"label_agreement":null},{"id":"W2254880623","doi":"","title":"The analysis longitudinal binary data.","year":2000,"lang":"it","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalized linear mixed model; Mixed model; Generalized linear model; Marginal model; Statistics; Covariate; Econometrics; Weighting; Interpretability; Random effects model; Population; Linear model; Estimator; Mathematics; Computer science; Regression analysis; Machine learning; Medicine","score_opus":0.017230468688854854,"score_gpt":0.226611531247391,"score_spread":0.20938106255853614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254880623","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":[0.03445921,0.0329782,0.8448674,0.012577721,0.0032259468,0.002491061,0.04521657,0.0015562712,0.022627596],"genre_scores_gemma":[0.3243866,0.027868833,0.5802936,0.008218645,0.0040814634,0.009836683,0.028892603,0.0006035361,0.015818046],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97971,0.013944312,0.000979941,0.0018075593,0.003309207,0.00024893958],"domain_scores_gemma":[0.9227067,0.04886793,0.00934802,0.011461639,0.0065934723,0.0010222459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02470831,0.00070457655,0.0015624528,0.0045742984,0.00081374374,0.0026914466,0.0013782628,0.0009974374,0.012101189],"category_scores_gemma":[0.09370327,0.00038388584,0.0013469029,0.0067050764,0.0012059802,0.0035348516,0.002893055,0.002177236,0.0029808357],"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":[0.00034666818,0.00021413549,0.071629815,0.0053333202,0.001678864,0.0003617942,0.0015879506,0.0023255683,0.0013012361,0.123490855,0.06490865,0.72682106],"study_design_scores_gemma":[0.00013179888,0.0014228747,0.12180393,0.0050380845,0.001356837,0.0028840515,0.0033802297,0.025857255,0.002484987,0.40628985,0.42902595,0.00032408425],"about_ca_topic_score_codex":0.0018184842,"about_ca_topic_score_gemma":0.0015532366,"teacher_disagreement_score":0.02470831,"about_ca_system_score_codex":0.0011448157,"about_ca_system_score_gemma":0.0029009487,"threshold_uncertainty_score":0.13067162},"labels":[],"label_agreement":null},{"id":"W2258328398","doi":"10.1007/978-1-59745-385-1_3","title":"Modeling Longitudinal Data, I: Principles of Multivariate Analysis","year":2008,"lang":"en","type":"review","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Statistics; Multivariate statistics; Econometrics; Component (thermodynamics); Confidence interval; Statistical model; Point estimation; Mathematics; Computer science","score_opus":0.37861015897843187,"score_gpt":0.5676699032039911,"score_spread":0.18905974422555927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258328398","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":[0.0006010926,0.14393,0.8454797,0.004800685,0.00074018864,0.00009661731,0.00048634873,0.00049620087,0.003369223],"genre_scores_gemma":[0.03354039,0.3663026,0.5851875,0.0031182116,0.0049187243,0.0012986633,0.0010934715,0.00031558707,0.0042248284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99499017,0.002830735,0.00031336845,0.00061298476,0.0011633538,0.00008940331],"domain_scores_gemma":[0.9894856,0.008494711,0.00050634885,0.0007647299,0.0006518017,0.00009673109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010860259,0.0017817894,0.0025580712,0.0033024915,0.00044755405,0.002813965,0.0029495808,0.0018419893,0.0018313725],"category_scores_gemma":[0.013960305,0.00090892176,0.0019112554,0.00448277,0.003873674,0.003783372,0.001809371,0.004291353,0.0014413628],"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":[0.000039405586,0.00005988977,0.0019662662,0.002642107,0.00046176932,0.00014561553,0.0003479294,0.026506905,0.00037588473,0.4858949,0.032079488,0.44947988],"study_design_scores_gemma":[0.000023084884,0.00004132496,0.0012272414,0.0010828183,0.0000833384,0.00024757456,0.00007197255,0.031319328,0.0002756899,0.8357783,0.12978543,0.00006386085],"about_ca_topic_score_codex":0.0028307363,"about_ca_topic_score_gemma":0.0015824693,"teacher_disagreement_score":0.010860259,"about_ca_system_score_codex":0.0019407442,"about_ca_system_score_gemma":0.0026313898,"threshold_uncertainty_score":0.057435274},"labels":[],"label_agreement":null},{"id":"W2272703140","doi":"","title":"Generalized Estimating Equations and Gaussian Estimation in Longitudinal Data Analysis","year":2011,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor","funders":"University of Windsor","keywords":"Mathematics; Generalized estimating equation; Estimator; Estimating equations; Statistics; Gaussian; Autocorrelation; Regression analysis; Marginal model; Applied mathematics","score_opus":0.19760158241598033,"score_gpt":0.35799683630595797,"score_spread":0.16039525388997763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272703140","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":[0.0013470916,0.0019431707,0.99512607,0.0004624983,0.000094231,0.000102573584,0.00018581489,0.00014097965,0.0005976677],"genre_scores_gemma":[0.066054545,0.007268554,0.92075425,0.0005707883,0.00046456276,0.0016533902,0.00092796324,0.00018139448,0.0021245696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94577265,0.04428989,0.0017605446,0.0041930475,0.0033674992,0.00061649736],"domain_scores_gemma":[0.9157422,0.07264177,0.0041603823,0.0036999078,0.003523205,0.00023256989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05563918,0.0025208162,0.0040502925,0.0049493127,0.00093223713,0.0027018648,0.0037641795,0.0028951024,0.004379722],"category_scores_gemma":[0.12851343,0.0013957316,0.0037179906,0.008746492,0.0034719,0.0049559204,0.00345595,0.005370487,0.0012720792],"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":[0.00004665425,0.00007941607,0.005777364,0.00093495386,0.0013614154,0.00028619438,0.0007664994,0.087601416,0.00026517163,0.75213736,0.006315289,0.1444283],"study_design_scores_gemma":[0.00006052287,0.00010111382,0.0026379107,0.0004838709,0.0003021619,0.00020644891,0.0002299325,0.26371115,0.00036912187,0.7139123,0.017845541,0.00013989363],"about_ca_topic_score_codex":0.009273838,"about_ca_topic_score_gemma":0.0060299947,"teacher_disagreement_score":0.05563918,"about_ca_system_score_codex":0.0026089426,"about_ca_system_score_gemma":0.0040337974,"threshold_uncertainty_score":0.29425162},"labels":[],"label_agreement":null},{"id":"W2285905196","doi":"10.1007/s13571-015-0106-2","title":"Inferences in Longitudinal Count Data Models with Measurement Errors in Time Dependent Covariates","year":2015,"lang":"en","type":"article","venue":"Sankhya B","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Estimator; Statistics; Observational error; Estimating equations; Mathematics; Regression analysis; Generalized estimating equation; Count data; Regression; Nuisance parameter; Linear regression","score_opus":0.3483836261418124,"score_gpt":0.39355235833486757,"score_spread":0.04516873219305517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2285905196","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":[0.0063277627,0.00096641754,0.99097824,0.00093509443,0.000077000266,0.000049735976,0.0001791391,0.000116760624,0.0003698065],"genre_scores_gemma":[0.28944343,0.0058993744,0.69029117,0.0013495222,0.0011648879,0.001705401,0.0018637716,0.00031261306,0.0079698125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96766686,0.025109923,0.001485966,0.0031933046,0.0020650665,0.00047880347],"domain_scores_gemma":[0.68594855,0.29457635,0.007674555,0.007412271,0.003384127,0.0010041918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.062102653,0.0019098661,0.0034222112,0.0035365399,0.0015757655,0.0043226536,0.0066127363,0.0041221534,0.0048065973],"category_scores_gemma":[0.30639732,0.0037436222,0.0036595229,0.0044708773,0.0043109143,0.009115174,0.004933835,0.0075027333,0.00079951086],"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":[0.0003680526,0.0002050247,0.009659896,0.0008330015,0.0013018311,0.0005253835,0.00089630176,0.15199852,0.00045688526,0.7477393,0.00406042,0.08195533],"study_design_scores_gemma":[0.00008627379,0.000043513486,0.0008235794,0.00011954547,0.00018433515,0.00013278479,0.000083842526,0.36033157,0.00024967885,0.63646114,0.0014459334,0.000037805712],"about_ca_topic_score_codex":0.010412185,"about_ca_topic_score_gemma":0.006192136,"teacher_disagreement_score":0.062102653,"about_ca_system_score_codex":0.0023799979,"about_ca_system_score_gemma":0.0038888238,"threshold_uncertainty_score":0.32843417},"labels":[],"label_agreement":null},{"id":"W2289217221","doi":"10.1214/14-bjps254","title":"Inferences in median regression models for asymmetric longitudinal data: A quasi-likelihood approach","year":2016,"lang":"en","type":"article","venue":"Brazilian Journal of Probability and Statistics","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Statistics; Autoregressive model; Pairwise comparison; Regression; Regression analysis; Independence (probability theory); Econometrics; Cross-sectional regression; Polynomial regression","score_opus":0.14686073957073334,"score_gpt":0.3858974427280153,"score_spread":0.23903670315728195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289217221","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":[0.0035280148,0.00010719442,0.9956617,0.0001719204,0.000014692619,0.0000320033,0.00006294266,0.000060308426,0.0003613273],"genre_scores_gemma":[0.29106206,0.0010081502,0.7010883,0.00043966444,0.0003468658,0.0008570625,0.0007795933,0.00020863666,0.004209583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98191833,0.014512979,0.0005480731,0.0016028709,0.0010526275,0.00036506716],"domain_scores_gemma":[0.9023206,0.08769469,0.0037733563,0.004049315,0.0017646742,0.00039724162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040332336,0.0011687591,0.0029255536,0.0021177852,0.001054582,0.00278801,0.0045654094,0.0023686637,0.0066615567],"category_scores_gemma":[0.12645875,0.0016615805,0.0025625925,0.0027991524,0.0027532605,0.005044057,0.003160321,0.0041255117,0.0008305479],"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":[0.00023409765,0.00014436666,0.005770298,0.00041894038,0.0005260872,0.00067359494,0.00068699365,0.22508308,0.00085938856,0.6859735,0.0023455992,0.077284165],"study_design_scores_gemma":[0.000037316335,0.00006994378,0.0008460555,0.000065565575,0.00005878785,0.00011583087,0.00007297389,0.6621735,0.00036409343,0.33479437,0.0013677106,0.000033910484],"about_ca_topic_score_codex":0.0038325326,"about_ca_topic_score_gemma":0.003571388,"teacher_disagreement_score":0.040332336,"about_ca_system_score_codex":0.0016425233,"about_ca_system_score_gemma":0.0020139138,"threshold_uncertainty_score":0.21330029},"labels":[],"label_agreement":null},{"id":"W2292183648","doi":"10.1177/1740774515606377","title":"A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome","year":2015,"lang":"en","type":"article","venue":"Clinical Trials","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":"Western University; University of Guelph","funders":"","keywords":"Intraclass correlation; Confidence interval; Statistics; Mathematics; Coverage probability; Estimator; Correlation coefficient; Sample size determination; Fisher transformation; Correlation ratio","score_opus":0.7679347356663273,"score_gpt":0.6752563290798085,"score_spread":0.09267840658651882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292183648","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","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":[0.07147449,0.034462094,0.87527347,0.0014987048,0.0019029177,0.007881525,0.00060991617,0.0010859787,0.0058109905],"genre_scores_gemma":[0.36096618,0.007617426,0.60563695,0.0010472068,0.00056447036,0.022042459,0.000704496,0.00053247984,0.0008882635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.43664923,0.5228972,0.009654625,0.0069654654,0.022911066,0.0009224037],"domain_scores_gemma":[0.1023746,0.85719585,0.015767438,0.013252573,0.010431046,0.0009784652],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.38162333,0.0022824188,0.0053674974,0.0062879412,0.0007823484,0.003663792,0.0055953874,0.0047834828,0.0043639876],"category_scores_gemma":[0.74914205,0.0013033213,0.006169641,0.0059763263,0.0037499834,0.00439835,0.0043826946,0.0068917326,0.0004670664],"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":[0.081518404,0.0016067908,0.02126975,0.017300013,0.026847836,0.00036693393,0.0043595103,0.10325566,0.0013282473,0.16966365,0.008421852,0.56406146],"study_design_scores_gemma":[0.03467388,0.026525259,0.03108252,0.011083099,0.017484168,0.0009920808,0.0012017722,0.71624476,0.004299796,0.1386733,0.016565802,0.0011735649],"about_ca_topic_score_codex":0.0018472439,"about_ca_topic_score_gemma":0.0010564186,"teacher_disagreement_score":0.38162333,"about_ca_system_score_codex":0.0033204423,"about_ca_system_score_gemma":0.0049958024,"threshold_uncertainty_score":0.76256853},"labels":[],"label_agreement":null},{"id":"W2294933899","doi":"10.1002/cjs.11275","title":"Jackknife empirical likelihood for comparing two Gini indices","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Georgia State University","keywords":"Jackknife resampling; Empirical likelihood; Statistics; Mathematics; Econometrics; Nuisance parameter; Statistic; Maximization; Missing data; Confidence interval; Estimator; Mathematical optimization","score_opus":0.1271092490982263,"score_gpt":0.394174626489652,"score_spread":0.2670653773914257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294933899","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":[0.02083202,0.00043575038,0.9760059,0.00022032732,0.000045265715,0.0001121665,0.00023336521,0.00024411097,0.0018709907],"genre_scores_gemma":[0.54226273,0.00047474293,0.45220503,0.00031843293,0.00014555527,0.0010317531,0.0015256833,0.000443841,0.0015921335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9770156,0.016143348,0.00092887523,0.0025669897,0.0028795595,0.00046558597],"domain_scores_gemma":[0.8540579,0.12815987,0.0054919384,0.007150866,0.0041712667,0.0009682399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038253102,0.0009073396,0.0026602054,0.0054401546,0.0011256963,0.0032214443,0.0035715015,0.0027295582,0.003983571],"category_scores_gemma":[0.26155508,0.0007661683,0.0014359533,0.003577043,0.0046248576,0.0046367287,0.003297148,0.003754445,0.0009883365],"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":[0.00074131915,0.00016512348,0.034879692,0.0008011608,0.0008945014,0.0006400451,0.0016940797,0.24339476,0.0021075471,0.4881533,0.00860515,0.21792333],"study_design_scores_gemma":[0.000053857442,0.00012406771,0.0056451834,0.00021849082,0.00006579278,0.0004929572,0.00033272707,0.53204566,0.0019510282,0.4539413,0.005030225,0.00009867572],"about_ca_topic_score_codex":0.0016178024,"about_ca_topic_score_gemma":0.00094240095,"teacher_disagreement_score":0.038253102,"about_ca_system_score_codex":0.0018306823,"about_ca_system_score_gemma":0.0014334277,"threshold_uncertainty_score":0.20230412},"labels":[],"label_agreement":null},{"id":"W2298956764","doi":"10.1080/02331888.2015.1053809","title":"Distribution approximation and modelling via orthogonal polynomial sequences","year":2015,"lang":"en","type":"article","venue":"Statistics","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Mathematics; Orthogonal polynomials; Basis (linear algebra); Moment (physics); Probability density function; Moment-generating function; Orthogonal basis; Applied mathematics; Random variable; Polynomial; Distribution (mathematics); Weight function; Sequence (biology); Function (biology); Method of moments (probability theory); Combinatorics; Statistics; Mathematical analysis; Estimator","score_opus":0.10087753345201891,"score_gpt":0.35665084666404845,"score_spread":0.25577331321202956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298956764","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":[0.0008875886,0.000042526615,0.9986998,0.000026611919,0.0000059379154,0.000009484411,0.000015475089,0.000042078413,0.00027043128],"genre_scores_gemma":[0.20675048,0.0012072646,0.7859468,0.00012078375,0.00015331164,0.0004309465,0.0003657146,0.00021318694,0.0048115305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970559,0.001422577,0.00011905822,0.00032957338,0.000861576,0.00021127265],"domain_scores_gemma":[0.9943686,0.0038746141,0.0005640402,0.00058036717,0.00051599677,0.00009640111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004915021,0.0010849925,0.0013936324,0.0024633561,0.0006485043,0.0017685461,0.0027237972,0.0013903612,0.0028486184],"category_scores_gemma":[0.019677859,0.0008112778,0.0016216448,0.0028706493,0.001688933,0.0029838844,0.0019870875,0.00283962,0.0010877474],"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":[0.00003988279,0.000033500673,0.00081486336,0.000103602004,0.000041591193,0.0001309707,0.00018978112,0.5532259,0.0015650352,0.40561324,0.0009067331,0.037334748],"study_design_scores_gemma":[0.0000044843478,0.000013153722,0.000100587065,0.00001708408,0.000004718048,0.000051464485,0.000013772736,0.92622584,0.00042125524,0.07183912,0.0012960475,0.000012496095],"about_ca_topic_score_codex":0.00532926,"about_ca_topic_score_gemma":0.0033812867,"teacher_disagreement_score":0.00532926,"about_ca_system_score_codex":0.0013357112,"about_ca_system_score_gemma":0.0016716277,"threshold_uncertainty_score":0.025993407},"labels":[],"label_agreement":null},{"id":"W2299186703","doi":"10.1007/s10260-019-00458-w","title":"Nonparametric imputation method for nonresponse in surveys","year":2019,"lang":"en","type":"preprint","venue":"Statistical Methods & Applications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Imputation (statistics); Covariate; Nonparametric statistics; Smoothing; Computer science; Econometrics; Statistics; Smoothing spline; Missing data; Mathematics","score_opus":0.11374161736666684,"score_gpt":0.5246406861970224,"score_spread":0.4108990688303556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299186703","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":[0.00095306465,0.00019414937,0.99746263,0.0003235448,0.00008416482,0.00005853091,0.00023841778,0.00022117539,0.00046432274],"genre_scores_gemma":[0.09619947,0.0008527503,0.88486636,0.0008413711,0.0006729887,0.0022159228,0.002567579,0.00047288524,0.011310608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9419577,0.049766175,0.0013372445,0.0028551912,0.0032464142,0.0008372732],"domain_scores_gemma":[0.8699938,0.09667908,0.00335907,0.023869373,0.005396646,0.00070203224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052318174,0.0012117969,0.003957006,0.0031914983,0.0016266999,0.0029277296,0.007685785,0.0045584347,0.016680034],"category_scores_gemma":[0.18110959,0.0018227154,0.003324287,0.0060117426,0.002290442,0.0047162157,0.0035876709,0.0062541817,0.004195023],"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":[0.0005343886,0.00033509533,0.0053777797,0.0009273192,0.0009856065,0.00030601144,0.0005941718,0.040284835,0.0005343544,0.61412954,0.024609853,0.311381],"study_design_scores_gemma":[0.00021077725,0.00010542296,0.0019101051,0.00024553662,0.00017717294,0.0003877826,0.00009166419,0.28743672,0.0006056845,0.6949942,0.013770067,0.0000647886],"about_ca_topic_score_codex":0.0027885707,"about_ca_topic_score_gemma":0.0024915992,"teacher_disagreement_score":0.052318174,"about_ca_system_score_codex":0.0014905491,"about_ca_system_score_gemma":0.004080143,"threshold_uncertainty_score":0.27668822},"labels":[],"label_agreement":null},{"id":"W2317634038","doi":"10.6000/1929-6029.2015.04.04.6","title":"Specification of Variance-Covariance Structure in Bivariate Mixed Model for Unequally Time-Spaced Longitudinal Data","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autocorrelation; Bivariate analysis; Covariate; Statistics; Multivariate statistics; Covariance; Mathematics; Data set; Bivariate data; Econometrics","score_opus":0.4536980794176892,"score_gpt":0.5561041835634061,"score_spread":0.10240610414571688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317634038","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":[0.007339227,0.00023333314,0.9914135,0.00019434212,0.000043110373,0.0001333108,0.00021861582,0.00019768308,0.00022690948],"genre_scores_gemma":[0.26070517,0.0009755043,0.73093414,0.00037872166,0.00017809469,0.0026951549,0.0018099997,0.00024182723,0.0020814114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96128064,0.030346308,0.0012788245,0.0042603933,0.0019211507,0.00091259216],"domain_scores_gemma":[0.9315835,0.058612026,0.0031992488,0.003629041,0.0025753002,0.00040079188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048277866,0.0019928575,0.0031886916,0.0026303302,0.0010645093,0.0028361212,0.004070242,0.0033268298,0.0028362062],"category_scores_gemma":[0.09170668,0.0016755133,0.0043524336,0.002853294,0.0022939502,0.003414017,0.0023705303,0.0041139517,0.0007626121],"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":[0.0007180439,0.00029204454,0.024711572,0.0010433452,0.0014120359,0.00096141675,0.0018130277,0.35567668,0.0027431424,0.5028508,0.003294269,0.10448368],"study_design_scores_gemma":[0.00008874271,0.00021062598,0.0028732491,0.00014939407,0.00021163515,0.00017119362,0.00013681196,0.8608453,0.0007681148,0.13208677,0.0023811907,0.00007688828],"about_ca_topic_score_codex":0.008931942,"about_ca_topic_score_gemma":0.008134987,"teacher_disagreement_score":0.048277866,"about_ca_system_score_codex":0.0020339729,"about_ca_system_score_gemma":0.004121212,"threshold_uncertainty_score":0.25532085},"labels":[],"label_agreement":null},{"id":"W2327164763","doi":"10.1080/10920277.2005.10596217","title":"“A Bayesian Generalized Linear Model for the Bornhuetter-Ferguson Method of Claims Reserving,” R. J. Verrall, July 2004","year":2005,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Statistical Methods and Bayesian 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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized linear model; Bayesian probability; Econometrics; Mathematics; Computer science; Statistics; Applied mathematics","score_opus":0.07426533128817449,"score_gpt":0.40076429457292334,"score_spread":0.32649896328474887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327164763","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":[0.0015762813,0.00092761836,0.9936114,0.0019361448,0.00022928863,0.000053503758,0.0001286573,0.00015308053,0.0013839655],"genre_scores_gemma":[0.09335583,0.0032684724,0.8778124,0.0016268957,0.0013228365,0.00056938076,0.00072651386,0.00047199882,0.02084562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926248,0.005449914,0.00016155478,0.000580678,0.00090972247,0.00027330796],"domain_scores_gemma":[0.99197656,0.0058482424,0.0004953836,0.0005906489,0.0009026661,0.00018659732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022099283,0.0013183482,0.001520709,0.001726436,0.0011299041,0.0018065588,0.005216645,0.002891435,0.009516259],"category_scores_gemma":[0.03604681,0.0014524886,0.0021638256,0.0016650162,0.0027656895,0.003425195,0.0021778976,0.0049751597,0.002572917],"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":[0.00018409459,0.00007676225,0.0012014257,0.00014247288,0.00017199527,0.00014064727,0.00027605775,0.14604476,0.0003330167,0.6995285,0.03784368,0.114056565],"study_design_scores_gemma":[0.000062646286,0.000046019584,0.0006050943,0.00008673248,0.000083383566,0.00010499237,0.000036513316,0.6109247,0.0003176684,0.37075928,0.016886443,0.000086473636],"about_ca_topic_score_codex":0.02300701,"about_ca_topic_score_gemma":0.03096386,"teacher_disagreement_score":0.02300701,"about_ca_system_score_codex":0.0025271813,"about_ca_system_score_gemma":0.0034679077,"threshold_uncertainty_score":0.11687362},"labels":[],"label_agreement":null},{"id":"W2328486734","doi":"10.17713/ajs.v35i2&3.365","title":"Empirical Likelihood Methods for Sample Survey Data: An Overview","year":2016,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"Carleton University","funders":"","keywords":"Sample (material); Survey sampling; Statistics; Empirical likelihood; Sampling design; Sampling (signal processing); Estimation; Confidence interval; Computer science; Population; Survey data collection; Econometrics; Mathematics; Engineering; Demography; Telecommunications","score_opus":0.8272905327295076,"score_gpt":0.7347107680961361,"score_spread":0.09257976463337148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328486734","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":[0.00025576568,0.03261723,0.9619842,0.0011006658,0.00015866639,0.00011470709,0.00030196545,0.00026977347,0.0031971475],"genre_scores_gemma":[0.017176565,0.08849411,0.8847161,0.0009107968,0.0016237342,0.0012595995,0.0012584648,0.00052016316,0.004040509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9716925,0.02072919,0.0014190025,0.0013598885,0.004587558,0.0002119123],"domain_scores_gemma":[0.94461656,0.04936108,0.0012453119,0.0019390709,0.00263562,0.00020241205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02560812,0.0017885639,0.0028991853,0.0057540615,0.00057074067,0.0037849648,0.0032712454,0.0027190077,0.009450894],"category_scores_gemma":[0.063786104,0.0014228433,0.0019743943,0.008974587,0.002433744,0.0052804747,0.0028276423,0.005197222,0.005976125],"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":[0.00005150839,0.00010096776,0.0014816332,0.0030357332,0.00021714855,0.00017158462,0.0004139524,0.015442753,0.0003442557,0.48233688,0.019857462,0.4765461],"study_design_scores_gemma":[0.00005123089,0.00007377452,0.0012356787,0.0013360216,0.00007062651,0.0004638402,0.00014525326,0.05799433,0.00055264443,0.6919896,0.2459834,0.00010361317],"about_ca_topic_score_codex":0.0023062595,"about_ca_topic_score_gemma":0.0013843772,"teacher_disagreement_score":0.02560812,"about_ca_system_score_codex":0.0020846485,"about_ca_system_score_gemma":0.0025478022,"threshold_uncertainty_score":0.13543034},"labels":[],"label_agreement":null},{"id":"W2329188561","doi":"10.4172/2155-6180.s7-007","title":"Consistent Estimation in Generalized Linear Mixed Models with Measurement Error","year":2012,"lang":"en","type":"article","venue":"Journal of Biometrics & Biostatistics","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 Manitoba","funders":"","keywords":"Computer science; Estimation; Generalized linear mixed model; Data mining; Statistics; Algorithm; Machine learning; Mathematics","score_opus":0.20778970505000477,"score_gpt":0.39057990435516676,"score_spread":0.182790199305162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329188561","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":[0.0010349412,0.00017096035,0.9984744,0.00011009003,0.00001601569,0.00001393967,0.000018722614,0.00003823338,0.00012264331],"genre_scores_gemma":[0.1397949,0.0012173889,0.8558556,0.00039248116,0.00020621739,0.0007964299,0.00040371055,0.00014817232,0.0011850875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9632803,0.031160213,0.0007155562,0.0019361012,0.0024344507,0.00047343547],"domain_scores_gemma":[0.94858634,0.043555785,0.003225965,0.0026437386,0.0017071092,0.0002811675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026251499,0.0014381633,0.0025230977,0.0024156075,0.00071617233,0.0025747423,0.0039098435,0.0022321215,0.0014534335],"category_scores_gemma":[0.09344584,0.0014872376,0.0027782645,0.0024069967,0.002536701,0.00239216,0.0036916516,0.0031810757,0.00045259233],"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":[0.00011955703,0.00008977212,0.004858043,0.00049551844,0.0009885594,0.00034992688,0.00038611263,0.2694262,0.001026692,0.6237001,0.00210012,0.09645946],"study_design_scores_gemma":[0.00007042384,0.0000792116,0.0006423496,0.00011065878,0.0001382818,0.00007350544,0.00004412306,0.65357345,0.0007574071,0.3414281,0.0030261169,0.000056387984],"about_ca_topic_score_codex":0.0021252444,"about_ca_topic_score_gemma":0.0020771855,"teacher_disagreement_score":0.026251499,"about_ca_system_score_codex":0.0011507648,"about_ca_system_score_gemma":0.0027855185,"threshold_uncertainty_score":0.13883281},"labels":[],"label_agreement":null},{"id":"W2329897787","doi":"10.1177/0013164415618240","title":"The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models","year":2015,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Statistical Methods and Bayesian 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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nesting (process); Nonparametric statistics; Latent class model; Class (philosophy); Econometrics; Multilevel model; Statistics; Structural equation modeling; Mathematics; Psychology; Computer science; Artificial intelligence; Engineering","score_opus":0.6967470229892968,"score_gpt":0.4920068167406266,"score_spread":0.20474020624867023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329897787","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":[0.11596159,0.0006609621,0.8798058,0.0016908894,0.00007558203,0.00014853297,0.00011216371,0.00016714702,0.0013772927],"genre_scores_gemma":[0.7502662,0.00038706616,0.24793376,0.0004972626,0.00006823384,0.00026226824,0.00017052892,0.00006627866,0.00034832713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8696067,0.11116012,0.004212779,0.0048285103,0.008974906,0.0012169903],"domain_scores_gemma":[0.43817082,0.5058205,0.020610133,0.02580079,0.008400633,0.0011971575],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11207558,0.000886059,0.0015919928,0.0014196903,0.0018689408,0.003192744,0.0029750012,0.0022272386,0.0011923267],"category_scores_gemma":[0.4296844,0.0010149896,0.0020699056,0.0023285719,0.003903744,0.0067381533,0.0035419348,0.0042217835,0.00017290987],"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":[0.0011128867,0.0004385553,0.15565123,0.0010438056,0.0019129711,0.0013509813,0.0069719325,0.4044527,0.0035207244,0.19648553,0.002427085,0.2246316],"study_design_scores_gemma":[0.00008446158,0.00046150776,0.023648653,0.0003776077,0.00042446365,0.00047374205,0.00079235015,0.8269434,0.0024563225,0.14263041,0.0015657607,0.0001414213],"about_ca_topic_score_codex":0.01047292,"about_ca_topic_score_gemma":0.01552334,"teacher_disagreement_score":0.88792443,"about_ca_system_score_codex":0.0025484143,"about_ca_system_score_gemma":0.004250127,"threshold_uncertainty_score":0.59271944},"labels":[],"label_agreement":null},{"id":"W2330135759","doi":"10.1097/jcp.0000000000000296","title":"Statistics Commentary Series","year":2015,"lang":"en","type":"editorial","venue":"Journal of Clinical Psychopharmacology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Library science; Series (stratigraphy); Psychiatry; Psychology; Gerontology; Medicine; Computer science","score_opus":0.14921712375812446,"score_gpt":0.5702894558530585,"score_spread":0.421072332094934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330135759","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003247639,0.006200058,0.00045278706,0.2751179,0.70871276,0.00010632535,0.001110209,0.00016917514,0.008098301],"genre_scores_gemma":[0.0009525698,0.0057791634,0.00060844235,0.20332168,0.7540068,0.00032070122,0.0005940393,0.00022740601,0.0341892],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98962796,0.0026824311,0.0012504144,0.0010927637,0.0048433486,0.0005031355],"domain_scores_gemma":[0.9274428,0.037628844,0.0025869454,0.0025722757,0.024865566,0.0049035167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01686146,0.0018584233,0.0028352798,0.0033132557,0.0023899272,0.005506054,0.0037744576,0.014105322,0.061549637],"category_scores_gemma":[0.10849487,0.0011965352,0.002875168,0.0022165542,0.0024560846,0.0026725738,0.001450805,0.024185723,0.030859647],"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":[0.000010737277,0.0000026838995,0.000007382121,0.00005930528,0.0000070283068,0.0000132767855,0.0000030784386,0.000010613855,0.0000045380457,0.00033287064,0.99752516,0.0020233276],"study_design_scores_gemma":[0.00007486118,0.000012070661,0.00015507085,0.0005491776,0.000024958785,0.00005231653,0.000010541267,0.000077895435,0.000026461827,0.0021570334,0.9968465,0.000012978749],"about_ca_topic_score_codex":0.006599387,"about_ca_topic_score_gemma":0.012547573,"teacher_disagreement_score":0.061549637,"about_ca_system_score_codex":0.005146106,"about_ca_system_score_gemma":0.011330777,"threshold_uncertainty_score":0.20590407},"labels":[],"label_agreement":null},{"id":"W2332566401","doi":"10.1177/0962280214552291","title":"Confidence intervals for a difference between lognormal means in cluster randomization trials","year":2014,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Robarts Clinical Trials; Western University","funders":"","keywords":"Confidence interval; Statistics; Randomization; Coverage probability; Sample size determination; Variance (accounting); Inference; Cluster randomised controlled trial; Mathematics; Cluster (spacecraft); Causal inference; Randomized controlled trial; Econometrics; Computer science; Medicine; Artificial intelligence","score_opus":0.41840400507326886,"score_gpt":0.6391732705030557,"score_spread":0.22076926542978687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332566401","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":[0.016126974,0.008507141,0.96731097,0.0017157984,0.00047050347,0.0009419695,0.00053730136,0.00070284423,0.0036864178],"genre_scores_gemma":[0.43054217,0.004032085,0.5542818,0.0017806068,0.00057250157,0.006226072,0.0010538806,0.00035861952,0.001152257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.72683024,0.23472585,0.009757478,0.01121737,0.016153377,0.0013156305],"domain_scores_gemma":[0.15457764,0.79112136,0.020061098,0.024279851,0.008800419,0.0011596142],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27889597,0.0017218876,0.004897626,0.0049435836,0.0012390906,0.0055982727,0.006282327,0.0059902905,0.005190907],"category_scores_gemma":[0.73637027,0.0012293198,0.0040403353,0.0050782613,0.006600256,0.0064511127,0.005116168,0.009255924,0.000748592],"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":[0.00777097,0.00032744816,0.0135154985,0.0065472405,0.0051821116,0.00070919545,0.002716631,0.10306522,0.0012774604,0.59831333,0.01069629,0.24987863],"study_design_scores_gemma":[0.0021073085,0.0015797942,0.007713255,0.004377333,0.0027926264,0.0010846144,0.0004283607,0.3316375,0.0034771238,0.6308863,0.013548544,0.0003671825],"about_ca_topic_score_codex":0.0017395373,"about_ca_topic_score_gemma":0.00083424844,"teacher_disagreement_score":0.721104,"about_ca_system_score_codex":0.0027369473,"about_ca_system_score_gemma":0.0030838007,"threshold_uncertainty_score":0.8892496},"labels":[],"label_agreement":null},{"id":"W2337224958","doi":"10.1007/s10474-016-0595-0","title":"Exploring functional CLT confidence intervals for a population mean in the domain of attraction of the normal law","year":2016,"lang":"en","type":"article","venue":"Acta Mathematica Academiae Scientiarum Hungaricae","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Manitoba","funders":"","keywords":"Mathematics; Central limit theorem; Asymptotic distribution; Confidence interval; Normal distribution; Limit (mathematics); Law of large numbers; Weak convergence; Statistic; Random variable; Population; Statistics; Normality; Applied mathematics; Mathematical analysis","score_opus":0.19966433745908319,"score_gpt":0.36735339689932783,"score_spread":0.16768905944024465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337224958","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":[0.080521554,0.000587314,0.9153573,0.00057948456,0.000036383244,0.000043116193,0.000116393974,0.00020060505,0.0025578814],"genre_scores_gemma":[0.8902639,0.000437266,0.10730562,0.00018464029,0.000102840546,0.00013269015,0.00044290585,0.00013252675,0.000997586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950713,0.0032094344,0.00017847218,0.0006522199,0.00064829696,0.00024029575],"domain_scores_gemma":[0.7760236,0.21197894,0.003664429,0.0027635647,0.0042395433,0.001329902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021054136,0.0007880173,0.0012619448,0.0038640462,0.0006117092,0.0037098778,0.0029505794,0.00197364,0.0033412392],"category_scores_gemma":[0.16367038,0.00065784896,0.001240286,0.0016047255,0.0029746012,0.0035039696,0.0038540985,0.0034614066,0.00022127584],"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":[0.00046124353,0.00011069147,0.009212461,0.0003807417,0.00020478354,0.00045702647,0.0007944581,0.5158636,0.0012848355,0.42927915,0.0012314781,0.040719528],"study_design_scores_gemma":[0.000022656372,0.000057616166,0.00082480366,0.00006364999,0.000019295556,0.00007867404,0.00007030679,0.89783585,0.00033860316,0.1002516,0.0004158405,0.0000210852],"about_ca_topic_score_codex":0.005137413,"about_ca_topic_score_gemma":0.0014839513,"teacher_disagreement_score":0.021054136,"about_ca_system_score_codex":0.0013117408,"about_ca_system_score_gemma":0.0014210003,"threshold_uncertainty_score":0.111346245},"labels":[],"label_agreement":null},{"id":"W2339680408","doi":"10.1002/cjs.11284","title":"Bayesian regression models adjusting for unidirectional covariate misclassification","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Northern Illinois University","keywords":"Covariate; Identifiability; Bayesian probability; Statistics; Regression analysis; Identification (biology); Computer science; Regression; Mathematics; Binary data; Econometrics; Binary number","score_opus":0.1401268564159234,"score_gpt":0.35062189918107534,"score_spread":0.21049504276515194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339680408","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":[0.101951994,0.0004962139,0.89293355,0.0015562107,0.00006614626,0.00012722454,0.00041092015,0.00035526548,0.002102548],"genre_scores_gemma":[0.8790768,0.000481856,0.11302072,0.00034165138,0.0001199213,0.00032958222,0.00075341394,0.00015283034,0.005723246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9792324,0.013601427,0.0007045659,0.0039642323,0.001600954,0.00089639216],"domain_scores_gemma":[0.8653229,0.10039773,0.015759232,0.011272028,0.0063928235,0.0008553226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051513813,0.0013989873,0.0030726679,0.0019662965,0.0010183773,0.0032932032,0.004448969,0.0030747922,0.0044384105],"category_scores_gemma":[0.18947089,0.0011584057,0.0021951152,0.0022341807,0.00267959,0.004073665,0.0025582293,0.003989401,0.0011906379],"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":[0.00045417927,0.00016402209,0.037337918,0.0002576085,0.00055224425,0.00052546896,0.00097217376,0.66787964,0.0007795022,0.20920584,0.0029744755,0.078896984],"study_design_scores_gemma":[0.0000428075,0.0000597471,0.0048109875,0.00009613041,0.00009995351,0.000090253205,0.00009760225,0.8423982,0.0003514497,0.15079565,0.0011078827,0.000049377482],"about_ca_topic_score_codex":0.014787484,"about_ca_topic_score_gemma":0.008737001,"teacher_disagreement_score":0.051513813,"about_ca_system_score_codex":0.0024036933,"about_ca_system_score_gemma":0.0019110006,"threshold_uncertainty_score":0.2724343},"labels":[],"label_agreement":null},{"id":"W2339701193","doi":"10.3141/2601-05","title":"Model-Based Versus Data-Driven Approach for Road Safety Analysis: Do More Data Help?","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Bootstrapping (finance); Computer science; Nonparametric statistics; Crash; Data mining; Parametric statistics; Machine learning; Econometrics; Statistics; Mathematics","score_opus":0.41715117341402413,"score_gpt":0.5065588998719871,"score_spread":0.08940772645796297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339701193","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":[0.07495887,0.009168789,0.8792646,0.02890381,0.0005107917,0.00025355982,0.0011074482,0.0007031848,0.0051289317],"genre_scores_gemma":[0.6059586,0.006162557,0.38091382,0.0034825006,0.0004996736,0.0004330369,0.0011420476,0.00027955443,0.0011282538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9827193,0.013352304,0.00046805062,0.0010330452,0.0022420357,0.00018532886],"domain_scores_gemma":[0.86581,0.11016302,0.003986113,0.012504468,0.0066899937,0.00084636733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03741955,0.00086098723,0.0021040007,0.0022078026,0.00066320196,0.0035014981,0.0023881225,0.0025532611,0.0023327887],"category_scores_gemma":[0.15160443,0.00067630125,0.0011789724,0.0035470796,0.0021249056,0.010028798,0.0025499796,0.003678722,0.00086355064],"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":[0.0012475885,0.00081310526,0.04527769,0.0019836605,0.0011507797,0.00023268888,0.00084525906,0.16931581,0.002320146,0.1665923,0.0136324875,0.59658843],"study_design_scores_gemma":[0.00026472966,0.0007994421,0.010947539,0.0007565607,0.00026525365,0.00024777255,0.0008730919,0.544843,0.0025156932,0.42196974,0.016294682,0.00022246587],"about_ca_topic_score_codex":0.0035745215,"about_ca_topic_score_gemma":0.0051154955,"teacher_disagreement_score":0.03741955,"about_ca_system_score_codex":0.0015919533,"about_ca_system_score_gemma":0.0028024286,"threshold_uncertainty_score":0.19789582},"labels":[],"label_agreement":null},{"id":"W2345136754","doi":"10.1002/sim.6955","title":"Linear models of coregionalization for multivariate lattice data: a general framework for coregionalized multivariate CAR models","year":2016,"lang":"en","type":"review","venue":"Statistics in Medicine","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Univariate; Autoregressive model; Covariance; Bayesian probability; Computer science; Multivariate analysis; Gaussian; Conditional independence; Multivariate normal distribution; Econometrics; Statistics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.39298559087138896,"score_gpt":0.5257090736074782,"score_spread":0.13272348273608925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345136754","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":[0.0012895917,0.010101403,0.9838971,0.001234504,0.00009870349,0.000032225325,0.00024392498,0.0001399755,0.002962547],"genre_scores_gemma":[0.2427621,0.075343885,0.65983194,0.0023898801,0.002208867,0.0011372307,0.0020456843,0.00045298896,0.013827358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99686,0.001819974,0.00015096627,0.00054831733,0.000497482,0.00012334547],"domain_scores_gemma":[0.989291,0.008208826,0.00075293117,0.0007088016,0.0009259962,0.00011247635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008053275,0.0014886857,0.0020269926,0.002241831,0.0004372006,0.002767139,0.0032636686,0.0018833831,0.0037514765],"category_scores_gemma":[0.014683959,0.0010499336,0.0027387792,0.003152762,0.0031226687,0.0039920546,0.0021409222,0.003948721,0.001480186],"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":[0.000016774346,0.000022713075,0.00087569555,0.0005383132,0.00018346614,0.00008447941,0.00017427719,0.08528703,0.0002306289,0.8252121,0.003872116,0.08350234],"study_design_scores_gemma":[0.000011568322,0.000035673744,0.00068916765,0.000299583,0.00007729994,0.00015338017,0.000047750178,0.2437448,0.00023503976,0.7277112,0.026940309,0.000054275486],"about_ca_topic_score_codex":0.006599281,"about_ca_topic_score_gemma":0.0039492706,"teacher_disagreement_score":0.008053275,"about_ca_system_score_codex":0.00247329,"about_ca_system_score_gemma":0.0022328014,"threshold_uncertainty_score":0.04259026},"labels":[],"label_agreement":null},{"id":"W2345476363","doi":"10.1080/10705511.2016.1169188","title":"Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models","year":2016,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":208,"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 Sherbrooke","funders":"","keywords":"Latent class model; Covariance; Mathematics; Statistics; Covariance matrix; Mixture model; Residual; Population; Variance (accounting); Econometrics; Algorithm","score_opus":0.12164574989365973,"score_gpt":0.3807370481958413,"score_spread":0.2590912983021816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345476363","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":[0.55665493,0.0008647219,0.43628323,0.0013166221,0.00008679453,0.00039034584,0.00038243787,0.00041601164,0.0036048363],"genre_scores_gemma":[0.91203004,0.00028386692,0.08591184,0.0002491493,0.000024611552,0.000306725,0.0005141041,0.00010996124,0.00056968885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9671943,0.024986168,0.0017924596,0.002736145,0.002430398,0.00086051103],"domain_scores_gemma":[0.41832045,0.5341681,0.016199593,0.022850193,0.007529321,0.0009323179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07840035,0.0013193745,0.0015456091,0.0015971382,0.0012790858,0.0026158043,0.001967106,0.0022519962,0.0018445783],"category_scores_gemma":[0.37273434,0.0009438346,0.0016507817,0.0013668109,0.0022696347,0.004740867,0.0031255332,0.0037138243,0.0003103074],"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":[0.0015872188,0.0003361187,0.05889632,0.0002773035,0.00053189613,0.00024643054,0.0014360052,0.84479946,0.0019017332,0.042996187,0.0009851075,0.046006143],"study_design_scores_gemma":[0.00012164448,0.00048628333,0.009659769,0.00019984797,0.00015848553,0.00017593852,0.00040763538,0.955644,0.0029918647,0.029214729,0.00083142705,0.00010833573],"about_ca_topic_score_codex":0.011644145,"about_ca_topic_score_gemma":0.011017592,"teacher_disagreement_score":0.07840035,"about_ca_system_score_codex":0.0025806685,"about_ca_system_score_gemma":0.0026649728,"threshold_uncertainty_score":0.41462564},"labels":[],"label_agreement":null},{"id":"W2348932631","doi":"10.1016/j.prevetmed.2016.04.003","title":"Multiple imputation in veterinary epidemiological studies: a case study and simulation","year":2016,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"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 Prince Edward Island","funders":"","keywords":"Imputation (statistics); Veterinary medicine; Statistics; Epidemiology; Missing data; Mathematics; Medicine","score_opus":0.32716516490505765,"score_gpt":0.5196535078981341,"score_spread":0.1924883429930765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2348932631","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":[0.24904458,0.0053589456,0.73461264,0.0055089397,0.00015349967,0.0005331444,0.0005714398,0.00018507066,0.0040317224],"genre_scores_gemma":[0.8117233,0.0015689728,0.18388139,0.00037508685,0.00010958142,0.0005472914,0.0002746236,0.00005176748,0.0014681296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.93771696,0.057738014,0.0013601211,0.0014712348,0.0011628912,0.0005508057],"domain_scores_gemma":[0.51821613,0.4627562,0.0065165693,0.008218365,0.00355476,0.0007379316],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.086161844,0.0009188801,0.0030175678,0.0018479141,0.0013564948,0.003139724,0.004205911,0.0059136976,0.0040971283],"category_scores_gemma":[0.21507332,0.0012457478,0.0039016274,0.0033215769,0.0018547871,0.0030590221,0.0019516263,0.0038136786,0.0003456685],"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":[0.0036767372,0.0013828581,0.068953246,0.0013472798,0.0024582397,0.0077468553,0.002003954,0.6859788,0.0005820079,0.12517235,0.0046068598,0.09609089],"study_design_scores_gemma":[0.00041629767,0.00059426797,0.003850436,0.00028644194,0.000509645,0.0020061375,0.00040369487,0.8977833,0.0002676459,0.092266135,0.0015320651,0.000083958366],"about_ca_topic_score_codex":0.0070149503,"about_ca_topic_score_gemma":0.0066590705,"teacher_disagreement_score":0.91383815,"about_ca_system_score_codex":0.0013844129,"about_ca_system_score_gemma":0.0019975994,"threshold_uncertainty_score":0.45567286},"labels":[],"label_agreement":null},{"id":"W2407972048","doi":"10.1007/s11749-016-0492-4","title":"Constrained Bayes estimation in small area models with functional measurement error","year":2016,"lang":"en","type":"article","venue":"Test","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Small area estimation; Bayes' theorem; Covariate; Statistics; Prior probability; Scale (ratio); Bayes estimator; Bayesian probability; Mathematics; Hierarchical database model; Sample size determination; Bayes error rate; Computer science; Bayesian hierarchical modeling; Econometrics; Data mining; Bayes classifier; Estimator; Geography","score_opus":0.21791278451851054,"score_gpt":0.327274948278693,"score_spread":0.10936216376018248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407972048","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":[0.005914547,0.00040521368,0.9925021,0.00040322493,0.000029197123,0.000026657195,0.00008206391,0.000102440776,0.00053455593],"genre_scores_gemma":[0.363495,0.0021389641,0.6216006,0.0006920261,0.000499739,0.0009961042,0.0010585005,0.00047065623,0.009048303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98746175,0.009786331,0.00041524688,0.0011822195,0.00083047396,0.00032397325],"domain_scores_gemma":[0.7679763,0.21915786,0.004553212,0.00475043,0.0027034888,0.0008587443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027137287,0.0022368992,0.0049569504,0.0032313094,0.0011782903,0.0034773548,0.0061645526,0.0037685381,0.0067358534],"category_scores_gemma":[0.15710595,0.0021239512,0.0018554626,0.003646219,0.0065878555,0.0068905912,0.004417569,0.0042257747,0.00069690845],"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":[0.0001459277,0.00008470741,0.0024295063,0.00035557162,0.0003744941,0.0002475439,0.0002550153,0.23463567,0.00030723892,0.7210428,0.002384925,0.037736613],"study_design_scores_gemma":[0.000036089143,0.000020477688,0.00041270812,0.000046845387,0.00003770596,0.000044398876,0.000029579183,0.48664513,0.00013258532,0.51176834,0.00080133806,0.000024730462],"about_ca_topic_score_codex":0.012052769,"about_ca_topic_score_gemma":0.0079981955,"teacher_disagreement_score":0.027137287,"about_ca_system_score_codex":0.0022548393,"about_ca_system_score_gemma":0.0034006485,"threshold_uncertainty_score":0.14351743},"labels":[],"label_agreement":null},{"id":"W2409405257","doi":"10.1007/978-1-4939-2428-8_5","title":"Longitudinal Studies 2: Modeling Data Using Multivariate Analysis","year":2015,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland; Foothills Medical Centre; University of Calgary","funders":"","keywords":"Multivariate statistics; Longitudinal data; Multivariate analysis; Computer science; Econometrics; Statistics; Data mining; Mathematics; Machine learning","score_opus":0.5605105742236646,"score_gpt":0.6070462698177319,"score_spread":0.04653569559406734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409405257","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":[0.006359283,0.0030598633,0.9859621,0.0028969997,0.0003487324,0.00012445387,0.0004490507,0.00032895058,0.00047047617],"genre_scores_gemma":[0.14427496,0.0078495545,0.83494914,0.0018287947,0.0022196434,0.0023011363,0.0009420439,0.00053002074,0.005104617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9720872,0.023818582,0.000637907,0.0021122233,0.0009959079,0.0003481466],"domain_scores_gemma":[0.870968,0.111914076,0.006190806,0.008146746,0.0017211835,0.0010591997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049254254,0.0020502468,0.0024505788,0.0028742673,0.00091696787,0.003161469,0.0030176467,0.0031352025,0.0064335284],"category_scores_gemma":[0.10568031,0.0017893814,0.0032557917,0.004113682,0.0028106035,0.004403494,0.0034924936,0.003996541,0.0007221606],"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":[0.00075338,0.00042414622,0.030730452,0.0019403086,0.004483499,0.00076303526,0.001651895,0.06086288,0.0014239764,0.57156366,0.022931093,0.30247164],"study_design_scores_gemma":[0.00018088132,0.00022077831,0.00624057,0.00034551497,0.00087706634,0.0004678892,0.00016497617,0.20385508,0.00041497554,0.77529025,0.011836236,0.000105729334],"about_ca_topic_score_codex":0.003718736,"about_ca_topic_score_gemma":0.0036472708,"teacher_disagreement_score":0.049254254,"about_ca_system_score_codex":0.0010544116,"about_ca_system_score_gemma":0.0034352131,"threshold_uncertainty_score":0.26048452},"labels":[],"label_agreement":null},{"id":"W2409945735","doi":"","title":"Generalized Linear Models in Family Studies","year":2016,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Generalized linear model; Categorical variable; Exponential family; Mathematics; Log-linear model; Probit; Linear model; Econometrics; Poisson distribution; Probit model; Statistics","score_opus":0.25746696802498964,"score_gpt":0.4446606016364087,"score_spread":0.18719363361141905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409945735","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":[0.007672264,0.05045584,0.9108151,0.008972629,0.0012513743,0.00028723667,0.0017681624,0.00044333749,0.018333985],"genre_scores_gemma":[0.3325725,0.10108446,0.53204775,0.0041174316,0.005865885,0.004351507,0.003297481,0.0004168951,0.016246064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9760566,0.019653449,0.0005677273,0.0016430068,0.0017010109,0.00037824348],"domain_scores_gemma":[0.95452887,0.039261017,0.0025272868,0.001867331,0.0015043492,0.0003111426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020383082,0.001995834,0.0024833572,0.0040860474,0.0011434517,0.0031735168,0.003074595,0.0028827186,0.012397648],"category_scores_gemma":[0.0498665,0.0008881828,0.001912724,0.009199416,0.0032154906,0.0034678038,0.0025925755,0.004646492,0.0021184343],"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":[0.000026253156,0.00006329391,0.0036924894,0.0005675478,0.00030175684,0.00029072998,0.0010650496,0.017483795,0.00006344566,0.88938487,0.012809888,0.0742509],"study_design_scores_gemma":[0.00002407723,0.000039778424,0.0013281318,0.00034843924,0.00006112013,0.0001376065,0.00024246116,0.022510795,0.000031038315,0.9532217,0.022020387,0.000034482393],"about_ca_topic_score_codex":0.010497667,"about_ca_topic_score_gemma":0.00673252,"teacher_disagreement_score":0.020383082,"about_ca_system_score_codex":0.0030699621,"about_ca_system_score_gemma":0.0035156105,"threshold_uncertainty_score":0.107797265},"labels":[],"label_agreement":null},{"id":"W2412812778","doi":"10.17269/cjph.106.5033","title":"Re: “Linking missing data to study outcomes using multiple imputations”","year":2015,"lang":"en","type":"letter","venue":"Canadian Journal of Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Missing data; Computer science; Statistics; Econometrics; Mathematics","score_opus":0.586694497723202,"score_gpt":0.5004997130874989,"score_spread":0.08619478463570318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412812778","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":[0.00013422026,0.001689372,0.0064948183,0.97299486,0.015574827,0.00005764951,0.00034689307,0.00020636186,0.0025010302],"genre_scores_gemma":[0.002067101,0.00088201603,0.007476641,0.9444477,0.039967634,0.00030818928,0.00012776071,0.00016822344,0.0045548296],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92356694,0.052791692,0.007869136,0.0044512334,0.00977949,0.0015414954],"domain_scores_gemma":[0.6144446,0.3375655,0.010585632,0.012667737,0.019820612,0.004915886],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08009158,0.0017953499,0.0037905122,0.0021292537,0.0035460533,0.005846365,0.005898826,0.05127597,0.011069227],"category_scores_gemma":[0.42615548,0.0020357186,0.002724599,0.0024358241,0.009152587,0.00609899,0.0050628274,0.061738815,0.013744823],"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":[0.00006296563,0.000014350099,0.00040137317,0.00012377645,0.0000830415,0.0001790311,0.00012455134,0.00014429563,0.00005613224,0.010436819,0.96736306,0.02101056],"study_design_scores_gemma":[0.0006240305,0.00007681165,0.0024911398,0.002797986,0.00035959942,0.0012117388,0.00032526784,0.004184853,0.0006412192,0.19987026,0.7871679,0.0002492113],"about_ca_topic_score_codex":0.012134463,"about_ca_topic_score_gemma":0.017514406,"teacher_disagreement_score":0.9199084,"about_ca_system_score_codex":0.0046676495,"about_ca_system_score_gemma":0.009762371,"threshold_uncertainty_score":0.4235698},"labels":[],"label_agreement":null},{"id":"W2413112715","doi":"10.1097/ede.0000000000000179","title":"Commentary","year":2014,"lang":"en","type":"letter","venue":"Epidemiology","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":"Simon Fraser University","funders":"","keywords":"Statistics; Confidence interval; Probabilistic logic; Random error; Standard error; Observational error; Mathematics; Funnel plot; Random effects model; Odds; Sample size determination; Type I and type II errors; Publication bias; Meta-analysis; Medicine; Logistic regression","score_opus":0.22269925516601258,"score_gpt":0.44887197447302785,"score_spread":0.22617271930701527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413112715","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":[0.00017749271,0.015203396,0.0004491501,0.71104664,0.2311671,0.000117272226,0.0016295018,0.00021791726,0.039991602],"genre_scores_gemma":[0.0036412852,0.013272788,0.00059402845,0.84376407,0.08407753,0.00029145993,0.00095530675,0.00028184883,0.053121693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98773324,0.0041255024,0.00088308717,0.0012393445,0.0046709743,0.0013477613],"domain_scores_gemma":[0.9378302,0.027743906,0.002400459,0.0038495148,0.023996761,0.004179155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011610438,0.0012059796,0.0016484124,0.002472764,0.0034564033,0.0070170094,0.006276849,0.020746859,0.15916355],"category_scores_gemma":[0.12390221,0.0007518195,0.0026360445,0.0018846232,0.0036309941,0.005842245,0.0042732875,0.016834604,0.057418313],"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":[0.000016930522,0.000003094235,0.000025128153,0.00021093928,0.000009461633,0.00003981943,0.000043316842,0.000010597265,0.0000071072145,0.0020359433,0.99069995,0.0068976567],"study_design_scores_gemma":[0.000024339719,0.000006448079,0.00009322496,0.0015699557,0.000016453581,0.00012775401,0.00010153995,0.000015965772,0.00003414884,0.0025333066,0.99546766,0.000009070194],"about_ca_topic_score_codex":0.01104007,"about_ca_topic_score_gemma":0.011632007,"teacher_disagreement_score":0.15916355,"about_ca_system_score_codex":0.0067271683,"about_ca_system_score_gemma":0.017956184,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2442085826","doi":"10.1007/978-1-59745-385-1_4","title":"Modeling Longitudinal Data, II: Standard Regression Models and Extensions","year":2008,"lang":"en","type":"review","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Longitudinal data; Outcome (game theory); Generalized linear model; Linear regression; Regression analysis; Statistics; Linear model; Generalized linear mixed model; Regression; Mixed model; Econometrics; Computer science; Mathematics; Data mining","score_opus":0.3665390658237885,"score_gpt":0.5666947209346396,"score_spread":0.2001556551108511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2442085826","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":[0.0007146276,0.14331318,0.84935594,0.0022963258,0.0005895293,0.0000636773,0.000505981,0.00034963212,0.0028111408],"genre_scores_gemma":[0.040536202,0.44333434,0.49896893,0.00186576,0.0037204793,0.0010710076,0.0016860801,0.0004902682,0.00832698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945943,0.0031096486,0.00032399446,0.0007866945,0.0010795007,0.000105839456],"domain_scores_gemma":[0.98517275,0.01200566,0.0007396042,0.00092412357,0.0010491244,0.00010877259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014487525,0.0023763198,0.0032006388,0.003403795,0.00039103604,0.0024942933,0.004358422,0.0029951148,0.0028493255],"category_scores_gemma":[0.021973163,0.0013910361,0.002415714,0.005906534,0.002518272,0.0049971985,0.0022247974,0.004637235,0.0024509262],"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":[0.00004826937,0.00009228959,0.0016351773,0.0027985543,0.0005957457,0.0001913759,0.00035009425,0.10196406,0.00026477737,0.47622386,0.025867293,0.3899685],"study_design_scores_gemma":[0.000030374431,0.000054285916,0.0007904353,0.0010159815,0.00013184718,0.0003663805,0.00007575347,0.11161162,0.00025298644,0.7749537,0.1106342,0.000082404054],"about_ca_topic_score_codex":0.0048632203,"about_ca_topic_score_gemma":0.0026028994,"teacher_disagreement_score":0.014487525,"about_ca_system_score_codex":0.0018636183,"about_ca_system_score_gemma":0.00235655,"threshold_uncertainty_score":0.076618254},"labels":[],"label_agreement":null},{"id":"W2460512890","doi":"10.1002/cjs.11290","title":"Correlation structure selection for longitudinal data with diverging cluster size","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Correlation; Consistency (knowledge bases); Statistics; Selection (genetic algorithm); Applied mathematics; Model selection; Algorithm; Computer science; Discrete mathematics; Artificial intelligence","score_opus":0.05870520795076562,"score_gpt":0.3224740743417092,"score_spread":0.2637688663909436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460512890","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":[0.094746046,0.00030477616,0.9033137,0.00044381944,0.000030425736,0.000086729255,0.00011672122,0.00028392146,0.0006738972],"genre_scores_gemma":[0.72355103,0.00022002342,0.27328694,0.00022406109,0.000069105474,0.0002713022,0.000751503,0.00013742215,0.0014886821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879171,0.009393803,0.00030118515,0.0010765579,0.0008831218,0.0004281919],"domain_scores_gemma":[0.92526245,0.059385143,0.0031121185,0.006662203,0.0043642195,0.0012138189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029697899,0.00071184774,0.0018212082,0.0027901253,0.0011151949,0.001289886,0.0025897496,0.001235372,0.0020046046],"category_scores_gemma":[0.09031115,0.0005769435,0.001241908,0.002104325,0.0023736537,0.0018123196,0.002773448,0.0029818804,0.00044029433],"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":[0.0009558152,0.0002633878,0.06944088,0.00027532293,0.0004909901,0.00094926596,0.00092942186,0.5030622,0.0029386985,0.17685948,0.008227334,0.23560722],"study_design_scores_gemma":[0.000042789885,0.000056935474,0.0042263493,0.00003134644,0.000022528373,0.000067881774,0.00006111788,0.9313536,0.0005349851,0.062990695,0.0005853863,0.00002640612],"about_ca_topic_score_codex":0.0072271405,"about_ca_topic_score_gemma":0.006783796,"teacher_disagreement_score":0.029697899,"about_ca_system_score_codex":0.0015526533,"about_ca_system_score_gemma":0.0023642045,"threshold_uncertainty_score":0.15705937},"labels":[],"label_agreement":null},{"id":"W2466825400","doi":"10.1002/jae.2530","title":"Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of Toronto","funders":"National Science Foundation","keywords":"Bayes' theorem; Nonparametric statistics; Econometrics; Dirichlet process; Prior probability; Estimator; Mathematics; Bayesian probability; Statistics; Bivariate analysis; Frequentist inference; Computer science; Bayesian inference","score_opus":0.40830278483465754,"score_gpt":0.5115459267521361,"score_spread":0.10324314191747852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466825400","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":[0.00075293763,0.001301338,0.9965605,0.00026581532,0.000058433765,0.000066196204,0.00020036568,0.00021668045,0.0005776789],"genre_scores_gemma":[0.061347768,0.003678323,0.9272111,0.0003923309,0.0004945627,0.0009952708,0.0012404708,0.000285087,0.004355007],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9854837,0.010565327,0.0006912967,0.0013984242,0.0016283826,0.00023290506],"domain_scores_gemma":[0.95556414,0.037480596,0.001772031,0.0028140696,0.0020441178,0.00032504374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030029742,0.0017152114,0.0030256351,0.0033478737,0.0009028031,0.00332206,0.0042077885,0.0025787498,0.007538478],"category_scores_gemma":[0.090204924,0.0017268431,0.0023892508,0.003310287,0.0017814772,0.0040249643,0.0025057744,0.004755859,0.0024749823],"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":[0.00021823369,0.00013628171,0.0033572055,0.00086799375,0.0006396585,0.0003673318,0.0004495776,0.22327629,0.0007086826,0.34098297,0.012291595,0.41670424],"study_design_scores_gemma":[0.00005101332,0.000043134372,0.0005545289,0.0002573075,0.00007375457,0.000128185,0.000050368533,0.58799815,0.00046142176,0.39998454,0.010346886,0.000050736046],"about_ca_topic_score_codex":0.0045546414,"about_ca_topic_score_gemma":0.0047344137,"teacher_disagreement_score":0.030029742,"about_ca_system_score_codex":0.0014678045,"about_ca_system_score_gemma":0.0029799063,"threshold_uncertainty_score":0.15881437},"labels":[],"label_agreement":null},{"id":"W2467224808","doi":"10.1093/biomet/asw022","title":"Accurate directional inference for vector parameters","year":2016,"lang":"en","type":"article","venue":"Biometrika","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; York University; Fondazione Cassa di Risparmio di Padova e Rovigo","keywords":"Mathematics; Nuisance parameter; Likelihood-ratio test; Inference; Score test; Statistical inference; Applied mathematics; Exponential family; Empirical likelihood; Exponential function; Indirect Inference; Likelihood principle; Statistics; Algorithm; Likelihood function; Maximum likelihood; Artificial intelligence; Computer science; Mathematical analysis; Quasi-maximum likelihood; Confidence interval; Estimator","score_opus":0.15068297292183588,"score_gpt":0.42135421615415575,"score_spread":0.27067124323231984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2467224808","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":[0.011641703,0.00015826803,0.98673093,0.0003221492,0.000023129513,0.000014588366,0.00008602799,0.000091717295,0.00093154446],"genre_scores_gemma":[0.51209956,0.00082216883,0.48251668,0.0005500671,0.00015177896,0.00018192107,0.0005456167,0.00019666344,0.0029354834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9925867,0.0042515397,0.0003576818,0.0010785183,0.001388406,0.00033708825],"domain_scores_gemma":[0.9602482,0.03007829,0.002849592,0.004224638,0.0020904022,0.00050889555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01761748,0.001054262,0.0016933235,0.0015889549,0.0006670538,0.0021929168,0.002532348,0.0024889875,0.002653794],"category_scores_gemma":[0.095116064,0.0007910252,0.001233665,0.0015946727,0.002985635,0.0057826666,0.0031486945,0.003136444,0.00078715646],"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":[0.00010638672,0.00003952051,0.0040064044,0.0001358487,0.00008963737,0.0002281876,0.00019860158,0.29658327,0.00169295,0.6617343,0.001205903,0.0339791],"study_design_scores_gemma":[0.000017475366,0.00003017141,0.00037984113,0.000024146751,0.000013996595,0.000092808004,0.000032696047,0.6334422,0.00074701954,0.3641426,0.0010509662,0.000026179796],"about_ca_topic_score_codex":0.0032286763,"about_ca_topic_score_gemma":0.0025351837,"teacher_disagreement_score":0.01761748,"about_ca_system_score_codex":0.0013478128,"about_ca_system_score_gemma":0.0013086624,"threshold_uncertainty_score":0.09317124},"labels":[],"label_agreement":null},{"id":"W2468405834","doi":"10.1007/978-0-387-68276-1_21","title":"Erratum to: Additional Statistical Applications","year":2011,"lang":"en","type":"erratum","venue":"Springer series in statistics","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":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.052464879780956625,"score_gpt":0.3569739998094363,"score_spread":0.30450912002847963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468405834","genre_codex":"editorial","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":[0.00030074286,0.0035791993,0.009154073,0.047016256,0.90505105,0.00010918309,0.002830429,0.00081219315,0.03114681],"genre_scores_gemma":[0.009448212,0.01082317,0.023451636,0.057762947,0.31064317,0.0004472727,0.006213392,0.0027869511,0.5784233],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.993785,0.0014897471,0.00097144773,0.000729792,0.0026528705,0.0003711672],"domain_scores_gemma":[0.96829,0.009407353,0.0009363365,0.0027695259,0.017674748,0.0009220798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044307164,0.003530333,0.0027580669,0.0064917686,0.0030330054,0.003557404,0.0035564883,0.005236211,0.16562222],"category_scores_gemma":[0.061876763,0.0011125676,0.0031897284,0.0054116743,0.00161688,0.0038216088,0.0022466574,0.0083429,0.08935035],"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":[0.000034554072,0.000016931343,0.000039238363,0.00014138568,0.00001132932,0.00014140307,0.000012541646,0.00014078787,0.000047119145,0.0031643107,0.9826911,0.013559312],"study_design_scores_gemma":[0.0000362802,0.00004235752,0.00083829387,0.00030061012,0.000051875002,0.0005950587,0.000050656792,0.0012538891,0.00029754,0.020365002,0.97610587,0.00006259492],"about_ca_topic_score_codex":0.0069515565,"about_ca_topic_score_gemma":0.01189132,"teacher_disagreement_score":0.16562222,"about_ca_system_score_codex":0.0034365407,"about_ca_system_score_gemma":0.0037653833,"threshold_uncertainty_score":0.55406153},"labels":[],"label_agreement":null},{"id":"W2469774875","doi":"10.1002/bimj.201500225","title":"Inverse probability weighting estimation of the volume under the ROC surface in the presence of verification bias","year":2016,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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":"DoD Alzheimer's Disease Neuroimaging Initiative; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Estimator; Jackknife resampling; Receiver operating characteristic; Weighting; Statistics; Mathematics; Inverse probability weighting; Variance (accounting); Statistical hypothesis testing; Computer science; Medicine; Radiology","score_opus":0.15672515822205366,"score_gpt":0.36997725460139896,"score_spread":0.2132520963793453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469774875","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":[0.00961688,0.00035389522,0.9893679,0.00009236003,0.00002786018,0.000057882433,0.000024139046,0.00012405026,0.00033506335],"genre_scores_gemma":[0.43915784,0.00093474315,0.55665,0.0002630786,0.00019393742,0.0005895832,0.0003037468,0.00017361855,0.001733569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9858731,0.008813392,0.0007404088,0.001847383,0.002281207,0.00044453854],"domain_scores_gemma":[0.91439074,0.07008169,0.004878834,0.0054269317,0.0047603487,0.0004615774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027418543,0.0012308406,0.0023763517,0.002701432,0.0005883684,0.0022261888,0.0024239363,0.0021535524,0.0014781083],"category_scores_gemma":[0.123540364,0.0006033087,0.0018617555,0.0021955695,0.002475312,0.0030310294,0.0025698754,0.0018862581,0.00041195663],"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":[0.00044698545,0.0001942231,0.031180615,0.00067458296,0.00063658896,0.0006808282,0.00071232935,0.3370539,0.006426605,0.17797749,0.0025852576,0.44143075],"study_design_scores_gemma":[0.000026876069,0.00015016523,0.0036013818,0.000073797804,0.00009506773,0.0003304185,0.000050108498,0.9272524,0.0022653437,0.06438191,0.0017121129,0.000060373484],"about_ca_topic_score_codex":0.002374442,"about_ca_topic_score_gemma":0.0010937534,"teacher_disagreement_score":0.027418543,"about_ca_system_score_codex":0.0011784323,"about_ca_system_score_gemma":0.001757136,"threshold_uncertainty_score":0.14500481},"labels":[],"label_agreement":null},{"id":"W2474654233","doi":"10.1037/met0000048","title":"Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.","year":2015,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","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":"McGill University Health Centre","funders":"National Cancer Institute; National Institute on Drug Abuse","keywords":"Covariate; Semiparametric regression; Nonparametric statistics; Econometrics; Population; Regression analysis; Regression; Statistics; Mathematics; Latent variable; Linear regression; Semiparametric model; Demography","score_opus":0.2803564483775052,"score_gpt":0.498751203955735,"score_spread":0.21839475557822985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474654233","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":[0.009116186,0.00043790042,0.98890555,0.00034544212,0.00006141482,0.00009662,0.00034450187,0.00023482837,0.00045764213],"genre_scores_gemma":[0.2886594,0.0016092595,0.7005462,0.0005221119,0.00030686,0.0014045666,0.0023175813,0.00018236194,0.0044515873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99312544,0.004580028,0.00030432493,0.0012288085,0.00053835684,0.00022299272],"domain_scores_gemma":[0.9685259,0.026167072,0.002203976,0.002080811,0.0006850178,0.00033722294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018806582,0.001473221,0.0019521697,0.0024121923,0.0008924847,0.002520875,0.003461538,0.0025364151,0.0029717898],"category_scores_gemma":[0.050460454,0.0014860549,0.0034857308,0.0024502454,0.0019431332,0.0033627495,0.003189915,0.0036518604,0.00055912416],"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":[0.0003048482,0.00021990901,0.02664401,0.0004432792,0.0013218679,0.00043241042,0.0013288646,0.41473535,0.0013033099,0.45406428,0.00446708,0.09473471],"study_design_scores_gemma":[0.000045954705,0.00007842337,0.0029206523,0.000075189215,0.00014729703,0.00015783779,0.00008395054,0.7838272,0.00027300182,0.20774487,0.0045935824,0.000051940286],"about_ca_topic_score_codex":0.012253273,"about_ca_topic_score_gemma":0.015285906,"teacher_disagreement_score":0.018806582,"about_ca_system_score_codex":0.0016084426,"about_ca_system_score_gemma":0.001651188,"threshold_uncertainty_score":0.09945989},"labels":[],"label_agreement":null},{"id":"W2478895115","doi":"10.1007/978-3-319-31260-6_3","title":"Zero-Inflated Spatial Models: Application and Interpretation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","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":"Western University; Simon Fraser University","funders":"","keywords":"Overdispersion; Statistics; Count data; Mathematics; Multivariate statistics; Econometrics; Generalized linear model; Computer science; Poisson distribution","score_opus":0.03482270418686196,"score_gpt":0.3273210291152349,"score_spread":0.29249832492837297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478895115","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":[0.003916191,0.0022818716,0.9739957,0.002173286,0.00030953038,0.000015656966,0.00040841204,0.00030779836,0.016591646],"genre_scores_gemma":[0.40496823,0.010576059,0.52533346,0.001995491,0.0017046264,0.00031314776,0.0021437597,0.0010094176,0.051955834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974638,0.001337725,0.00015470102,0.00043633094,0.00048094164,0.00012644826],"domain_scores_gemma":[0.9912115,0.00559224,0.0008242613,0.0012528244,0.00093633815,0.00018286573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005594812,0.0014810637,0.0015791535,0.002399786,0.0009154271,0.0047871773,0.0055220746,0.003469078,0.009430204],"category_scores_gemma":[0.020124085,0.0012317748,0.0026806197,0.004367924,0.0040741544,0.005667422,0.0029260584,0.004680734,0.0027261102],"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":[0.000007717169,0.000010168725,0.0003155359,0.00007462725,0.00003330936,0.00007013504,0.00013734642,0.0172342,0.000097206306,0.9660618,0.0044132783,0.011544664],"study_design_scores_gemma":[0.0000024811163,0.000004891055,0.000073271476,0.000026767219,0.000013151353,0.000064681335,0.00003493455,0.03304196,0.00005589365,0.96168995,0.0049824766,0.000009448844],"about_ca_topic_score_codex":0.0039912886,"about_ca_topic_score_gemma":0.0029868449,"teacher_disagreement_score":0.009430204,"about_ca_system_score_codex":0.0016520229,"about_ca_system_score_gemma":0.0013284638,"threshold_uncertainty_score":0.03154719},"labels":[],"label_agreement":null},{"id":"W2505248766","doi":"10.1007/978-3-319-31260-6_6","title":"Dynamic Models for Longitudinal Ordinal Non-stationary Categorical Data","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Categorical variable; Ordinal data; Ordinal regression; Longitudinal data; Econometrics; Computer science; Mathematics; Statistics; Data mining","score_opus":0.12132006274639773,"score_gpt":0.3954614555441705,"score_spread":0.2741413927977728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505248766","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":[0.0023976911,0.0026908573,0.9895846,0.0015326538,0.00020128983,0.000026701087,0.00062753545,0.00022753704,0.0027111394],"genre_scores_gemma":[0.33731058,0.02117724,0.55502075,0.0020615472,0.002506675,0.0019024402,0.008131036,0.001015201,0.07087453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960091,0.002305385,0.0002124201,0.0007972415,0.0004779972,0.00019784724],"domain_scores_gemma":[0.9781762,0.018379234,0.0012204,0.0011753907,0.0007624773,0.00028628574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008091931,0.0017993656,0.0024667624,0.0016346431,0.0006712321,0.0028690253,0.0048143496,0.0030513476,0.013325701],"category_scores_gemma":[0.030213729,0.0017117074,0.0024471823,0.0035540808,0.0020202105,0.0043908022,0.002167889,0.006422611,0.0037262905],"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":[0.000044580054,0.000045306537,0.0011978335,0.00024399672,0.00016528249,0.00010414498,0.00029254582,0.0837487,0.0002819944,0.8597803,0.00877923,0.045316126],"study_design_scores_gemma":[0.000013049407,0.000021183661,0.00029861188,0.00006808263,0.000041365816,0.00006072037,0.00003021932,0.22656217,0.000057450525,0.7670229,0.0057952027,0.000029085759],"about_ca_topic_score_codex":0.005811206,"about_ca_topic_score_gemma":0.0074816006,"teacher_disagreement_score":0.013325701,"about_ca_system_score_codex":0.0024492934,"about_ca_system_score_gemma":0.0017319764,"threshold_uncertainty_score":0.04457885},"labels":[],"label_agreement":null},{"id":"W2508625972","doi":"10.1214/16-ba1024","title":"Optimal Robustness Results for Relative Belief Inferences and the Relationship to Prior-Data Conflict","year":2016,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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":"Robustness (evolution); Inference; Computer science; Bayesian probability; Econometrics; Bayesian inference; Prior probability; Data mining; Machine learning; Mathematics; Artificial intelligence","score_opus":0.1422005404427989,"score_gpt":0.39962657890489983,"score_spread":0.25742603846210094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508625972","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":[0.012191319,0.0025070729,0.97491837,0.0018869737,0.00011428337,0.00013114478,0.0002760584,0.00022762887,0.007747223],"genre_scores_gemma":[0.5996292,0.0069525302,0.3827483,0.0017701387,0.001675873,0.0013409081,0.0010672518,0.00080680905,0.0040090573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94617844,0.036441583,0.0030605071,0.0059513394,0.0067389067,0.0016291476],"domain_scores_gemma":[0.44505873,0.51642084,0.015276751,0.013820716,0.0075956443,0.0018273278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06774786,0.003489335,0.0046486123,0.0072790612,0.0019115346,0.008744086,0.0052090986,0.0050137155,0.007066846],"category_scores_gemma":[0.38547137,0.0022752243,0.0041910727,0.005097847,0.0118189445,0.013269279,0.0076796603,0.008865428,0.0010554494],"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":[0.00046781424,0.00008610089,0.0014947886,0.0008064926,0.0007040998,0.00028764937,0.00043585876,0.19203667,0.0010611857,0.76702815,0.0018086581,0.033782497],"study_design_scores_gemma":[0.000050244893,0.00010805894,0.00046854184,0.00017495693,0.000117018884,0.000119405195,0.000051752548,0.19633156,0.000757841,0.8009167,0.0008388568,0.00006506043],"about_ca_topic_score_codex":0.0019913514,"about_ca_topic_score_gemma":0.0009969671,"teacher_disagreement_score":0.06774786,"about_ca_system_score_codex":0.0059896572,"about_ca_system_score_gemma":0.002967619,"threshold_uncertainty_score":0.35828924},"labels":[],"label_agreement":null},{"id":"W2514259613","doi":"10.1002/cjs.11302","title":"Probability‐scale residuals for continuous, discrete, and censored data","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Residual; Statistics; Quantile; Mathematics; Scale (ratio); Ordinal regression; Econometrics; Probability distribution; Proportional hazards model; Regression analysis; Outcome (game theory); Quantile regression; Cumulative distribution function; Regression; Ordinal data; Probability density function; Algorithm; Geography","score_opus":0.11667351654085495,"score_gpt":0.3441755786954863,"score_spread":0.22750206215463137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514259613","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":[0.009216987,0.00032932087,0.98832464,0.00045584724,0.00008205789,0.000045472396,0.0003489056,0.00035677553,0.00084009016],"genre_scores_gemma":[0.4622517,0.00079448137,0.52931124,0.0005219258,0.00029289338,0.0005679278,0.0019831061,0.00049627345,0.0037804376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98748624,0.008310586,0.0007516243,0.0011650458,0.0019779485,0.0003085413],"domain_scores_gemma":[0.92884177,0.052771926,0.005549618,0.009448554,0.002842918,0.0005452133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02532562,0.00069748913,0.001100709,0.0019692436,0.00038426256,0.0016597084,0.002590961,0.00145273,0.0040300237],"category_scores_gemma":[0.116263255,0.00038071114,0.0015406515,0.002545351,0.0027440705,0.0027733226,0.0021283987,0.0028956488,0.00089768996],"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":[0.00019385143,0.000082233,0.013973139,0.00043942838,0.00024531726,0.00040968353,0.00048224017,0.1877545,0.0010913668,0.68370587,0.0071735643,0.10444885],"study_design_scores_gemma":[0.000066249304,0.00019982141,0.0058026044,0.00012374757,0.00006766176,0.0002897494,0.00013947282,0.5781863,0.0008190943,0.4007898,0.01342743,0.00008813377],"about_ca_topic_score_codex":0.0047272,"about_ca_topic_score_gemma":0.003944011,"teacher_disagreement_score":0.02532562,"about_ca_system_score_codex":0.0013047875,"about_ca_system_score_gemma":0.001940325,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2515095143","doi":"10.1177/0008068320090106","title":"A Zero-Inflated Bivariate Poisson Regression Model and Application to Some Dental Epidemiological Data","year":2009,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor; The King's University","funders":"","keywords":"Covariate; Statistics; Mathematics; Poisson regression; Zero-inflated model; Bivariate analysis; Poisson distribution; Count data; Regression analysis; Generalized linear model; Quasi-likelihood; Zero (linguistics); Population; Medicine","score_opus":0.0649466904730365,"score_gpt":0.39394542361452145,"score_spread":0.328998733141485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515095143","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":[0.008204143,0.00020660498,0.99042386,0.00039275296,0.000024571767,0.000048838192,0.00011968662,0.0001283597,0.00045113382],"genre_scores_gemma":[0.2221989,0.0016192581,0.7711123,0.0003378599,0.0001978178,0.00070617,0.0007683432,0.00013950525,0.0029198714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99197984,0.0056588203,0.0003259398,0.00090854545,0.00091041025,0.0002165018],"domain_scores_gemma":[0.97714394,0.018592706,0.0014560726,0.0015615748,0.0010803496,0.00016534408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017886208,0.0005451813,0.0014822581,0.00224701,0.0007823439,0.0013831676,0.0029602353,0.0015439359,0.0027437066],"category_scores_gemma":[0.051122323,0.0006343757,0.0020732551,0.0035972036,0.0015748349,0.0016546077,0.0016320911,0.00255009,0.0006200398],"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":[0.00014335592,0.00010969646,0.012688537,0.00043541103,0.00023350892,0.0009975093,0.00080623524,0.2921562,0.0029127162,0.55582196,0.0034884196,0.13020648],"study_design_scores_gemma":[0.000028139599,0.000059391085,0.0021658482,0.000039150542,0.000052423948,0.0003694229,0.000080031255,0.87633836,0.0005984474,0.11685016,0.0033661802,0.000052427145],"about_ca_topic_score_codex":0.0058365148,"about_ca_topic_score_gemma":0.0033858102,"teacher_disagreement_score":0.017886208,"about_ca_system_score_codex":0.0012612178,"about_ca_system_score_gemma":0.0013463765,"threshold_uncertainty_score":0.09459245},"labels":[],"label_agreement":null},{"id":"W2515445137","doi":"10.20982/tqmp.07.1.p001","title":"Non-central t distribution and the power of the t test: A rejoinder","year":2011,"lang":"en","type":"article","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières","funders":"","keywords":"Envelope (radar); Mathematics; Probability density function; Distribution (mathematics); Statistics; Variance (accounting); Normal distribution; Power (physics); Function (biology); Econometrics; Statistical physics; Physics; Mathematical analysis; Economics; Quantum mechanics; Computer science; Telecommunications","score_opus":0.19091560464498766,"score_gpt":0.5069770458439566,"score_spread":0.31606144119896895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515445137","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":[0.0030562154,0.01205828,0.9389218,0.031611364,0.005716788,0.00018286098,0.000100207384,0.000313476,0.008039069],"genre_scores_gemma":[0.14319184,0.010983911,0.79306257,0.024189426,0.018934134,0.0015749377,0.00015616987,0.0012168463,0.006690169],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9322543,0.042296305,0.004251117,0.009186678,0.011314473,0.0006971701],"domain_scores_gemma":[0.5991587,0.35785684,0.004959306,0.019448949,0.016456213,0.0021200276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13664836,0.001986041,0.006315355,0.0050495164,0.0035636942,0.007310885,0.0049431943,0.009701359,0.005226612],"category_scores_gemma":[0.2627803,0.0014714397,0.0035574771,0.0031008094,0.03820891,0.014600857,0.007876054,0.032520182,0.002464133],"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":[0.00016878953,0.000035864512,0.0007651949,0.00042417215,0.00014469672,0.00042844244,0.0013561463,0.002668216,0.00034439334,0.9259814,0.009786483,0.05789622],"study_design_scores_gemma":[0.000040762116,0.00008035272,0.00024676562,0.00019208965,0.000041277945,0.000419342,0.00014390446,0.0047163446,0.00032247658,0.97712225,0.016606528,0.000067944806],"about_ca_topic_score_codex":0.0015559531,"about_ca_topic_score_gemma":0.0008986283,"teacher_disagreement_score":0.13664836,"about_ca_system_score_codex":0.0033402548,"about_ca_system_score_gemma":0.0035315042,"threshold_uncertainty_score":0.72267425},"labels":[],"label_agreement":null},{"id":"W2515545921","doi":"10.1177/0962280216660419","title":"Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs","year":2016,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Univariate; Autoregressive model; Computer science; Multivariate analysis; Context (archaeology); Covariance; Linear model; Generalized linear mixed model; Econometrics; Statistics; Mathematics; Machine learning","score_opus":0.44791645230417537,"score_gpt":0.6086216280736643,"score_spread":0.1607051757694889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515545921","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":[0.007903079,0.00030844193,0.9901411,0.00031562126,0.000032024764,0.000033104894,0.00020111723,0.00015515367,0.0009103829],"genre_scores_gemma":[0.5462888,0.002346853,0.43187475,0.00091152976,0.0005607718,0.00085708324,0.001931887,0.0006482985,0.014580075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99453413,0.0028849994,0.00022225204,0.0011458838,0.0007763712,0.00043636432],"domain_scores_gemma":[0.97865146,0.013674728,0.0031126325,0.002595142,0.0015264414,0.000439596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010306928,0.0014327528,0.0020439073,0.0020227607,0.00077166926,0.0028850245,0.004073394,0.0019131502,0.0037369428],"category_scores_gemma":[0.027122002,0.0010693653,0.0028258425,0.0024556243,0.0032023157,0.0041347495,0.0034496807,0.004777035,0.0009937902],"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":[0.000060528946,0.00003999655,0.0024640164,0.00011755562,0.00013414267,0.00014395195,0.00027625414,0.23320419,0.00075313373,0.73390937,0.0022783612,0.026618501],"study_design_scores_gemma":[0.000010219586,0.000021715896,0.00056715845,0.000025724916,0.000028062017,0.00005142278,0.000034502358,0.7678937,0.00021421375,0.2289305,0.002188172,0.00003461603],"about_ca_topic_score_codex":0.009707115,"about_ca_topic_score_gemma":0.008820283,"teacher_disagreement_score":0.010306928,"about_ca_system_score_codex":0.0021585596,"about_ca_system_score_gemma":0.0022155493,"threshold_uncertainty_score":0.054508865},"labels":[],"label_agreement":null},{"id":"W2516009469","doi":"10.1002/cjs.11296","title":"Unit level small area estimation with copulas","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Statistics Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Copula (linguistics); Statistics; Estimator; Multivariate statistics; Econometrics","score_opus":0.16790825498622003,"score_gpt":0.3276110990539593,"score_spread":0.15970284406773927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516009469","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":[0.014722675,0.00013022345,0.9843047,0.000070643706,0.000009628093,0.000019917972,0.00007773076,0.00010081945,0.00056362234],"genre_scores_gemma":[0.7372364,0.00065036834,0.25838998,0.00008367887,0.00007009169,0.00023817802,0.00062554615,0.00016719988,0.0025384936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99748373,0.0017466441,0.00006972103,0.00033687148,0.00025709067,0.000105960644],"domain_scores_gemma":[0.9850745,0.011854834,0.0010721876,0.0009959883,0.00084230234,0.00016014499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059421877,0.0007957845,0.0011984811,0.0014532026,0.00039310835,0.0015272857,0.0017277497,0.00074377126,0.002479289],"category_scores_gemma":[0.024286594,0.00073141727,0.0010830861,0.001879301,0.000885445,0.001665135,0.0014364395,0.0016492208,0.00044623754],"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":[0.000025356172,0.000026699774,0.003909314,0.00004783348,0.0001133936,0.00006941945,0.00007054291,0.93959117,0.00025603702,0.03484699,0.00076416845,0.020279039],"study_design_scores_gemma":[0.0000018740433,0.000006723975,0.0005396892,0.000008055307,0.0000058939736,0.000005928101,0.000009953876,0.98360217,0.00009405711,0.01551,0.00021081672,0.000004800095],"about_ca_topic_score_codex":0.01028316,"about_ca_topic_score_gemma":0.0066384426,"teacher_disagreement_score":0.01028316,"about_ca_system_score_codex":0.0008418983,"about_ca_system_score_gemma":0.0010043364,"threshold_uncertainty_score":0.031425655},"labels":[],"label_agreement":null},{"id":"W2516839318","doi":"10.1002/sim.7079","title":"Estimation for zero-inflated over-dispersed count data model with missing response","year":2016,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Windsor","funders":"","keywords":"Count data; Negative binomial distribution; Missing data; Poisson distribution; Zero-inflated model; Expectation–maximization algorithm; Statistics; Estimator; Maximum likelihood; Mathematics; Zero (linguistics); Overdispersion; Mixture model; Censoring (clinical trials); Poisson regression; Applied mathematics; Econometrics; Population","score_opus":0.1297925260737236,"score_gpt":0.4484117459185901,"score_spread":0.3186192198448665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516839318","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":[0.01090461,0.00023366768,0.9883609,0.00010949226,0.000019463685,0.000038781465,0.00008595367,0.0000761341,0.00017098413],"genre_scores_gemma":[0.28454894,0.0010301464,0.7094812,0.00035346468,0.00020446006,0.00048413075,0.001469418,0.0001326173,0.0022956668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9864564,0.008505342,0.00075370073,0.0027234838,0.0012338314,0.00032728232],"domain_scores_gemma":[0.9505313,0.038168658,0.0045069885,0.004538804,0.001886643,0.00036762768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02467386,0.0011485312,0.002913336,0.0018791854,0.0006729885,0.0018523935,0.0051715714,0.0023842733,0.0030099542],"category_scores_gemma":[0.075900644,0.0011121384,0.0017909254,0.002665121,0.0020018513,0.003596988,0.0028159919,0.0033666366,0.0008581369],"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":[0.0005307899,0.00024101044,0.03650434,0.0016571262,0.0010264324,0.002766629,0.002275781,0.38298514,0.004788875,0.34410346,0.003364151,0.21975625],"study_design_scores_gemma":[0.000072585965,0.00017864909,0.004176084,0.00017648125,0.00016776401,0.00093638327,0.00028275614,0.8143304,0.001695183,0.17414823,0.0037418578,0.00009354952],"about_ca_topic_score_codex":0.0017133084,"about_ca_topic_score_gemma":0.0011316291,"teacher_disagreement_score":0.02467386,"about_ca_system_score_codex":0.0008492529,"about_ca_system_score_gemma":0.0010838603,"threshold_uncertainty_score":0.13048941},"labels":[],"label_agreement":null},{"id":"W2518023653","doi":"10.1007/s00477-016-1309-4","title":"Multiple imputation framework for data assignment in truncated pluri-Gaussian simulation","year":2016,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"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":"Categorical variable; Latent variable; Imputation (statistics); Local independence; Missing data; Statistics; Latent variable model; Mathematics; Latent class model; Gaussian; Econometrics; Realization (probability); Computer science; Data mining","score_opus":0.1572263267314668,"score_gpt":0.48919197841650197,"score_spread":0.33196565168503517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518023653","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":[0.0012034983,0.00007714301,0.998147,0.00014976002,0.000022355296,0.000028773973,0.000085904605,0.00008859695,0.0001969976],"genre_scores_gemma":[0.14014156,0.00061586173,0.849718,0.0004967914,0.00026895976,0.0011652269,0.0017718906,0.00031090027,0.005510808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9829116,0.011656121,0.0009784363,0.0022061232,0.0015312961,0.00071646314],"domain_scores_gemma":[0.9243165,0.059842665,0.0028036432,0.006900478,0.0049712094,0.0011655123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036050934,0.0013665278,0.0050609205,0.0019997337,0.0019561856,0.0038575963,0.010328075,0.0043031946,0.008226269],"category_scores_gemma":[0.09448549,0.002191961,0.004369753,0.0044097323,0.003478834,0.0054792045,0.0054087546,0.0062466026,0.0018118669],"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":[0.00025301243,0.000092194714,0.001845784,0.00019710418,0.000374629,0.0002603068,0.00039184027,0.46096155,0.0003205452,0.49542943,0.002773626,0.03709993],"study_design_scores_gemma":[0.000044196087,0.000023145336,0.00015913621,0.000034098848,0.000041696425,0.000045708435,0.00001741788,0.8400794,0.0001337875,0.15845805,0.00094000873,0.000023426453],"about_ca_topic_score_codex":0.011198879,"about_ca_topic_score_gemma":0.0077492055,"teacher_disagreement_score":0.036050934,"about_ca_system_score_codex":0.0025036002,"about_ca_system_score_gemma":0.005623896,"threshold_uncertainty_score":0.1906578},"labels":[],"label_agreement":null},{"id":"W2518891199","doi":"10.1080/07474946.2016.1206386","title":"Multistage estimation of the difference of locations of two negative exponential populations under a modified Linex loss function: Real data illustrations from cancer studies and reliability analysis","year":2016,"lang":"en","type":"article","venue":"Sequential Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"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":"Mathematics; Statistics; Exponential function; Applied mathematics; Function (biology); Taylor series; Series (stratigraphy); Exponential distribution; Mathematical analysis","score_opus":0.2180346798713618,"score_gpt":0.4490526077710861,"score_spread":0.23101792789972428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518891199","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":[0.051974747,0.00043485602,0.9463126,0.00028216586,0.000022260876,0.00009035332,0.000088409644,0.00007483792,0.0007198194],"genre_scores_gemma":[0.5576446,0.00061369885,0.4376269,0.0001468703,0.0000573988,0.00045655403,0.0003264896,0.000053280834,0.0030742004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99324083,0.0048654806,0.00027397432,0.00077673775,0.00064397295,0.00019901381],"domain_scores_gemma":[0.9559895,0.037150156,0.0019934725,0.0020473085,0.0024852348,0.0003343683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020952623,0.000792402,0.0011362355,0.0012253807,0.00047820542,0.0011404438,0.0019316514,0.0016191747,0.0020074425],"category_scores_gemma":[0.044605073,0.00051698927,0.0023556354,0.001064387,0.0014410054,0.001777828,0.0024408693,0.0020441904,0.00032556523],"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":[0.00070942094,0.00019560885,0.036477394,0.00066654093,0.00041052568,0.0010705584,0.0017256002,0.64683735,0.007024775,0.14489728,0.0020865859,0.15789828],"study_design_scores_gemma":[0.000066855304,0.00042486395,0.007575901,0.00008091945,0.000116833704,0.00030917706,0.00024202594,0.93142724,0.00279323,0.05470232,0.0021760575,0.00008459173],"about_ca_topic_score_codex":0.0051447046,"about_ca_topic_score_gemma":0.003328875,"teacher_disagreement_score":0.020952623,"about_ca_system_score_codex":0.001019443,"about_ca_system_score_gemma":0.00094928994,"threshold_uncertainty_score":0.110809386},"labels":[],"label_agreement":null},{"id":"W2520267412","doi":"10.1016/j.spl.2016.08.020","title":"The Bayesian restricted Conway–Maxwell-Binomial model to control dispersion in count data","year":2016,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Count data; Negative binomial distribution; Binomial distribution; Bayesian probability; Continuity correction; Binomial (polynomial); Binomial proportion confidence interval; Statistics; Applied mathematics; Negative multinomial distribution; Beta-binomial distribution; Statistical physics; Poisson distribution; Physics","score_opus":0.0688019171194428,"score_gpt":0.3448547847241888,"score_spread":0.276052867604746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520267412","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":[0.012216611,0.00039951582,0.9852696,0.0004534842,0.00011000902,0.000059801572,0.00018587806,0.0002776096,0.0010274451],"genre_scores_gemma":[0.45807564,0.0010572001,0.5246326,0.0010550959,0.00049030123,0.0008687636,0.001124114,0.0006395789,0.012056804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9756034,0.017552823,0.0010061744,0.0030964494,0.0018564163,0.00088483456],"domain_scores_gemma":[0.8995829,0.079999834,0.0042427117,0.011422224,0.0034130148,0.001339372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039668,0.0012739259,0.0035547093,0.0025465703,0.0016936628,0.0026973921,0.007939631,0.0037974021,0.0061154473],"category_scores_gemma":[0.130468,0.0018545251,0.0023460602,0.004174058,0.0039537526,0.00807402,0.0044120545,0.005309948,0.0011384371],"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":[0.00062199665,0.00022556794,0.0063222745,0.0002626155,0.0006710731,0.0002850323,0.0008061798,0.27936035,0.0009105338,0.63474375,0.0047584893,0.071032144],"study_design_scores_gemma":[0.00007728184,0.00005286978,0.000830036,0.000043126984,0.00008928146,0.00009825981,0.00003677045,0.80133355,0.0002749239,0.19509123,0.0020265481,0.00004611467],"about_ca_topic_score_codex":0.010351999,"about_ca_topic_score_gemma":0.008631136,"teacher_disagreement_score":0.039668,"about_ca_system_score_codex":0.0022259504,"about_ca_system_score_gemma":0.0034531164,"threshold_uncertainty_score":0.20978701},"labels":[],"label_agreement":null},{"id":"W2522861822","doi":"10.1037/met0000084","title":"The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models.","year":2016,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":145,"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 Sherbrooke","funders":"","keywords":"Covariate; Statistics; Mathematics; Bayesian information criterion; Akaike information criterion; Econometrics; Population; Sample size determination; Latent class model; Demography","score_opus":0.08867699414247705,"score_gpt":0.46423015804126466,"score_spread":0.3755531638987876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522861822","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":[0.3133751,0.006089495,0.6691847,0.0019688958,0.0004045394,0.0006469709,0.0012436527,0.0013954634,0.0056911693],"genre_scores_gemma":[0.8689276,0.0010079845,0.12515137,0.0005046226,0.00016204575,0.00045242917,0.002274244,0.00024210644,0.0012776542],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9060982,0.077596195,0.0038781005,0.006834638,0.004309989,0.0012828807],"domain_scores_gemma":[0.5130673,0.4471374,0.012806916,0.019530255,0.005849234,0.0016089298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11404058,0.0024686756,0.0027449618,0.0017859642,0.0020071408,0.004803291,0.002271265,0.00236025,0.0030515755],"category_scores_gemma":[0.34834903,0.0012069753,0.0052996296,0.0023536975,0.0032049625,0.0059349723,0.0054319436,0.00403807,0.0006687887],"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":[0.004807894,0.0004358901,0.37641165,0.0017478359,0.009253886,0.00081699085,0.003127393,0.21926951,0.0027090856,0.047689337,0.0033688555,0.33036175],"study_design_scores_gemma":[0.00044071843,0.0027580021,0.14125986,0.0010505654,0.008096762,0.00077382,0.0016188544,0.7364724,0.0086297905,0.08381944,0.014655947,0.00042381356],"about_ca_topic_score_codex":0.008959607,"about_ca_topic_score_gemma":0.008789447,"teacher_disagreement_score":0.11404058,"about_ca_system_score_codex":0.0013962694,"about_ca_system_score_gemma":0.00336668,"threshold_uncertainty_score":0.60311145},"labels":[],"label_agreement":null},{"id":"W2530745759","doi":"","title":"Combined composite likelihood","year":2015,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pairwise comparison; Quasi-maximum likelihood; Likelihood function; Likelihood principle; Constant (computer programming); Conditional independence; Range (aeronautics); Econometrics; Statistics; Mathematics; Maximum likelihood; Independence (probability theory); Composite number; Restricted maximum likelihood; Marginal likelihood; Value (mathematics); Function (biology); Computer science; Engineering; Algorithm","score_opus":0.12398426855734868,"score_gpt":0.39474499573105165,"score_spread":0.270760727173703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530745759","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":[0.00246153,0.0007853422,0.9864703,0.000510085,0.000125146,0.00006987613,0.00058585516,0.0004888961,0.00850302],"genre_scores_gemma":[0.16965482,0.0013912545,0.807916,0.000662075,0.0006468525,0.00049824425,0.0023175993,0.0012092848,0.015703872],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895174,0.0060599903,0.0004121341,0.0015212977,0.0021131705,0.00037604768],"domain_scores_gemma":[0.9621709,0.023874903,0.0015683731,0.006382092,0.005033012,0.0009706595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012968024,0.0017113147,0.003078595,0.0039000395,0.001124624,0.006368209,0.004127926,0.0026680976,0.02448659],"category_scores_gemma":[0.058308598,0.000877847,0.0028734133,0.0050767437,0.0021258206,0.008069324,0.006020188,0.0052260226,0.006653591],"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":[0.0006310183,0.00016978836,0.006277085,0.0010607867,0.0007671009,0.0005545079,0.00041912877,0.08795041,0.0027345957,0.5617548,0.027891679,0.30978915],"study_design_scores_gemma":[0.00006650572,0.00017643931,0.0023823625,0.00024413916,0.00024530047,0.0010047377,0.00015286014,0.3313493,0.0024152335,0.6171752,0.044638813,0.00014917311],"about_ca_topic_score_codex":0.0018719452,"about_ca_topic_score_gemma":0.0030776763,"teacher_disagreement_score":0.02448659,"about_ca_system_score_codex":0.0019770523,"about_ca_system_score_gemma":0.0034176267,"threshold_uncertainty_score":0.081915855},"labels":[],"label_agreement":null},{"id":"W2550654637","doi":"10.1007/s10463-016-0590-9","title":"Inferences in semi-parametric dynamic mixed models for longitudinal count data","year":2016,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Estimator; Parametric statistics; Count data; Random effects model; Consistency (knowledge bases); Statistics; Quasi-likelihood; Strong consistency; Applied mathematics; Poisson distribution; Econometrics","score_opus":0.30337942978221777,"score_gpt":0.4466509848132453,"score_spread":0.1432715550310275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550654637","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":[0.004790186,0.00053653604,0.9933339,0.000540838,0.00006583517,0.00007236567,0.00018330413,0.00016394774,0.00031310288],"genre_scores_gemma":[0.2545498,0.0030140593,0.72993344,0.0013035975,0.0011062765,0.0022718909,0.0025391516,0.0005025847,0.00477919],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89795536,0.08259114,0.004304514,0.009372926,0.004639231,0.0011368828],"domain_scores_gemma":[0.42346197,0.544237,0.012099033,0.014600896,0.00408081,0.0015202276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11154262,0.0032156282,0.00616301,0.0066354563,0.0024384882,0.008032945,0.011167848,0.0056342254,0.006444651],"category_scores_gemma":[0.42034096,0.005779482,0.0076816664,0.0061720884,0.009003884,0.012090084,0.008878532,0.010260764,0.0009940071],"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":[0.00058326294,0.00021072644,0.006601891,0.00094677485,0.0021499898,0.00067154225,0.0014398781,0.16508017,0.000598029,0.75125235,0.0025484883,0.06791681],"study_design_scores_gemma":[0.000103375336,0.000078190234,0.00060286245,0.00013522293,0.00023691726,0.00017786794,0.00009352587,0.31806913,0.00027597134,0.678999,0.001164288,0.0000637642],"about_ca_topic_score_codex":0.008207888,"about_ca_topic_score_gemma":0.007341725,"teacher_disagreement_score":0.11154262,"about_ca_system_score_codex":0.0040262965,"about_ca_system_score_gemma":0.005565896,"threshold_uncertainty_score":0.5899008},"labels":[],"label_agreement":null},{"id":"W2555286646","doi":"10.1002/cjs.11304","title":"Gaussian process emulators for spatial individual‐level models of infectious disease","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Guelph; University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Likelihood function; Computer science; Gaussian process; Bayesian inference; Statistical inference; Inference; Bayesian probability; Algorithm; Context (archaeology); Data mining; Gaussian; Machine learning; Artificial intelligence; Statistics; Mathematics; Estimation theory","score_opus":0.09271043050733802,"score_gpt":0.34044275637971383,"score_spread":0.2477323258723758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555286646","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":[0.0032058605,0.00015307525,0.99562585,0.0001873951,0.000017246983,0.00002868348,0.000057915262,0.000119718585,0.00060419756],"genre_scores_gemma":[0.19044353,0.0011702009,0.7992375,0.00041101602,0.00016622497,0.0008390114,0.0007948613,0.00046752204,0.006470126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964552,0.0025215505,0.00013857463,0.00034726103,0.00040606904,0.00013134792],"domain_scores_gemma":[0.9732328,0.022410013,0.0014384618,0.001328408,0.0012878915,0.0003023193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013553942,0.0010030087,0.0017334702,0.0025504925,0.0008049096,0.0017491817,0.0034400127,0.0018903753,0.0053325696],"category_scores_gemma":[0.057256307,0.00112595,0.0017086822,0.0023745324,0.0025621464,0.0029706357,0.0031039403,0.003399065,0.0014232519],"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":[0.000049530292,0.00003959749,0.0012930896,0.00008356464,0.00010100701,0.00007708797,0.00013805363,0.5534349,0.00031375317,0.41517258,0.0024457856,0.02685105],"study_design_scores_gemma":[0.000011573277,0.000008787272,0.00017158444,0.000024934396,0.000009279634,0.000020927158,0.000014584356,0.857495,0.0001557764,0.14075984,0.0013145301,0.000013116843],"about_ca_topic_score_codex":0.0070750923,"about_ca_topic_score_gemma":0.0071925516,"teacher_disagreement_score":0.013553942,"about_ca_system_score_codex":0.001989171,"about_ca_system_score_gemma":0.00264569,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2558351252","doi":"","title":"Regression Composite Estimation for the Canadian Labour Force Survey: Evaluation and Implementation","year":2001,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"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":"Estimation; Econometrics; Variance (accounting); Publication; Regression; Series (stratigraphy); Statistics; Sample (material); Perspective (graphical); Mathematics; Computer science; Economics; Artificial intelligence; Accounting; Advertising; Business","score_opus":0.14820633525792312,"score_gpt":0.48306926553143925,"score_spread":0.3348629302735161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558351252","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":[0.11668887,0.013655242,0.7620239,0.007890682,0.0015999873,0.015955888,0.03044857,0.009427802,0.042309005],"genre_scores_gemma":[0.21665284,0.0064447806,0.7480831,0.0007799374,0.00024614602,0.0061056754,0.013790601,0.0010028,0.0068941154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9234471,0.046776652,0.0021575335,0.0034541327,0.023216948,0.00094769406],"domain_scores_gemma":[0.8398424,0.08706798,0.0054039736,0.010893832,0.053775553,0.0030162623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13544388,0.0015263938,0.0017849462,0.0044621355,0.0014831108,0.0020765401,0.004586748,0.0011947375,0.009517149],"category_scores_gemma":[0.2783027,0.00090081355,0.0017301142,0.008386173,0.001106205,0.0022543557,0.00277418,0.0013602906,0.0014257826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005588455,0.0006131378,0.075997256,0.0023408032,0.0012248829,0.00007037665,0.0011572758,0.023849338,0.0006014627,0.020903248,0.045568295,0.8220854],"study_design_scores_gemma":[0.003455722,0.0048186467,0.35876513,0.0019946638,0.003298105,0.00036082137,0.0020996456,0.4667831,0.0030616059,0.013731854,0.14093092,0.0006998284],"about_ca_topic_score_codex":0.70403796,"about_ca_topic_score_gemma":0.6078842,"teacher_disagreement_score":0.9888809,"about_ca_system_score_codex":0.011119155,"about_ca_system_score_gemma":0.025871325,"threshold_uncertainty_score":0.7163043},"labels":[],"label_agreement":null},{"id":"W2558645513","doi":"10.1002/sim.7189","title":"Estimation of state occupancy probabilities in multistate models with dependent intermittent observation, with application to HIV viral rebounds","year":2016,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":true,"ca_institutions":"University of Waterloo; Lunenfeld-Tanenbaum Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Smoothing; Context (archaeology); Estimation; Occupancy; Nonparametric statistics; Statistics; Observational study; Econometrics; Inverse probability; Cohort; Human immunodeficiency virus (HIV); Computer science; Medicine; Mathematics; Biology; Virology; Bayesian probability; Economics","score_opus":0.04233541563109173,"score_gpt":0.35590498400765236,"score_spread":0.31356956837656064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558645513","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":[0.026104072,0.00040094825,0.97236633,0.00038348322,0.00003098115,0.00006172247,0.0001403516,0.00014090643,0.0003711997],"genre_scores_gemma":[0.62802786,0.0016077087,0.36346558,0.00021203021,0.00021872325,0.00055933185,0.0008980533,0.00017672683,0.0048340214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99610126,0.0027323756,0.00014473032,0.0005467298,0.00028709674,0.00018777874],"domain_scores_gemma":[0.9570879,0.037771445,0.0023154037,0.0014680132,0.00096510624,0.0003921725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01599074,0.00095763104,0.0019249904,0.0013633241,0.0009926902,0.0016958937,0.003249154,0.0018856819,0.0017092575],"category_scores_gemma":[0.058534585,0.0011895286,0.0019567832,0.0018151565,0.0023981323,0.0021950745,0.0027955782,0.0031177416,0.00023323501],"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":[0.00016011423,0.000104815175,0.013263705,0.00017345214,0.00028868672,0.00022885289,0.000444381,0.83156145,0.00037314938,0.10931454,0.0012850463,0.042801842],"study_design_scores_gemma":[0.000015419138,0.000018153898,0.0009350148,0.000016953953,0.000024645658,0.000023403709,0.000037492475,0.9576242,0.00008171768,0.04073598,0.00046944796,0.000017492113],"about_ca_topic_score_codex":0.040730514,"about_ca_topic_score_gemma":0.03427812,"teacher_disagreement_score":0.040730514,"about_ca_system_score_codex":0.0016144948,"about_ca_system_score_gemma":0.0025825766,"threshold_uncertainty_score":0.08456808},"labels":[],"label_agreement":null},{"id":"W2558923764","doi":"10.1007/978-981-10-2594-5_10","title":"Improving the Robustness of Parametric Imputation","year":2016,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Missing data; Imputation (statistics); Estimator; Parametric statistics; Robustness (evolution); Computer science; Parametric model; Statistics; Econometrics; Mathematics","score_opus":0.044618176534586655,"score_gpt":0.32866564117468655,"score_spread":0.2840474646400999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558923764","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":[0.0013711542,0.0015484863,0.9910909,0.0010629207,0.00027881525,0.000021125097,0.00023542746,0.00088964205,0.0035014467],"genre_scores_gemma":[0.097510025,0.0036194283,0.8756433,0.0021298595,0.0021198872,0.00026699662,0.0022882766,0.0038356576,0.012586476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9663825,0.023446202,0.0012727557,0.0035934267,0.0046888604,0.000616317],"domain_scores_gemma":[0.83391494,0.13218307,0.0034712518,0.024041293,0.0056469208,0.00074258423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034552917,0.0026529597,0.0037654834,0.002518598,0.0011728873,0.0051373905,0.007317563,0.0040749265,0.012420404],"category_scores_gemma":[0.21100317,0.002235417,0.0037880999,0.004147845,0.004260097,0.0067147315,0.007096661,0.011391732,0.0070517724],"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":[0.0005451875,0.00014137832,0.0036850541,0.0006272749,0.0011871365,0.00034601951,0.0005172676,0.14783594,0.0025129467,0.30047214,0.042010244,0.5001194],"study_design_scores_gemma":[0.00007361742,0.000084904765,0.0010073988,0.00024353653,0.00022911129,0.00028091818,0.00006229848,0.3869871,0.0031283717,0.5873403,0.020458426,0.00010399289],"about_ca_topic_score_codex":0.0025859147,"about_ca_topic_score_gemma":0.0018911824,"teacher_disagreement_score":0.034552917,"about_ca_system_score_codex":0.001399649,"about_ca_system_score_gemma":0.0024301487,"threshold_uncertainty_score":0.1827355},"labels":[],"label_agreement":null},{"id":"W2564410009","doi":"10.1080/03610918.2015.1005230","title":"Weighting methods for ties between event times and covariate change times","year":2017,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Weighting; Jump; Event (particle physics); Statistics; Mathematics; Standard error; Regression; Proportional hazards model; Econometrics","score_opus":0.4797585865448165,"score_gpt":0.5893264604100661,"score_spread":0.10956787386524958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564410009","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":[0.0031631256,0.0004191536,0.99538594,0.00013234175,0.00010020538,0.00015785597,0.00006748033,0.00016132362,0.0004125039],"genre_scores_gemma":[0.15863031,0.0013686826,0.8313555,0.00039292587,0.00053852797,0.002201668,0.00074447657,0.00035014554,0.0044177063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96518177,0.021859633,0.0024248608,0.0048869853,0.0049118213,0.00073489535],"domain_scores_gemma":[0.7953302,0.16363393,0.012858104,0.02018861,0.006789694,0.0011994207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06610937,0.0018330895,0.0024054996,0.004777883,0.001504339,0.002974262,0.0060036187,0.0037304864,0.007872319],"category_scores_gemma":[0.22807427,0.001422032,0.0030158467,0.0047450666,0.0023794896,0.0076700835,0.00426342,0.005114484,0.0014753374],"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":[0.00083843234,0.00022255731,0.011869562,0.0010083723,0.0010710068,0.00045170158,0.0013060275,0.10143424,0.0019842067,0.28545383,0.0044061346,0.58995396],"study_design_scores_gemma":[0.00030890427,0.0003319946,0.004514543,0.0004352735,0.0005145138,0.00056917226,0.0002299223,0.60722256,0.0031641573,0.3657853,0.016745983,0.0001776031],"about_ca_topic_score_codex":0.0025081616,"about_ca_topic_score_gemma":0.0026488802,"teacher_disagreement_score":0.06610937,"about_ca_system_score_codex":0.0017637023,"about_ca_system_score_gemma":0.0018705167,"threshold_uncertainty_score":0.34962392},"labels":[],"label_agreement":null},{"id":"W2567199705","doi":"","title":"Adjustments to the signed likelihood root and analysis of an embedded experiment in a survey","year":2016,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Root (linguistics); Statistics; Econometrics; Mathematics; Psychology; Computer science; Linguistics; Philosophy","score_opus":0.058225919001626915,"score_gpt":0.448503495826981,"score_spread":0.3902775768253541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567199705","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":[0.0129181305,0.00015499671,0.9852249,0.0002052578,0.000045482684,0.00005530585,0.00005090118,0.00011220919,0.0012327348],"genre_scores_gemma":[0.27505043,0.0005346497,0.72004473,0.00027363023,0.00012326671,0.00059296057,0.00016914603,0.00017216284,0.0030390862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97170043,0.022299703,0.00068787887,0.0020999848,0.0029035641,0.00030858253],"domain_scores_gemma":[0.8829487,0.09288337,0.0066047413,0.012676562,0.0043577724,0.0005288484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028207678,0.00049128075,0.0010582962,0.0015078225,0.0004251277,0.0016765416,0.0015538897,0.0010779703,0.004142359],"category_scores_gemma":[0.14585476,0.00043811154,0.0009879938,0.0018821598,0.0021566532,0.0029633304,0.0019907022,0.001793885,0.0006298681],"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":[0.0001507324,0.000120469944,0.008024684,0.00044872053,0.00015404653,0.00008859851,0.0006673193,0.045755077,0.005041768,0.66854656,0.002204445,0.26879746],"study_design_scores_gemma":[0.00007333264,0.0004817464,0.010076006,0.00016614592,0.00009003216,0.00016933691,0.00026406097,0.445905,0.0073199472,0.5181881,0.017140264,0.0001259798],"about_ca_topic_score_codex":0.00064003875,"about_ca_topic_score_gemma":0.00058661104,"teacher_disagreement_score":0.028207678,"about_ca_system_score_codex":0.0012751155,"about_ca_system_score_gemma":0.0014068392,"threshold_uncertainty_score":0.1491782},"labels":[],"label_agreement":null},{"id":"W2569089902","doi":"10.1080/03610918.2019.1577975","title":"Sample size calculations for hierarchical Poisson and zero-inflated Poisson regression models","year":2019,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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":"Université du Québec à Montréal; HEC Montréal","funders":"","keywords":"Poisson regression; Zero-inflated model; Poisson distribution; Statistics; Mathematics; Zero (linguistics); Overdispersion; Sample (material); Sample size determination; Regression analysis; Regression; Count data; Statistical physics; Econometrics; Physics; Population; Medicine; Thermodynamics","score_opus":0.2052446019183401,"score_gpt":0.49230259907917684,"score_spread":0.28705799716083674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569089902","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":[0.005888165,0.0005051193,0.99162817,0.00029733108,0.000121546975,0.00025360487,0.0001370203,0.0002114656,0.00095753244],"genre_scores_gemma":[0.11598998,0.0006741134,0.878038,0.00045939165,0.00024403249,0.0025041276,0.00079860026,0.00029336364,0.0009983827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9748048,0.019386843,0.0010474202,0.0016131868,0.0028177307,0.0003300432],"domain_scores_gemma":[0.86295104,0.11903201,0.004119628,0.008830437,0.0043599196,0.0007070216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037020784,0.00078140217,0.0013656808,0.0033449903,0.00086997106,0.0013024935,0.0037062545,0.0024040134,0.0052383123],"category_scores_gemma":[0.24533202,0.0006931026,0.0015038428,0.0028047746,0.0017260704,0.002767559,0.0025603436,0.002960749,0.0009892408],"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":[0.0011360889,0.00031924798,0.018544532,0.0012275161,0.0005857668,0.0010163344,0.00095734076,0.2172096,0.004547789,0.35878387,0.018515918,0.37715593],"study_design_scores_gemma":[0.00028175898,0.00034309932,0.004158348,0.0002818356,0.0001194247,0.00057458517,0.00019855022,0.5836237,0.0033304892,0.39701888,0.009978108,0.00009118719],"about_ca_topic_score_codex":0.0017699829,"about_ca_topic_score_gemma":0.0023860412,"teacher_disagreement_score":0.037020784,"about_ca_system_score_codex":0.0011780317,"about_ca_system_score_gemma":0.0018369997,"threshold_uncertainty_score":0.19578695},"labels":[],"label_agreement":null},{"id":"W2569952854","doi":"10.1177/0962280216684671","title":"Detecting and correcting for publication bias in meta-analysis – A truncated normal distribution approach","year":2016,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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":"","keywords":"Publication bias; Funnel plot; Estimator; Statistics; Selection bias; Meta-analysis; Random effects model; Parametric statistics; Truncation (statistics); Econometrics; Computer science; Mathematics; Confidence interval; Medicine","score_opus":0.5688831534431038,"score_gpt":0.6084837730401228,"score_spread":0.03960061959701899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569952854","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":[0.0016217466,0.0044558644,0.9915946,0.0009023272,0.00022440554,0.0003431717,0.00014312632,0.00030227087,0.0004124337],"genre_scores_gemma":[0.13024493,0.008228942,0.85432357,0.0015978278,0.00061142707,0.0031942187,0.00046377737,0.00027692196,0.0010583935],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7450805,0.20314088,0.018476704,0.011388195,0.020854961,0.0010586975],"domain_scores_gemma":[0.50517917,0.43616295,0.019899782,0.02624972,0.011823763,0.00068461726],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25294513,0.0023618944,0.006708583,0.009349948,0.0011714168,0.005849988,0.007639614,0.00540468,0.0037012557],"category_scores_gemma":[0.51543343,0.0016512437,0.009916366,0.008869125,0.005272824,0.007589507,0.004641498,0.006344784,0.0006387231],"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":[0.0013896726,0.00019459904,0.010736189,0.0136286495,0.020201165,0.0015529675,0.0016822604,0.103356965,0.0024391585,0.18316807,0.0075220885,0.65412813],"study_design_scores_gemma":[0.0014127302,0.0007851806,0.0037814626,0.00410119,0.00949728,0.0014659296,0.00024328318,0.30498463,0.004839178,0.6545139,0.014014662,0.00036068517],"about_ca_topic_score_codex":0.0023220223,"about_ca_topic_score_gemma":0.0015698023,"teacher_disagreement_score":0.7470549,"about_ca_system_score_codex":0.0033461,"about_ca_system_score_gemma":0.009301124,"threshold_uncertainty_score":0.92125165},"labels":[],"label_agreement":null},{"id":"W2576106406","doi":"10.1016/j.prevetmed.2017.01.006","title":"STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models","year":2017,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":219,"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 Prince Edward Island; McGill University Health Centre","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs; McGill University","keywords":"Latent class model; Bayesian probability; Computer science; Test (biology); Checklist; Diagnostic accuracy; Class (philosophy); Artificial intelligence; Machine learning; Medical physics; Data mining; Medicine; Psychology","score_opus":0.5081186604439791,"score_gpt":0.523944825892012,"score_spread":0.015826165448032836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576106406","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014093725,0.009702308,0.8943738,0.016324071,0.00484914,0.009516114,0.039968215,0.014804775,0.009052243],"genre_scores_gemma":[0.015017765,0.005049769,0.90897113,0.008424545,0.0013502488,0.029804034,0.02628366,0.0032132878,0.0018854552],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.31315568,0.38400838,0.22516055,0.008550707,0.066695295,0.0024293598],"domain_scores_gemma":[0.05925088,0.7201771,0.049055494,0.09356641,0.074822545,0.0031275172],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4717311,0.0056315097,0.010829727,0.03762177,0.0035506275,0.01942465,0.022478234,0.020567715,0.02487748],"category_scores_gemma":[0.8774673,0.0076934807,0.013424641,0.03268406,0.010681697,0.009724957,0.019938678,0.025865626,0.017765366],"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":[0.0019848838,0.0006019105,0.005165822,0.040563904,0.004304934,0.00039541928,0.0023705685,0.012310501,0.0022860996,0.15245245,0.48368362,0.2938799],"study_design_scores_gemma":[0.0024926949,0.0008615973,0.01049066,0.053327102,0.002955873,0.0014849674,0.0009479478,0.03348014,0.010512838,0.36237597,0.5199855,0.0010847378],"about_ca_topic_score_codex":0.007127723,"about_ca_topic_score_gemma":0.0055259173,"teacher_disagreement_score":0.52826893,"about_ca_system_score_codex":0.009224367,"about_ca_system_score_gemma":0.038488384,"threshold_uncertainty_score":0.6514496},"labels":[],"label_agreement":null},{"id":"W2577537660","doi":"10.18637/jss.v076.i01","title":"<i>Stan</i> : A Probabilistic Programming Language","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7378,"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 Center for Research Resources; Institute of Education Sciences; U.S. Department of Energy; National Science Foundation; National Institutes of Health; Harvard University","keywords":"Python (programming language); Computer science; Markov chain Monte Carlo; Algorithm; Hybrid Monte Carlo; Monte Carlo method; Probabilistic logic; Bayesian inference; Importance sampling; Statistical inference; Inference; Monte Carlo integration; Applied mathematics; Bayesian probability; Mathematical optimization; Mathematics; Programming language; Artificial intelligence; Statistics","score_opus":0.05011962462799613,"score_gpt":0.3917699605443133,"score_spread":0.34165033591631716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577537660","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032531668,0.00018853624,0.8889701,0.0005256013,0.00014165303,0.00015732208,0.012222602,0.090229586,0.0072393594],"genre_scores_gemma":[0.010079351,0.00059414265,0.8950227,0.0019069046,0.00021019927,0.0016692957,0.017653944,0.06180873,0.011054799],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709356,0.0009587079,0.00041685635,0.00056134776,0.0007694348,0.00020018037],"domain_scores_gemma":[0.9906446,0.00620515,0.0007649272,0.000964483,0.0011999671,0.00022088639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005296637,0.0029431176,0.0017734363,0.0020509015,0.0008684856,0.005120043,0.0049127894,0.0018008326,0.11569443],"category_scores_gemma":[0.021377606,0.002622225,0.0030360695,0.0027662367,0.0015440336,0.005046561,0.0034340085,0.0052781985,0.07530208],"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":[0.00019145742,0.00008589782,0.0013267365,0.0013443795,0.00020513526,0.00043281761,0.00041799428,0.01653264,0.0026667381,0.21370077,0.64043677,0.12265865],"study_design_scores_gemma":[0.00015482483,0.00003715806,0.00049557694,0.0004044862,0.00005710252,0.0005946974,0.000048391987,0.09834083,0.0068153366,0.20920832,0.68370813,0.00013510347],"about_ca_topic_score_codex":0.0036919285,"about_ca_topic_score_gemma":0.00568725,"teacher_disagreement_score":0.11569443,"about_ca_system_score_codex":0.0010780735,"about_ca_system_score_gemma":0.003688184,"threshold_uncertainty_score":0.38703644},"labels":[],"label_agreement":null},{"id":"W2582570636","doi":"10.1016/j.jeconom.2016.12.003","title":"A simple consistent test of conditional symmetry in symmetrically trimmed tobit models","year":2017,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Statistical Methods and Bayesian Inference","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 Waterloo","funders":"","keywords":"Mathematics; Test statistic; Nonparametric statistics; Econometrics; Statistics; Statistic; Smoothing; Tobit model; Kolmogorov–Smirnov test; Statistical hypothesis testing","score_opus":0.294315572289793,"score_gpt":0.37556831935192975,"score_spread":0.08125274706213675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582570636","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":[0.22173569,0.00050846726,0.7630157,0.0017864172,0.00039394113,0.0005107317,0.001787093,0.0011652014,0.009096837],"genre_scores_gemma":[0.8650755,0.00023392032,0.12824242,0.0005721107,0.00034134055,0.00048603374,0.0018983051,0.0002212448,0.00292906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96787184,0.023144804,0.0017434496,0.0034025428,0.0029169512,0.0009204005],"domain_scores_gemma":[0.69624317,0.25634864,0.012002721,0.026291318,0.0070872,0.002026979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039145216,0.0011786007,0.0033017837,0.0030514754,0.0012625518,0.0032684696,0.00506621,0.0032035003,0.02211855],"category_scores_gemma":[0.26258606,0.0010437071,0.0031469918,0.003982754,0.0030850617,0.005939298,0.0033594319,0.0036529815,0.0023384031],"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":[0.004072635,0.001230075,0.063127235,0.0010519574,0.0051647252,0.0029380228,0.0014047669,0.06770059,0.0034004394,0.6309699,0.01762284,0.20131683],"study_design_scores_gemma":[0.0013763603,0.0015887812,0.019989567,0.0002654658,0.0009561166,0.0009865001,0.0008955041,0.40555924,0.0025928018,0.5605315,0.005031162,0.0002270308],"about_ca_topic_score_codex":0.0018731743,"about_ca_topic_score_gemma":0.001452602,"teacher_disagreement_score":0.039145216,"about_ca_system_score_codex":0.00084588473,"about_ca_system_score_gemma":0.0032694982,"threshold_uncertainty_score":0.20702219},"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":"W2584362917","doi":"10.1111/bmsp.12085","title":"Population models and simulation methods: The case of the Spearman rank correlation","year":2017,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":76,"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":"Spearman's rank correlation coefficient; Rank correlation; Correlation; Statistic; Copula (linguistics); Statistics; Rank (graph theory); Population; Sample size determination; Mathematics; Nonparametric statistics; Econometrics; Test statistic; Statistical hypothesis testing; Demography","score_opus":0.12336615640232285,"score_gpt":0.48009945331021286,"score_spread":0.35673329690789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584362917","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":[0.004519532,0.005914594,0.9711019,0.0065247505,0.00045597056,0.000066461966,0.000084478175,0.00009952776,0.011232756],"genre_scores_gemma":[0.39086863,0.014520099,0.58273786,0.002802189,0.002097622,0.0010208203,0.00017199077,0.00031433662,0.005466385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9550154,0.03897704,0.00079067796,0.0016540999,0.003124074,0.0004386905],"domain_scores_gemma":[0.846248,0.13884321,0.0044670617,0.0060290066,0.003616999,0.0007957325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0440298,0.0012151287,0.0019944287,0.002989142,0.0012244134,0.00488032,0.0022707991,0.004354761,0.0037334014],"category_scores_gemma":[0.15136415,0.00089821656,0.0015419892,0.0042149937,0.009297171,0.007504661,0.004326064,0.0063170576,0.0006646744],"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":[0.000015331625,0.000013661025,0.0007772573,0.00012882156,0.00007151486,0.000095995536,0.0002570738,0.03734398,0.000045049088,0.94647896,0.001345226,0.013427068],"study_design_scores_gemma":[0.000013003097,0.000028072123,0.0002668799,0.0001239445,0.000019001578,0.000089733,0.00008556808,0.095150985,0.000078479155,0.8960834,0.008034235,0.000026681666],"about_ca_topic_score_codex":0.0056865406,"about_ca_topic_score_gemma":0.002803516,"teacher_disagreement_score":0.0440298,"about_ca_system_score_codex":0.0024868005,"about_ca_system_score_gemma":0.0036062647,"threshold_uncertainty_score":0.2328546},"labels":[],"label_agreement":null},{"id":"W2588907513","doi":"10.1093/aje/163.suppl_11.s28-d","title":"Sequential Choropleth Mapping of Disability-Adjusted Life Year (DALY): A Bayesian Daly Method for Spatiotemporal Injury Surveillance","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"","keywords":"Bayesian probability; Statistics; Quality-adjusted life year; Computer science; Econometrics; Mathematics; Cost effectiveness","score_opus":0.08559953900103422,"score_gpt":0.41708225537664756,"score_spread":0.33148271637561333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588907513","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":[0.017116535,0.00014077428,0.9814509,0.00015201818,0.000024951221,0.0000568058,0.0003972335,0.00015874086,0.0005021032],"genre_scores_gemma":[0.41280854,0.0004401369,0.58157486,0.00011188528,0.000085322725,0.0005903863,0.0016446841,0.000110510104,0.0026336426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983022,0.0010207677,0.00007392527,0.00033840793,0.00019697206,0.000067691006],"domain_scores_gemma":[0.9941321,0.004213402,0.00046790641,0.0006146427,0.00043915393,0.00013281785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042407895,0.0006007439,0.0012111085,0.0018768447,0.000540122,0.00092145207,0.0015341378,0.0007953126,0.0022855394],"category_scores_gemma":[0.022849083,0.0006241781,0.0012508098,0.0016033219,0.00061209325,0.00142342,0.0020530235,0.0012951092,0.00029004496],"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":[0.0005400613,0.00019544404,0.03220212,0.00027903786,0.0005784124,0.00023589107,0.0005697149,0.49859774,0.0018306505,0.11870607,0.005751187,0.34051374],"study_design_scores_gemma":[0.000033081073,0.00005086908,0.0031569507,0.000027100048,0.000041378004,0.000108798515,0.00004200545,0.9413915,0.0003103392,0.0532309,0.0015775806,0.000029457278],"about_ca_topic_score_codex":0.016397247,"about_ca_topic_score_gemma":0.011346405,"teacher_disagreement_score":0.016397247,"about_ca_system_score_codex":0.0005984514,"about_ca_system_score_gemma":0.0016609567,"threshold_uncertainty_score":0.032603562},"labels":[],"label_agreement":null},{"id":"W2594511273","doi":"10.1186/s13063-017-1833-7","title":"Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples","year":2017,"lang":"en","type":"article","venue":"Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":173,"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 for Health and Care Research","keywords":"Econometrics; Random effects model; Treatment effect; Confounding; Medicine; Statistics; Psychological intervention; Computer science; Mathematics; Meta-analysis","score_opus":0.4153827757707162,"score_gpt":0.5217674289473853,"score_spread":0.10638465317666906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594511273","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":[0.090809524,0.017600277,0.759142,0.0045078862,0.0061080907,0.11242865,0.0025187249,0.0018887114,0.004996177],"genre_scores_gemma":[0.49181154,0.0021466932,0.3490272,0.0026556565,0.0005232937,0.15054339,0.0009815623,0.00015652289,0.002154123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.61820084,0.34070966,0.015065857,0.014829805,0.009316543,0.0018773283],"domain_scores_gemma":[0.56144786,0.36197087,0.02802952,0.038812477,0.008030083,0.001709163],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2656677,0.0032920563,0.00956916,0.0029297154,0.0010374542,0.0032193947,0.00569736,0.005441489,0.01334053],"category_scores_gemma":[0.4101131,0.001688237,0.012178061,0.0027406486,0.005145366,0.004010716,0.0026619635,0.0054982393,0.0010886597],"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":[0.16048515,0.0038853756,0.033103634,0.08998206,0.12474449,0.0019919488,0.002807438,0.14895181,0.0024798955,0.19221441,0.019632477,0.21972126],"study_design_scores_gemma":[0.08966848,0.032807637,0.012781623,0.014813955,0.0393429,0.0008603199,0.00081370067,0.43573314,0.0057614953,0.34263906,0.024054524,0.0007231608],"about_ca_topic_score_codex":0.0016997689,"about_ca_topic_score_gemma":0.0010981011,"teacher_disagreement_score":0.2656677,"about_ca_system_score_codex":0.003981315,"about_ca_system_score_gemma":0.0058900495,"threshold_uncertainty_score":0.90556246},"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":"W2598291149","doi":"10.1002/cjs.11349","title":"On the minimum coverage probability of model averaged tail area confidence intervals","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coverage probability; Credible interval; Robust confidence intervals; Confidence interval; CDF-based nonparametric confidence interval; Upper and lower bounds; Confidence distribution; Interval (graph theory); Imprecise probability; Linear regression","score_opus":0.11133431607578505,"score_gpt":0.32934560247347705,"score_spread":0.21801128639769202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598291149","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":[0.038339246,0.002771038,0.9518962,0.001208118,0.00009336689,0.00009280222,0.00046304255,0.00040891586,0.004727256],"genre_scores_gemma":[0.78344417,0.0017787114,0.20966916,0.0006265248,0.0006736056,0.0006220507,0.0014845188,0.00059172633,0.0011094562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9630727,0.024549117,0.0014376428,0.0036070112,0.006226229,0.0011073154],"domain_scores_gemma":[0.47217867,0.48840135,0.008909161,0.016050454,0.012336041,0.0021243393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0472715,0.0013238499,0.0043910523,0.006269338,0.0016181668,0.0043463423,0.005159677,0.0034866852,0.004331424],"category_scores_gemma":[0.38431075,0.0012798273,0.0019389652,0.004409745,0.0054944106,0.006404225,0.005260795,0.004797322,0.00054476986],"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":[0.00096377364,0.000103206454,0.021314276,0.0010443382,0.00079103484,0.000718519,0.0010142975,0.4143759,0.0017832399,0.45223522,0.006505324,0.09915094],"study_design_scores_gemma":[0.00007510031,0.00019359715,0.0029843866,0.0005363457,0.00013220106,0.0005179342,0.00015122218,0.6536096,0.0016913556,0.33727288,0.002734097,0.00010133056],"about_ca_topic_score_codex":0.0038110276,"about_ca_topic_score_gemma":0.0013981988,"teacher_disagreement_score":0.0472715,"about_ca_system_score_codex":0.00197183,"about_ca_system_score_gemma":0.0013929569,"threshold_uncertainty_score":0.24999863},"labels":[],"label_agreement":null},{"id":"W2603287415","doi":"","title":"Discrete-Time Survival Trees","year":2007,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Statistical Methods and Bayesian Inference","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":"Université de Montréal; Research Unit on Children's Psychosocial Maladjustment; University of British Columbia; HEC Montréal","funders":"","keywords":"Categorical variable; Statistics; Covariate; Interpretability; Mathematics; Survival analysis; Artificial intelligence; Computer science","score_opus":0.024504729530312845,"score_gpt":0.31628695031437537,"score_spread":0.2917822207840625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603287415","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":[0.018786952,0.003618632,0.9627901,0.0010160664,0.00050219486,0.00020927451,0.005411672,0.0014838122,0.0061813304],"genre_scores_gemma":[0.46283817,0.006551096,0.4902855,0.0007062584,0.0007861751,0.00093894865,0.017322676,0.0006461838,0.019925069],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978968,0.00092187565,0.00016156485,0.0004484062,0.00039179486,0.00017945856],"domain_scores_gemma":[0.9872547,0.009385672,0.0008734124,0.0009259281,0.0012285215,0.00033184368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045983423,0.0008919843,0.0011858693,0.0038678583,0.00087270676,0.0031848592,0.0018676912,0.001803054,0.0124314455],"category_scores_gemma":[0.021366306,0.0005796609,0.0021321794,0.004526128,0.00084970176,0.0023980276,0.001516467,0.0025714526,0.0037845871],"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":[0.0003646414,0.0001283242,0.020644339,0.00096296036,0.00038835386,0.0005104859,0.0008084817,0.27437332,0.0011474383,0.222123,0.0343608,0.4441878],"study_design_scores_gemma":[0.00006929354,0.00012583785,0.0045440434,0.00029774726,0.00014263309,0.0006912111,0.00022569038,0.6050927,0.00077219674,0.33295497,0.054992683,0.0000909795],"about_ca_topic_score_codex":0.0029644251,"about_ca_topic_score_gemma":0.0036992745,"teacher_disagreement_score":0.0124314455,"about_ca_system_score_codex":0.0014109006,"about_ca_system_score_gemma":0.0013331776,"threshold_uncertainty_score":0.041587293},"labels":[],"label_agreement":null},{"id":"W2605130264","doi":"10.1002/sim.7298","title":"A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":47,"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":"Frequentist inference; Bayes' theorem; Prior probability; Sensitivity (control systems); Bayesian probability; Econometrics; Monte Carlo method; Statistics; Confounding; Posterior probability; Contrast (vision); Computer science; Mathematics; Bayesian inference; Artificial intelligence","score_opus":0.1480890340324776,"score_gpt":0.49242406635217234,"score_spread":0.34433503231969476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605130264","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":[0.03258826,0.015560908,0.9323462,0.0050230455,0.0007363323,0.0020770724,0.0008165822,0.00046393814,0.01038773],"genre_scores_gemma":[0.6829131,0.0076300134,0.2989583,0.003361765,0.0005462538,0.004114117,0.000610673,0.00027533813,0.0015904163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.669489,0.3056965,0.005266724,0.004786397,0.0132854255,0.0014759016],"domain_scores_gemma":[0.2607548,0.70614845,0.01082846,0.012571695,0.009003098,0.00069349376],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.24223456,0.0023586405,0.0042798296,0.006811631,0.001337885,0.004689804,0.0035947652,0.004330519,0.0060982965],"category_scores_gemma":[0.5311684,0.0015265058,0.009391157,0.005135446,0.004001137,0.008129991,0.0050995545,0.004694429,0.00041023036],"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":[0.002994785,0.00033124804,0.013972437,0.0058169314,0.01160135,0.00049933343,0.0016112841,0.4412928,0.00067889754,0.3782015,0.006826407,0.13617298],"study_design_scores_gemma":[0.0008101885,0.00075702317,0.008339328,0.00300017,0.0034092078,0.0006754105,0.00048386937,0.5008111,0.0010236605,0.4673674,0.012859143,0.00046341371],"about_ca_topic_score_codex":0.0054178247,"about_ca_topic_score_gemma":0.0036986414,"teacher_disagreement_score":0.7577654,"about_ca_system_score_codex":0.0050918935,"about_ca_system_score_gemma":0.0058103716,"threshold_uncertainty_score":0.9344597},"labels":[],"label_agreement":null},{"id":"W2607081621","doi":"10.1002/cjs.11318","title":"Bayesian analysis of a density ratio model","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Mathematics; Markov chain Monte Carlo; Statistics; Econometrics; Humanities; Philosophy","score_opus":0.07925885678258944,"score_gpt":0.3541440477006121,"score_spread":0.27488519091802266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607081621","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":[0.008766921,0.00018844374,0.9892943,0.00027636584,0.000017019693,0.00003358215,0.00008364788,0.00015941255,0.0011803282],"genre_scores_gemma":[0.6813757,0.00095445383,0.30734545,0.00047352238,0.00023696087,0.00045558118,0.0006463583,0.00037508606,0.008136928],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99051356,0.0063932966,0.00023308441,0.0010439501,0.0014534569,0.00036266094],"domain_scores_gemma":[0.96500266,0.028898247,0.0018323316,0.0016184325,0.0021977099,0.0004506974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017426278,0.0009221947,0.0020149585,0.0026789668,0.0006114691,0.002686498,0.0037001108,0.0021081134,0.0064860196],"category_scores_gemma":[0.0581261,0.0011208791,0.0022643318,0.0015515699,0.0028034027,0.0036425588,0.0023822985,0.0025796315,0.0010310875],"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":[0.00015300297,0.00008677112,0.002525198,0.00020228142,0.00025969013,0.00035718086,0.00026689944,0.38221037,0.0017162302,0.5711686,0.002509794,0.038543988],"study_design_scores_gemma":[0.000016167227,0.000026795506,0.00047057052,0.000022822025,0.00003036369,0.00008050163,0.000020411324,0.9040933,0.00022663813,0.09409773,0.00088474224,0.00003002689],"about_ca_topic_score_codex":0.0069381264,"about_ca_topic_score_gemma":0.0030610745,"teacher_disagreement_score":0.017426278,"about_ca_system_score_codex":0.00206937,"about_ca_system_score_gemma":0.0014578959,"threshold_uncertainty_score":0.092160106},"labels":[],"label_agreement":null},{"id":"W2607492691","doi":"10.1177/1094428103006003003","title":"How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method","year":2003,"lang":"en","type":"article","venue":"Organizational Research Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Categorical variable; Missing data; Imputation (statistics); Bayesian probability; Computer science; Regression; Statistics; Regression analysis; Data mining; Econometrics; Mathematics","score_opus":0.2853940616044742,"score_gpt":0.5576315963265147,"score_spread":0.2722375347220405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607492691","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":[0.056633234,0.00088246504,0.9283861,0.0047755637,0.00036095438,0.0009205301,0.00031940974,0.0005554719,0.007166335],"genre_scores_gemma":[0.48922917,0.0006714789,0.5021169,0.0021021916,0.0004585983,0.0020451325,0.00074387353,0.00042875097,0.0022038806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.77876425,0.19108126,0.005411193,0.00871221,0.01418011,0.0018509411],"domain_scores_gemma":[0.12020708,0.8464698,0.008478763,0.015820924,0.0075327936,0.0014907119],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2292475,0.0017348785,0.0043678633,0.0046889163,0.002018428,0.004393498,0.005867321,0.005907318,0.01379709],"category_scores_gemma":[0.6935367,0.0011878461,0.0050524063,0.004494958,0.006952363,0.011537217,0.0052123833,0.0057000667,0.0017671678],"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":[0.0058114687,0.0015915725,0.05141714,0.0019466343,0.0076572807,0.00084874354,0.003255806,0.08569056,0.0011188909,0.35916644,0.016945425,0.4645501],"study_design_scores_gemma":[0.0016181439,0.001850434,0.014189594,0.0005956928,0.0011354324,0.00059039507,0.0010943183,0.48122138,0.001953547,0.4867821,0.008721984,0.0002470068],"about_ca_topic_score_codex":0.0026845222,"about_ca_topic_score_gemma":0.0010448413,"teacher_disagreement_score":0.2292475,"about_ca_system_score_codex":0.001692131,"about_ca_system_score_gemma":0.00514635,"threshold_uncertainty_score":0.95047504},"labels":[],"label_agreement":null},{"id":"W2609350886","doi":"10.4236/ojs.2017.72019","title":"Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"Robarts Clinical Trials; Western University","funders":"","keywords":"Statistics; Confidence interval; Mathematics; Monte Carlo method; Wald test; Inference; Interval estimation; Coverage probability; Cluster (spacecraft); Correlation; Variance (accounting); Aggregate (composite); Econometrics; Statistical hypothesis testing; Binary data; Statistical inference; Binary number; Computer science","score_opus":0.2740426777950981,"score_gpt":0.5118138189660502,"score_spread":0.23777114117095205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609350886","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":[0.0133748455,0.004102302,0.9794322,0.0012812249,0.00026032992,0.00030155457,0.00029562722,0.000083142026,0.00086872693],"genre_scores_gemma":[0.32876217,0.0064888913,0.6580909,0.0010737209,0.0008628626,0.0025774376,0.00066330313,0.00010827507,0.0013724961],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.87662476,0.10472267,0.0034445534,0.008087536,0.006525514,0.0005949203],"domain_scores_gemma":[0.5955327,0.35018066,0.018075535,0.031851593,0.0037232821,0.000636149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12518968,0.0012836471,0.003648137,0.004563124,0.0010959348,0.0035162352,0.004997877,0.0050452515,0.0024216934],"category_scores_gemma":[0.3954268,0.0011759503,0.0025104615,0.0062302165,0.0048314203,0.0050009224,0.0042171795,0.004301785,0.0003437636],"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":[0.00050329784,0.00018902382,0.06515299,0.0039305976,0.007022173,0.0016514381,0.002120186,0.063286744,0.0008841614,0.6880222,0.0029903492,0.16424681],"study_design_scores_gemma":[0.000116339106,0.00026027716,0.012958665,0.0008668359,0.0010554185,0.00077755103,0.00040852366,0.10445609,0.00087985565,0.87037724,0.007737345,0.00010578591],"about_ca_topic_score_codex":0.0037534945,"about_ca_topic_score_gemma":0.0017979959,"teacher_disagreement_score":0.12518968,"about_ca_system_score_codex":0.0013834126,"about_ca_system_score_gemma":0.0019245095,"threshold_uncertainty_score":0.6620742},"labels":[],"label_agreement":null},{"id":"W2611208896","doi":"10.1101/132753","title":"Modeling zero-inflated count data with glmmTMB","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":428,"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","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Count data; Laplace's method; Generalized linear mixed model; Negative binomial distribution; Overdispersion; Markov chain Monte Carlo; Computer science; Poisson distribution; Statistics; Quasi-likelihood; Range (aeronautics); Generalized linear model; Bayesian probability; Algorithm; Mathematics; Applied mathematics; Engineering","score_opus":0.08572515254872412,"score_gpt":0.3282695346689558,"score_spread":0.2425443821202317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611208896","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":[0.010506677,0.00043858468,0.945194,0.0007098252,0.00029032948,0.00017317642,0.00994848,0.03153687,0.0012020333],"genre_scores_gemma":[0.07406588,0.00040536997,0.8879511,0.0008534541,0.00011920061,0.001885256,0.013632397,0.019061128,0.002026277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99143326,0.005048185,0.0005513958,0.0016182062,0.000995888,0.0003531985],"domain_scores_gemma":[0.9614024,0.028248878,0.0026306296,0.003627213,0.0035968723,0.00049404876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023122275,0.002955207,0.0028938376,0.0038553418,0.0013481753,0.0044605997,0.005955279,0.0027591346,0.019568896],"category_scores_gemma":[0.079209976,0.0023308683,0.005333316,0.0052356073,0.001600607,0.00394946,0.0041357665,0.005595555,0.011623283],"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":[0.00084648427,0.00046638114,0.06659151,0.0043990477,0.004187834,0.0011291684,0.0024676737,0.3399932,0.011584012,0.07109128,0.23109323,0.26615015],"study_design_scores_gemma":[0.00018913313,0.00019782402,0.010738845,0.00057661365,0.00046024597,0.00041406357,0.00026201463,0.7792813,0.005178975,0.114534274,0.08776294,0.00040378037],"about_ca_topic_score_codex":0.014414857,"about_ca_topic_score_gemma":0.016448338,"teacher_disagreement_score":0.023122275,"about_ca_system_score_codex":0.001834432,"about_ca_system_score_gemma":0.0031121962,"threshold_uncertainty_score":0.12228376},"labels":[],"label_agreement":null},{"id":"W2612728037","doi":"","title":"MAXIMUM LIKELIHOOD INFERENCE IN ROBUST LINEAR MIXED-EFFECTS MODELS USING MULTIVARIATE t DISTRIBUTIONS","year":2007,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"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; University of Waterloo","funders":"","keywords":"Akaike information criterion; Outlier; Restricted maximum likelihood; Estimator; Random effects model; Multivariate statistics; Generalized linear mixed model; Mathematics; Mixed model; Maximum likelihood; Statistics; Inference; Expectation–maximization algorithm; Bayesian information criterion; Degrees of freedom (physics and chemistry); Maximum likelihood sequence estimation; Linear model; M-estimator; Computer science; Artificial intelligence","score_opus":0.11992058314677775,"score_gpt":0.41746524140847996,"score_spread":0.2975446582617022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612728037","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":[0.0007776585,0.00018628797,0.99868387,0.00006436031,0.000008636867,0.00002159397,0.000023995715,0.00009859658,0.00013488256],"genre_scores_gemma":[0.059387833,0.00088912103,0.9378976,0.00012605224,0.00010512491,0.000521286,0.00025345362,0.00019175907,0.00062780705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9772703,0.019425921,0.00059620454,0.0013456129,0.0011309077,0.00023105361],"domain_scores_gemma":[0.9072016,0.08681141,0.0028238792,0.0018473889,0.0010796594,0.0002360235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030817565,0.0023474556,0.0036440836,0.0036274027,0.0009651653,0.0026089177,0.0038945598,0.0028452494,0.0029984957],"category_scores_gemma":[0.12784044,0.0016178872,0.003735115,0.0042482587,0.0030264966,0.0035026944,0.0033300682,0.003629699,0.00094829104],"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":[0.00022686203,0.000097406926,0.0013502139,0.00071667193,0.00071729964,0.0003311698,0.00036255317,0.5915732,0.00076691713,0.27986193,0.0019069742,0.12208882],"study_design_scores_gemma":[0.00005829127,0.000044627275,0.00021520893,0.000071207556,0.0000523601,0.00005850669,0.000031704476,0.730365,0.0004165883,0.2675628,0.0010895437,0.00003412935],"about_ca_topic_score_codex":0.0041863727,"about_ca_topic_score_gemma":0.0037611427,"teacher_disagreement_score":0.030817565,"about_ca_system_score_codex":0.0015797038,"about_ca_system_score_gemma":0.0025148548,"threshold_uncertainty_score":0.16298085},"labels":[],"label_agreement":null},{"id":"W2616554581","doi":"10.1002/pds.4223","title":"Correcting hazard ratio estimates for outcome misclassification using multiple imputation with internal validation data","year":2017,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Hazard ratio; Confidence interval; Diabetes mellitus; Proportional hazards model; Imputation (statistics); Observational study; Statistics; Pharmacoepidemiology; Internal medicine; Missing data; Mathematics; Endocrinology","score_opus":0.32972263983433714,"score_gpt":0.5118574114712745,"score_spread":0.18213477163693736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616554581","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":[0.072978295,0.001456988,0.92028487,0.0010857738,0.00021530762,0.00071250164,0.0011317136,0.0008378133,0.001296754],"genre_scores_gemma":[0.63686746,0.00051064213,0.3582583,0.0007935241,0.00013987192,0.001299966,0.001225842,0.00021160088,0.00069283857],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.79298896,0.18515018,0.0074038785,0.0071173753,0.0060902582,0.0012493015],"domain_scores_gemma":[0.62086767,0.28376347,0.037177134,0.044258147,0.013331864,0.000601797],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.22589074,0.0018100741,0.0021907643,0.0018495832,0.001016258,0.0024928155,0.0037787524,0.0020509406,0.0015633123],"category_scores_gemma":[0.35292026,0.0010914063,0.005166645,0.0031958108,0.0016746122,0.0018561081,0.0026859704,0.0026517855,0.0004015484],"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":[0.0022731507,0.00042480382,0.5367037,0.0023174798,0.018636681,0.00077008276,0.002925999,0.18070538,0.0025374158,0.020982116,0.006881428,0.22484174],"study_design_scores_gemma":[0.0017043024,0.0019119711,0.23395084,0.0025235973,0.008162696,0.001706695,0.00048053675,0.6579354,0.0153367985,0.06278732,0.013047954,0.00045197777],"about_ca_topic_score_codex":0.006227867,"about_ca_topic_score_gemma":0.00461751,"teacher_disagreement_score":0.77410924,"about_ca_system_score_codex":0.0015095695,"about_ca_system_score_gemma":0.0029414096,"threshold_uncertainty_score":0.9546145},"labels":[],"label_agreement":null},{"id":"W2618058430","doi":"10.1002/sim.7336","title":"Intermediate and advanced topics in multilevel logistic regression analysis","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":657,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Vetenskapsrådet; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Marginal model; Logistic regression; Covariate; Multilevel model; Statistics; Hierarchical clustering; Econometrics; Regression analysis; Population; Odds ratio; Regression; Cluster analysis; Computer science; Mathematics; Demography","score_opus":0.1141231773850907,"score_gpt":0.47828034943776926,"score_spread":0.36415717205267856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618058430","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":[0.004759708,0.042782024,0.90992975,0.022421474,0.0029564255,0.00032621494,0.0016682983,0.0014545217,0.013701613],"genre_scores_gemma":[0.1237067,0.048938278,0.78533006,0.0072334465,0.014445159,0.0023805648,0.00446634,0.0022663285,0.011233218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.964048,0.025929125,0.0021216215,0.0026724956,0.0046835863,0.00054511917],"domain_scores_gemma":[0.8925722,0.09010526,0.0054546753,0.0060950844,0.0047205086,0.0010523826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028343363,0.0014705481,0.002011997,0.005637617,0.0010886597,0.00432164,0.0024974607,0.0025269564,0.017196676],"category_scores_gemma":[0.13324374,0.0009230289,0.0036031443,0.009889791,0.0030119696,0.0044966484,0.005159437,0.008467041,0.0058280467],"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":[0.00016895018,0.00015490218,0.014345222,0.0040883957,0.0011576968,0.0006751585,0.0014489723,0.011388416,0.0008708251,0.4339261,0.12571655,0.4060588],"study_design_scores_gemma":[0.00004978274,0.00018303789,0.0060369424,0.0021817584,0.00025287428,0.00065927615,0.00033182086,0.046344817,0.00065013237,0.7539292,0.18925026,0.00013015997],"about_ca_topic_score_codex":0.0019302104,"about_ca_topic_score_gemma":0.0017646108,"teacher_disagreement_score":0.028343363,"about_ca_system_score_codex":0.00169445,"about_ca_system_score_gemma":0.0035671857,"threshold_uncertainty_score":0.14989585},"labels":[],"label_agreement":null},{"id":"W2620826610","doi":"10.1016/j.jclinepi.2017.05.017","title":"A systematic survey on reporting and methods for handling missing participant data for continuous outcomes in randomized controlled trials","year":2017,"lang":"en","type":"review","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":34,"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; McMaster University; Impact","funders":"","keywords":"Missing data; Randomized controlled trial; Medicine; Interquartile range; Imputation (statistics); MEDLINE; Clinical trial; Statistics; Surgery; Internal medicine; Mathematics","score_opus":0.9405744797648787,"score_gpt":0.763690149918479,"score_spread":0.17688432984639968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620826610","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011711699,0.95231074,0.03015415,0.007993059,0.0012352259,0.0043893247,0.0016295796,0.00017952482,0.0009372088],"genre_scores_gemma":[0.020068366,0.8556011,0.09838827,0.008693773,0.00094079034,0.014239129,0.0014916637,0.0001901863,0.0003868513],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.49246556,0.2835225,0.17025456,0.00766639,0.044246066,0.0018449712],"domain_scores_gemma":[0.16136847,0.735537,0.057616334,0.015488104,0.028476918,0.001513101],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.40741128,0.002865523,0.015702441,0.014906944,0.0015055757,0.007779796,0.00629167,0.008212206,0.0046298676],"category_scores_gemma":[0.7438943,0.004556388,0.018083973,0.014507164,0.005241066,0.010428471,0.00578855,0.013704958,0.0009262856],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018114657,0.00008485567,0.00199144,0.5956739,0.029692924,0.00008400899,0.0010026466,0.0011887917,0.000292501,0.008132471,0.018604884,0.34144014],"study_design_scores_gemma":[0.0023056108,0.0006070937,0.0039115716,0.8570258,0.069324374,0.00045117844,0.00034559748,0.0013337134,0.0006350366,0.018702473,0.0450834,0.00027422988],"about_ca_topic_score_codex":0.009930172,"about_ca_topic_score_gemma":0.013792783,"teacher_disagreement_score":0.5925887,"about_ca_system_score_codex":0.010877223,"about_ca_system_score_gemma":0.04139325,"threshold_uncertainty_score":0.73076737},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["metaresearch","metaepi_broad"],"domain":"methods","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W2622376831","doi":"10.4236/ojs.2017.73029","title":"Confidence Intervals for the Mean of Non-Normal Distribution: Transform or Not to Transform","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"York University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Confidence interval; Mathematics; Normality; CDF-based nonparametric confidence interval; Confidence distribution; Sample size determination; Coverage probability; Robust confidence intervals; Transformation (genetics); Normal distribution; Parametric statistics; Confidence region; Data transformation; Power transform; Computer science; Data mining","score_opus":0.13757602959727303,"score_gpt":0.45764766421103736,"score_spread":0.3200716346137643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622376831","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":[0.013213126,0.005935332,0.9721129,0.001651677,0.00073875027,0.00012715768,0.00045085012,0.00055869087,0.0052114967],"genre_scores_gemma":[0.5277591,0.0055716042,0.45818973,0.0018035935,0.0011244323,0.0011635982,0.0016244599,0.0006973428,0.002066113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9659823,0.018327119,0.0022007993,0.0046912697,0.007960105,0.0008383027],"domain_scores_gemma":[0.6470501,0.3042013,0.014722092,0.020403326,0.012423514,0.0011996797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04226244,0.0011228096,0.0020956835,0.003803692,0.00101536,0.0041041756,0.0035641007,0.0037794258,0.005599149],"category_scores_gemma":[0.3645871,0.00055899384,0.0018479942,0.0040638433,0.0053888904,0.007222595,0.0033629332,0.0071759303,0.0011430581],"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":[0.0008210765,0.00018161624,0.018599942,0.0020502077,0.00084577675,0.0010434836,0.0018633861,0.050679293,0.00218001,0.64564943,0.016443713,0.259642],"study_design_scores_gemma":[0.00018939728,0.00031902795,0.008568317,0.0016403832,0.00031428115,0.0015219908,0.0007329792,0.168455,0.0050646374,0.78056246,0.032329176,0.0003022104],"about_ca_topic_score_codex":0.0017461193,"about_ca_topic_score_gemma":0.0007960466,"teacher_disagreement_score":0.04226244,"about_ca_system_score_codex":0.0015849234,"about_ca_system_score_gemma":0.0017964183,"threshold_uncertainty_score":0.22350776},"labels":[],"label_agreement":null},{"id":"W2623116935","doi":"10.1002/jrsm.1244","title":"Paule‐Mandel estimators for network meta‐analysis with random inconsistency effects","year":2017,"lang":"en","type":"article","venue":"Research Synthesis Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Medical Research Council","keywords":"Estimator; Computer science; Meta-analysis; Random effects model; Statistics; Econometrics; Mathematics; Medicine","score_opus":0.4494998410577451,"score_gpt":0.5848635729691072,"score_spread":0.13536373191136208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623116935","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":[0.0013207485,0.007959214,0.98669225,0.0008044967,0.00020969265,0.0010913123,0.0005698928,0.0003476155,0.0010047662],"genre_scores_gemma":[0.08138989,0.0058838776,0.9001518,0.0011274114,0.00033084408,0.00884672,0.00089044665,0.00027235132,0.001106649],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7967812,0.18010731,0.007448379,0.008147091,0.0068993215,0.000616766],"domain_scores_gemma":[0.6417436,0.31884497,0.01276487,0.021786196,0.00449688,0.00036348333],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.16407828,0.0025617857,0.0065183295,0.0077301487,0.0012603062,0.004064706,0.0061755464,0.0039557437,0.0069451467],"category_scores_gemma":[0.3717702,0.0017645857,0.013065256,0.0076219896,0.0026820528,0.0053666267,0.003845809,0.006101129,0.0009039156],"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":[0.0010528464,0.00014694044,0.008810808,0.017816424,0.045868143,0.0005678733,0.0007905255,0.09455443,0.0010326402,0.42041636,0.014711326,0.39423168],"study_design_scores_gemma":[0.0014674455,0.00055654306,0.0047408883,0.0039103865,0.018402945,0.0006423904,0.00016195553,0.20757675,0.002438068,0.7137189,0.046076402,0.00030733584],"about_ca_topic_score_codex":0.0023101883,"about_ca_topic_score_gemma":0.0027016567,"teacher_disagreement_score":0.8359217,"about_ca_system_score_codex":0.0031264871,"about_ca_system_score_gemma":0.0050893095,"threshold_uncertainty_score":0.86773926},"labels":[],"label_agreement":null},{"id":"W2625352524","doi":"10.1002/sim.7346","title":"Analysis of panel data under hidden mover-stayer models","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inference; Expectation–maximization algorithm; Econometrics; Latent variable; Maximum likelihood; Panel data; Maximization; Population; Statistics; Data mining; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.31928921598111903,"score_gpt":0.479378919571194,"score_spread":0.16008970359007496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625352524","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":[0.04638058,0.0003430246,0.95156866,0.000517051,0.000037231664,0.000089478155,0.00045722115,0.00017321196,0.00043345775],"genre_scores_gemma":[0.7587324,0.00090329075,0.2331556,0.00040845512,0.00019643713,0.00053212617,0.002203884,0.00009425534,0.0037736187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9818288,0.014237037,0.00052262685,0.0020877162,0.00083863724,0.00048525142],"domain_scores_gemma":[0.88969964,0.09445087,0.007528656,0.006762402,0.0011453732,0.00041305934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04159486,0.00089296437,0.0025997174,0.001546739,0.0008748227,0.0021070843,0.0034080448,0.0024474103,0.0029321457],"category_scores_gemma":[0.085176095,0.0009512958,0.0025080233,0.00235207,0.0019452382,0.002721296,0.0022610656,0.0034813609,0.00045480125],"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":[0.0006220526,0.00020924465,0.056686476,0.00043948094,0.002496127,0.0011345658,0.0010876957,0.5697369,0.00135653,0.27953306,0.0030642715,0.08363363],"study_design_scores_gemma":[0.000070064765,0.000092664544,0.005636436,0.00004032993,0.00021074177,0.00008932879,0.00008749006,0.8474089,0.00048263682,0.14486879,0.0009779644,0.000034774894],"about_ca_topic_score_codex":0.006918287,"about_ca_topic_score_gemma":0.0050343815,"teacher_disagreement_score":0.04159486,"about_ca_system_score_codex":0.0011411307,"about_ca_system_score_gemma":0.0013680239,"threshold_uncertainty_score":0.21997732},"labels":[],"label_agreement":null},{"id":"W2735500398","doi":"10.1007/s12561-019-09234-6","title":"Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies","year":2019,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Computer science; Bayesian probability; Sensitivity (control systems); Data mining; Econometrics; Statistics; Outcome (game theory); Contrast (vision); Machine learning; Artificial intelligence; Mathematics","score_opus":0.19193641977606773,"score_gpt":0.4641900731555287,"score_spread":0.27225365337946095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735500398","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":[0.013295877,0.006780431,0.9733138,0.003099892,0.00029969277,0.0004361076,0.0008509908,0.0002795152,0.001643769],"genre_scores_gemma":[0.6582479,0.010352669,0.31487063,0.0033509606,0.0012811313,0.0032128228,0.0016343384,0.00052181626,0.006527619],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8464191,0.13917494,0.0038144798,0.0056452365,0.003639672,0.0013066264],"domain_scores_gemma":[0.2733433,0.70406467,0.007043681,0.01178123,0.0029625762,0.0008045391],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18066321,0.0024354928,0.0079677785,0.0050098132,0.0015825861,0.005259702,0.006367319,0.0058812667,0.008346411],"category_scores_gemma":[0.48258272,0.0032465886,0.007099409,0.0036975911,0.004675234,0.006453524,0.0072338185,0.009042949,0.0004928666],"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":[0.0016517832,0.00040397723,0.0135331275,0.0043512033,0.01282073,0.0022556358,0.0011415733,0.4413422,0.0009073885,0.44390595,0.0071239034,0.070562504],"study_design_scores_gemma":[0.00021454193,0.00014468782,0.0023410576,0.0005099169,0.0016976041,0.00043754422,0.000120627126,0.37049004,0.0003875416,0.62031686,0.0032228983,0.000116669224],"about_ca_topic_score_codex":0.010719086,"about_ca_topic_score_gemma":0.0051841843,"teacher_disagreement_score":0.8193368,"about_ca_system_score_codex":0.0040498506,"about_ca_system_score_gemma":0.003880482,"threshold_uncertainty_score":0.9554498},"labels":[],"label_agreement":null},{"id":"W2744100613","doi":"10.1186/s40488-017-0068-1","title":"On Poisson–Tweedie mixtures","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Distributions and Applications","topic":"Statistical Methods and Bayesian 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":"University of Toronto; York University","keywords":"Negative binomial distribution; Mathematics; Poisson distribution; Compound Poisson distribution; Natural exponential family; Exponential family; Applied mathematics; Exponential function; Measure (data warehouse); Dispersion (optics); Statistical physics; Mathematical analysis; Statistics; Poisson regression; Population","score_opus":0.04768297974065813,"score_gpt":0.40651203903575867,"score_spread":0.35882905929510056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744100613","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":[0.06198552,0.0011808772,0.9262941,0.0005529524,0.000082849256,0.000047188125,0.000117737945,0.00018288291,0.009556009],"genre_scores_gemma":[0.8769801,0.0026182334,0.10385444,0.00041290352,0.00045287344,0.00023607793,0.0005121527,0.00021736388,0.014715862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871516,0.0004653081,0.000054323013,0.00020714034,0.00037861042,0.00017949835],"domain_scores_gemma":[0.99381185,0.0041299183,0.0007219816,0.00043766806,0.0005447194,0.00035379967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030643009,0.0009833552,0.0009767923,0.0027378767,0.0011072609,0.0025078072,0.0016571088,0.0017050933,0.003458631],"category_scores_gemma":[0.020457873,0.0006780515,0.0013519529,0.0014579174,0.0031359713,0.0040936046,0.0023792607,0.0023878783,0.0008331819],"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":[0.000040972114,0.000022646984,0.001136501,0.000045978974,0.000022715163,0.0001758716,0.00018394503,0.085665904,0.0011548002,0.9053821,0.00079140975,0.0053771585],"study_design_scores_gemma":[0.000008586385,0.000018456323,0.00047448947,0.000032059757,0.000010895972,0.00013096913,0.00006786093,0.65783244,0.0004522352,0.3392418,0.0016989567,0.000031287418],"about_ca_topic_score_codex":0.0037395696,"about_ca_topic_score_gemma":0.0017504026,"teacher_disagreement_score":0.0037395696,"about_ca_system_score_codex":0.0018124535,"about_ca_system_score_gemma":0.0007068679,"threshold_uncertainty_score":0.016205788},"labels":[],"label_agreement":null},{"id":"W2744687314","doi":"10.1002/cjs.11325","title":"Bayes factor biases for non‐nested models and corrections","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayes factor; Bayes' theorem; Bayesian probability; Mathematics; Humanities; Statistics; Philosophy","score_opus":0.1979826975087588,"score_gpt":0.38852159299297445,"score_spread":0.19053889548421565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744687314","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":[0.028371008,0.0012484307,0.9595106,0.0019802393,0.0005315442,0.00012423964,0.0001488885,0.0010416078,0.00704348],"genre_scores_gemma":[0.6605501,0.00068897026,0.33186135,0.001528043,0.00048759583,0.00033349675,0.00016053118,0.00065381516,0.0037360783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96284735,0.024291702,0.0016031575,0.0042975345,0.0059916736,0.0009686426],"domain_scores_gemma":[0.600412,0.35172376,0.0125073595,0.023899216,0.010021205,0.0014365738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0530053,0.0011119918,0.0016790617,0.002300027,0.001425216,0.002751551,0.0034489871,0.0024393604,0.009988614],"category_scores_gemma":[0.4611277,0.0008477228,0.0015478993,0.0013114698,0.0045815106,0.004732228,0.00306644,0.0053709405,0.0010987038],"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":[0.0003092377,0.00012512201,0.010124219,0.00045502078,0.00036057775,0.0009276906,0.0013339928,0.081918746,0.0013602133,0.7673573,0.010283407,0.12544449],"study_design_scores_gemma":[0.000060625553,0.000056876823,0.0016006076,0.00028813301,0.0000864275,0.00036283518,0.00011591435,0.22690332,0.0016820111,0.7629515,0.00580417,0.000087689965],"about_ca_topic_score_codex":0.008475573,"about_ca_topic_score_gemma":0.0070311744,"teacher_disagreement_score":0.0530053,"about_ca_system_score_codex":0.0032972924,"about_ca_system_score_gemma":0.0027629766,"threshold_uncertainty_score":0.28032213},"labels":[],"label_agreement":null},{"id":"W2744856250","doi":"10.1002/sim.7427","title":"Bayesian analysis of pair‐matched case‐control studies subject to outcome misclassification","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"British Columbia Centre of Excellence for Women's Health; Vancouver Coastal Health; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Multiple Sclerosis Society","keywords":"Outcome (game theory); Bayesian probability; Computer science; Subject (documents); Statistics; Econometrics; Artificial intelligence; Mathematics","score_opus":0.17087201834420754,"score_gpt":0.48955020471738364,"score_spread":0.31867818637317613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744856250","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":[0.075069174,0.004863337,0.91697127,0.0010479082,0.0001330315,0.0004979185,0.00042056333,0.00024675598,0.00075019104],"genre_scores_gemma":[0.7722879,0.0027253747,0.22012594,0.0007716999,0.00025594994,0.0015867767,0.0010664922,0.0000840265,0.001095872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.87371933,0.10995962,0.004077927,0.0068347678,0.0047209235,0.00068748207],"domain_scores_gemma":[0.42688504,0.529922,0.021181954,0.016968032,0.00427773,0.0007652608],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19141617,0.0018841908,0.0037267066,0.0043434254,0.0010807028,0.0034049351,0.0048772967,0.0033301406,0.0022317956],"category_scores_gemma":[0.45363492,0.001624711,0.0036168003,0.0031905049,0.0025518239,0.0022186441,0.0028485954,0.0027018378,0.00031782498],"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":[0.004110856,0.00047999463,0.17461297,0.0026050669,0.019044297,0.0024676982,0.0018611858,0.3635143,0.0020136079,0.15759765,0.0035728307,0.26811954],"study_design_scores_gemma":[0.0007990337,0.00058517733,0.023070136,0.0005480581,0.004304119,0.0012065256,0.00015690601,0.73282814,0.0012297039,0.23220672,0.0029296898,0.0001357858],"about_ca_topic_score_codex":0.0054367036,"about_ca_topic_score_gemma":0.0027702646,"teacher_disagreement_score":0.19141617,"about_ca_system_score_codex":0.0014529562,"about_ca_system_score_gemma":0.0014567329,"threshold_uncertainty_score":0.99712783},"labels":[],"label_agreement":null},{"id":"W2747622113","doi":"10.3390/e19100555","title":"The Prior Can Often Only Be Understood in the Context of the Likelihood","year":2017,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":451,"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":"Advanced Research Projects Agency; Office of Naval Research; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Inference; Bayesian probability; Context (archaeology); Entropy (arrow of time); Prior probability; Key (lock); Bayesian inference; Principle of maximum entropy","score_opus":0.058945951541757156,"score_gpt":0.3561339327717054,"score_spread":0.2971879812299483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747622113","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":[0.0018580762,0.004801868,0.96453995,0.011589809,0.0006482432,0.000027128908,0.00031594868,0.00015457388,0.016064283],"genre_scores_gemma":[0.29614398,0.02287021,0.6452211,0.009823866,0.0063181994,0.00050977117,0.0008412496,0.0009776038,0.01729402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923409,0.0040229107,0.00046490852,0.0015067426,0.0014729602,0.00019162212],"domain_scores_gemma":[0.9742949,0.020743428,0.0011683706,0.0026714625,0.00080706924,0.00031463997],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01279468,0.0015307392,0.002049091,0.0032100468,0.0016355639,0.005472458,0.0033547701,0.0047005713,0.009515355],"category_scores_gemma":[0.050935127,0.0010651539,0.0012731222,0.0034270806,0.013357104,0.01727772,0.004209901,0.011698664,0.0038699317],"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":[0.000008081304,0.000006991492,0.00011969238,0.0000997252,0.00001534592,0.000041319083,0.00012653602,0.0014654279,0.00016605823,0.98656297,0.0019228421,0.009465115],"study_design_scores_gemma":[0.0000025777297,0.0000072090015,0.000094376395,0.00007327616,0.000008648202,0.00008201752,0.000026168915,0.0027477618,0.0001214977,0.98751056,0.00931342,0.000012509728],"about_ca_topic_score_codex":0.0017639778,"about_ca_topic_score_gemma":0.0016451958,"teacher_disagreement_score":0.9872053,"about_ca_system_score_codex":0.0021000009,"about_ca_system_score_gemma":0.0016201563,"threshold_uncertainty_score":0.06766552},"labels":[],"label_agreement":null},{"id":"W2747702080","doi":"10.1111/rssa.12308","title":"Clustering in Small Area Estimation with Area Level Linear Mixed Models","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Statistical Methods and Bayesian 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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Estimator; Small area estimation; Statistics; Variance (accounting); Homogeneity (statistics); Mean squared error; Mathematics; Euclidean distance; Computer science; Artificial intelligence","score_opus":0.127003631371638,"score_gpt":0.3472797529928348,"score_spread":0.2202761216211968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747702080","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":[0.028125798,0.0003652151,0.9705493,0.00024522678,0.000027979895,0.00006810546,0.00010161591,0.0001422067,0.0003745958],"genre_scores_gemma":[0.54711115,0.00044599365,0.44946316,0.00018574156,0.0001272229,0.00047679368,0.000595641,0.00010078355,0.0014934265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98382956,0.013358041,0.00036588885,0.0016064189,0.0005939823,0.00024605013],"domain_scores_gemma":[0.9321323,0.05835122,0.0040001934,0.003063534,0.002086052,0.00036676053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019034421,0.0010215245,0.0019218872,0.0023411936,0.0008337772,0.0018361091,0.0026420334,0.0015015705,0.0015713561],"category_scores_gemma":[0.0631657,0.0008083689,0.0017590424,0.0025018454,0.0017342749,0.0018670887,0.0022456269,0.002021321,0.0002923735],"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":[0.0002904943,0.00009979782,0.02322206,0.00029230842,0.0009174691,0.00020150085,0.00042757596,0.8409321,0.00066798873,0.06848974,0.001470185,0.062988795],"study_design_scores_gemma":[0.000014734982,0.00004596664,0.0016617124,0.000024008443,0.000043711305,0.0000188256,0.000043332897,0.9568494,0.00022327414,0.04054927,0.0005071758,0.000018531631],"about_ca_topic_score_codex":0.008094607,"about_ca_topic_score_gemma":0.007880937,"teacher_disagreement_score":0.019034421,"about_ca_system_score_codex":0.0014880139,"about_ca_system_score_gemma":0.0013064855,"threshold_uncertainty_score":0.100664794},"labels":[],"label_agreement":null},{"id":"W2748169160","doi":"10.1111/insr.12230","title":"Stratification of Skewed Populations: A Comparison of Optimisation‐based versus Approximate Methods","year":2017,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Statistics Canada","funders":"","keywords":"Estimator; Mathematics; Flexibility (engineering); Mathematical optimization; Population; Stratum; Computer science; Statistics; Stratification (seeds); Sample size determination; Econometrics; Engineering","score_opus":0.4317204470670315,"score_gpt":0.6023179494079958,"score_spread":0.17059750234096427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748169160","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":[0.015715845,0.018635843,0.9561442,0.0023013665,0.0002110883,0.00029627263,0.00011145563,0.00016062781,0.0064232647],"genre_scores_gemma":[0.36213154,0.02295258,0.6084237,0.0012509564,0.000565215,0.0010603119,0.0005093112,0.00024033942,0.0028659375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9561408,0.03873268,0.0010266743,0.0009431695,0.0028451022,0.00031159876],"domain_scores_gemma":[0.9262015,0.06487757,0.0024017019,0.003907265,0.0022947043,0.00031731473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039180804,0.00071855035,0.0019511052,0.0018338443,0.00042574192,0.0020669508,0.0017648402,0.0014054825,0.0023560533],"category_scores_gemma":[0.112330966,0.00051282556,0.0014910934,0.0021340684,0.0020196405,0.0025958414,0.0026356045,0.0016755635,0.0005200129],"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":[0.0006168187,0.00008952668,0.007519481,0.0018583427,0.00082082377,0.00007612487,0.001108007,0.16664484,0.00030037874,0.31854066,0.0052151056,0.49720982],"study_design_scores_gemma":[0.0004320636,0.00069349434,0.010061519,0.001547685,0.00037452963,0.0002396792,0.0005444078,0.47995174,0.00062554196,0.47971812,0.025690274,0.00012106291],"about_ca_topic_score_codex":0.0019754563,"about_ca_topic_score_gemma":0.0014499403,"teacher_disagreement_score":0.039180804,"about_ca_system_score_codex":0.0015588766,"about_ca_system_score_gemma":0.0018320055,"threshold_uncertainty_score":0.20721036},"labels":[],"label_agreement":null},{"id":"W2752933376","doi":"","title":"PEMODELAN REGRESI MULTILEVEL ZERO-INFLATED GENERALIZED POISSON DAN REGRESI MULTILEVEL ZERO-INFLATED POISSON PADA DATA RESPON COUNT","year":2017,"lang":"id","type":"other","venue":"Hasanuddin University Repository","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Poisson distribution; Count data; Mathematics; Zero (linguistics); Statistics; Zero-inflated model; Poisson regression; Population; Medicine","score_opus":0.08590855819995726,"score_gpt":0.3269987620624191,"score_spread":0.24109020386246188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752933376","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":[0.11023705,0.0035312201,0.84385127,0.0031824505,0.0013104656,0.0028400195,0.020019637,0.0041518523,0.010876141],"genre_scores_gemma":[0.5315461,0.0027741513,0.3771799,0.0019397564,0.0005480559,0.009872387,0.024354711,0.0020112144,0.049773782],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895085,0.0062906696,0.0005894753,0.0021025077,0.0008772902,0.0006315896],"domain_scores_gemma":[0.9828256,0.011588911,0.0012902159,0.0023625134,0.0016487577,0.00028416165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016891379,0.0022228546,0.0027422898,0.0013666943,0.0012727386,0.0032548574,0.0061358903,0.0019504015,0.032157052],"category_scores_gemma":[0.038306072,0.0011300698,0.0064001237,0.002642128,0.0010706303,0.0022057078,0.0031439804,0.005129133,0.007210102],"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":[0.0024560657,0.0009505412,0.2610985,0.0059416476,0.009421968,0.0024016045,0.005161617,0.14385825,0.004038722,0.11605629,0.0676952,0.38091955],"study_design_scores_gemma":[0.00051196205,0.002136017,0.100662656,0.0023890631,0.005404311,0.0015576725,0.004465842,0.5877811,0.0072792685,0.13414991,0.15313025,0.0005319794],"about_ca_topic_score_codex":0.01656008,"about_ca_topic_score_gemma":0.013048082,"teacher_disagreement_score":0.032157052,"about_ca_system_score_codex":0.0015238791,"about_ca_system_score_gemma":0.0041904794,"threshold_uncertainty_score":0.10757607},"labels":[],"label_agreement":null},{"id":"W2755786236","doi":"10.1002/sim.8013","title":"One‐sample aggregate data meta‐analysis of medians","year":2018,"lang":"en","type":"preprint","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé","keywords":"Median; Meta-analysis; Statistics; Weighting; Sample size determination; Pooled variance; Standard deviation; Variance (accounting); Sample (material); Outcome (game theory); Mathematics; Data set; Computer science; Confidence interval; Medicine","score_opus":0.327383040627426,"score_gpt":0.4826645932734693,"score_spread":0.15528155264604326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755786236","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":[0.025737053,0.17690244,0.77733845,0.0029563387,0.0020788312,0.002457805,0.0072623244,0.0019017801,0.003365029],"genre_scores_gemma":[0.55521744,0.04056615,0.38505524,0.0025396042,0.0012330242,0.007346069,0.0048692,0.0008044326,0.0023688234],"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90149504,0.08572168,0.0039560604,0.0053071612,0.0031002618,0.00041969548],"domain_scores_gemma":[0.8698329,0.11179847,0.0045736097,0.011122853,0.0023760705,0.00029610674],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08599191,0.0032044125,0.009793665,0.0067839082,0.0008007033,0.004286417,0.002925723,0.0023204528,0.0055986736],"category_scores_gemma":[0.18966226,0.0012193094,0.026977796,0.005911604,0.0011533425,0.0033676415,0.0023013547,0.004024423,0.00053828745],"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":[0.0070102704,0.00020858005,0.014027215,0.045922942,0.6601808,0.0005231783,0.00034849066,0.06816944,0.0014711774,0.031995684,0.009502305,0.16063985],"study_design_scores_gemma":[0.0055820504,0.0019692678,0.009966072,0.007757929,0.6101407,0.00057858473,0.00026376214,0.113625355,0.003960551,0.21081045,0.03503544,0.0003099427],"about_ca_topic_score_codex":0.002156603,"about_ca_topic_score_gemma":0.002176486,"teacher_disagreement_score":0.9140081,"about_ca_system_score_codex":0.0017630551,"about_ca_system_score_gemma":0.0021537705,"threshold_uncertainty_score":0.45477414},"labels":[],"label_agreement":null},{"id":"W2755882125","doi":"10.2174/1876527001708010027","title":"Bayesian Inference for Three Bivariate Beta Binomial Models","year":2017,"lang":"en","type":"article","venue":"The Open Statistics & Probability Journal","topic":"Statistical Methods and Bayesian 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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Markov chain Monte Carlo; Beta-binomial distribution; Bayesian probability; Bayesian inference; Mathematics; Negative binomial distribution; Likelihood function; Statistics; Inference; Econometrics; Computer science; Maximum likelihood; Poisson distribution; Artificial intelligence","score_opus":0.2415924702581883,"score_gpt":0.4466584665223155,"score_spread":0.2050659962641272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755882125","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":[0.036000136,0.0007519871,0.9584994,0.0008612953,0.000036170117,0.00009249312,0.00038889973,0.00017639184,0.0031932367],"genre_scores_gemma":[0.74679196,0.0021114904,0.24525796,0.00041944694,0.00016976499,0.00073686556,0.0011643128,0.00009893129,0.0032493246],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907602,0.0063732136,0.00036617505,0.0010315945,0.0010210414,0.0004478436],"domain_scores_gemma":[0.94043756,0.053223953,0.0025734315,0.0018815799,0.0013186528,0.0005648132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024834502,0.0010878661,0.0021545547,0.0030085822,0.0010146864,0.0038274333,0.0029960305,0.0023774868,0.005034484],"category_scores_gemma":[0.082976334,0.0010304502,0.0021409183,0.0033030834,0.0031369731,0.0037624957,0.0031104095,0.0035669908,0.0005974623],"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":[0.00024863333,0.00014112171,0.010458679,0.00037970237,0.00029350613,0.00042551933,0.0007449564,0.28633773,0.0006488376,0.6366717,0.0025543799,0.061095156],"study_design_scores_gemma":[0.000056601475,0.000032155956,0.0018816009,0.0001141382,0.00008426593,0.00020763831,0.00007748547,0.5731234,0.00020025959,0.42268553,0.0014920775,0.000044849126],"about_ca_topic_score_codex":0.0056119896,"about_ca_topic_score_gemma":0.0034986343,"teacher_disagreement_score":0.024834502,"about_ca_system_score_codex":0.0023362383,"about_ca_system_score_gemma":0.0016394397,"threshold_uncertainty_score":0.13133895},"labels":[],"label_agreement":null},{"id":"W2756553568","doi":"10.1177/0962280217729573","title":"Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Estimator; Confidence interval; Mathematics; Weibull distribution; Missing data; Econometrics; Computer science","score_opus":0.5664562877201138,"score_gpt":0.6143606527333666,"score_spread":0.04790436501325279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756553568","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":[0.005533879,0.00022103939,0.99356914,0.000299741,0.00002175605,0.000047060654,0.00007326719,0.00008777778,0.0001463145],"genre_scores_gemma":[0.35392046,0.0013130651,0.6352452,0.0008514048,0.00042157804,0.0020169448,0.0011551051,0.00022868159,0.0048474655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9810704,0.012855557,0.000704281,0.0028267486,0.0018678253,0.000675197],"domain_scores_gemma":[0.8794816,0.10014461,0.00942362,0.0066514444,0.0031863858,0.0011123266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04836108,0.0016005886,0.0037125526,0.002153201,0.00082639226,0.0024243526,0.0062952866,0.003734086,0.0022097456],"category_scores_gemma":[0.11461899,0.0018634949,0.0035204296,0.0025165442,0.0036008218,0.004384401,0.0047230334,0.0046748994,0.0006237675],"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":[0.0005612159,0.00040338392,0.03392945,0.00092453393,0.001447963,0.0012046938,0.0017378877,0.51195365,0.0031681452,0.32321993,0.002662706,0.11878642],"study_design_scores_gemma":[0.00006895047,0.0001685562,0.001601887,0.00008131706,0.00011247264,0.00023948327,0.00005139139,0.89374095,0.0007823807,0.10099539,0.0021027243,0.00005450006],"about_ca_topic_score_codex":0.0043059005,"about_ca_topic_score_gemma":0.0026410862,"teacher_disagreement_score":0.04836108,"about_ca_system_score_codex":0.0017333851,"about_ca_system_score_gemma":0.003448343,"threshold_uncertainty_score":0.2557609},"labels":[],"label_agreement":null},{"id":"W2756610413","doi":"10.1002/sim.7532","title":"Measures of clustering and heterogeneity in multilevel <scp>P</scp>oisson regression analyses of rates/count data","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":206,"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 Prince Edward Island; University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Vetenskapsrådet; Heart and Stroke Foundation of Canada","keywords":"Statistics; Poisson regression; Multilevel model; Poisson distribution; Hazard ratio; Proportional hazards model; Count data; Random effects model; Regression analysis; Cluster analysis; Confidence interval; Mathematics; Odds ratio; Rate ratio; Linear regression; Hierarchical clustering; Cluster (spacecraft); Econometrics; Medicine; Meta-analysis; Computer science; Internal medicine; Population","score_opus":0.4042531616015181,"score_gpt":0.5285301629397802,"score_spread":0.12427700133826208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756610413","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":[0.23211281,0.003102074,0.73889863,0.004013534,0.0003837951,0.0007307314,0.008288891,0.0009012612,0.011568237],"genre_scores_gemma":[0.89734703,0.0005351271,0.09524191,0.00046047088,0.00022302534,0.0011801878,0.0030531508,0.00021192418,0.0017471695],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9649846,0.02073218,0.003008292,0.004096145,0.006469579,0.000709293],"domain_scores_gemma":[0.81461823,0.1374074,0.024037508,0.018035416,0.005137029,0.0007643289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02922282,0.0004972554,0.001091739,0.0047212453,0.0007556032,0.0019752579,0.0017615971,0.0010450681,0.004373983],"category_scores_gemma":[0.17080471,0.00038010243,0.0023389815,0.006826541,0.002038914,0.0020467814,0.0021587948,0.0017541035,0.00053573685],"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":[0.0003745402,0.00018061147,0.5183472,0.0013663252,0.006040739,0.001478373,0.0031401527,0.083669215,0.0013700634,0.18639962,0.02021203,0.17742112],"study_design_scores_gemma":[0.000089017994,0.00034026854,0.3625159,0.0008440107,0.0008011307,0.0016999057,0.0020073825,0.24733981,0.0022809163,0.35853666,0.02327658,0.0002684401],"about_ca_topic_score_codex":0.0057877763,"about_ca_topic_score_gemma":0.0038820233,"teacher_disagreement_score":0.02922282,"about_ca_system_score_codex":0.0015634194,"about_ca_system_score_gemma":0.001049616,"threshold_uncertainty_score":0.15454686},"labels":[],"label_agreement":null},{"id":"W2760713401","doi":"10.1080/24709360.2017.1359356","title":"Analysis of progressive multi-state models with misclassified states: likelihood and pairwise likelihood methods","year":2017,"lang":"en","type":"article","venue":"Biostatistics & Epidemiology","topic":"Statistical Methods and Bayesian Inference","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Inference; Computer science; Maximum likelihood; State (computer science); Data mining; Artificial intelligence; Machine learning; Econometrics; Statistics; Mathematics; Algorithm","score_opus":0.19242330087441353,"score_gpt":0.4705267785926885,"score_spread":0.2781034777182749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760713401","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":[0.0065030716,0.00028948663,0.99259126,0.00026910982,0.000023182893,0.000049496328,0.000056693134,0.00004890522,0.0001689105],"genre_scores_gemma":[0.4363988,0.0014756852,0.5564958,0.0003396647,0.00030847877,0.0008701027,0.00082675397,0.00016097608,0.0031237588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98029697,0.014842065,0.0006341262,0.0023397547,0.0015197005,0.00036753085],"domain_scores_gemma":[0.81832707,0.16498704,0.0072347797,0.0061913193,0.002301898,0.0009578791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052806962,0.0017583057,0.0029346352,0.002459469,0.00082516123,0.0026604927,0.005806346,0.0026610494,0.0029320342],"category_scores_gemma":[0.16559592,0.0013114087,0.0036589873,0.0024593626,0.0034986224,0.0045985365,0.0041857623,0.005258273,0.00041560887],"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":[0.0002430041,0.00015302576,0.016906923,0.0003879428,0.0011735618,0.00062750105,0.00083594787,0.5736148,0.0005792502,0.3069158,0.0013757381,0.097186536],"study_design_scores_gemma":[0.000041092142,0.00008237202,0.0015297673,0.00007576086,0.00011127346,0.00016277788,0.00006169231,0.7792315,0.00030602916,0.21737218,0.0009768215,0.000048737103],"about_ca_topic_score_codex":0.0041884286,"about_ca_topic_score_gemma":0.0031220526,"teacher_disagreement_score":0.052806962,"about_ca_system_score_codex":0.0015030417,"about_ca_system_score_gemma":0.0024549726,"threshold_uncertainty_score":0.2792732},"labels":[],"label_agreement":null},{"id":"W2761595227","doi":"10.1002/cjs.11338","title":"Censored regression models with autoregressive errors: A likelihood‐based perspective","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Iowa Department of Natural Resources; University of Connecticut; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Society for Academic Emergency Medicine; National Center for Atmospheric Research","keywords":"Censoring (clinical trials); Autoregressive model; Unobservable; Computer science; Series (stratigraphy); Censored regression model; Time series; Regression analysis; Statistics; Regression; Likelihood function; Econometrics; Mathematics; Algorithm; Estimation theory","score_opus":0.060044582272418955,"score_gpt":0.3486902968055343,"score_spread":0.2886457145331154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761595227","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":[0.003094553,0.00060581614,0.9946791,0.00062070513,0.000025207819,0.000019554902,0.000058711583,0.00011720941,0.0007792481],"genre_scores_gemma":[0.42694515,0.0033679535,0.5597428,0.0005318433,0.00053975947,0.00041004995,0.0007522101,0.00030845788,0.007401807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992829,0.005582498,0.00020870578,0.0005310179,0.0006893583,0.00015936205],"domain_scores_gemma":[0.9369654,0.057014916,0.0026287427,0.0017717669,0.0013464233,0.00027278543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019269451,0.0011453922,0.0021236583,0.002526512,0.00055907696,0.00268388,0.0042326567,0.002552285,0.0035523605],"category_scores_gemma":[0.084094256,0.0011615172,0.0013672177,0.0031657263,0.002865707,0.0035792275,0.002340787,0.0036336002,0.00081032433],"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":[0.00006292932,0.000053103908,0.0032193644,0.00023001025,0.00020412254,0.00026195857,0.00023021609,0.5334482,0.00027830154,0.41984287,0.0024260029,0.039742872],"study_design_scores_gemma":[0.000014076822,0.000017782282,0.00048759344,0.000072442715,0.000024497904,0.00006945122,0.000029171326,0.82421046,0.0001710886,0.17309447,0.0017819955,0.000026988433],"about_ca_topic_score_codex":0.008128022,"about_ca_topic_score_gemma":0.005787897,"teacher_disagreement_score":0.019269451,"about_ca_system_score_codex":0.00164661,"about_ca_system_score_gemma":0.0016635323,"threshold_uncertainty_score":0.10190779},"labels":[],"label_agreement":null},{"id":"W2761969460","doi":"10.1002/env.2478","title":"Bayesian inference in time‐varying additive hazards models with applications to disease mapping","year":2017,"lang":"en","type":"article","venue":"Environmetrics","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":"University of Windsor; TD Bank Group","funders":"National Institute of Dental and Craniofacial Research; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Inference; Bayesian probability; Bayesian inference; Econometrics; Computer science; Frequentist inference; Statistics; Mathematics; Artificial intelligence","score_opus":0.06314494535418518,"score_gpt":0.3432266484521709,"score_spread":0.2800817030979857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761969460","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":[0.0027779767,0.0006699954,0.99539137,0.0005230145,0.00002785961,0.000025867465,0.00008265203,0.00008941864,0.00041174566],"genre_scores_gemma":[0.2821388,0.0045889397,0.70510024,0.00059422315,0.00063341286,0.0007597856,0.00079933397,0.00021768318,0.0051675574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9932787,0.005304697,0.00021286127,0.0005625618,0.00047712706,0.00016392957],"domain_scores_gemma":[0.92958474,0.065990366,0.0017144072,0.0012216845,0.0010857321,0.0004030277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019581962,0.0011783122,0.002057354,0.0026927781,0.0010440905,0.0018772421,0.003250533,0.0021423898,0.0034338783],"category_scores_gemma":[0.070641495,0.0014971915,0.0019826915,0.0030362082,0.0028576658,0.0024572567,0.0030589695,0.00394279,0.00044170974],"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":[0.00006107102,0.0000668212,0.0026456818,0.00022432016,0.00024298177,0.00019173327,0.00031491398,0.61157525,0.00031618375,0.33342677,0.0018330315,0.04910117],"study_design_scores_gemma":[0.000027851549,0.000018583058,0.00034304545,0.000039299644,0.00002680977,0.00003873843,0.000029998559,0.67504364,0.000108882254,0.32263196,0.001670008,0.000021212096],"about_ca_topic_score_codex":0.019136298,"about_ca_topic_score_gemma":0.017201003,"teacher_disagreement_score":0.019581962,"about_ca_system_score_codex":0.00219686,"about_ca_system_score_gemma":0.002742445,"threshold_uncertainty_score":0.10356051},"labels":[],"label_agreement":null},{"id":"W2763506269","doi":"10.1007/s40300-017-0128-9","title":"Multiply robust imputation procedures for zero-inflated distributions in surveys","year":2017,"lang":"en","type":"article","venue":"METRON","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"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 General Medical Sciences; University of Oklahoma Health Sciences Center; National Institutes of Health; Health Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Jackknife resampling; Imputation (statistics); Missing data; Estimator; Statistics; Econometrics; Variance (accounting); Computer science; Mathematics","score_opus":0.10356926532101822,"score_gpt":0.40664734774567796,"score_spread":0.3030780824246597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763506269","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":[0.0018817688,0.0006096451,0.99602085,0.00047574894,0.00006163283,0.00004610947,0.00022668231,0.0002636426,0.00041400298],"genre_scores_gemma":[0.109260306,0.0015837643,0.8773726,0.00068835914,0.00066401303,0.0013761078,0.0015867078,0.0004817488,0.0069863205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9323112,0.057728346,0.0019777317,0.0041109957,0.002950445,0.00092123443],"domain_scores_gemma":[0.7466975,0.20827353,0.008896104,0.029800776,0.0053632776,0.0009687913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0652912,0.0019702204,0.0057195066,0.0041222963,0.0023906594,0.0045283716,0.010074044,0.0049272897,0.0132909985],"category_scores_gemma":[0.26483124,0.0028056926,0.0043078735,0.008361181,0.004629707,0.007987114,0.00497515,0.007376945,0.0025108485],"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":[0.00026791496,0.00012459073,0.003368245,0.00063258334,0.0010210288,0.00022184025,0.00059691013,0.04736085,0.00023881401,0.84279007,0.009278146,0.09409898],"study_design_scores_gemma":[0.00008221089,0.000049718983,0.00078848243,0.00015722481,0.00014518802,0.00013474339,0.00006488945,0.13473342,0.00029315392,0.85801584,0.005481477,0.000053695658],"about_ca_topic_score_codex":0.0044756997,"about_ca_topic_score_gemma":0.005064641,"teacher_disagreement_score":0.0652912,"about_ca_system_score_codex":0.0024865118,"about_ca_system_score_gemma":0.003484578,"threshold_uncertainty_score":0.34529704},"labels":[],"label_agreement":null},{"id":"W2764295523","doi":"10.1007/s10985-017-9410-7","title":"Practical considerations when analyzing discrete survival times using the grouped relative risk model","year":2017,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"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; Mathematics; Efficiency; Statistics; Parametric statistics; Parametric model; Econometrics; Regression analysis","score_opus":0.25476507849948166,"score_gpt":0.47067153930695055,"score_spread":0.2159064608074689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764295523","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":[0.004320091,0.0008791244,0.98952454,0.0037089386,0.00016333864,0.00008718059,0.00010568861,0.0001416462,0.0010694122],"genre_scores_gemma":[0.11535165,0.0015980125,0.87779045,0.0017111389,0.0005270937,0.0007717184,0.00022386468,0.0002393625,0.0017868163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91793734,0.07418479,0.0016315561,0.0025190199,0.0032801928,0.00044710006],"domain_scores_gemma":[0.6864568,0.29480004,0.004199959,0.010271596,0.0033930046,0.0008785329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12305378,0.0014080327,0.0035063198,0.002568795,0.0011968487,0.004962412,0.006250001,0.0039402363,0.005600803],"category_scores_gemma":[0.2739239,0.001292855,0.0027950422,0.003469886,0.0052283206,0.005944478,0.003239937,0.0107110655,0.0009557032],"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":[0.0003877843,0.00021872173,0.0051778546,0.0006552113,0.0007942549,0.00086828135,0.0012565968,0.084510736,0.00079191144,0.7499055,0.0077257487,0.14770733],"study_design_scores_gemma":[0.000085828986,0.00012450444,0.00093786354,0.0001794292,0.00012763942,0.00044403353,0.00035183606,0.11756043,0.000491021,0.87490785,0.0047357953,0.000053743406],"about_ca_topic_score_codex":0.005185591,"about_ca_topic_score_gemma":0.0059514134,"teacher_disagreement_score":0.12305378,"about_ca_system_score_codex":0.002033546,"about_ca_system_score_gemma":0.004155425,"threshold_uncertainty_score":0.6507784},"labels":[],"label_agreement":null},{"id":"W2765604597","doi":"","title":"Bayesian inference and model comparison for random choice structures","year":2013,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian Inference","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":"University of Victoria; Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Inference; Axiom; Bayesian inference; Bayesian probability; Conjugate prior; Class (philosophy); Joint probability distribution; Mathematics; Computer science; Posterior probability; Prior probability; Econometrics; Mathematical economics; Statistics; Artificial intelligence","score_opus":0.08881758202042353,"score_gpt":0.425511058224314,"score_spread":0.33669347620389045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765604597","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":[0.03728958,0.0005733384,0.95523703,0.001614949,0.00007067186,0.00017698987,0.00022394044,0.00014067515,0.0046727112],"genre_scores_gemma":[0.6539265,0.00069271337,0.3405907,0.00074880995,0.00021404521,0.0011561866,0.0007519701,0.00013922424,0.001779825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8550765,0.13115323,0.0017016506,0.0048524127,0.006090565,0.0011255684],"domain_scores_gemma":[0.5513887,0.42507866,0.007634614,0.011221667,0.0034839443,0.0011924421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10283594,0.0015214426,0.0036713735,0.0047648596,0.0018181725,0.0046728076,0.0047292085,0.00312693,0.007955151],"category_scores_gemma":[0.3096756,0.0014177657,0.0034127154,0.0035936283,0.0066289473,0.008407506,0.0051374757,0.006587206,0.0005108329],"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":[0.00028989438,0.00014195156,0.0022811715,0.00019968006,0.0005730078,0.000064695814,0.00031047311,0.12435033,0.00013419545,0.84956104,0.0012750812,0.020818394],"study_design_scores_gemma":[0.000062847575,0.000044769477,0.0003805527,0.00003625522,0.00003822754,0.000016123115,0.000031494546,0.29525816,0.00009581602,0.7034596,0.00055670016,0.000019463107],"about_ca_topic_score_codex":0.00450268,"about_ca_topic_score_gemma":0.00452266,"teacher_disagreement_score":0.10283594,"about_ca_system_score_codex":0.0056382986,"about_ca_system_score_gemma":0.0029191927,"threshold_uncertainty_score":0.54385495},"labels":[],"label_agreement":null},{"id":"W2765783863","doi":"10.1002/cjs.11471","title":"On asymptotic inference in stochastic differential equations with time‐varying covariates","year":2018,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Asymptotic distribution; Consistency (knowledge bases); Covariate; Stochastic differential equation; Applied mathematics; Mathematics; Bayesian inference; Population; Inference; Bayesian probability; Strong consistency; Set (abstract data type); Normality; Computer science; Econometrics; Statistics; Artificial intelligence; Discrete mathematics; Estimator","score_opus":0.054252100133297065,"score_gpt":0.32787520411251153,"score_spread":0.27362310397921447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765783863","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":[0.010416766,0.00076356984,0.98591995,0.0015302479,0.000091755865,0.0000598075,0.00015750507,0.00012548755,0.0009349558],"genre_scores_gemma":[0.4720219,0.0042184456,0.5106017,0.0018814014,0.0011336033,0.0009643901,0.001501632,0.00041110828,0.0072659277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9830494,0.012333198,0.0007152937,0.0016745364,0.0017675448,0.00045997056],"domain_scores_gemma":[0.75601244,0.22608621,0.007080408,0.0047681215,0.004994102,0.0010587098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045378294,0.0014927194,0.003546679,0.0031166153,0.0011707742,0.0028155153,0.004322194,0.0032062896,0.005047923],"category_scores_gemma":[0.21247096,0.0015677521,0.0024539896,0.0031704502,0.0071963323,0.004504572,0.005495974,0.005314995,0.0005519323],"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":[0.000093799055,0.000106121945,0.005820279,0.00030193268,0.00042021027,0.00036402218,0.00032299658,0.288681,0.00042923357,0.6771188,0.0023306087,0.024010979],"study_design_scores_gemma":[0.00005793364,0.00002847527,0.00065372523,0.00006557496,0.000040980147,0.00004546479,0.0000316974,0.6253509,0.00016889657,0.372406,0.0011211003,0.000029281578],"about_ca_topic_score_codex":0.024182927,"about_ca_topic_score_gemma":0.015111676,"teacher_disagreement_score":0.045378294,"about_ca_system_score_codex":0.0034300114,"about_ca_system_score_gemma":0.00583306,"threshold_uncertainty_score":0.23998624},"labels":[],"label_agreement":null},{"id":"W2765966791","doi":"10.1002/sim.7515","title":"A mechanistic nonlinear model for censored and mismeasured covariates in longitudinal models, with application in AIDS studies","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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; HIV Legal Network; St. Paul's Hospital","funders":"","keywords":"Covariate; Censoring (clinical trials); Econometrics; Inference; Observational error; Computer science; Statistics; Mathematics; Artificial intelligence","score_opus":0.1472845397461962,"score_gpt":0.438050019936488,"score_spread":0.2907654801902918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765966791","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":[0.015098332,0.0005765319,0.9810176,0.0013800988,0.0000755975,0.00009045713,0.0004345001,0.00013474858,0.0011921722],"genre_scores_gemma":[0.64307255,0.0032573119,0.33165094,0.001354425,0.0005343599,0.0012954314,0.001499011,0.00015960823,0.017176423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995849,0.002739799,0.00015013527,0.00069452374,0.0003740783,0.0001923784],"domain_scores_gemma":[0.9798877,0.015670743,0.0021754864,0.0011940547,0.0007663831,0.00030563763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014261661,0.0011620473,0.0013809123,0.0017157625,0.0011377551,0.0019417568,0.0036771162,0.002575042,0.003916624],"category_scores_gemma":[0.035910927,0.00093828724,0.002039733,0.0024297035,0.0023917968,0.0036019154,0.0022101672,0.0035748833,0.0006312877],"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":[0.00013561398,0.00010953444,0.013033066,0.0002493682,0.0002161053,0.00060812844,0.0007214098,0.33918306,0.0007708935,0.6039257,0.0032546595,0.03779257],"study_design_scores_gemma":[0.000048040718,0.00007514743,0.002145808,0.00006828379,0.00009365092,0.00027585635,0.00008541109,0.72517616,0.00021281731,0.26717827,0.0045811865,0.00005931564],"about_ca_topic_score_codex":0.007938186,"about_ca_topic_score_gemma":0.009036611,"teacher_disagreement_score":0.014261661,"about_ca_system_score_codex":0.0018109004,"about_ca_system_score_gemma":0.0022257196,"threshold_uncertainty_score":0.07542378},"labels":[],"label_agreement":null},{"id":"W2766515099","doi":"10.1111/rssb.12255","title":"From Multiple Gaussian Sequences to Functional Data and Beyond: A Stein Estimation Approach","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Oracle; Robustness (evolution); Computer science; Pooling; Gaussian; Sequence (biology); Algorithm; Leverage (statistics); Variance (accounting); Projection (relational algebra); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.20672806798050292,"score_gpt":0.41093011743552704,"score_spread":0.20420204945502413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766515099","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":[0.0028745092,0.00019066685,0.9959557,0.0003457648,0.000016789209,0.00001496846,0.000019373769,0.000041808642,0.0005404544],"genre_scores_gemma":[0.36792222,0.0015386877,0.62379354,0.0009101359,0.000290062,0.00041209042,0.00017091051,0.0001680068,0.0047944747],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949498,0.0033268512,0.00018984755,0.0006407783,0.0007281085,0.00016455934],"domain_scores_gemma":[0.9778316,0.016282495,0.0014559526,0.002750243,0.0012417631,0.000437994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018601457,0.0011867875,0.0016839876,0.001978578,0.00050067843,0.0016905996,0.0024430265,0.0018711078,0.002970589],"category_scores_gemma":[0.04230834,0.0009293229,0.0017140099,0.0012400143,0.0047548003,0.0036028444,0.004203541,0.0036496297,0.00043068494],"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":[0.00006405149,0.000041480835,0.000874014,0.00018734009,0.00010453491,0.00018493975,0.0001693893,0.2029619,0.0017289724,0.753003,0.00081991736,0.039860472],"study_design_scores_gemma":[0.000012420224,0.00005168159,0.00031076395,0.00003148978,0.000013569702,0.000043432072,0.000025874495,0.452325,0.00046222753,0.5454117,0.0012888155,0.000023039645],"about_ca_topic_score_codex":0.0027144228,"about_ca_topic_score_gemma":0.0018582145,"teacher_disagreement_score":0.018601457,"about_ca_system_score_codex":0.0015146913,"about_ca_system_score_gemma":0.0020403478,"threshold_uncertainty_score":0.09837508},"labels":[],"label_agreement":null},{"id":"W2766830023","doi":"10.5539/jmr.v9n6p106","title":"Estimation of Causal Functional Linear Regression Models","year":2017,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Mathematics; Combinatorics; Tensor product; Order (exchange); Linear operators; Orthonormal basis; Tensor (intrinsic definition); Linear form; Product (mathematics); Linear regression; Mathematical analysis; Geometry; Statistics; Pure mathematics; Physics","score_opus":0.46517733873585654,"score_gpt":0.5526505462554957,"score_spread":0.08747320751963916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766830023","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":[0.0020072986,0.00008985061,0.99703634,0.00015283404,0.000017405167,0.000023514607,0.00008793779,0.00014145976,0.00044331947],"genre_scores_gemma":[0.22603606,0.00088532356,0.765475,0.00040803937,0.00023796504,0.0008200931,0.0014303769,0.00027256607,0.0044346377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908097,0.0067770327,0.0002821773,0.0009262054,0.0008593496,0.00034546867],"domain_scores_gemma":[0.97712713,0.017091127,0.0020154293,0.0022441023,0.0012417646,0.00028046852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0118927555,0.0017046388,0.001637719,0.0035145849,0.00084657304,0.001828795,0.0034706015,0.0021675248,0.0062856534],"category_scores_gemma":[0.051347855,0.0014659942,0.002601758,0.002665968,0.0020876434,0.002950268,0.0027256045,0.003345809,0.0010880412],"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":[0.000072789735,0.000117197094,0.0035067075,0.0002322174,0.00035204407,0.00016333799,0.0002462612,0.33672935,0.00090322766,0.58444494,0.002947775,0.07028427],"study_design_scores_gemma":[0.000016113609,0.000036258214,0.00048704314,0.000045561294,0.000049975195,0.00004401732,0.00003987312,0.7739352,0.0004527937,0.22242859,0.0024324744,0.000032034553],"about_ca_topic_score_codex":0.009968019,"about_ca_topic_score_gemma":0.009367539,"teacher_disagreement_score":0.0118927555,"about_ca_system_score_codex":0.0018347418,"about_ca_system_score_gemma":0.003253484,"threshold_uncertainty_score":0.062895656},"labels":[],"label_agreement":null},{"id":"W2767955851","doi":"10.1002/sta4.159","title":"A class of flexible models for analysis of complex structured correlated data with application to clustered longitudinal data","year":2017,"lang":"en","type":"article","venue":"Stat","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary; Western University; University of Waterloo","funders":"","keywords":"Pairwise comparison; Computer science; Class (philosophy); Generalized linear mixed model; Longitudinal data; Data mining; Linear model; Generalized linear model; Theoretical computer science; Machine learning; Artificial intelligence","score_opus":0.36079339415936035,"score_gpt":0.47553549211404533,"score_spread":0.11474209795468499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767955851","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":[0.00096948235,0.00019284504,0.99822646,0.00016696415,0.000019922416,0.00005423764,0.00009096949,0.00008500039,0.0001941734],"genre_scores_gemma":[0.105159566,0.001545677,0.8871254,0.00044212796,0.00024250467,0.001919339,0.00079782587,0.00021720673,0.0025502953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98581356,0.010203816,0.00048815014,0.0016396038,0.0014743218,0.00038054856],"domain_scores_gemma":[0.9708683,0.022115557,0.0026677318,0.002727765,0.0012646922,0.0003559724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023729436,0.0025412648,0.0021895324,0.0028143506,0.0011446391,0.0026567793,0.0051642144,0.002509529,0.004434797],"category_scores_gemma":[0.0600576,0.0011726996,0.0043405164,0.004267438,0.0031284227,0.0043391506,0.004693237,0.005154987,0.0011103621],"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":[0.00010045079,0.000078577505,0.0032605673,0.0004765687,0.00054488354,0.00035154397,0.0007448932,0.1757174,0.001081696,0.6893416,0.0043008123,0.12400101],"study_design_scores_gemma":[0.000038066344,0.00009956052,0.000684094,0.00011657312,0.000106184474,0.00017334313,0.00009416551,0.4820201,0.0002877221,0.51168895,0.0046303854,0.00006073738],"about_ca_topic_score_codex":0.0030944748,"about_ca_topic_score_gemma":0.0042942856,"teacher_disagreement_score":0.023729436,"about_ca_system_score_codex":0.0014509589,"about_ca_system_score_gemma":0.0029466306,"threshold_uncertainty_score":0.12549472},"labels":[],"label_agreement":null},{"id":"W2770123802","doi":"10.1002/cjs.11527","title":"Estimating prediction error for complex samples","year":2019,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health","keywords":"Estimator; Statistics; Sampling (signal processing); Generalization; Context (archaeology); Sample size determination; Computer science; Population; Sample (material); Mean squared error; Econometrics; Mathematics","score_opus":0.2079316552992227,"score_gpt":0.39054116759513174,"score_spread":0.18260951229590905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770123802","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":[0.017195638,0.00029306664,0.9815663,0.0002466431,0.000035702386,0.000046931465,0.00007246773,0.000105346444,0.00043793087],"genre_scores_gemma":[0.50831026,0.0008565677,0.48644984,0.00042889756,0.0001695387,0.00051754166,0.0007769602,0.00015601472,0.0023343153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9881158,0.007156698,0.0006909038,0.0020787006,0.0016454519,0.00031246728],"domain_scores_gemma":[0.87729484,0.10474237,0.0056677773,0.007955162,0.0039083795,0.00043143472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035734423,0.0010322202,0.002007582,0.0019327116,0.0006208281,0.0022514604,0.0023678658,0.0020794799,0.0016267534],"category_scores_gemma":[0.16037488,0.0008651707,0.0010736831,0.0017728431,0.0026754136,0.003943492,0.002894508,0.002992264,0.00034589908],"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":[0.00018369463,0.0001041457,0.018225024,0.00024233546,0.0004332602,0.00019285304,0.00039458915,0.6627637,0.0009972034,0.19902445,0.0021719444,0.115266874],"study_design_scores_gemma":[0.00001969117,0.000035349516,0.0018596903,0.00003582171,0.000018890618,0.000041308347,0.000023881252,0.89416176,0.0004245032,0.10268139,0.0006803938,0.00001734109],"about_ca_topic_score_codex":0.0057012504,"about_ca_topic_score_gemma":0.0033498472,"teacher_disagreement_score":0.035734423,"about_ca_system_score_codex":0.0018311178,"about_ca_system_score_gemma":0.0015388876,"threshold_uncertainty_score":0.18898398},"labels":[],"label_agreement":null},{"id":"W2770230962","doi":"10.1002/cjs.11496","title":"Checking validity of monotone domain mean estimators","year":2019,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Estimator; Monotonic function; Domain (mathematical analysis); Monotone polygon; Inference; Mathematics; Population; Applied mathematics; Mathematical optimization; Measure (data warehouse); Statistics; Computer science; Artificial intelligence; Mathematical analysis; Data mining","score_opus":0.10650738430771883,"score_gpt":0.3567251940120499,"score_spread":0.25021780970433105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770230962","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":[0.090056896,0.00041568695,0.90540314,0.00053432817,0.000031461703,0.00008570385,0.00033936312,0.00017773922,0.0029556493],"genre_scores_gemma":[0.7174053,0.00023777674,0.279714,0.00030336142,0.000082251536,0.00026226928,0.0008261359,0.00015263277,0.0010162509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97199064,0.018723963,0.0010690219,0.0028002884,0.0047823065,0.0006337016],"domain_scores_gemma":[0.6384281,0.31032157,0.014724995,0.01668144,0.017690914,0.002152967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0546155,0.0005817464,0.0016961374,0.003092175,0.0011759093,0.0025602754,0.003067815,0.0019793569,0.002791009],"category_scores_gemma":[0.31030208,0.0007338428,0.0011265632,0.0017706495,0.0043281796,0.00368841,0.004155281,0.002811201,0.00038999438],"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":[0.0006401323,0.000216215,0.10003448,0.00049083383,0.0004530177,0.00059113506,0.0012187436,0.211301,0.004213267,0.5500008,0.004369155,0.1264712],"study_design_scores_gemma":[0.000065461034,0.0001410824,0.007087851,0.00015930677,0.000032868258,0.00023951048,0.00019946382,0.7217835,0.0030750558,0.2652005,0.0019612054,0.000054193904],"about_ca_topic_score_codex":0.0040160567,"about_ca_topic_score_gemma":0.002118013,"teacher_disagreement_score":0.0546155,"about_ca_system_score_codex":0.0014574808,"about_ca_system_score_gemma":0.002756382,"threshold_uncertainty_score":0.2888378},"labels":[],"label_agreement":null},{"id":"W2770711354","doi":"10.1177/0962280217737566","title":"Bayesian latent time joint mixed effect models for multicohort longitudinal data","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":81,"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 on Aging; Canadian Institutes of Health Research","keywords":"Bayesian probability; Mixed model; Longitudinal data; Latent variable; Econometrics; Computer science; Statistics; Event data; Mathematics; Data mining; Covariate","score_opus":0.5151171744634669,"score_gpt":0.6288287204909679,"score_spread":0.113711546027501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770711354","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":[0.007729513,0.0010654827,0.988734,0.0009821755,0.00008755194,0.00013475443,0.00064056664,0.00020814527,0.00041773953],"genre_scores_gemma":[0.32933334,0.0039642774,0.6527897,0.0009195889,0.0006172221,0.0033266642,0.0036855647,0.0002261481,0.0051374706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98424596,0.012328337,0.0005291271,0.0018170716,0.0006851339,0.0003943952],"domain_scores_gemma":[0.89912933,0.08821893,0.0055419207,0.0045806896,0.0017299918,0.00079922244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038453847,0.0018738235,0.0031514287,0.0032805966,0.0011976765,0.0030119952,0.005134026,0.003147846,0.0041680923],"category_scores_gemma":[0.087378845,0.0015948399,0.003029001,0.0039539854,0.0031946963,0.004686402,0.0029411232,0.0049432293,0.0007518589],"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":[0.0003502561,0.00018950898,0.011307926,0.00048402828,0.0011599226,0.00041674104,0.00082344026,0.20790747,0.0005464658,0.7258013,0.0043922137,0.04662076],"study_design_scores_gemma":[0.00010002537,0.00008846169,0.0015321523,0.00011493586,0.00023050977,0.00012308177,0.00009575066,0.46678102,0.00018530407,0.5277758,0.0029048007,0.00006818724],"about_ca_topic_score_codex":0.008731807,"about_ca_topic_score_gemma":0.009760588,"teacher_disagreement_score":0.038453847,"about_ca_system_score_codex":0.0026721545,"about_ca_system_score_gemma":0.002645066,"threshold_uncertainty_score":0.2033658},"labels":[],"label_agreement":null},{"id":"W2774445575","doi":"10.1002/sim.7555","title":"Bayesian inference for unidirectional misclassification of a binary response trait","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identifiability; Covariate; Computer science; Inference; Poisson distribution; Identification (biology); Binary data; Bayesian probability; Binary number; Statistics; Econometrics; Bayesian inference; Binary classification; Bayes' theorem; Machine learning; Artificial intelligence; Mathematics","score_opus":0.1210735242408162,"score_gpt":0.4597409341384469,"score_spread":0.3386674098976307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774445575","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":[0.028872812,0.00032606896,0.9690048,0.00074811454,0.000051972493,0.00008034617,0.000108349006,0.00010474401,0.00070284895],"genre_scores_gemma":[0.6271313,0.0008394304,0.3671009,0.0009900165,0.00022315387,0.0006701166,0.0006103146,0.00010303971,0.0023316543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96004456,0.029468434,0.0015960581,0.0059611653,0.0022525238,0.0006772484],"domain_scores_gemma":[0.7811842,0.18807313,0.011958734,0.014922787,0.0030541618,0.0008069668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.090335384,0.0013044203,0.0026152509,0.0022285297,0.0014350347,0.0030012925,0.0037399195,0.0027965007,0.002274116],"category_scores_gemma":[0.2870932,0.0013508507,0.0019476764,0.0017631878,0.0044069467,0.0049870396,0.0037338766,0.005413296,0.0004780133],"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":[0.0006030508,0.00025108614,0.042619556,0.0007315519,0.0009994911,0.00057205366,0.0023836764,0.14644358,0.0018826657,0.6197959,0.0027140344,0.18100345],"study_design_scores_gemma":[0.00008108968,0.0000989811,0.0047192206,0.00023223492,0.00022288426,0.00025089874,0.00015915094,0.39781615,0.0014704022,0.59342563,0.0014576446,0.00006570689],"about_ca_topic_score_codex":0.0039612222,"about_ca_topic_score_gemma":0.00373733,"teacher_disagreement_score":0.090335384,"about_ca_system_score_codex":0.0021187016,"about_ca_system_score_gemma":0.0021288553,"threshold_uncertainty_score":0.47774488},"labels":[],"label_agreement":null},{"id":"W2782608753","doi":"10.1111/bmsp.12126","title":"On the solution multiplicity of the Fleishman method and its impact in simulation studies","year":2018,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","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 British Columbia","funders":"","keywords":"Normality; Monte Carlo method; Multiplicity (mathematics); Population; Applied mathematics; Mathematics; Computer science; Algorithm; Statistics; Mathematical analysis","score_opus":0.13750207654009666,"score_gpt":0.50524852138227,"score_spread":0.3677464448421734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782608753","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":[0.04462082,0.0040239915,0.9299486,0.0068777907,0.00042241468,0.00032587885,0.0001079325,0.0003376555,0.0133348275],"genre_scores_gemma":[0.3983811,0.00227613,0.59474903,0.0010630232,0.00029810218,0.0009255906,0.00012389699,0.00035197052,0.0018312437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.88945055,0.09661645,0.0019879453,0.0034942834,0.007684547,0.000766176],"domain_scores_gemma":[0.27488032,0.6924819,0.009860682,0.011178855,0.010225617,0.0013725642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11367284,0.001301416,0.0020619014,0.003948549,0.001956622,0.0049510035,0.0026006622,0.0042483937,0.0071453126],"category_scores_gemma":[0.599131,0.0009525251,0.0017775232,0.0037471377,0.007052567,0.008906875,0.0058272895,0.0058739167,0.0007345711],"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":[0.0007694045,0.00023901883,0.011534074,0.0009961355,0.00030192704,0.0002538748,0.0022501135,0.14596939,0.0010051659,0.68750405,0.004286917,0.1448899],"study_design_scores_gemma":[0.00018186765,0.00050934806,0.001873652,0.001110963,0.00012288599,0.00026906581,0.00052994007,0.5088646,0.0016604861,0.47696525,0.007754896,0.00015710795],"about_ca_topic_score_codex":0.0038383426,"about_ca_topic_score_gemma":0.0033875047,"teacher_disagreement_score":0.11367284,"about_ca_system_score_codex":0.0030725903,"about_ca_system_score_gemma":0.004322305,"threshold_uncertainty_score":0.6011666},"labels":[],"label_agreement":null},{"id":"W2784307041","doi":"10.1002/sim.7553","title":"Modeling clustering and treatment effect heterogeneity in parallel and stepped‐wedge cluster randomized trials","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada","funders":"National Health and Medical Research Council; Medical Research Council; National Institute for Health and Care Research","keywords":"Cluster analysis; Cluster (spacecraft); Computer science; Random effects model; Econometrics; Wedge (geometry); Statistics; Data mining; Mathematics; Medicine; Artificial intelligence; Meta-analysis","score_opus":0.1332079247993277,"score_gpt":0.4520500531089921,"score_spread":0.3188421283096644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784307041","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":[0.016776472,0.0007667121,0.9769644,0.0016101344,0.00015137767,0.0015033328,0.00025162273,0.00021738326,0.0017585281],"genre_scores_gemma":[0.3816721,0.0009967551,0.6042442,0.0015525295,0.00016971707,0.007803386,0.00048616756,0.00012613577,0.0029490027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.87187153,0.11169243,0.0039465753,0.005861364,0.0053986777,0.0012293897],"domain_scores_gemma":[0.7256604,0.23939161,0.014234611,0.015914598,0.0038047,0.0009940489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16432996,0.0017744015,0.004289493,0.0024385485,0.0010088673,0.002975023,0.006592457,0.0056446325,0.0059832335],"category_scores_gemma":[0.28702456,0.0019958757,0.005132027,0.0031810466,0.0047082095,0.0045033707,0.00399515,0.0060943323,0.00074813166],"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":[0.0023369875,0.0002677782,0.0053563938,0.0015990123,0.0014706888,0.0007764974,0.0010431898,0.35277832,0.0006088744,0.57228124,0.0035121671,0.057968825],"study_design_scores_gemma":[0.0013607263,0.00055217603,0.0008784479,0.0003943764,0.000512552,0.00024831272,0.00011824726,0.42268834,0.00057110674,0.5692797,0.0033051101,0.00009096246],"about_ca_topic_score_codex":0.0026182886,"about_ca_topic_score_gemma":0.002167927,"teacher_disagreement_score":0.16432996,"about_ca_system_score_codex":0.0035397373,"about_ca_system_score_gemma":0.004114038,"threshold_uncertainty_score":0.8690703},"labels":[],"label_agreement":null},{"id":"W2785817299","doi":"10.1136/bmj.j5779","title":"Concerns about composite reference standards in diagnostic research","year":2018,"lang":"en","type":"article","venue":"BMJ","topic":"Statistical Methods and Bayesian Inference","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":"McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Composite number; Imperfect; Reference model; Reference data; Computer science; Test (biology); Statistics; Class (philosophy); Reference values; Econometrics; Data mining; Artificial intelligence; Mathematics; Algorithm; Medicine","score_opus":0.26943144837595806,"score_gpt":0.5502487348604476,"score_spread":0.2808172864844895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785817299","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":[0.011465395,0.036956474,0.74353856,0.16818252,0.008644762,0.001759217,0.0011383599,0.0007243094,0.027590431],"genre_scores_gemma":[0.26913592,0.010079597,0.61042696,0.08557947,0.012547921,0.007234081,0.0010091225,0.0006268661,0.0033600498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.19397528,0.6534689,0.039993577,0.02763188,0.08294727,0.0019830402],"domain_scores_gemma":[0.060465533,0.83634,0.023724018,0.048493844,0.029154036,0.0018226282],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.7773238,0.0020524296,0.005962058,0.010218999,0.005140701,0.01196718,0.016251262,0.0119648455,0.004996222],"category_scores_gemma":[0.8733042,0.0030339297,0.004749158,0.012718295,0.04097485,0.01905654,0.013058704,0.025780516,0.002123053],"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":[0.00079557695,0.00012114852,0.012412205,0.0030984972,0.00094297435,0.00038623158,0.0084347185,0.003743071,0.0004032507,0.7819633,0.03173224,0.15596677],"study_design_scores_gemma":[0.00017471713,0.00028534373,0.005111328,0.004910392,0.0002644934,0.0010569224,0.0010342393,0.007986789,0.00091192935,0.9301335,0.04792982,0.00020058487],"about_ca_topic_score_codex":0.0082678,"about_ca_topic_score_gemma":0.0064205998,"teacher_disagreement_score":0.22267622,"about_ca_system_score_codex":0.014002179,"about_ca_system_score_gemma":0.010417861,"threshold_uncertainty_score":0.2745995},"labels":[],"label_agreement":null},{"id":"W2789931592","doi":"10.1017/pan.2017.43","title":"When Can Multiple Imputation Improve Regression Estimates?","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"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; Université de Montréal","funders":"","keywords":"Missing data; Imputation (statistics); Estimator; Regression; Computer science; Regression analysis; Statistics; Panacea (medicine); Meta-regression; Econometrics; Data mining; Meta-analysis; Mathematics; Machine learning; Medicine","score_opus":0.05043689094324021,"score_gpt":0.39517078885719203,"score_spread":0.3447338979139518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789931592","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":[0.0042425506,0.01949581,0.8646559,0.10145328,0.0029671069,0.00015337202,0.0006107729,0.0009904383,0.0054306956],"genre_scores_gemma":[0.1399248,0.01906512,0.80466163,0.023295145,0.006288103,0.00093637255,0.0008249125,0.0016176737,0.003386238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7756833,0.19439305,0.006264934,0.009807569,0.01206251,0.0017887008],"domain_scores_gemma":[0.45939416,0.46400124,0.016984278,0.039767668,0.018162346,0.0016903337],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20853336,0.00189335,0.005010674,0.0032316025,0.0017194746,0.009277209,0.0046982137,0.006181224,0.008532705],"category_scores_gemma":[0.66680753,0.0021815877,0.0038235046,0.0064078514,0.004950867,0.017409647,0.0073561426,0.012563979,0.005601751],"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":[0.00048679547,0.00018524662,0.016934402,0.0035545311,0.0032156487,0.0003190699,0.0037343116,0.0147997355,0.00068597955,0.29267958,0.061911933,0.60149276],"study_design_scores_gemma":[0.00021550417,0.00023809749,0.0054860646,0.0040663294,0.0006958769,0.00032053856,0.0009257774,0.035051543,0.0014032656,0.8602237,0.09112984,0.00024343417],"about_ca_topic_score_codex":0.004246216,"about_ca_topic_score_gemma":0.0045290915,"teacher_disagreement_score":0.79146665,"about_ca_system_score_codex":0.002235565,"about_ca_system_score_gemma":0.005325912,"threshold_uncertainty_score":0.97601926},"labels":[],"label_agreement":null},{"id":"W2790909449","doi":"10.1002/wics.110","title":"Likelihood inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","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 Toronto","funders":"","keywords":"Likelihood function; Likelihood principle; Inference; Empirical likelihood; Maximum likelihood; Restricted maximum likelihood; Marginal likelihood; Parametric statistics; Quasi-maximum likelihood; Computer science; Bayesian inference; Bayesian probability; Likelihood-ratio test; Econometrics; Mathematics; Artificial intelligence; Statistics","score_opus":0.11955205169657247,"score_gpt":0.4714508139783933,"score_spread":0.3518987622818208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790909449","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":[0.0004675658,0.011793027,0.95584905,0.0022259862,0.0004408098,0.00015032136,0.0008586047,0.0009381879,0.027276436],"genre_scores_gemma":[0.080436915,0.053247035,0.8042477,0.0029711025,0.002938885,0.0010865513,0.005690175,0.0013907135,0.047990944],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99237144,0.004078253,0.00042651896,0.0010487604,0.0018989601,0.00017611375],"domain_scores_gemma":[0.98399943,0.010730991,0.0007248698,0.0021766126,0.0021913694,0.00017668308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009688513,0.0016507078,0.0025125972,0.004261473,0.00080834585,0.0050077913,0.004577209,0.002828082,0.034588303],"category_scores_gemma":[0.039402496,0.0009937657,0.0018657331,0.003586621,0.0024030919,0.0046059154,0.0032176601,0.0035180503,0.02386963],"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":[0.000045058398,0.00004626972,0.00079067703,0.0013707245,0.00030926763,0.00016983015,0.00012983558,0.022107838,0.00038258824,0.45103505,0.046262003,0.47735074],"study_design_scores_gemma":[0.000036358342,0.000024278339,0.0004375402,0.00089012645,0.00008124997,0.00042372133,0.000067998204,0.050680015,0.00086640596,0.7352894,0.21115218,0.000050759663],"about_ca_topic_score_codex":0.0017710355,"about_ca_topic_score_gemma":0.0012876948,"teacher_disagreement_score":0.034588303,"about_ca_system_score_codex":0.0017135392,"about_ca_system_score_gemma":0.0028291242,"threshold_uncertainty_score":0.115709424},"labels":[],"label_agreement":null},{"id":"W2791458756","doi":"10.2196/medinform.8960","title":"Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":174,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; University of Pennsylvania; National Institutes of Health; Pennsylvania Department of Health","keywords":"Missing data; Imputation (statistics); Health records; Computer science; Data science; Electronic health record; Data mining; Health care; Machine learning","score_opus":0.08677746776859628,"score_gpt":0.4334871571936644,"score_spread":0.3467096894250681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791458756","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":[0.014988471,0.0020851735,0.97486943,0.004452876,0.00012640795,0.0009405634,0.0012790996,0.0006104618,0.00064764795],"genre_scores_gemma":[0.121642895,0.0021631105,0.8708085,0.0009797481,0.00025068506,0.0016481325,0.0021154932,0.00016771123,0.00022367778],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.7957572,0.16838822,0.013418589,0.006478245,0.014775038,0.0011827078],"domain_scores_gemma":[0.48489636,0.42409915,0.03891189,0.03155181,0.01918129,0.0013595641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16180548,0.0015564257,0.002786629,0.0069064344,0.0026696078,0.005935527,0.0062353765,0.0029750622,0.0016158535],"category_scores_gemma":[0.38506773,0.0017863249,0.0039550485,0.012263527,0.003473086,0.008335262,0.005702194,0.005043277,0.0007409051],"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":[0.00045724583,0.0006800584,0.18531087,0.009158053,0.0033801333,0.00066944544,0.007679041,0.12601814,0.0022726927,0.07612578,0.023049545,0.565199],"study_design_scores_gemma":[0.0003459515,0.00088502996,0.047706015,0.009884449,0.0012617747,0.0014737057,0.006127028,0.43948603,0.010233125,0.441892,0.040024903,0.00067989656],"about_ca_topic_score_codex":0.0036912465,"about_ca_topic_score_gemma":0.003521664,"teacher_disagreement_score":0.16180548,"about_ca_system_score_codex":0.0028171155,"about_ca_system_score_gemma":0.009961076,"threshold_uncertainty_score":0.8557194},"labels":[],"label_agreement":null},{"id":"W2792754000","doi":"10.3103/s1066530719020017","title":"Asymptotic Theory for Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights","year":2019,"lang":"en","type":"preprint","venue":"Mathematical Methods of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Mathematics; Estimator; Statistics; Covariate; Generalized estimating equation; Longitudinal data; Inverse probability; Gee; Applied mathematics; Econometrics; Demography; Posterior probability; Bayesian probability","score_opus":0.22391204494280095,"score_gpt":0.4538299206569673,"score_spread":0.22991787571416636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792754000","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":[0.0010603223,0.00026458062,0.99787974,0.00020762281,0.000035148096,0.000016510558,0.00004198508,0.0000630919,0.0004309409],"genre_scores_gemma":[0.17651989,0.00433522,0.8052559,0.0011217799,0.0012945058,0.001985328,0.0010382265,0.00040926973,0.008039839],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9870459,0.00865466,0.00047281326,0.0010403985,0.0024463828,0.00033986662],"domain_scores_gemma":[0.9098972,0.075318895,0.0033719535,0.0063312342,0.0045928373,0.00048782487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03752273,0.0014678856,0.0024195358,0.0032859412,0.0008359656,0.0019405182,0.004415642,0.0022943965,0.00562726],"category_scores_gemma":[0.13509831,0.0012114363,0.0023540985,0.0029272747,0.0039552287,0.0055779256,0.0033422923,0.0054291314,0.0012351555],"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":[0.000048202754,0.00006357069,0.002130638,0.00034384805,0.00022938751,0.00017186411,0.00029997824,0.06673069,0.00068587955,0.8809158,0.0023756034,0.046004597],"study_design_scores_gemma":[0.00003346872,0.00005889425,0.0008648795,0.000083039784,0.00005132088,0.000157507,0.00005632724,0.36371297,0.00035199863,0.63061154,0.0039841714,0.00003393475],"about_ca_topic_score_codex":0.0024193525,"about_ca_topic_score_gemma":0.0017420275,"teacher_disagreement_score":0.03752273,"about_ca_system_score_codex":0.0017367037,"about_ca_system_score_gemma":0.0023020566,"threshold_uncertainty_score":0.19844157},"labels":[],"label_agreement":null},{"id":"W2795014879","doi":"10.1007/s00184-018-0656-1","title":"Shrinkage estimation in linear mixed models for longitudinal data","year":2018,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Lasso (programming language); Covariate; Mathematics; Penalty method; Shrinkage; Linear model; Statistics; Random effects model; Mixed model; Mathematical optimization; Applied mathematics; Computer science","score_opus":0.2786613555191302,"score_gpt":0.4533145887574207,"score_spread":0.17465323323829052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795014879","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":[0.001547486,0.0008706934,0.9966413,0.00049643696,0.00004698743,0.000025115349,0.00006879785,0.000107878375,0.00019532535],"genre_scores_gemma":[0.12087909,0.005787938,0.8615372,0.00072511233,0.0010332939,0.0014930128,0.0011097081,0.00054723915,0.0068873717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9773392,0.018216385,0.00088738196,0.0019827615,0.0012078388,0.00036643643],"domain_scores_gemma":[0.837644,0.14892246,0.0043190024,0.0057273936,0.0027302431,0.0006569223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04210999,0.0023545565,0.0037498216,0.0029429162,0.0012060255,0.0031412079,0.005102882,0.0039648153,0.0036232825],"category_scores_gemma":[0.16454688,0.0032334435,0.0031044893,0.0041312836,0.005573634,0.005492451,0.004635235,0.007942145,0.0010299211],"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":[0.00021679848,0.00012204012,0.0024851032,0.0011745514,0.00077888137,0.00026219012,0.0007549596,0.14674476,0.0011272317,0.7496695,0.004472017,0.092191964],"study_design_scores_gemma":[0.000054766828,0.00004481322,0.0004526318,0.00011928159,0.00010934387,0.000102280545,0.000049785984,0.38996738,0.00034904014,0.6058871,0.002814443,0.000049219758],"about_ca_topic_score_codex":0.0068077636,"about_ca_topic_score_gemma":0.0066318177,"teacher_disagreement_score":0.04210999,"about_ca_system_score_codex":0.0021045562,"about_ca_system_score_gemma":0.0038353007,"threshold_uncertainty_score":0.22270155},"labels":[],"label_agreement":null},{"id":"W2799699001","doi":"10.1007/s11136-018-1861-0","title":"A systematic review of the quality of reporting of simulation studies about methods for the analysis of complex longitudinal patient-reported outcomes data","year":2018,"lang":"en","type":"review","venue":"Quality of Life Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Providence Health Care; University of Calgary; Western University; University of Manitoba","funders":"Canadian Institutes of Health Research","keywords":"Quality of Life Research; Public health; Quality (philosophy); Longitudinal data; Systematic review; MEDLINE; Medicine; Psychology; Data science; Computer science; Data mining; Nursing; Political science","score_opus":0.936527074074851,"score_gpt":0.7564366288651042,"score_spread":0.18009044520974682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799699001","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008780397,0.9939115,0.0025239608,0.0007760254,0.00032841845,0.00063418946,0.0006331168,0.000029035597,0.0002856096],"genre_scores_gemma":[0.028786475,0.9580874,0.007468055,0.0019613344,0.000349204,0.0025095567,0.0006277279,0.000058562004,0.00015167188],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.84469,0.081760705,0.052676648,0.0055065393,0.014520242,0.00084595644],"domain_scores_gemma":[0.49928802,0.44336677,0.036564376,0.006321853,0.013566871,0.0008921782],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11359563,0.0027941838,0.015642613,0.0106149465,0.0010986809,0.0062888674,0.00430735,0.00454066,0.0052099884],"category_scores_gemma":[0.48005357,0.0022734054,0.019569004,0.008762282,0.0032722016,0.004958809,0.0039659133,0.005119463,0.00038504234],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069270807,0.000017826635,0.00072105846,0.9156371,0.03571291,0.000062375126,0.0002624481,0.00042962193,0.00010854176,0.0010940261,0.001524077,0.04373725],"study_design_scores_gemma":[0.0008724617,0.00023524431,0.0020272327,0.828781,0.14844044,0.00028627357,0.0001732346,0.0004234325,0.0002717854,0.0028790566,0.015528783,0.000081106744],"about_ca_topic_score_codex":0.011747212,"about_ca_topic_score_gemma":0.017229732,"teacher_disagreement_score":0.8864044,"about_ca_system_score_codex":0.00866621,"about_ca_system_score_gemma":0.022199916,"threshold_uncertainty_score":0.6007583},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"reporting","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["metaresearch"],"domain":"reporting","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2799810076","doi":"10.1111/insr.12263","title":"Vine Copulas for Imputation of Monotone Non‐response","year":2018,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian 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":"Université Laval; The Scarborough Hospital; University of Toronto","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":"Vine copula; Bivariate analysis; Mathematics; Joint probability distribution; Copula (linguistics); Imputation (statistics); Missing data; Econometrics; Statistics; Conditional probability distribution; Marginal distribution; Random variable","score_opus":0.07513017960498651,"score_gpt":0.4841740229055865,"score_spread":0.40904384330059995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799810076","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":[0.0016154848,0.00094620266,0.99625105,0.00033384052,0.000055896224,0.000047389265,0.00018584215,0.00011598945,0.00044829515],"genre_scores_gemma":[0.17154269,0.0065737003,0.80905116,0.0009856626,0.0007931064,0.0019801373,0.0023266484,0.0005745214,0.00617237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9787587,0.017289814,0.0007505901,0.0015891539,0.0012231092,0.00038865494],"domain_scores_gemma":[0.9157863,0.06922712,0.0039915126,0.0074527836,0.003035924,0.00050637906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036781035,0.0017316847,0.0040173596,0.0029415314,0.0007780515,0.0027248543,0.004284211,0.002469014,0.005839355],"category_scores_gemma":[0.118481934,0.0018868329,0.0037391235,0.004103022,0.00213064,0.0028692097,0.0030498656,0.005582603,0.0012370587],"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":[0.000119108554,0.00008896248,0.0055273287,0.00083070167,0.0015148283,0.00042211424,0.00057186227,0.23099114,0.00054671924,0.64674705,0.008841846,0.10379841],"study_design_scores_gemma":[0.000032350366,0.0000391323,0.0011535698,0.00022748338,0.00009335357,0.0000889287,0.000053449934,0.4975478,0.00025122572,0.49526098,0.005213363,0.00003836078],"about_ca_topic_score_codex":0.003699988,"about_ca_topic_score_gemma":0.002770187,"teacher_disagreement_score":0.036781035,"about_ca_system_score_codex":0.0016787666,"about_ca_system_score_gemma":0.00198379,"threshold_uncertainty_score":0.19451898},"labels":[],"label_agreement":null},{"id":"W2800451635","doi":"10.6000/1929-6029.2018.07.02.4","title":"Bayesian Analysis of Markov Based Logistic Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayes factor; Exponential function; Statistics; Mathematics; Bayes' theorem; Variable-order Bayesian network; Bayes estimator; Function (biology); Applied mathematics; Logistic regression; Markov model; Bayesian inference; Econometrics; Computer science; Markov chain","score_opus":0.2158784806776032,"score_gpt":0.5573796766179351,"score_spread":0.3415011959403319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800451635","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":[0.02845463,0.0011384451,0.9663173,0.0008171092,0.00006900124,0.000071364775,0.00048337792,0.00022333558,0.0024255961],"genre_scores_gemma":[0.7709018,0.0053917556,0.20122291,0.00047540755,0.0005441664,0.0006405305,0.0031258566,0.0002601741,0.01743737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99675035,0.0018049554,0.000120817786,0.00049480336,0.0005659595,0.00026313664],"domain_scores_gemma":[0.9896702,0.008397214,0.0006963733,0.00036547912,0.0006666224,0.0002041432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006212457,0.00067689066,0.0016106119,0.0018745388,0.00073261856,0.0016111663,0.0018451263,0.0011786534,0.0051257326],"category_scores_gemma":[0.022695938,0.0007191786,0.0016001696,0.0016030989,0.0009837662,0.0026788802,0.0017677315,0.0024049743,0.0007552843],"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":[0.00028173198,0.000121989026,0.009655201,0.00032595155,0.00031726484,0.00044169198,0.00041253184,0.5030254,0.0011168096,0.39654887,0.0060253562,0.081727155],"study_design_scores_gemma":[0.000015389314,0.0000251236,0.000842208,0.00003130385,0.000028509658,0.000067824665,0.000021023587,0.90847516,0.00011658399,0.08916515,0.001186834,0.00002476107],"about_ca_topic_score_codex":0.011252515,"about_ca_topic_score_gemma":0.007860741,"teacher_disagreement_score":0.011252515,"about_ca_system_score_codex":0.0013756355,"about_ca_system_score_gemma":0.001904953,"threshold_uncertainty_score":0.032855034},"labels":[],"label_agreement":null},{"id":"W2800840470","doi":"10.1016/j.jmva.2018.04.001","title":"Small area estimation with multiple covariates measured with errors: A nested error linear regression approach of combining multiple surveys","year":2018,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Small area estimation; Estimator; Statistics; Mathematics; Linear regression; Mean squared error; Regression analysis; Econometrics; Estimation; Regression","score_opus":0.12216047879963632,"score_gpt":0.3569225119051644,"score_spread":0.23476203310552807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800840470","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":[0.007909297,0.00013494748,0.991442,0.00009059107,0.000027898413,0.00004201181,0.000057646652,0.00005594391,0.00023954769],"genre_scores_gemma":[0.25372607,0.0004610154,0.74126494,0.00018719395,0.0001952116,0.00051985955,0.0005566898,0.000099385834,0.0029896214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95185244,0.037845276,0.0011735706,0.0054907924,0.0026693358,0.0009684515],"domain_scores_gemma":[0.89063644,0.08301617,0.006858445,0.0139594795,0.004285882,0.0012436341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048489753,0.0015179883,0.004799001,0.0021236187,0.0010969897,0.0027652357,0.006620089,0.0026509522,0.002406581],"category_scores_gemma":[0.13840364,0.0024535472,0.0039274846,0.004301779,0.0027367196,0.0062939893,0.0054287533,0.0034158763,0.0005162304],"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":[0.00060546456,0.0006507672,0.053123888,0.00058944395,0.00309805,0.0006920593,0.0020371308,0.46905228,0.0017675932,0.3186738,0.0031050867,0.14660452],"study_design_scores_gemma":[0.000071510454,0.000247819,0.0045116628,0.000081370454,0.00032678028,0.0001586161,0.00016996101,0.84843636,0.00053507014,0.14307518,0.0023152733,0.000070342394],"about_ca_topic_score_codex":0.01372629,"about_ca_topic_score_gemma":0.014577639,"teacher_disagreement_score":0.048489753,"about_ca_system_score_codex":0.0013146632,"about_ca_system_score_gemma":0.0027510945,"threshold_uncertainty_score":0.25644135},"labels":[],"label_agreement":null},{"id":"W2801251405","doi":"10.1002/sim.7680","title":"Time series analysis of fMRI data: Spatial modelling and Bayesian computation","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Victoria","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Computation; Approximate Bayesian computation; Bayesian probability; Bayes' theorem; Statistical inference; Algorithm; Inference; Bayesian inference; Statistical parametric mapping; Parametric statistics; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.07775302964608646,"score_gpt":0.4042363175240131,"score_spread":0.3264832878779267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801251405","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":[0.0018245026,0.00037423384,0.99681807,0.0002949865,0.00001452315,0.000013894877,0.000039764618,0.00012684174,0.0004931798],"genre_scores_gemma":[0.17896879,0.0030942112,0.8143055,0.00033651898,0.0001871323,0.0002829375,0.00039669216,0.0002705154,0.002157648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983272,0.00079880666,0.000092482784,0.00024590586,0.00047046968,0.00006511794],"domain_scores_gemma":[0.99164104,0.0069227265,0.000555041,0.0003949227,0.00038828028,0.000097989905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039559784,0.0007332933,0.0012520392,0.0014801592,0.0005380902,0.002197801,0.0019400045,0.0019151843,0.0019167254],"category_scores_gemma":[0.021325225,0.00102496,0.0012992647,0.002093979,0.0017884022,0.003012197,0.0016387634,0.0024568522,0.0006027047],"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":[0.00004126248,0.000035414643,0.0015165083,0.00022450191,0.000102219645,0.000102772115,0.0001770339,0.6934745,0.0020738703,0.22493665,0.002085742,0.07522952],"study_design_scores_gemma":[0.0000030357542,0.000005586323,0.00018431457,0.000018574943,0.000006941545,0.000029825718,0.000009214402,0.9402241,0.00024817875,0.058391698,0.0008684739,0.000010128614],"about_ca_topic_score_codex":0.011621141,"about_ca_topic_score_gemma":0.00861286,"teacher_disagreement_score":0.011621141,"about_ca_system_score_codex":0.0015702372,"about_ca_system_score_gemma":0.0017440801,"threshold_uncertainty_score":0.023106992},"labels":[],"label_agreement":null},{"id":"W2802435502","doi":"10.5539/ijsp.v7n5p86","title":"Testing Simultaneous Marginal Homogeneity for Clustered Matched-Pair Multinomial Data","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","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":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Homogeneity (statistics); Categorical variable; Multinomial distribution; Mathematics; Statistics; Econometrics; Statistical hypothesis testing; Ordinal data; Stochastic ordering; Monte Carlo method","score_opus":0.13779369467881458,"score_gpt":0.4089543162245287,"score_spread":0.27116062154571413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802435502","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":[0.21025774,0.00025698118,0.78660893,0.00019330977,0.0000560715,0.0004316663,0.0005407678,0.0004149292,0.0012396123],"genre_scores_gemma":[0.8089584,0.00012359538,0.18854246,0.0001133709,0.0000616555,0.000722501,0.0009498799,0.00007986374,0.00044846645],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9539215,0.030018765,0.0020950534,0.007817326,0.0050018886,0.0011453766],"domain_scores_gemma":[0.7110144,0.24921185,0.011597955,0.021474335,0.00500179,0.0016996195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047448043,0.00095545786,0.0027406174,0.0032167837,0.0014220171,0.0021288139,0.003984311,0.0016511211,0.004459044],"category_scores_gemma":[0.21061611,0.00069054705,0.0031534682,0.0036329993,0.00457429,0.0036148264,0.0040282286,0.0021070603,0.0006476007],"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":[0.005620287,0.0012190338,0.2955178,0.0014959043,0.0057427306,0.0036616263,0.005121135,0.121742964,0.0119693875,0.21467786,0.0034011723,0.32983017],"study_design_scores_gemma":[0.00049299205,0.0015458581,0.07800119,0.00015810817,0.00075953465,0.0014235205,0.0014356592,0.61788297,0.011568589,0.28316617,0.003264823,0.00030065628],"about_ca_topic_score_codex":0.0016025665,"about_ca_topic_score_gemma":0.0014521879,"teacher_disagreement_score":0.047448043,"about_ca_system_score_codex":0.001049667,"about_ca_system_score_gemma":0.002002911,"threshold_uncertainty_score":0.25093222},"labels":[],"label_agreement":null},{"id":"W2802826956","doi":"10.1111/rssc.12279","title":"A Non-Linear Model for Censored and Mismeasured Time Varying Covariates in Survival Models, with Applications in Human Immunodeficiency Virus and Acquired Immune Deficiency Syndrome Studies","year":2018,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"College of Staten Island, City University of New York; Research Foundation of The City University of New York; City University of New York; National Science Foundation","keywords":"Covariate; Proportional hazards model; Statistics; Linear model; Econometrics; Survival analysis; Mathematics","score_opus":0.04311923441272773,"score_gpt":0.3284523403737827,"score_spread":0.28533310596105493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802826956","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":[0.004449737,0.00079760293,0.99329925,0.0007926347,0.000057953817,0.000038375154,0.000113616305,0.00007953344,0.00037116255],"genre_scores_gemma":[0.34896916,0.0038456067,0.62691814,0.0011155348,0.0006797049,0.0012721948,0.0010752057,0.00030763546,0.015816785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98717415,0.010540279,0.000298076,0.0010841619,0.0006649332,0.00023845874],"domain_scores_gemma":[0.90059584,0.09212575,0.0034496407,0.0018533303,0.0014748808,0.0005005287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030466596,0.001355678,0.001865149,0.0018037603,0.00086828956,0.0018990962,0.003346547,0.0028345922,0.0045517324],"category_scores_gemma":[0.06571619,0.0010191196,0.0025143535,0.0023570086,0.0026253997,0.0031963077,0.0024614458,0.004655508,0.0007783867],"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":[0.00013897188,0.000085319196,0.0052597583,0.00038191854,0.00027761087,0.00048030994,0.00044341836,0.4171264,0.00050431764,0.53606606,0.003246314,0.035989657],"study_design_scores_gemma":[0.000029105842,0.00008029096,0.00085537403,0.00008793631,0.00006473439,0.00013576008,0.00004916417,0.7571178,0.0001987749,0.23768926,0.0036474473,0.000044294386],"about_ca_topic_score_codex":0.007318929,"about_ca_topic_score_gemma":0.0076288446,"teacher_disagreement_score":0.030466596,"about_ca_system_score_codex":0.0019001097,"about_ca_system_score_gemma":0.0019645744,"threshold_uncertainty_score":0.1611247},"labels":[],"label_agreement":null},{"id":"W2802842452","doi":"10.1002/cjs.11351","title":"Combining ROC curves using MAMSE weighted distributions","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Receiver operating characteristic; Statistics; Monte Carlo method; Mathematics; Variable (mathematics)","score_opus":0.08642992134130285,"score_gpt":0.357319782066272,"score_spread":0.2708898607249692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802842452","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":[0.015963074,0.001250808,0.9805697,0.00031960505,0.00007990371,0.00023519028,0.00022644488,0.0004508723,0.000904433],"genre_scores_gemma":[0.42227417,0.0014442876,0.57117164,0.00039378335,0.0004011204,0.0011488249,0.0011218261,0.0003114352,0.0017328518],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9652138,0.02464365,0.0018283037,0.003537534,0.0042452435,0.00053150806],"domain_scores_gemma":[0.847833,0.12765685,0.006176933,0.008724249,0.008813801,0.000795181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057734974,0.0021592632,0.0034160858,0.011723091,0.0006774634,0.004949625,0.0024910246,0.0026911073,0.0028723394],"category_scores_gemma":[0.20097345,0.0012676554,0.0035051135,0.005327382,0.0018017774,0.0049794153,0.0040557226,0.0033025104,0.0010055744],"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":[0.0010524396,0.00022135387,0.03279309,0.0009216945,0.0026608,0.0006595543,0.0007497988,0.38296118,0.0029160294,0.08942734,0.0044637253,0.48117298],"study_design_scores_gemma":[0.0000772876,0.000333426,0.0056157326,0.00022168361,0.0002572372,0.00039674883,0.00011403429,0.8482962,0.0015190716,0.13832225,0.004712666,0.0001335382],"about_ca_topic_score_codex":0.0021695802,"about_ca_topic_score_gemma":0.0011988521,"teacher_disagreement_score":0.057734974,"about_ca_system_score_codex":0.001591169,"about_ca_system_score_gemma":0.0011338358,"threshold_uncertainty_score":0.3053354},"labels":[],"label_agreement":null},{"id":"W2804923947","doi":"10.1109/wts.2018.8363929","title":"On improving imputation accuracy of LTE spectrum measurements data","year":2018,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Imputation (statistics); Univariate; Computer science; Kalman filter; Multivariate statistics; Missing data; Statistics; Data mining; Artificial intelligence; Mathematics; Machine learning","score_opus":0.25369175481901846,"score_gpt":0.44703598027567704,"score_spread":0.19334422545665858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804923947","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":[0.017954381,0.0006257014,0.9787385,0.00052881334,0.000126937,0.000037682836,0.00045918918,0.00080445764,0.00072433136],"genre_scores_gemma":[0.40430453,0.0009291036,0.5885785,0.0005062357,0.00052914803,0.0001577685,0.0028195132,0.00026012192,0.0019149946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99123454,0.005567187,0.0004953018,0.0012380834,0.0010993576,0.0003653978],"domain_scores_gemma":[0.9483678,0.036967795,0.0025090661,0.0074629514,0.0042233,0.0004692157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023935508,0.0009716256,0.002005409,0.0013450679,0.0010607748,0.0016500808,0.002581351,0.0015290296,0.0016693106],"category_scores_gemma":[0.07040163,0.0006318095,0.0016154328,0.0026940769,0.0007953395,0.002376762,0.00181277,0.0025668666,0.0009420441],"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":[0.0009603201,0.000255776,0.043676507,0.00034018376,0.0009780694,0.0002786967,0.00042609556,0.443743,0.003249014,0.024098832,0.0101311505,0.47186244],"study_design_scores_gemma":[0.00003419065,0.00007457028,0.0052725724,0.00006895248,0.000091989496,0.00011529807,0.000056132016,0.9723231,0.0027575237,0.016131394,0.0030325097,0.000041789226],"about_ca_topic_score_codex":0.0065808846,"about_ca_topic_score_gemma":0.007659986,"teacher_disagreement_score":0.023935508,"about_ca_system_score_codex":0.0007505464,"about_ca_system_score_gemma":0.001985542,"threshold_uncertainty_score":0.12658459},"labels":[],"label_agreement":null},{"id":"W2807335968","doi":"10.1155/2018/1581979","title":"Mixed Effects Models with Censored Covariates, with Applications in HIV/AIDS Studies","year":2018,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Econometrics; Missing data; Inference; Statistics; Mixed model; Survival analysis; Longitudinal data; Mathematics; Computer science; Data mining; Artificial intelligence","score_opus":0.07282324793261316,"score_gpt":0.352778102447665,"score_spread":0.27995485451505187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807335968","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":[0.00089949556,0.026169907,0.9668102,0.00208951,0.00032551299,0.000071066475,0.00032020884,0.00017886907,0.003135259],"genre_scores_gemma":[0.06455665,0.06618589,0.8550614,0.0024089399,0.0027190186,0.0011142868,0.0009672074,0.00035111676,0.006635486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9898301,0.008056971,0.0003801784,0.00071861106,0.00087125425,0.00014291143],"domain_scores_gemma":[0.9633461,0.03272449,0.0017146864,0.00092162576,0.0010027761,0.00029029034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01601191,0.0018764518,0.0021325478,0.0029676997,0.0010133273,0.002456352,0.0031914134,0.0037702196,0.005988533],"category_scores_gemma":[0.051227186,0.0012453693,0.002646912,0.005865636,0.0024989995,0.0036106242,0.0027872755,0.0058944128,0.0018173376],"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":[0.000052461834,0.000053505788,0.0016204925,0.0012881818,0.000288865,0.00037291166,0.0005323407,0.025138145,0.00025047507,0.8805779,0.011058126,0.07876653],"study_design_scores_gemma":[0.000029823326,0.00005202352,0.00043891728,0.0005129131,0.0001193757,0.0002335391,0.00007861154,0.05537429,0.00015743263,0.90318245,0.03977189,0.000048763934],"about_ca_topic_score_codex":0.004533858,"about_ca_topic_score_gemma":0.004678999,"teacher_disagreement_score":0.01601191,"about_ca_system_score_codex":0.0014724201,"about_ca_system_score_gemma":0.0021434051,"threshold_uncertainty_score":0.08468008},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W2807355199","doi":"10.5539/ijsp.v7n4p27","title":"Extended Marginal Homogeneity Model Based on Complementary Log-Log Transform for Square Tables","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contingency table; Mathematics; Homogeneity (statistics); Extension (predicate logic); Statistics; Logit; Log-linear model; Square (algebra); Column (typography); Econometrics; Combinatorics; Computer science; Linear model; Geometry","score_opus":0.07488447368805586,"score_gpt":0.38739421098457055,"score_spread":0.3125097372965147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807355199","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":[0.023786847,0.00037636983,0.96955127,0.0005492325,0.00008618145,0.00015887743,0.0007428973,0.00049160776,0.004256751],"genre_scores_gemma":[0.76231366,0.0007183226,0.21520558,0.0005099388,0.0003934758,0.00095055613,0.0015739469,0.00034937504,0.017985068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937257,0.0029735342,0.00021916749,0.001301857,0.0012249539,0.0005546987],"domain_scores_gemma":[0.98384297,0.010854453,0.0011111932,0.0019699023,0.0018541086,0.00036741263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00906082,0.00072448223,0.0014617153,0.0021790948,0.00094851153,0.0024573817,0.0037707263,0.0013101915,0.014194428],"category_scores_gemma":[0.032338705,0.00061973644,0.0022079127,0.003054327,0.0025656193,0.004052555,0.0020908068,0.0024039827,0.0021675806],"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":[0.00029858056,0.00008975331,0.0073836492,0.0001819316,0.00017049671,0.00083261676,0.0012474098,0.074291125,0.0014661177,0.8570017,0.00681902,0.05021747],"study_design_scores_gemma":[0.00005609905,0.00009344818,0.0028696344,0.000036311223,0.00007700676,0.00057064893,0.00017250233,0.4658923,0.0006568724,0.5225896,0.006914497,0.00007103307],"about_ca_topic_score_codex":0.007353149,"about_ca_topic_score_gemma":0.003605004,"teacher_disagreement_score":0.014194428,"about_ca_system_score_codex":0.0020587035,"about_ca_system_score_gemma":0.0015154023,"threshold_uncertainty_score":0.047918737},"labels":[],"label_agreement":null},{"id":"W2808363984","doi":"10.1002/ecm.1309","title":"Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference","year":2018,"lang":"en","type":"review","venue":"Ecological Monographs","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":404,"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 Calgary","funders":"Seventh Framework Programme; Engineering and Physical Sciences Research Council; University of Melbourne; Bundesministerium für Bildung und Forschung; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Deutsche Forschungsgemeinschaft; Australian Research Council; Alexander von Humboldt-Stiftung","keywords":"Variance (accounting); Model selection; Bayesian inference; Covariance; Context (archaeology); Bayesian probability; Computer science; Ecology; Range (aeronautics); Inference; Econometrics; Mathematics; Statistics; Artificial intelligence","score_opus":0.17902802682896768,"score_gpt":0.41108136183373206,"score_spread":0.23205333500476438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808363984","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006486547,0.7824931,0.20651548,0.0060246666,0.00085064734,0.00004855617,0.00016479814,0.00017116158,0.0030829145],"genre_scores_gemma":[0.027129425,0.87840354,0.085914135,0.0019707358,0.0052860323,0.00017945559,0.00022450632,0.00013813585,0.00075404986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943169,0.0028872935,0.0004844644,0.0007317752,0.0014576985,0.00012189804],"domain_scores_gemma":[0.9724788,0.023439122,0.0008998037,0.0008807739,0.002049012,0.00025237337],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016478915,0.0023449957,0.0040043117,0.0073830825,0.0009139177,0.0040482907,0.0045474605,0.0032591748,0.0017245002],"category_scores_gemma":[0.023363333,0.001311666,0.002846792,0.007866387,0.0053560766,0.007533467,0.0024494042,0.0059671747,0.0008589722],"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":[0.00006077521,0.00011645933,0.0015107472,0.010499582,0.00093347876,0.00019940485,0.00041471765,0.044504784,0.0005747816,0.40716445,0.020609403,0.51341146],"study_design_scores_gemma":[0.000025135012,0.00014489052,0.0019613148,0.0072657852,0.00040493658,0.00048832025,0.00015482494,0.06813534,0.0007151128,0.75820374,0.16223322,0.00026735818],"about_ca_topic_score_codex":0.0057619265,"about_ca_topic_score_gemma":0.0030773485,"teacher_disagreement_score":0.9835211,"about_ca_system_score_codex":0.0031909447,"about_ca_system_score_gemma":0.0031455322,"threshold_uncertainty_score":0.08714986},"labels":[],"label_agreement":null},{"id":"W2808396915","doi":"10.1002/sim.7841","title":"Tweedie family of generalized linear models with distribution‐free random effects for skewed longitudinal data","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"University of New Brunswick","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Random effects model; Skewness; Generalized linear mixed model; Covariance; Mathematics; Econometrics; Linear model; Mixed model; Statistics; Flexibility (engineering); Population; Computer science; Medicine","score_opus":0.13676609651227098,"score_gpt":0.4275039299316327,"score_spread":0.29073783341936177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808396915","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":[0.00035877712,0.00036286647,0.99829036,0.0002648261,0.00008790661,0.00011599523,0.00018081233,0.00013226242,0.00020612408],"genre_scores_gemma":[0.023298493,0.0029448404,0.963037,0.00061170786,0.0003687222,0.0035895468,0.0008667588,0.0002837972,0.0049991244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97935355,0.01598406,0.00086812535,0.0017921008,0.0016686718,0.00033347888],"domain_scores_gemma":[0.9596441,0.03470002,0.0014734016,0.0025617841,0.0013243988,0.00029623427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03395786,0.0026563662,0.0035997066,0.004015729,0.0010544083,0.0032669895,0.0072798464,0.0033644952,0.009507601],"category_scores_gemma":[0.08714507,0.0023067663,0.0047259596,0.0042938064,0.002749597,0.0050551393,0.0033847305,0.009154061,0.0032042498],"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":[0.0001436248,0.000103553495,0.0019482863,0.0008092418,0.0010022679,0.00072710303,0.00059280195,0.066208586,0.00070711126,0.68706423,0.009696996,0.23099625],"study_design_scores_gemma":[0.00014418695,0.00014637143,0.00095856504,0.00029920173,0.00025788028,0.00046505142,0.00008199175,0.38133034,0.0005043461,0.5840985,0.031577013,0.00013657141],"about_ca_topic_score_codex":0.0043415637,"about_ca_topic_score_gemma":0.004823979,"teacher_disagreement_score":0.03395786,"about_ca_system_score_codex":0.0016668076,"about_ca_system_score_gemma":0.0035663552,"threshold_uncertainty_score":0.17958844},"labels":[],"label_agreement":null},{"id":"W2808447433","doi":"10.1002/sim.7845","title":"Estimation for zero‐inflated beta‐binomial regression model with missing response data","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overdispersion; Statistics; Missing data; Mathematics; Negative binomial distribution; Expectation–maximization algorithm; Count data; Zero-inflated model; Quasi-likelihood; Mean squared error; Regression analysis; Econometrics; Maximum likelihood; Poisson regression; Poisson distribution","score_opus":0.16683890694722295,"score_gpt":0.47400700736848506,"score_spread":0.3071681004212621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808447433","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":[0.0068718824,0.00021177852,0.99246687,0.00010911284,0.000011880326,0.00002497245,0.000054814653,0.00008554675,0.00016317941],"genre_scores_gemma":[0.30371454,0.001738676,0.6899432,0.00034274915,0.00014466193,0.0005860222,0.0009827184,0.00015338151,0.0023940634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9873018,0.009617415,0.00040916816,0.0013288516,0.0010049111,0.00033791887],"domain_scores_gemma":[0.95092493,0.04217597,0.002819181,0.0024350695,0.0013875501,0.00025733974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03082645,0.0010551277,0.0024919228,0.0014319848,0.00055037916,0.0014726593,0.0036334742,0.002267523,0.0017882055],"category_scores_gemma":[0.064352,0.0009712291,0.0018033131,0.0019363251,0.0016874397,0.0030448183,0.0018257804,0.0030606557,0.0006395287],"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":[0.00047482894,0.00016933438,0.01648637,0.0010437901,0.00070133153,0.0010625257,0.00082541583,0.55818945,0.005317956,0.23384039,0.0028851514,0.17900345],"study_design_scores_gemma":[0.000042727712,0.000103041326,0.0018323244,0.00008038531,0.00008244288,0.0003610076,0.00006771549,0.8940758,0.0012355187,0.1005554,0.0015155016,0.000048126494],"about_ca_topic_score_codex":0.0021997772,"about_ca_topic_score_gemma":0.0014759303,"teacher_disagreement_score":0.03082645,"about_ca_system_score_codex":0.0007285209,"about_ca_system_score_gemma":0.0012359739,"threshold_uncertainty_score":0.16302782},"labels":[],"label_agreement":null},{"id":"W2809856602","doi":"10.1111/anzs.12234","title":"Semi‐parametric small‐area estimation by combining time‐series and cross‐sectional data methods","year":2018,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"University of Manitoba; Manitoba Health; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Small area estimation; Statistics; Estimation; Mathematics; Covariate; Generalized linear mixed model; Spline (mechanical); Linear model; Generalized linear model; Scale (ratio); Parametric statistics; Econometrics; Population; Regression analysis; Linear regression; Geography; Medicine; Cartography","score_opus":0.13143028913928637,"score_gpt":0.43330651755798205,"score_spread":0.30187622841869566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809856602","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":[0.004875706,0.00015319603,0.99428326,0.00009595305,0.000037967595,0.00007623257,0.000090641086,0.00014795078,0.00023905854],"genre_scores_gemma":[0.30141813,0.00092705514,0.6894801,0.00026177533,0.0003535422,0.0017675913,0.001613421,0.00030441422,0.0038739524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9717862,0.02251796,0.0009656438,0.0029512246,0.0013838424,0.00039506308],"domain_scores_gemma":[0.86652654,0.112145744,0.0069322176,0.008101447,0.005497757,0.00079624954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03655229,0.0013925544,0.0022884957,0.003978659,0.0007162416,0.0024887447,0.0041865394,0.0016693074,0.005628661],"category_scores_gemma":[0.09068711,0.0015644088,0.0045650518,0.004238841,0.0018823462,0.003459142,0.0038910192,0.0031603165,0.001097448],"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":[0.00031006982,0.0004268735,0.043258302,0.0009403657,0.0025413253,0.0006880604,0.0011330592,0.51494616,0.0013401042,0.112790234,0.0048034247,0.31682202],"study_design_scores_gemma":[0.000017732944,0.00011113106,0.003227916,0.00006877442,0.00008232735,0.00006945304,0.00011186775,0.9525619,0.00024122889,0.040890843,0.0025788122,0.000037946807],"about_ca_topic_score_codex":0.007258412,"about_ca_topic_score_gemma":0.0060456707,"teacher_disagreement_score":0.03655229,"about_ca_system_score_codex":0.0009022098,"about_ca_system_score_gemma":0.0021967513,"threshold_uncertainty_score":0.1933093},"labels":[],"label_agreement":null},{"id":"W2810172697","doi":"10.1111/anzs.12235","title":"Generalised quasi‐likelihood inference in a semi‐parametric binary dynamic mixed logit model","year":2018,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","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":"Carleton University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Parametric statistics; Context (archaeology); Quasi-likelihood; Statistics; Parametric model; Binary data; Inference; Consistency (knowledge bases); Restricted maximum likelihood; Count data; Semiparametric model; Random effects model; Econometrics; Estimation theory; Binary number; Computer science; Artificial intelligence","score_opus":0.07063219302783064,"score_gpt":0.37903191850335644,"score_spread":0.3083997254755258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810172697","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":[0.013260427,0.000158263,0.9852562,0.00033517828,0.000018190674,0.000049931485,0.000105906765,0.000108531756,0.00070740824],"genre_scores_gemma":[0.5620564,0.00035108344,0.43274772,0.0002588692,0.00006554087,0.00045227297,0.0004423627,0.00010850369,0.0035173174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98267454,0.01537513,0.00026097384,0.0005866284,0.0008579269,0.0002447812],"domain_scores_gemma":[0.9075566,0.08512818,0.0027295041,0.0024485032,0.001778753,0.00035852368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02475333,0.00061630836,0.0016652532,0.0012495595,0.00051950786,0.0019680762,0.0037119037,0.0018557999,0.0059524206],"category_scores_gemma":[0.07724742,0.0009873812,0.0014050216,0.0017982937,0.0024812412,0.0026203415,0.0026292084,0.0024590571,0.00061934436],"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":[0.00015596484,0.000090607755,0.0020447234,0.00026206608,0.00013354585,0.0002840029,0.00030161854,0.6169045,0.00040522506,0.3506963,0.0010800876,0.027641436],"study_design_scores_gemma":[0.000024156707,0.000030136993,0.0003150534,0.000020877316,0.000010251435,0.00003086589,0.00002675207,0.91774404,0.00008512443,0.08116525,0.00053245004,0.0000149978105],"about_ca_topic_score_codex":0.0067127217,"about_ca_topic_score_gemma":0.0049494673,"teacher_disagreement_score":0.02475333,"about_ca_system_score_codex":0.0016423005,"about_ca_system_score_gemma":0.0013621924,"threshold_uncertainty_score":0.13090968},"labels":[],"label_agreement":null},{"id":"W2810197347","doi":"10.1016/j.cmpb.2018.06.014","title":"Measuring the Impact of Nonignorable Missingness Using the R Package isni","year":2018,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Canada; Simon Fraser University","funders":"National Cancer Institute","keywords":"Missing data; Computer science; R package; Statistics; Econometrics; Mathematics; Machine learning; Computational science","score_opus":0.2526076017060687,"score_gpt":0.4803102280519936,"score_spread":0.22770262634592486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810197347","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":[0.1085182,0.0022620333,0.85671747,0.0013044127,0.0004644296,0.00055937865,0.01234418,0.013299109,0.0045308825],"genre_scores_gemma":[0.31429377,0.00051255955,0.66398156,0.0006726064,0.00009627357,0.0009939326,0.011708313,0.0063642785,0.0013768424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8987455,0.0674165,0.007912339,0.0102747185,0.014357349,0.0012937002],"domain_scores_gemma":[0.64104617,0.28225008,0.012794464,0.04864376,0.013064456,0.0022010652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10199327,0.0028031773,0.0033852584,0.004068062,0.0016880318,0.0046692383,0.0045949365,0.0018709984,0.0073478646],"category_scores_gemma":[0.38959035,0.0014068345,0.0042307978,0.0076520094,0.0026479003,0.0033026233,0.0044305944,0.0042016273,0.002726826],"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":[0.0035404859,0.0006785908,0.3227878,0.0070702136,0.018295335,0.0015659367,0.0040305606,0.16916095,0.021293318,0.07360625,0.05089584,0.32707474],"study_design_scores_gemma":[0.0008673005,0.0017086419,0.10043768,0.0011738291,0.007014085,0.0032514543,0.001277294,0.59985906,0.04821617,0.15318854,0.082147434,0.0008584441],"about_ca_topic_score_codex":0.005856415,"about_ca_topic_score_gemma":0.007008201,"teacher_disagreement_score":0.10199327,"about_ca_system_score_codex":0.0012795269,"about_ca_system_score_gemma":0.00507396,"threshold_uncertainty_score":0.53939843},"labels":[],"label_agreement":null},{"id":"W2884004329","doi":"10.1016/j.jval.2018.06.007","title":"Performance of a Bayesian Approach for Imputing Missing Data on the SF-12 Health-Related Quality-of-Life Measure","year":2018,"lang":"en","type":"article","venue":"Value in Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Missing data; Imputation (statistics); Statistics; Cohort; Categorical variable; SF-36; Bayesian probability; Medicine; Computer science; Mathematics; Health related quality of life","score_opus":0.4348050802861287,"score_gpt":0.45449927568290693,"score_spread":0.019694195396778247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884004329","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":[0.13915502,0.0015083832,0.85089874,0.0021091902,0.00017315325,0.00020656327,0.0006479091,0.0010987723,0.0042023147],"genre_scores_gemma":[0.54579216,0.0006185115,0.44886753,0.0005577447,0.0001281838,0.0002899046,0.0016454348,0.000272768,0.001827688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9812615,0.014779235,0.0007397013,0.0011294868,0.0017816103,0.00030836574],"domain_scores_gemma":[0.8643273,0.12467159,0.002051582,0.0034662555,0.0045718043,0.0009114243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04345988,0.0011022971,0.0018468386,0.0019154823,0.000999287,0.0025010388,0.0023479494,0.0037303362,0.004173939],"category_scores_gemma":[0.1545302,0.0010008429,0.0015996919,0.0017193216,0.0010578673,0.0033581546,0.003101389,0.0031716211,0.0007709199],"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":[0.005409365,0.00083012757,0.015286125,0.0005080284,0.0011276187,0.00014401606,0.00047675672,0.63587826,0.002080389,0.029973328,0.0043766247,0.3039094],"study_design_scores_gemma":[0.00018713913,0.00017023279,0.0020374858,0.000054295164,0.00008370186,0.00005706621,0.00003786923,0.9836031,0.000595052,0.012551988,0.0005823029,0.000039906052],"about_ca_topic_score_codex":0.017132435,"about_ca_topic_score_gemma":0.01146148,"teacher_disagreement_score":0.04345988,"about_ca_system_score_codex":0.0015268995,"about_ca_system_score_gemma":0.005124857,"threshold_uncertainty_score":0.22984052},"labels":[],"label_agreement":null},{"id":"W2884390433","doi":"10.1097/ede.0000000000000889","title":"A Call for Caution in Using Information Criteria to Select the Working Correlation Structure in Generalized Estimating Equations","year":2018,"lang":"en","type":"letter","venue":"Epidemiology","topic":"Statistical Methods and Bayesian 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","funders":"Canadian Institutes of Health Research","keywords":"Estimator; Covariate; Correlation; Computer science; Generalized estimating equation; Parametric statistics; Statistics; Parametric model; Econometrics; Data mining; Mathematics","score_opus":0.24030195272005409,"score_gpt":0.4690944263504464,"score_spread":0.22879247363039232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884390433","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":[0.0004375504,0.005728301,0.019423943,0.68377537,0.28781277,0.00010500308,0.0005507508,0.0006448409,0.0015215314],"genre_scores_gemma":[0.006884818,0.0046294876,0.025105601,0.4667154,0.4914501,0.00037381967,0.00015700744,0.00053026364,0.004153499],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90470225,0.056975055,0.015314335,0.0056822794,0.016170528,0.0011555519],"domain_scores_gemma":[0.58176804,0.31117716,0.017460275,0.0151953325,0.07088241,0.0035167804],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09747475,0.003198256,0.004528277,0.0037551408,0.0026100357,0.009333471,0.013827138,0.02339215,0.010462385],"category_scores_gemma":[0.46828687,0.0019511238,0.0036761013,0.004925929,0.011748132,0.008926566,0.0037841164,0.060677085,0.008845829],"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":[0.00006183362,0.000040280633,0.00046122487,0.00071717077,0.00013976611,0.00027618403,0.00039327255,0.00027768489,0.00008334958,0.008317477,0.9654588,0.023772994],"study_design_scores_gemma":[0.00020596669,0.00013961268,0.0019011248,0.004303534,0.0002733212,0.0013344905,0.0005796715,0.005685472,0.00081636006,0.06405153,0.920292,0.00041684695],"about_ca_topic_score_codex":0.0033323271,"about_ca_topic_score_gemma":0.005259537,"teacher_disagreement_score":0.90252525,"about_ca_system_score_codex":0.003026157,"about_ca_system_score_gemma":0.0055085137,"threshold_uncertainty_score":0.51550186},"labels":[],"label_agreement":null},{"id":"W2884919358","doi":"10.1002/sim.7908","title":"Modeling the random effects covariance matrix for longitudinal data with covariates measurement error","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","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":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Random effects model; Statistics; Estimator; Covariance; Covariance matrix; Mathematics; Generalized linear mixed model; Computer science","score_opus":0.18409707406414916,"score_gpt":0.4458210375468594,"score_spread":0.26172396348271026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884919358","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":[0.0010554832,0.0002572651,0.998033,0.00018285659,0.000040417493,0.00004326292,0.00012084377,0.00010673603,0.00016011932],"genre_scores_gemma":[0.121261895,0.002431234,0.86854297,0.00074880675,0.0004048955,0.0014290478,0.0014325217,0.00025155093,0.003497046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9827444,0.011112161,0.0007697947,0.0034398271,0.0015013634,0.0004325452],"domain_scores_gemma":[0.96832746,0.024381587,0.0029664105,0.0027877176,0.0013171267,0.00021971569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029407343,0.002262357,0.00261963,0.0032471642,0.0009875002,0.0021936835,0.0046348344,0.0034810365,0.0033032435],"category_scores_gemma":[0.06969568,0.0015327191,0.0036829659,0.0039707534,0.0025791887,0.003565259,0.002869245,0.0054233824,0.001148028],"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":[0.0001380021,0.00012862473,0.013002749,0.0007701673,0.0011604152,0.00068005634,0.0006124005,0.33895332,0.0017120583,0.52149475,0.005021317,0.11632622],"study_design_scores_gemma":[0.00006261845,0.000119067154,0.0027493744,0.00021655438,0.000293032,0.00028312072,0.00008580855,0.60426515,0.0006764927,0.3803007,0.010851387,0.000096772026],"about_ca_topic_score_codex":0.0102345655,"about_ca_topic_score_gemma":0.008857689,"teacher_disagreement_score":0.029407343,"about_ca_system_score_codex":0.0017075471,"about_ca_system_score_gemma":0.0032547012,"threshold_uncertainty_score":0.1555227},"labels":[],"label_agreement":null},{"id":"W2885675882","doi":"10.1002/jrsm.1316","title":"A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses","year":2018,"lang":"en","type":"article","venue":"Research Synthesis Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1026,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital","funders":"Medical Research Council; National Institute for Health and Care Research","keywords":"Estimator; Meta-analysis; Random effects model; Variance (accounting); Econometrics; Statistics; Variance components; Computer science; Estimation; Mathematics; Economics; Medicine","score_opus":0.6474146937413818,"score_gpt":0.6854525192511277,"score_spread":0.03803782550974588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885675882","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":[0.061070446,0.02347018,0.90693784,0.0017377419,0.00047873933,0.0017636485,0.0009823936,0.0006663234,0.0028927475],"genre_scores_gemma":[0.4825108,0.007577873,0.5020256,0.00080174155,0.00014231987,0.004968366,0.0012827192,0.00030222154,0.00038833485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.69207436,0.28556857,0.009278676,0.0040124035,0.0085107265,0.0005552674],"domain_scores_gemma":[0.35816997,0.60889274,0.008296924,0.015032086,0.009127112,0.0004812483],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.24626918,0.0014609952,0.0045779757,0.0043426193,0.00063957076,0.0030980618,0.0031219264,0.0035471676,0.0021471574],"category_scores_gemma":[0.5461177,0.0014066497,0.009982529,0.0037080278,0.0016427727,0.005290114,0.0024038362,0.0029343576,0.00031867877],"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":[0.0107856635,0.00041239735,0.019783787,0.016088432,0.087219186,0.00045978333,0.0016526497,0.47399345,0.001568175,0.11119362,0.0045822305,0.27226064],"study_design_scores_gemma":[0.00944315,0.0041393437,0.015540326,0.009241984,0.031269196,0.00080236077,0.0007589807,0.6790972,0.0039202017,0.23171192,0.01336705,0.0007083464],"about_ca_topic_score_codex":0.0013127927,"about_ca_topic_score_gemma":0.0010960121,"teacher_disagreement_score":0.75373083,"about_ca_system_score_codex":0.002586264,"about_ca_system_score_gemma":0.0028035801,"threshold_uncertainty_score":0.9294843},"labels":[],"label_agreement":null},{"id":"W2886524654","doi":"10.1080/00949655.2018.1511713","title":"Non-penalty shrinkage estimation of random effect models for longitudinal data with AR(1) errors","year":2018,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","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":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Covariate; Shrinkage; Mathematics; Autoregressive model; Statistics; Shrinkage estimator; Lasso (programming language); Random effects model; Econometrics; Applied mathematics; Efficient estimator; Computer science; Minimum-variance unbiased estimator","score_opus":0.09544597903472186,"score_gpt":0.4317216881018583,"score_spread":0.33627570906713644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886524654","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":[0.002398321,0.00021784648,0.9969689,0.00010862899,0.000020688083,0.000024655628,0.000024845524,0.00005986038,0.00017623685],"genre_scores_gemma":[0.15915371,0.0019135239,0.8322035,0.00036838066,0.00031229077,0.0009233591,0.00059047824,0.00026293707,0.0042718807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9889637,0.00840189,0.000342349,0.00093458546,0.0011397402,0.0002176109],"domain_scores_gemma":[0.96462494,0.030364254,0.0018667915,0.0016795244,0.0012320613,0.00023243077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02192646,0.0011997535,0.0024775995,0.0012078546,0.0004971284,0.0011916414,0.002782261,0.0019052984,0.0017061342],"category_scores_gemma":[0.060721103,0.0010317832,0.0019323113,0.0013758353,0.0019180314,0.0027671668,0.0023630722,0.0034126458,0.000589679],"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":[0.00019473989,0.00013544643,0.00387029,0.0006010998,0.0003981875,0.00035904982,0.0003621321,0.6440235,0.0019495909,0.22251463,0.0022236614,0.12336768],"study_design_scores_gemma":[0.000027489321,0.000055522436,0.0004054946,0.000043461976,0.000030551655,0.00006915337,0.000018745337,0.9373225,0.0003691792,0.05986943,0.0017642941,0.000024159639],"about_ca_topic_score_codex":0.0015362309,"about_ca_topic_score_gemma":0.0012868906,"teacher_disagreement_score":0.02192646,"about_ca_system_score_codex":0.00058055244,"about_ca_system_score_gemma":0.0012910997,"threshold_uncertainty_score":0.115959585},"labels":[],"label_agreement":null},{"id":"W2887069295","doi":"10.1111/sjos.12351","title":"A unified empirical likelihood approach for testing MCAR and subsequent estimation","year":2018,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian 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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Missing data; Statistics; Estimating equations; Set (abstract data type); Estimation; Applied mathematics; Computer science","score_opus":0.1259905556521418,"score_gpt":0.40060571553261254,"score_spread":0.27461515988047075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887069295","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":[0.0030998853,0.00017032344,0.99538773,0.00033107994,0.000029182153,0.0001378305,0.00006104152,0.00010392251,0.0006790916],"genre_scores_gemma":[0.22007683,0.000511731,0.77458966,0.00057879067,0.00030931647,0.0021686784,0.0005183763,0.00013206342,0.0011144455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8550113,0.12441273,0.003835596,0.0066053476,0.0090267705,0.0011082962],"domain_scores_gemma":[0.700746,0.25530985,0.01362515,0.018977186,0.010068377,0.0012735422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10988058,0.0020914497,0.0040246546,0.0064481758,0.0013533734,0.003216793,0.005352113,0.0039424826,0.006802736],"category_scores_gemma":[0.3607941,0.0015853908,0.0032243365,0.006171524,0.0057232147,0.006624681,0.0072887396,0.006162272,0.0008679425],"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":[0.00023854432,0.00025627008,0.008718191,0.0004870139,0.0009977251,0.00037854238,0.00080591213,0.069607645,0.0009845789,0.747143,0.002977962,0.1674047],"study_design_scores_gemma":[0.00018494093,0.00037336652,0.003399079,0.00028521643,0.0002135941,0.0002799531,0.00023923539,0.41009697,0.0013856424,0.57858133,0.004835504,0.00012519148],"about_ca_topic_score_codex":0.0017986157,"about_ca_topic_score_gemma":0.00089830393,"teacher_disagreement_score":0.10988058,"about_ca_system_score_codex":0.0021347508,"about_ca_system_score_gemma":0.0041681062,"threshold_uncertainty_score":0.581111},"labels":[],"label_agreement":null},{"id":"W2887833865","doi":"10.1016/j.econlet.2018.07.040","title":"Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers","year":2018,"lang":"en","type":"article","venue":"Economics Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"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":"Fogarty International Center; National Institute on Aging; Queen's University; National Institutes of Health; Bundesministerium für Bildung und Forschung; National Institute of Allergy and Infectious Diseases; Alexander von Humboldt-Stiftung; Wellcome Trust; Harvard University; Queen's University Belfast; Eunice Kennedy Shriver National Institute of Child Health and Human Development; European Commission","keywords":"Voucher; Missing data; Econometrics; Selection bias; Bivariate analysis; Non-response bias; Imputation (statistics); Normality; Statistics; Economics; Computer science; Mathematics","score_opus":0.28401813026250555,"score_gpt":0.4262184200531246,"score_spread":0.14220028979061905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887833865","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":[0.01485625,0.000429621,0.98024154,0.0017982931,0.000087956294,0.00074300874,0.00007798339,0.0002958794,0.0014695853],"genre_scores_gemma":[0.22861831,0.00058917794,0.7668134,0.00068015273,0.00016783965,0.0016441984,0.00008030173,0.00011387682,0.0012927246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.74848473,0.23576365,0.002963092,0.0032405166,0.008558815,0.0009892051],"domain_scores_gemma":[0.4729693,0.4736566,0.01848887,0.02454196,0.009420191,0.0009230734],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18741144,0.0013589459,0.0024224366,0.004279499,0.001908126,0.0021719537,0.0038795993,0.004804077,0.0047786683],"category_scores_gemma":[0.38493526,0.0012764418,0.002961712,0.0060378877,0.004411533,0.004158595,0.0048541063,0.0030392276,0.00062945165],"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":[0.0008454115,0.0007631705,0.05826249,0.0016959206,0.0012803941,0.0011588276,0.0044869725,0.07752797,0.0012926159,0.27326486,0.00482294,0.5745985],"study_design_scores_gemma":[0.0005891296,0.0011314895,0.0137816705,0.0009118902,0.0003874134,0.0011040798,0.0010036591,0.48247415,0.0027835225,0.47921348,0.016365245,0.00025421407],"about_ca_topic_score_codex":0.0045379447,"about_ca_topic_score_gemma":0.0026592023,"teacher_disagreement_score":0.8125886,"about_ca_system_score_codex":0.0025839477,"about_ca_system_score_gemma":0.004127117,"threshold_uncertainty_score":0.9911383},"labels":[],"label_agreement":null},{"id":"W2888737001","doi":"10.5539/mas.v12n9p159","title":"Bayesian Inference in a Joint Model for Longitudinal and Time to Event Data with Gompertz Baseline Hazards","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Pan African University; Jomo Kenyatta University of Agriculture and Technology","keywords":"Weibull distribution; Gompertz function; Covariate; Statistics; Event (particle physics); Joint probability distribution; Bayesian probability; Computer science; Gibbs sampling; Bayesian inference; Proportional hazards model; Mathematics; Econometrics","score_opus":0.10440911731285804,"score_gpt":0.3849044296789931,"score_spread":0.280495312366135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888737001","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":[0.015031915,0.00037051542,0.983442,0.00037834782,0.000029199073,0.00006126465,0.000181463,0.00012252806,0.0003827571],"genre_scores_gemma":[0.5208746,0.0018252534,0.46875873,0.00048849714,0.0002910063,0.0011196361,0.0014699803,0.00015014918,0.0050220904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99079835,0.0059261294,0.00039090528,0.0017182946,0.00077352975,0.0003927413],"domain_scores_gemma":[0.95692366,0.037995353,0.0020493008,0.0016989153,0.0009772822,0.00035542037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027917055,0.0011450709,0.0030955386,0.0027256915,0.00090222957,0.002452722,0.0032958665,0.002293252,0.0023323915],"category_scores_gemma":[0.06406203,0.0016140898,0.0028517882,0.0029256106,0.0025632118,0.0035771383,0.002420467,0.0036259552,0.00042756254],"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":[0.00026883758,0.00013908684,0.01118581,0.00030708802,0.0006400017,0.00043774402,0.000650044,0.6061369,0.00078606367,0.3214745,0.0015921554,0.056381796],"study_design_scores_gemma":[0.00005478468,0.000056934787,0.0014813336,0.000041801544,0.00012124273,0.00009322352,0.000050182858,0.855265,0.00023744852,0.14146514,0.001093281,0.000039531944],"about_ca_topic_score_codex":0.016737396,"about_ca_topic_score_gemma":0.012261633,"teacher_disagreement_score":0.027917055,"about_ca_system_score_codex":0.0020615472,"about_ca_system_score_gemma":0.0033159903,"threshold_uncertainty_score":0.1476413},"labels":[],"label_agreement":null},{"id":"W2888844257","doi":"10.1002/sim.7942","title":"Estimation in generalized linear models under censored covariates with an application to MIREC data","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"Carleton University; Health Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Health Canada; Ministry of Natural Resources","keywords":"Covariate; Estimator; Generalized linear model; Statistics; Econometrics; Generalized estimating equation; Computer science; Linear model; Mathematics","score_opus":0.1248335094346998,"score_gpt":0.45313467404832947,"score_spread":0.3283011646136297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888844257","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":[0.010717333,0.00094896223,0.9869266,0.0006081414,0.0000327634,0.000058714515,0.0002310298,0.00021746273,0.0002589413],"genre_scores_gemma":[0.28099796,0.0029392547,0.7085554,0.00060154294,0.00040599483,0.0008374263,0.0019648473,0.0002481607,0.0034493848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99127734,0.007007091,0.0002644132,0.00072031684,0.0005214416,0.00020927918],"domain_scores_gemma":[0.9376957,0.05581203,0.00284534,0.0020115539,0.0013355032,0.0002999395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020960767,0.0010793698,0.0018345123,0.0017113895,0.0006693968,0.0014325824,0.0026263376,0.0019663817,0.0022531655],"category_scores_gemma":[0.06504246,0.0008271829,0.0019705317,0.0023129785,0.0015385147,0.0017185222,0.0024955452,0.0029493705,0.00045256427],"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":[0.00032156284,0.00013675923,0.008628736,0.00068177306,0.0004977495,0.00076877204,0.00052396656,0.7056285,0.0013101951,0.19007473,0.002953942,0.088473305],"study_design_scores_gemma":[0.000054854496,0.00006673471,0.0014737898,0.000045090288,0.0000372957,0.00011109482,0.000051572966,0.9106771,0.00030459856,0.08428454,0.0028578977,0.000035465906],"about_ca_topic_score_codex":0.008777188,"about_ca_topic_score_gemma":0.0070534716,"teacher_disagreement_score":0.020960767,"about_ca_system_score_codex":0.0010556651,"about_ca_system_score_gemma":0.0015882454,"threshold_uncertainty_score":0.11085242},"labels":[],"label_agreement":null},{"id":"W2888945867","doi":"10.1111/anzs.12245","title":"Hybrid pairwise‐likelihood estimation methods for incomplete longitudinal binary data","year":2018,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Missing data; Covariate; Estimator; Mathematics; Computation; Statistics; Random effects model; Algorithm; Mathematical optimization; Data mining; Computer science","score_opus":0.17308903330891826,"score_gpt":0.4590126952504464,"score_spread":0.2859236619415282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888945867","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":[0.001629782,0.00014231862,0.99785537,0.00006488277,0.000012015116,0.00002171084,0.00003257679,0.000072989744,0.00016836791],"genre_scores_gemma":[0.0923665,0.00034549687,0.9044099,0.000090611386,0.000087599474,0.0003826312,0.00034185327,0.00018113319,0.0017942768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99144804,0.0067327763,0.00022433577,0.0005993502,0.00084911747,0.00014650049],"domain_scores_gemma":[0.97525394,0.020657483,0.001179205,0.0012169004,0.0013442909,0.00034812267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01415071,0.0011848198,0.0015080889,0.0018772761,0.0005809277,0.0011750706,0.004366433,0.0014455719,0.004115294],"category_scores_gemma":[0.03790467,0.0008834814,0.0021569836,0.0023418998,0.0012989306,0.0024575945,0.0033750806,0.0026683551,0.00088259124],"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":[0.00042644204,0.00015938556,0.006262523,0.0006834078,0.000702954,0.00039825795,0.0005970037,0.3986211,0.0027020862,0.24325192,0.004800533,0.34139442],"study_design_scores_gemma":[0.000050486462,0.00007896316,0.0006932753,0.000041990927,0.00005484337,0.00012736322,0.00004547781,0.8872152,0.0008568227,0.107892185,0.0028989643,0.000044402714],"about_ca_topic_score_codex":0.0020000564,"about_ca_topic_score_gemma":0.0023661747,"teacher_disagreement_score":0.01415071,"about_ca_system_score_codex":0.00080929266,"about_ca_system_score_gemma":0.0015652112,"threshold_uncertainty_score":0.07483697},"labels":[],"label_agreement":null},{"id":"W2890626091","doi":"10.1002/sim.7963","title":"Modeling semicontinuous longitudinal data with order constraints","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Inference; Computer science; Longitudinal data; Statistical inference; Econometrics; Statistical hypothesis testing; Joint (building); Machine learning; Statistics; Data mining; Artificial intelligence; Mathematics","score_opus":0.12914742725631273,"score_gpt":0.43149439201528045,"score_spread":0.3023469647589677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890626091","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":[0.031910986,0.0006583119,0.9650033,0.00078271737,0.000042196076,0.00007458296,0.00046470587,0.0001729036,0.00089016097],"genre_scores_gemma":[0.62450415,0.001994464,0.36525849,0.0008880154,0.00025027373,0.0008776425,0.0017728587,0.00019592722,0.0042582015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9882079,0.0075302264,0.00066920056,0.001828086,0.0011990177,0.00056555035],"domain_scores_gemma":[0.8763337,0.10751427,0.007343202,0.005992546,0.0020622278,0.0007540903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028524878,0.0012510928,0.0027913009,0.0017156181,0.000752939,0.0028409124,0.0036401593,0.0024491886,0.0032474818],"category_scores_gemma":[0.08301015,0.0014688289,0.0022621073,0.0027521031,0.003270574,0.003989852,0.002902214,0.004398377,0.00046199808],"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":[0.00032224355,0.00012644572,0.015194575,0.0003560311,0.00032719766,0.0012777492,0.0009455065,0.49417093,0.0009452243,0.44101915,0.0019956667,0.043319337],"study_design_scores_gemma":[0.000048450347,0.00006449849,0.0018641618,0.00006709052,0.000048717182,0.00016749701,0.000054518416,0.6625278,0.00020127243,0.33379483,0.0011272191,0.00003400884],"about_ca_topic_score_codex":0.009968839,"about_ca_topic_score_gemma":0.0087991115,"teacher_disagreement_score":0.028524878,"about_ca_system_score_codex":0.0017676969,"about_ca_system_score_gemma":0.0029066245,"threshold_uncertainty_score":0.15085578},"labels":[],"label_agreement":null},{"id":"W2891328715","doi":"10.1111/insr.12291","title":"Some Theoretical and Practical Aspects of Empirical Likelihood Methods for Complex Surveys","year":2018,"lang":"en","type":"article","venue":"International Statistical Review","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Sample (material); Sample size determination; Empirical likelihood; Sampling (signal processing); Point estimation; Survey sampling; Model selection; Statistical inference; Population; Mathematics; Statistics; Machine learning; Artificial intelligence","score_opus":0.1937269300936042,"score_gpt":0.5690216844931857,"score_spread":0.3752947543995815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891328715","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":[0.0011112607,0.019338522,0.9684797,0.0044134604,0.00021352145,0.000037130092,0.000063752996,0.00008922911,0.0062534274],"genre_scores_gemma":[0.110250525,0.055410054,0.81910616,0.0022830053,0.0033005301,0.00090925023,0.0002537614,0.00029378806,0.008192912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98632693,0.010410208,0.00054145243,0.0006382312,0.0019241244,0.00015912001],"domain_scores_gemma":[0.95000416,0.044997934,0.0008912184,0.0022029697,0.0016840869,0.00021948028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024463948,0.0009711235,0.0015340501,0.002884704,0.00060390326,0.0038679633,0.0020071962,0.0022244842,0.004984246],"category_scores_gemma":[0.061171133,0.0011957763,0.0013850782,0.003228876,0.00642262,0.00483217,0.0022081197,0.0054851607,0.0014391292],"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":[0.0000095435935,0.000022444254,0.00023010637,0.00024926884,0.000030466048,0.000058467565,0.00016491258,0.010582385,0.00016782939,0.9405278,0.0025193936,0.045437496],"study_design_scores_gemma":[0.000010420107,0.000019611478,0.0003228681,0.00018613382,0.000008906519,0.00010660171,0.00003915529,0.036110945,0.00017528696,0.9310488,0.031943563,0.000027779375],"about_ca_topic_score_codex":0.0015475339,"about_ca_topic_score_gemma":0.0007076312,"teacher_disagreement_score":0.024463948,"about_ca_system_score_codex":0.0019761324,"about_ca_system_score_gemma":0.0013916132,"threshold_uncertainty_score":0.12937927},"labels":[],"label_agreement":null},{"id":"W2891741935","doi":"10.1002/mpr.1742","title":"A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders","year":2018,"lang":"en","type":"article","venue":"International Journal of Methods in Psychiatric Research","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; Memorial University of Newfoundland","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Superordinate goals; Multivariate statistics; Epidemiology; Nosology; Operationalization; Bayesian probability; Multivariate analysis; Clinical psychology; Medicine; Anxiety; Psychology; Statistics; Psychiatry; Mathematics; Social psychology; Pathology","score_opus":0.17314136634677477,"score_gpt":0.559634731498844,"score_spread":0.3864933651520692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891741935","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":[0.00592513,0.00033316508,0.99237776,0.0004880468,0.000030981442,0.00012297907,0.00018855343,0.000103150596,0.00043020566],"genre_scores_gemma":[0.14380395,0.00068103324,0.85242504,0.00036823755,0.00019251175,0.00085676025,0.0005448518,0.000089476256,0.0010381244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.980649,0.015443839,0.000706673,0.0017579809,0.0011988006,0.00024360057],"domain_scores_gemma":[0.9444429,0.04600253,0.003332015,0.003443315,0.0022655937,0.0005136396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033742074,0.0012560483,0.0020812657,0.004272462,0.0011174451,0.0021220571,0.0028844255,0.0020419066,0.004332154],"category_scores_gemma":[0.09453974,0.000989386,0.0030685065,0.0034003253,0.0020389976,0.0022846197,0.0026580961,0.0030588298,0.00046135025],"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":[0.000881467,0.00025130494,0.02943708,0.001125855,0.0028444934,0.000573187,0.0012978293,0.33851114,0.0028668428,0.277417,0.0077291615,0.33706465],"study_design_scores_gemma":[0.00017130953,0.00019230986,0.0073215025,0.0003052867,0.0006333429,0.00042487838,0.00012590633,0.7336185,0.0007397421,0.25145808,0.004894825,0.00011419492],"about_ca_topic_score_codex":0.009312445,"about_ca_topic_score_gemma":0.010690767,"teacher_disagreement_score":0.033742074,"about_ca_system_score_codex":0.0015458489,"about_ca_system_score_gemma":0.0023604475,"threshold_uncertainty_score":0.17844725},"labels":[],"label_agreement":null},{"id":"W2896344278","doi":"10.1007/s00184-018-0690-z","title":"An approximate method for generalized linear and nonlinear mixed effects models with a mechanistic nonlinear covariate measurement error model","year":2018,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia","funders":"City University of New York; National Science Foundation","keywords":"Covariate; Mathematics; Estimator; Nonlinear system; Applied mathematics; Consistency (knowledge bases); Inference; Asymptotic distribution; Linear model; Statistics; Errors-in-variables models; Computer science; Artificial intelligence","score_opus":0.1623960712519726,"score_gpt":0.40362027872458406,"score_spread":0.24122420747261145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896344278","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":[0.00022564802,0.000090620975,0.9994173,0.00006156349,0.000016300246,0.000014828201,0.000023474391,0.000033671917,0.00011662194],"genre_scores_gemma":[0.01700623,0.00043671316,0.97920585,0.00020017271,0.00013098835,0.0006317342,0.00017211278,0.0001721658,0.0020440097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99218386,0.0059059495,0.0002574449,0.0006474158,0.00084849575,0.00015679227],"domain_scores_gemma":[0.97735274,0.019012576,0.0007232864,0.0014699299,0.001148953,0.00029248704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014464135,0.0016463732,0.0027070916,0.002305927,0.0011001021,0.0019828132,0.005711543,0.0030428374,0.0059557254],"category_scores_gemma":[0.045206565,0.0017450334,0.002988926,0.003125441,0.0025835852,0.0032935604,0.0040479293,0.004957823,0.0013612153],"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":[0.00018300705,0.000107232125,0.0009292775,0.0006333642,0.00054329855,0.00022178474,0.00040626252,0.17848782,0.0016935548,0.6627736,0.0038184454,0.15020233],"study_design_scores_gemma":[0.00005008825,0.00007082247,0.00029876706,0.00009843571,0.00013759271,0.00017443448,0.00004212363,0.6506726,0.0005184787,0.33925575,0.008625359,0.00005552635],"about_ca_topic_score_codex":0.0071795527,"about_ca_topic_score_gemma":0.010900728,"teacher_disagreement_score":0.014464135,"about_ca_system_score_codex":0.0019076049,"about_ca_system_score_gemma":0.0042669214,"threshold_uncertainty_score":0.076494575},"labels":[],"label_agreement":null},{"id":"W2896849089","doi":"10.5539/ijsp.v7n6p113","title":"Estimation of the Poisson Parameter with Moment Generating Method","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Efficiency; Moment (physics); Mathematics; Goodness of fit; Poisson distribution; Trimmed estimator; Computation; Applied mathematics; Efficient estimator; Statistics; Stein's unbiased risk estimate; Bias of an estimator; Minimax estimator; Consistent estimator; Minimum-variance unbiased estimator; Mathematical optimization; Algorithm","score_opus":0.04841376477779027,"score_gpt":0.3877994640064101,"score_spread":0.33938569922861983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896849089","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":[0.002145123,0.00016238126,0.9968489,0.00006769032,0.000040281397,0.00004151728,0.000049920232,0.00015571885,0.00048845215],"genre_scores_gemma":[0.1966508,0.0012406143,0.79653025,0.00033672652,0.000452779,0.0006445618,0.0007435889,0.00030472374,0.0030960392],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951143,0.0024841535,0.00020285927,0.0006145554,0.001379147,0.00020489223],"domain_scores_gemma":[0.9874429,0.008303121,0.0013972858,0.0010542057,0.0016256369,0.00017693145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008010985,0.0007343258,0.0018239651,0.003340312,0.00059581635,0.0018917962,0.0031469394,0.0019379513,0.0045782235],"category_scores_gemma":[0.036694184,0.00066098175,0.0013355553,0.0025386526,0.0010816844,0.0028815798,0.0019229356,0.0023194652,0.0013710617],"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":[0.0002693775,0.00017796733,0.011565109,0.00057382527,0.00029882957,0.00066813664,0.00031516448,0.25979808,0.009232882,0.42420623,0.009052763,0.2838417],"study_design_scores_gemma":[0.000034579516,0.000059044916,0.0018159956,0.00006830824,0.000062113206,0.0004938196,0.00003440862,0.9053698,0.0028143888,0.083548374,0.005608914,0.00009028399],"about_ca_topic_score_codex":0.00091268413,"about_ca_topic_score_gemma":0.0005052465,"teacher_disagreement_score":0.008010985,"about_ca_system_score_codex":0.0011137595,"about_ca_system_score_gemma":0.0015159415,"threshold_uncertainty_score":0.042366624},"labels":[],"label_agreement":null},{"id":"W2896915321","doi":"10.1080/02664763.2021.1957789","title":"A new GEE method to account for heteroscedasticity using asymmetric least-square regressions","year":2021,"lang":"en","type":"preprint","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université du Québec à Montréal; McGill University; Jewish General Hospital","funders":"","keywords":"Heteroscedasticity; Estimator; Mathematics; Generalized estimating equation; Estimating equations; Statistics; Asymptotic distribution; Inference; Econometrics; Covariance matrix; Applied mathematics; Computer science","score_opus":0.13253678431776542,"score_gpt":0.45245195313507025,"score_spread":0.31991516881730486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896915321","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":[0.0011458935,0.00007654925,0.99803084,0.00008540322,0.000031005096,0.000032707234,0.000117473,0.0002728179,0.0002073853],"genre_scores_gemma":[0.05599256,0.0003955108,0.93697095,0.0003570799,0.00018649177,0.00057775434,0.0011562981,0.0010557022,0.0033076704],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923205,0.004878204,0.00039062713,0.0011756667,0.001010499,0.00022460299],"domain_scores_gemma":[0.98211586,0.012282243,0.0010081014,0.0024980416,0.001875452,0.0002202401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012576443,0.0014324009,0.0021829044,0.0020377997,0.0005165398,0.0014814663,0.0031025347,0.0015207828,0.0066469978],"category_scores_gemma":[0.054691777,0.00086774695,0.002640113,0.0026380597,0.00083435973,0.0028467376,0.0023257977,0.0033268423,0.0019078156],"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":[0.00018696101,0.0003009261,0.013584035,0.0005773619,0.0019424515,0.00063921214,0.00057525444,0.16185933,0.0044486565,0.23478226,0.020118678,0.5609848],"study_design_scores_gemma":[0.0000947675,0.00010529596,0.003343125,0.00010244592,0.0002660384,0.00045476906,0.00006446289,0.7802958,0.0013388757,0.19073172,0.023107009,0.00009574132],"about_ca_topic_score_codex":0.0043955133,"about_ca_topic_score_gemma":0.005858006,"teacher_disagreement_score":0.012576443,"about_ca_system_score_codex":0.00058940507,"about_ca_system_score_gemma":0.0025261382,"threshold_uncertainty_score":0.06651139},"labels":[],"label_agreement":null},{"id":"W2899843281","doi":"10.5061/dryad.m2v4m","title":"Data from: Using multiple imputation to estimate missing data in meta-regression","year":2014,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Statistical Methods and Bayesian 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 Prince Edward Island; Trent University","funders":"","keywords":"Missing data; Imputation (statistics); Statistics; Regression; Meta-regression; Computer science; Econometrics; Data mining; Mathematics; Meta-analysis; Medicine","score_opus":0.2425731247560988,"score_gpt":0.4414774353459856,"score_spread":0.1989043105898868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899843281","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004029358,0.048596803,0.8939561,0.010287977,0.0036888595,0.004970061,0.026842624,0.0045908755,0.0030373638],"genre_scores_gemma":[0.09188258,0.015312785,0.8531913,0.005130339,0.0012461737,0.017298758,0.011638309,0.0025812518,0.0017185754],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.7109175,0.26135933,0.012546182,0.008494338,0.0060404697,0.000642203],"domain_scores_gemma":[0.68264294,0.2633869,0.012752358,0.03445262,0.006184604,0.0005806261],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.13767709,0.0032803142,0.007841865,0.00681548,0.0011836789,0.0067246356,0.005931044,0.005877518,0.01966965],"category_scores_gemma":[0.47445515,0.0027975715,0.017675616,0.01114886,0.0015559543,0.005675987,0.0047102543,0.006269935,0.0032252807],"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":[0.0042421515,0.00023245926,0.013180929,0.14689037,0.18911012,0.0007501555,0.0018909262,0.06360607,0.0012861785,0.06389812,0.12683125,0.38808134],"study_design_scores_gemma":[0.007586187,0.0013250263,0.011506414,0.057284527,0.11103383,0.0008604744,0.0003864027,0.10603753,0.004945221,0.415713,0.28208414,0.0012372773],"about_ca_topic_score_codex":0.0031619335,"about_ca_topic_score_gemma":0.003438558,"teacher_disagreement_score":0.8623229,"about_ca_system_score_codex":0.0018999956,"about_ca_system_score_gemma":0.005663723,"threshold_uncertainty_score":0.7281147},"labels":[],"label_agreement":null},{"id":"W2900914706","doi":"10.1080/03610926.2018.1473882","title":"Simultaneous estimation of Cronbach’s alpha coefficients","year":2018,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","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":"Brock University","funders":"","keywords":"Estimator; Cronbach's alpha; Monte Carlo method; Mathematics; Statistics; Homogeneity (statistics); Psychometrics","score_opus":0.058766595952790476,"score_gpt":0.47660730264610096,"score_spread":0.41784070669331047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900914706","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":[0.10417218,0.00059703656,0.8899999,0.00023246442,0.00011926744,0.00027773227,0.00035903166,0.00035657923,0.0038857858],"genre_scores_gemma":[0.661907,0.00054759416,0.33421585,0.00010952568,0.00012506006,0.0011873689,0.00065538666,0.00013571567,0.00111646],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9613458,0.023261582,0.0025978063,0.0059367144,0.006090677,0.0007674247],"domain_scores_gemma":[0.8196818,0.12465771,0.011590251,0.02343948,0.019687066,0.0009437227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056710888,0.0011431904,0.0019101431,0.0045273313,0.0006077425,0.0024331952,0.0014252756,0.0010360789,0.0021735488],"category_scores_gemma":[0.23675469,0.0009150172,0.00275495,0.0047700424,0.0014011861,0.0030286296,0.003216874,0.0022224751,0.0010233237],"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":[0.00046708225,0.00017944486,0.13139063,0.00093177415,0.0029917748,0.0003170571,0.0046591554,0.037633747,0.0052612647,0.06572279,0.005012722,0.7454326],"study_design_scores_gemma":[0.000212574,0.0018639803,0.24150145,0.00097058155,0.0018341346,0.0009492036,0.0030339425,0.32688406,0.015635362,0.38844118,0.018243445,0.00042997478],"about_ca_topic_score_codex":0.00082789763,"about_ca_topic_score_gemma":0.0007736931,"teacher_disagreement_score":0.056710888,"about_ca_system_score_codex":0.000599978,"about_ca_system_score_gemma":0.0018054929,"threshold_uncertainty_score":0.29991943},"labels":[],"label_agreement":null},{"id":"W2902082626","doi":"10.18192/osurj.v1i1.3702","title":"Expressing the randomity of events – An analysis of random number generation with given distributions","year":2018,"lang":"en","type":"article","venue":"University of Ottawa Science Undergraduate Research Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Inverse distribution; Monte Carlo method; Computer science; Computation; Probability distribution; Simple (philosophy); Point (geometry); Algorithm; Mathematics; Statistics; Heavy-tailed distribution","score_opus":0.10137712508984174,"score_gpt":0.41177660500210034,"score_spread":0.3103994799122586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902082626","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":[0.003821696,0.00014834909,0.99449015,0.00026172725,0.000015605712,0.00006192892,0.00003199499,0.00005809511,0.0011104663],"genre_scores_gemma":[0.3629843,0.0012863919,0.63025403,0.00044062786,0.00018437672,0.0011049698,0.00026464136,0.00023055983,0.00325019],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9789488,0.014668064,0.0008224323,0.0019094798,0.0031673955,0.00048391573],"domain_scores_gemma":[0.84204626,0.1404537,0.007027867,0.007335217,0.002629014,0.0005078948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035530195,0.0011536727,0.0017088808,0.0028507188,0.0008676655,0.004278233,0.0031296962,0.0022000377,0.004041817],"category_scores_gemma":[0.1365105,0.0008265253,0.0016987668,0.002195792,0.0060995244,0.0077970596,0.0025864153,0.0032731271,0.0007623207],"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":[0.000028423508,0.000022016582,0.0014319341,0.00012376152,0.00005199904,0.00010768514,0.0003066798,0.096860036,0.00041782623,0.8845767,0.0004111296,0.01566191],"study_design_scores_gemma":[0.000016465012,0.000052430893,0.00045047214,0.00008960784,0.000025230474,0.00019195753,0.000088233486,0.4451727,0.0005676091,0.55131775,0.0019934857,0.00003403138],"about_ca_topic_score_codex":0.001227163,"about_ca_topic_score_gemma":0.000652992,"teacher_disagreement_score":0.035530195,"about_ca_system_score_codex":0.0023468805,"about_ca_system_score_gemma":0.0015406557,"threshold_uncertainty_score":0.18790388},"labels":[],"label_agreement":null},{"id":"W2902397990","doi":"10.1002/9780470057339.val018.pub2","title":"Longitudinal Studies","year":2012,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Statistical Methods and Bayesian 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":"Simon Fraser University; Western University","funders":"","keywords":"Longitudinal study; Longitudinal data; Computer science; Psychology; Statistics; Mathematics; Data mining","score_opus":0.09303640190191041,"score_gpt":0.37484169160877423,"score_spread":0.2818052897068638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902397990","genre_codex":"other","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":[0.12670057,0.18034369,0.11136415,0.031791583,0.012566113,0.0071360613,0.22509882,0.001204388,0.30379462],"genre_scores_gemma":[0.6330533,0.09034497,0.069541834,0.02073897,0.007480047,0.013025134,0.079972714,0.0005514347,0.08529155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.980231,0.011678481,0.0015836229,0.0030980457,0.0027468964,0.00066199346],"domain_scores_gemma":[0.9392787,0.02269383,0.012142249,0.010473454,0.013130149,0.0022816025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0149533,0.0006844035,0.001192555,0.0040873303,0.0014048249,0.002183399,0.0013065321,0.0013309967,0.048512],"category_scores_gemma":[0.07275599,0.0004017391,0.0011682649,0.006909336,0.0010222493,0.0027895044,0.002231923,0.0018277926,0.0069208294],"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":[0.0011062114,0.00054551894,0.23998581,0.007725061,0.0040759332,0.0009483586,0.004676819,0.00087406184,0.0008158952,0.1454923,0.2600721,0.33368194],"study_design_scores_gemma":[0.00020646605,0.00091364066,0.1409373,0.008801312,0.0017410825,0.0022710161,0.002809541,0.0006920817,0.0007366314,0.04944233,0.7913073,0.00014134112],"about_ca_topic_score_codex":0.005471147,"about_ca_topic_score_gemma":0.004555203,"teacher_disagreement_score":0.048512,"about_ca_system_score_codex":0.0015353697,"about_ca_system_score_gemma":0.0035789178,"threshold_uncertainty_score":0.16228884},"labels":[],"label_agreement":null},{"id":"W2902420601","doi":"10.21307/stattrans-2020-037","title":"An evaluation of design-based properties of different composite estimators","year":2020,"lang":"en","type":"preprint","venue":"Statistics in Transition New Series","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Mean squared error; Population; Mathematics; Sigma; Sample (material); Variance (accounting); Sample size determination; Linear regression; Current Population Survey; Econometrics; Computer science; Economics; Physics","score_opus":0.1830131001062286,"score_gpt":0.3946642502491629,"score_spread":0.2116511501429343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902420601","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":[0.22622538,0.003170756,0.7613877,0.0007714496,0.0002753381,0.00087844813,0.00070883526,0.0008686935,0.005713283],"genre_scores_gemma":[0.6808048,0.00053793844,0.31496343,0.00024830754,0.00016063389,0.000707913,0.0013704759,0.00026628742,0.0009401678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9016601,0.07909933,0.0038104916,0.0047467877,0.0097600175,0.0009233573],"domain_scores_gemma":[0.30572876,0.6152226,0.019118322,0.036810055,0.021672474,0.0014478255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17740701,0.0011100369,0.0018474532,0.0031872902,0.00089729147,0.0026626987,0.0022154292,0.002563068,0.003231435],"category_scores_gemma":[0.42933065,0.00068757235,0.002225422,0.0030274997,0.0017775309,0.0041877404,0.0026008354,0.0016135912,0.00047544533],"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":[0.0071636615,0.0009765738,0.12726524,0.0016844339,0.0025166853,0.00028564187,0.0007195294,0.48838475,0.0025638652,0.078976676,0.004080344,0.28538254],"study_design_scores_gemma":[0.0005803057,0.0036531857,0.028441552,0.00027392962,0.00068809773,0.00034626693,0.00026655686,0.93338996,0.00330046,0.025606986,0.0033120983,0.00014069003],"about_ca_topic_score_codex":0.0016817605,"about_ca_topic_score_gemma":0.001223626,"teacher_disagreement_score":0.17740701,"about_ca_system_score_codex":0.0020342534,"about_ca_system_score_gemma":0.0022006237,"threshold_uncertainty_score":0.93822914},"labels":[],"label_agreement":null},{"id":"W2903064176","doi":"10.1111/insr.12305","title":"Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review","year":2018,"lang":"en","type":"review","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","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":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Missing data; Estimator; Statistics; Computer science; Econometrics; Variance (accounting); Data mining; Mathematics","score_opus":0.2397411221076692,"score_gpt":0.5238129779630646,"score_spread":0.2840718558553954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903064176","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029941551,0.9976979,0.0006946455,0.0010529616,0.00024675482,0.0000068608206,0.000012542721,0.0000056995154,0.0002526358],"genre_scores_gemma":[0.00037137594,0.9977003,0.00085358566,0.0005226341,0.0004343881,0.000020213274,0.00001716349,0.0000037665518,0.00007663096],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9956209,0.0021296598,0.00064495456,0.0004052768,0.0010950164,0.00010417748],"domain_scores_gemma":[0.94104934,0.05162207,0.0019111464,0.0005848989,0.004459564,0.00037303736],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013108811,0.001071078,0.0024976707,0.00578754,0.000516075,0.0018323027,0.0021556346,0.002281804,0.004483348],"category_scores_gemma":[0.04269929,0.0007293427,0.0014560339,0.0077111926,0.002018206,0.0036873624,0.0013045458,0.0035278595,0.001642009],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007504998,0.000053203792,0.00038448715,0.06522814,0.00027784533,0.000092582755,0.00021842134,0.0005438887,0.0001270644,0.01601138,0.038310256,0.8786777],"study_design_scores_gemma":[0.000032832533,0.00012471709,0.0020703645,0.07729898,0.0005905338,0.0008793819,0.00024208949,0.00040292743,0.00021701754,0.023181416,0.894875,0.00008480717],"about_ca_topic_score_codex":0.0027324425,"about_ca_topic_score_gemma":0.003504566,"teacher_disagreement_score":0.9868912,"about_ca_system_score_codex":0.0016499716,"about_ca_system_score_gemma":0.0047964756,"threshold_uncertainty_score":0.06932682},"labels":[],"label_agreement":null},{"id":"W2904405044","doi":"10.1515/ijb-2017-0002","title":"Parametric Regression Analysis with Covariate Misclassification in Main Study/Validation Study Designs","year":2018,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","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":"University of Waterloo","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Covariate; Inference; Computer science; Statistics; Statistical inference; Observational error; Data mining; Econometrics; Causal inference; Parametric statistics; Nonparametric statistics; Machine learning; Mathematics; Artificial intelligence","score_opus":0.16327130706442697,"score_gpt":0.4423361246666251,"score_spread":0.27906481760219815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904405044","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":[0.009192783,0.0013612907,0.98675984,0.0008576653,0.0002480917,0.0007333314,0.00010091586,0.00019908433,0.0005470396],"genre_scores_gemma":[0.3279396,0.000857675,0.66177064,0.0021473288,0.0003700096,0.0051552285,0.00036181288,0.00016218165,0.0012354926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.50175136,0.44846553,0.014999668,0.019049445,0.013655355,0.0020787437],"domain_scores_gemma":[0.35334244,0.51375407,0.03867785,0.08393291,0.009135232,0.0011575113],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.41240248,0.0021240644,0.0036486504,0.0024140382,0.0021904234,0.0034527343,0.008321663,0.006198337,0.0023684348],"category_scores_gemma":[0.6400835,0.0016092184,0.0060105766,0.0034136712,0.007813836,0.0049831914,0.007074141,0.0060577802,0.00059978047],"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":[0.003139499,0.0004810475,0.1072096,0.004002241,0.007573558,0.001471674,0.0050427793,0.06816195,0.0023950345,0.3951339,0.007983134,0.39740548],"study_design_scores_gemma":[0.0009758107,0.0018113115,0.024979144,0.001810885,0.0033583299,0.0014703056,0.0005923744,0.2766283,0.0062637897,0.6656975,0.016123697,0.00028855048],"about_ca_topic_score_codex":0.002353916,"about_ca_topic_score_gemma":0.0020491749,"teacher_disagreement_score":0.41240248,"about_ca_system_score_codex":0.0022419847,"about_ca_system_score_gemma":0.0052088816,"threshold_uncertainty_score":0.72461236},"labels":[],"label_agreement":null},{"id":"W2911912076","doi":"10.1002/9781118445112.stat05746","title":"Errors in the Measurement of Covariates","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Covariate; Extrapolation; Statistics; Regression; Replication (statistics); Calibration; Observational error; Regression analysis; Econometrics; Computer science; Mathematics","score_opus":0.12153765697573136,"score_gpt":0.3875607195407237,"score_spread":0.2660230625649923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911912076","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":[0.021889936,0.0077106287,0.9099616,0.011216375,0.0026897895,0.0003991476,0.007644571,0.0011583809,0.037329547],"genre_scores_gemma":[0.6673507,0.010366069,0.26877376,0.005146598,0.0020477963,0.0015280768,0.007974225,0.0008720676,0.035940617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94309413,0.032527994,0.0048257615,0.0065112314,0.012114198,0.0009267114],"domain_scores_gemma":[0.8656466,0.080377616,0.015122929,0.031437594,0.007002528,0.00041273743],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.044386763,0.00091285247,0.0014432603,0.0026954573,0.00078232045,0.0034661535,0.0023128055,0.0017087929,0.0116336895],"category_scores_gemma":[0.26746807,0.0007177192,0.0010384085,0.0060779797,0.002592752,0.002535332,0.002996881,0.0031351903,0.0046442105],"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":[0.00023507881,0.0001131717,0.043955967,0.0012810765,0.0005381395,0.00036720716,0.0013057519,0.014997995,0.0012712185,0.50779647,0.053254824,0.37488303],"study_design_scores_gemma":[0.00009916575,0.00015543579,0.042538993,0.0022351523,0.00032276317,0.0007691797,0.00029909273,0.045373537,0.0075898776,0.75288105,0.14759544,0.00014034366],"about_ca_topic_score_codex":0.006051897,"about_ca_topic_score_gemma":0.0027670707,"teacher_disagreement_score":0.95561326,"about_ca_system_score_codex":0.002031129,"about_ca_system_score_gemma":0.002198832,"threshold_uncertainty_score":0.2347424},"labels":[],"label_agreement":null},{"id":"W2913308422","doi":"10.1002/cjs.11483","title":"Locally efficient semiparametric estimators for a class of Poisson models with measurement error","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institutes of Health; National Science Foundation","keywords":"Estimator; Poisson distribution; Covariate; Semiparametric regression; Observational error; Semiparametric model; Econometrics; Statistics; Poisson regression; Computer science; Sample (material); Errors-in-variables models; Mathematics; Medicine","score_opus":0.08521191812292296,"score_gpt":0.3139629932551407,"score_spread":0.22875107513221776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913308422","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":[0.007618094,0.00030057417,0.9911584,0.0002445684,0.000011128273,0.00005597321,0.00006857556,0.00007829619,0.00046440787],"genre_scores_gemma":[0.44915783,0.0011935132,0.5451792,0.00027589337,0.00015821413,0.0008659103,0.0006423311,0.00017101383,0.0023561867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98864424,0.008957224,0.0003463611,0.00074517075,0.0010587481,0.0002482653],"domain_scores_gemma":[0.89347744,0.09194981,0.006246996,0.0047568986,0.0031025514,0.00046633434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023648486,0.0007726656,0.0018906873,0.0026516593,0.00045521845,0.0023144213,0.0029114392,0.0015506883,0.003137789],"category_scores_gemma":[0.10977912,0.00069534907,0.0015108802,0.0019044643,0.0023385722,0.0031387033,0.0035719974,0.0027684213,0.000515059],"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":[0.00012050306,0.00016334785,0.008576906,0.0005864732,0.00043913926,0.00028056692,0.0006075896,0.35553256,0.0016798342,0.49992865,0.003382599,0.12870182],"study_design_scores_gemma":[0.00004140634,0.0000744446,0.0017555394,0.000116029645,0.00006538822,0.0001441159,0.000100281206,0.70914865,0.0005035286,0.2863438,0.0016678816,0.000038962404],"about_ca_topic_score_codex":0.0014464243,"about_ca_topic_score_gemma":0.0014778536,"teacher_disagreement_score":0.023648486,"about_ca_system_score_codex":0.0013046968,"about_ca_system_score_gemma":0.0017805523,"threshold_uncertainty_score":0.12506664},"labels":[],"label_agreement":null},{"id":"W2913778194","doi":"10.1093/biomet/asy059","title":"Testing for independence in arbitrary distributions","year":2018,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"HEC Montréal; McGill University","funders":"","keywords":"Mathematics; Contingency table; Copula (linguistics); Multilinear map; Independence (probability theory); Range (aeronautics); Statistical hypothesis testing; Dimension (graph theory); Marginal distribution; Statistics; Sample size determination; Applied mathematics; Random variable; Econometrics; Combinatorics","score_opus":0.15773052256857184,"score_gpt":0.4213547188414348,"score_spread":0.263624196272863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913778194","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":[0.040855233,0.0005757144,0.9540058,0.00045618787,0.00010418118,0.00010797646,0.00056386396,0.00027745293,0.0030536053],"genre_scores_gemma":[0.7451287,0.0009330398,0.24914187,0.0005044098,0.00050204044,0.00090772816,0.0015759928,0.00013339144,0.0011728114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9756821,0.015536159,0.0014673374,0.003530782,0.00309882,0.00068473053],"domain_scores_gemma":[0.80752254,0.16724314,0.009876154,0.010867075,0.003362675,0.0011284043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024703806,0.0009180208,0.0017053718,0.0033939292,0.0008421848,0.0022550412,0.0019239758,0.001268458,0.005403665],"category_scores_gemma":[0.16203536,0.00061131787,0.0015950842,0.0030466933,0.004295346,0.0042697955,0.0030924617,0.0023871458,0.00066916837],"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":[0.0005802271,0.00012478682,0.035278045,0.00062567194,0.0009948224,0.00094800495,0.0008250576,0.04351266,0.0017856716,0.6773243,0.0046364,0.23336434],"study_design_scores_gemma":[0.00010776489,0.00033621833,0.009835397,0.00018076473,0.0001373931,0.0005748703,0.00021805282,0.15696575,0.0014797397,0.82581645,0.0042586783,0.00008897955],"about_ca_topic_score_codex":0.00052209717,"about_ca_topic_score_gemma":0.0003303848,"teacher_disagreement_score":0.024703806,"about_ca_system_score_codex":0.00065444055,"about_ca_system_score_gemma":0.0015255676,"threshold_uncertainty_score":0.13064772},"labels":[],"label_agreement":null},{"id":"W2917498738","doi":"10.1080/01621459.2023.2183130","title":"Hypotheses Testing from Complex Survey Data Using Bootstrap Weights: A Unified Approach","year":2023,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","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":"Carleton University","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Stroke Foundation; National Science Foundation","keywords":"Type I and type II errors; Statistics; Categorical variable; Wald test; Statistical hypothesis testing; Computer science; Likelihood-ratio test; Goodness of fit; Mathematics; Nominal level; Econometrics; Data mining; Confidence interval","score_opus":0.5140034967384134,"score_gpt":0.45464535895251196,"score_spread":0.05935813778590143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917498738","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":[0.0008452565,0.00010246281,0.99848264,0.00008021119,0.00002223982,0.00014770919,0.000032653214,0.00007052051,0.00021641084],"genre_scores_gemma":[0.030700905,0.00045542783,0.9661479,0.00014433706,0.00016218582,0.0018473893,0.00017621128,0.0000765428,0.00028909164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9371778,0.050455432,0.0025007578,0.0026194998,0.006791479,0.00045504846],"domain_scores_gemma":[0.8929196,0.085682325,0.004270361,0.009983753,0.006358099,0.0007858313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.078073926,0.0017957439,0.003713791,0.0078077363,0.001369268,0.0035578387,0.004104731,0.0023429797,0.004140011],"category_scores_gemma":[0.16851321,0.0015520325,0.0025854106,0.005483031,0.0037221347,0.005558698,0.0060769,0.004467129,0.0012068029],"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":[0.00016184774,0.00030616098,0.0054999273,0.0011580351,0.00071687024,0.0004877547,0.001453807,0.044778615,0.001934794,0.5387155,0.0060670543,0.3987196],"study_design_scores_gemma":[0.00012993847,0.00025690722,0.0022651628,0.00042302243,0.00014641791,0.00022439733,0.00030173088,0.304254,0.0012566264,0.68004483,0.0105855055,0.0001114336],"about_ca_topic_score_codex":0.0011665762,"about_ca_topic_score_gemma":0.0011365894,"teacher_disagreement_score":0.078073926,"about_ca_system_score_codex":0.0013095083,"about_ca_system_score_gemma":0.0032102726,"threshold_uncertainty_score":0.41289932},"labels":[],"label_agreement":null},{"id":"W2917654268","doi":"","title":"A method of determining the winsorization threshold, with an application to domain estimation","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","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":"Statistics Canada; Université de Montréal","funders":"","keywords":"Estimator; Context (archaeology); Consistency (knowledge bases); Constant (computer programming); Estimation; Econometrics; Statistics; Sample (material); Population; Computer science; Mathematics; Economics; Physics; Geology","score_opus":0.041971658593731455,"score_gpt":0.3425491466978793,"score_spread":0.3005774881041478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917654268","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":[0.00081474474,0.000110173416,0.99840385,0.0000656359,0.00004055323,0.000023235938,0.000023176053,0.0001592623,0.00035934307],"genre_scores_gemma":[0.03130278,0.0003150877,0.9648091,0.00011368563,0.00014200545,0.00015691624,0.00012807852,0.0002630967,0.0027691976],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99657106,0.0013564308,0.00022593797,0.00087359874,0.00076961936,0.00020338615],"domain_scores_gemma":[0.9879453,0.007518468,0.0006028453,0.0015929792,0.001888791,0.00045154104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005886664,0.0011320858,0.001955668,0.0035364828,0.0015432334,0.0028858918,0.0029168453,0.0024217546,0.00636083],"category_scores_gemma":[0.029310385,0.00091045897,0.0014913994,0.0029735465,0.002872453,0.003462239,0.0042924206,0.0044994173,0.0023256883],"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":[0.00040842828,0.00017472575,0.0021896395,0.00055687357,0.00021585979,0.00023902344,0.00043523777,0.06997668,0.021805726,0.34209117,0.009449022,0.5524576],"study_design_scores_gemma":[0.00008053294,0.00011465278,0.0010775353,0.00010398248,0.00011046153,0.0004766105,0.0001290387,0.7361737,0.015006737,0.23355186,0.013060624,0.0001142523],"about_ca_topic_score_codex":0.0024757583,"about_ca_topic_score_gemma":0.0037280736,"teacher_disagreement_score":0.00636083,"about_ca_system_score_codex":0.0010534839,"about_ca_system_score_gemma":0.002464799,"threshold_uncertainty_score":0.031131983},"labels":[],"label_agreement":null},{"id":"W2918868073","doi":"10.1155/2019/7173416","title":"Improved Small Sample Inference on the Ratio of Two Coefficients of Variation of Two Independent Lognormal Distributions","year":2019,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Statistical Methods and Bayesian Inference","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":"York University","funders":"","keywords":"Log-normal distribution; Statistics; Mathematics; Generalizability theory; Inference; Sample size determination; Variation (astronomy); Reliability (semiconductor); Econometrics; Sample (material); Coefficient of variation; Statistical inference; Computer science; Artificial intelligence; Power (physics)","score_opus":0.0627976781574616,"score_gpt":0.3530260594081218,"score_spread":0.2902283812506602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918868073","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":[0.0070595765,0.00019461065,0.9920575,0.00008032026,0.000028263736,0.000035911227,0.000037099613,0.00010976309,0.0003969001],"genre_scores_gemma":[0.313808,0.00063801993,0.6827611,0.00025279858,0.00020747479,0.00031171463,0.00041428473,0.00015987016,0.0014467757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9836406,0.011589111,0.0005116263,0.002074193,0.0018728294,0.00031169262],"domain_scores_gemma":[0.8661043,0.11911125,0.0036089653,0.0056119915,0.004942446,0.0006210684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0259498,0.0010047279,0.0019477681,0.002855308,0.00068015786,0.0019422135,0.0029898083,0.0014492307,0.00252619],"category_scores_gemma":[0.13162264,0.00078871596,0.0017431936,0.0020627612,0.0025732915,0.003358359,0.0021017953,0.0033683777,0.00050487655],"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":[0.0008088371,0.0002606608,0.0129281385,0.00051424484,0.0006480751,0.0006334894,0.0006451822,0.5573031,0.004831512,0.23777275,0.002876016,0.18077812],"study_design_scores_gemma":[0.000093714414,0.00012305376,0.0018552985,0.000048119386,0.000096481876,0.0002365641,0.00003250956,0.9374482,0.0014673597,0.057028793,0.0015167929,0.000053167132],"about_ca_topic_score_codex":0.003783088,"about_ca_topic_score_gemma":0.0029792546,"teacher_disagreement_score":0.0259498,"about_ca_system_score_codex":0.0012727143,"about_ca_system_score_gemma":0.0018492717,"threshold_uncertainty_score":0.13723731},"labels":[],"label_agreement":null},{"id":"W2919652104","doi":"10.1017/asb.2018.41","title":"FREQUENTIST INFERENCE IN INSURANCE RATEMAKING MODELS ADJUSTING FOR MISREPRESENTATION","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"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":"Misrepresentation; Frequentist inference; Inference; Econometrics; Actuarial science; Underwriting; Context (archaeology); Statistical inference; Statistics; Computer science; Economics; Mathematics; Bayesian inference; Artificial intelligence; Bayesian probability; Law","score_opus":0.09612516858208361,"score_gpt":0.3923013302203296,"score_spread":0.296176161638246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919652104","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":[0.10302356,0.00019257815,0.8938278,0.0012250532,0.000038459842,0.00010421112,0.00014102817,0.00020139049,0.0012457964],"genre_scores_gemma":[0.85602367,0.0003081545,0.14053948,0.00031699086,0.00018162071,0.00021295037,0.0002891469,0.0000728739,0.0020551109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98245054,0.0128716715,0.00060508196,0.0019940236,0.0014438655,0.00063494255],"domain_scores_gemma":[0.7346615,0.2392242,0.011160832,0.0107034445,0.0034162062,0.0008337755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050666463,0.00087618036,0.0028038414,0.0022656778,0.0012722161,0.003056554,0.0041330378,0.0026319781,0.0033863555],"category_scores_gemma":[0.23633467,0.0012200959,0.001973009,0.0023738986,0.004077644,0.0049174847,0.0028212243,0.004449402,0.00045091048],"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":[0.00037487946,0.00028063598,0.022134183,0.00018481437,0.00045677178,0.0005792563,0.001117316,0.6062892,0.0005302214,0.29929376,0.0021363206,0.06662263],"study_design_scores_gemma":[0.00003790421,0.00003825505,0.0016389539,0.000036952995,0.0000440839,0.000086394604,0.00007112905,0.76705515,0.00023927541,0.23035839,0.0003661174,0.000027438937],"about_ca_topic_score_codex":0.009150524,"about_ca_topic_score_gemma":0.0066470737,"teacher_disagreement_score":0.050666463,"about_ca_system_score_codex":0.002400528,"about_ca_system_score_gemma":0.0015627086,"threshold_uncertainty_score":0.2679531},"labels":[],"label_agreement":null},{"id":"W2921359494","doi":"10.1002/9781118445112.stat05847","title":"The Theory of Estimating Functions","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian 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; University of Guelph","funders":"","keywords":"Overdispersion; Biostatistics; Unification; Inference; Computer science; Econometrics; Statistical inference; Parametric statistics; Statistics; Sampling (signal processing); Mathematics; Artificial intelligence; Count data; Programming language; Poisson distribution; Medicine","score_opus":0.07766754231727015,"score_gpt":0.3828921664326531,"score_spread":0.305224624115383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921359494","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":[0.0009425649,0.0012835476,0.99223447,0.001439274,0.00009857471,0.000044144323,0.0001967928,0.00007504313,0.0036856432],"genre_scores_gemma":[0.18022957,0.011403019,0.7884808,0.0018139984,0.0019335648,0.0019063799,0.0015324673,0.00042162812,0.012278466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93890285,0.050691333,0.0016556465,0.00332405,0.0046611447,0.00076496287],"domain_scores_gemma":[0.73614436,0.24092764,0.004908311,0.009992274,0.0074416352,0.0005857233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059361916,0.0025982799,0.0037958152,0.0061442824,0.0013921381,0.0061516915,0.004343083,0.0041759396,0.0065303245],"category_scores_gemma":[0.1755861,0.0018815957,0.0030286168,0.005822584,0.008998035,0.008183253,0.0040607085,0.008364606,0.0021850835],"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":[0.0000099155395,0.000014132687,0.00046290847,0.00016480462,0.0000794864,0.000042616135,0.00013991774,0.014375995,0.000044042066,0.96767676,0.0017156821,0.01527365],"study_design_scores_gemma":[0.00001126861,0.000015157441,0.00018702124,0.00013043717,0.00003149711,0.00004849655,0.000030676714,0.0657583,0.000080744954,0.92863977,0.005048902,0.000017807499],"about_ca_topic_score_codex":0.005514512,"about_ca_topic_score_gemma":0.0019265192,"teacher_disagreement_score":0.059361916,"about_ca_system_score_codex":0.0041762986,"about_ca_system_score_gemma":0.0041192984,"threshold_uncertainty_score":0.31393957},"labels":[],"label_agreement":null},{"id":"W2923957350","doi":"10.1177/0962280219889080","title":"Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis","year":2020,"lang":"en","type":"preprint","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":83,"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; McGill University Health Centre","funders":"National Institute on Minority Health and Health Disparities; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Fonds de Recherche du Québec - Santé; National Center for Research Resources; National Institute of General Medical Sciences; National Institute of Mental Health; Agency for Healthcare Research and Quality; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Centers for Disease Control and Prevention","keywords":"Standard deviation; Quartile; Statistics; Quantile; Sample mean and sample covariance; Sample size determination; Meta-analysis; Standard error; Normal distribution; Sample (material); Econometrics; Outcome (game theory); Mathematics; Pooled variance; Absolute deviation; Confidence interval; Medicine","score_opus":0.5909202286661872,"score_gpt":0.6355710282862124,"score_spread":0.04465079962002516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923957350","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":[0.0052594645,0.013704461,0.9770857,0.0015943154,0.0003576584,0.00043727653,0.0004910312,0.00051869615,0.00055139535],"genre_scores_gemma":[0.16799901,0.0109116305,0.8136118,0.0015393889,0.0006960683,0.0031291158,0.0011915413,0.00047094605,0.00045048798],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.79776275,0.17459701,0.008130092,0.009536839,0.009155491,0.00081770774],"domain_scores_gemma":[0.51198786,0.44607717,0.013753612,0.020231482,0.0073203095,0.0006294741],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1939273,0.0025133886,0.0069235833,0.009009775,0.0012063265,0.0065120575,0.005901113,0.00487688,0.002266109],"category_scores_gemma":[0.5278686,0.0020608068,0.010018218,0.00985101,0.003881638,0.0058613666,0.0047537694,0.0073429476,0.00065113825],"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":[0.0020679266,0.0002239863,0.030449014,0.0146965375,0.037944995,0.0008134469,0.0016039297,0.25656435,0.0016756916,0.16497035,0.016720425,0.47226942],"study_design_scores_gemma":[0.0012195098,0.0005420992,0.008267705,0.0040885075,0.00903687,0.0005359458,0.00030005717,0.3104346,0.003139248,0.64711845,0.014909332,0.0004077591],"about_ca_topic_score_codex":0.003293413,"about_ca_topic_score_gemma":0.002574065,"teacher_disagreement_score":0.8060727,"about_ca_system_score_codex":0.002798821,"about_ca_system_score_gemma":0.005268236,"threshold_uncertainty_score":0.99403113},"labels":[],"label_agreement":null},{"id":"W2924070044","doi":"10.1002/cjs.11493","title":"Empirical likelihood confidence intervals under imputation for missing survey data from stratified simple random sampling","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Employment and Social Development Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Categorical variable; Missing data; Statistics; Empirical likelihood; Mathematics; Simple random sample; Inference; Confidence interval; Sample size determination; Econometrics; Population; Computer science","score_opus":0.2856349706985442,"score_gpt":0.4403929466688877,"score_spread":0.15475797597034346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924070044","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":[0.017942313,0.00081414456,0.9795425,0.00018240286,0.00003760981,0.00008316534,0.00015851758,0.00035010412,0.0008891179],"genre_scores_gemma":[0.5218768,0.0011330652,0.47371382,0.00021022787,0.00018085001,0.00066995126,0.0011462427,0.00021514168,0.00085385505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95031554,0.038743086,0.0017186397,0.0029036598,0.005528543,0.0007905867],"domain_scores_gemma":[0.5973332,0.35763508,0.015807528,0.01677838,0.011271329,0.0011744789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07143269,0.0010309421,0.0030027223,0.0046311994,0.000791383,0.0031420665,0.00442081,0.0021947299,0.0038753024],"category_scores_gemma":[0.38831565,0.0009178293,0.0018435874,0.005614009,0.0040844316,0.0037842118,0.0031610797,0.0027150642,0.0007675875],"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":[0.00102029,0.00017059004,0.022512745,0.0010681805,0.000933986,0.0008118198,0.0012541162,0.30949545,0.00095887424,0.4570169,0.0047759684,0.19998115],"study_design_scores_gemma":[0.00012409218,0.00014729032,0.0052577145,0.00035707766,0.00014475975,0.0003382814,0.00018127804,0.7237116,0.0009383282,0.2663976,0.0023222328,0.00007968316],"about_ca_topic_score_codex":0.003157773,"about_ca_topic_score_gemma":0.0014905034,"teacher_disagreement_score":0.07143269,"about_ca_system_score_codex":0.0019415601,"about_ca_system_score_gemma":0.0019599856,"threshold_uncertainty_score":0.37777668},"labels":[],"label_agreement":null},{"id":"W2924255108","doi":"10.1515/ijb-2018-0090","title":"A Joint Poisson State-Space Modelling Approach to Analysis of Binomial Series with Random Cluster Sizes","year":2019,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Mathematics; Negative binomial distribution; Poisson distribution; Overdispersion; Statistics; Randomness; Series (stratigraphy); Binomial (polynomial); Binomial distribution; State space; Random effects model","score_opus":0.04154293692951882,"score_gpt":0.313282682610338,"score_spread":0.27173974568081916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924255108","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":[0.0015527444,0.000091756796,0.9977297,0.00011328065,0.000020270492,0.000026474945,0.00005919176,0.000052026247,0.0003545492],"genre_scores_gemma":[0.25871864,0.0020096507,0.7277377,0.00042777567,0.00036867266,0.0013243143,0.00090037996,0.00020233003,0.008310398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9940195,0.004079081,0.00022383635,0.00066240405,0.0007956672,0.00021946657],"domain_scores_gemma":[0.9833134,0.013248776,0.0012116502,0.001011954,0.001011628,0.00020254857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011974648,0.0009508052,0.0014793772,0.0018557875,0.0007999757,0.0017940497,0.0031276196,0.0014771768,0.004088734],"category_scores_gemma":[0.027437627,0.000762165,0.0023650695,0.0021706312,0.0018822835,0.0023286971,0.0021755146,0.0034312783,0.0007029839],"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":[0.000040325915,0.00006226935,0.0031250375,0.00018678873,0.00014988454,0.0001780597,0.00032798716,0.26715663,0.0011548066,0.6946583,0.0017453555,0.031214604],"study_design_scores_gemma":[0.000008224822,0.000041727606,0.00063402974,0.000030289897,0.000031762254,0.00006917608,0.000037271137,0.8233508,0.00029427337,0.17355923,0.0019110924,0.0000321411],"about_ca_topic_score_codex":0.005021083,"about_ca_topic_score_gemma":0.004088324,"teacher_disagreement_score":0.011974648,"about_ca_system_score_codex":0.0012305095,"about_ca_system_score_gemma":0.001970225,"threshold_uncertainty_score":0.06332874},"labels":[],"label_agreement":null},{"id":"W2933939320","doi":"10.1097/pts.0000000000000595","title":"Managing Missing Data in the Hospital Survey on Patient Safety Culture: A Simulation Study","year":2019,"lang":"en","type":"article","venue":"Journal of Patient Safety","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Agence Nationale de la Recherche","keywords":"Imputation (statistics); Missing data; Statistics; Mean squared error; Computer science; Data mining; Mathematics","score_opus":0.08128046112991551,"score_gpt":0.3899131081419704,"score_spread":0.3086326470120549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2933939320","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":[0.95835555,0.00025802606,0.03845342,0.0005728455,0.000028197612,0.0004844078,0.00051582156,0.00008312071,0.0012485675],"genre_scores_gemma":[0.97942823,0.00012956592,0.019071413,0.00009575356,0.000015366397,0.00062366144,0.00037761233,0.000011666851,0.0002467159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97582257,0.021945726,0.0004952574,0.00060174713,0.0005653428,0.0005693765],"domain_scores_gemma":[0.74829215,0.23300111,0.0064548436,0.0056913374,0.0048865625,0.0016739859],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.034787968,0.0008827287,0.0011915881,0.0013242692,0.00091208785,0.0011125135,0.0021681632,0.0023095105,0.002420118],"category_scores_gemma":[0.08109299,0.0007249781,0.0021756813,0.001726031,0.0010509539,0.0015172029,0.0016607889,0.001954623,0.00016720749],"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":[0.0022680932,0.0023205222,0.09445852,0.00032305962,0.0009096812,0.00046135017,0.0007923484,0.87886894,0.00033363793,0.005853892,0.00091992866,0.012490161],"study_design_scores_gemma":[0.000710358,0.0019437829,0.011773432,0.00013183642,0.00029021298,0.00018043205,0.00048522057,0.97985625,0.0007026326,0.0033472858,0.00051037484,0.000068126785],"about_ca_topic_score_codex":0.018228568,"about_ca_topic_score_gemma":0.008122895,"teacher_disagreement_score":0.96521205,"about_ca_system_score_codex":0.0025752587,"about_ca_system_score_gemma":0.0030350478,"threshold_uncertainty_score":0.18397856},"labels":[],"label_agreement":null},{"id":"W2936048997","doi":"10.6000/1929-6029.2019.08.01","title":"Bayesian Model Averaging for Selection of a Risk Prediction Model for Death within Thirty Days of Discharge: The SILVER-AMI Study","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Akaike information criterion; Bayesian information criterion; Statistics; Model selection; Observational study; Bayes' theorem; Selection (genetic algorithm); Statistic; Context (archaeology); Bayesian probability; Posterior probability; Mathematics; Econometrics; Medicine; Computer science; Artificial intelligence","score_opus":0.11784041491150081,"score_gpt":0.4817388968654298,"score_spread":0.363898481953929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936048997","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":[0.3910046,0.0007353135,0.60525554,0.0010129153,0.00006184044,0.0005284247,0.00044696056,0.0002044027,0.00074999646],"genre_scores_gemma":[0.6945981,0.00035533935,0.3022565,0.00030960576,0.00009949467,0.00097294204,0.0009774115,0.00006244079,0.00036817614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9810952,0.017386727,0.00038256778,0.00039006435,0.00060562976,0.00013985163],"domain_scores_gemma":[0.96095043,0.03423488,0.0016070367,0.0018051176,0.001002464,0.00039999862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039452575,0.00069546635,0.0015786407,0.0010171711,0.0005927509,0.0007679833,0.0016571332,0.00061313726,0.00090583955],"category_scores_gemma":[0.07481188,0.00045823577,0.001960687,0.0010638823,0.00039028117,0.00060195324,0.001560291,0.0018215629,0.00013381639],"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":[0.0038573537,0.0011365447,0.25693667,0.0005826972,0.005794968,0.001666542,0.0017702422,0.43202338,0.003515659,0.03757101,0.007398759,0.24774618],"study_design_scores_gemma":[0.00051266194,0.0007332224,0.014246308,0.00008353674,0.00049079483,0.00017253544,0.00012399074,0.9495738,0.0006471014,0.03186957,0.001495932,0.000050433977],"about_ca_topic_score_codex":0.005653444,"about_ca_topic_score_gemma":0.006767219,"teacher_disagreement_score":0.039452575,"about_ca_system_score_codex":0.0005236229,"about_ca_system_score_gemma":0.0017587127,"threshold_uncertainty_score":0.20864761},"labels":[],"label_agreement":null},{"id":"W2944599912","doi":"10.1186/s12874-019-0742-8","title":"The relationship between statistical power and predictor distribution in multilevel logistic regression: a simulation-based approach","year":2019,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Learning Partnership; University of British Columbia","funders":"Lawson Foundation","keywords":"Logistic regression; Multilevel model; Statistics; Statistical power; Regression analysis; Computer science; Regression; Econometrics; Distribution (mathematics); Psychology; Mathematics","score_opus":0.6827948773904509,"score_gpt":0.5984411841336096,"score_spread":0.08435369325684128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944599912","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":[0.049510494,0.00058277143,0.94400895,0.0012644514,0.000065993125,0.0005166554,0.00012161329,0.00036720253,0.0035617806],"genre_scores_gemma":[0.66427743,0.000472717,0.33192605,0.00034466278,0.00006445596,0.0020824547,0.0001544432,0.00018023672,0.0004975418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9282547,0.06467404,0.0012656067,0.0019016864,0.003407959,0.0004960469],"domain_scores_gemma":[0.5264463,0.4541779,0.007282035,0.007094158,0.0043258118,0.00067380315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.072849885,0.0006925935,0.001352351,0.0019857944,0.0007429935,0.0025075234,0.0025955515,0.0022159342,0.004641956],"category_scores_gemma":[0.30822593,0.0007606735,0.0022785487,0.0017494855,0.002498228,0.0026549578,0.0028540373,0.0027492323,0.0004561741],"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":[0.0010956653,0.00038542564,0.07106851,0.001791243,0.0020051913,0.00064242556,0.002361814,0.64578545,0.0019038917,0.13704696,0.0037409228,0.13217247],"study_design_scores_gemma":[0.00013676946,0.0003579888,0.004653884,0.00037402587,0.00022611492,0.00020722656,0.00016235287,0.91903824,0.0009488958,0.0717715,0.0020838531,0.00003907875],"about_ca_topic_score_codex":0.00239342,"about_ca_topic_score_gemma":0.0017157823,"teacher_disagreement_score":0.072849885,"about_ca_system_score_codex":0.0018448541,"about_ca_system_score_gemma":0.0024103285,"threshold_uncertainty_score":0.3852716},"labels":[],"label_agreement":null},{"id":"W2946269157","doi":"10.1080/00949655.2019.1615911","title":"R package for analysis of data with mixed measurement error and misclassification in covariates: augSIMEX","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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; Inference; Observational error; Statistics; Mathematics; R package; Errors-in-variables models; Extrapolation; Data mining; Computer science; Econometrics; Artificial intelligence","score_opus":0.21941753681024354,"score_gpt":0.43850791572289216,"score_spread":0.21909037891264863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946269157","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003555588,0.0020263118,0.6129858,0.0022968973,0.0008529456,0.0013351511,0.21281183,0.15423645,0.009899095],"genre_scores_gemma":[0.027701449,0.0014870353,0.73581064,0.0021075937,0.00037067762,0.009005934,0.09565762,0.11608109,0.011777954],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942409,0.0031327745,0.0005794061,0.0007353959,0.0010942193,0.00021730403],"domain_scores_gemma":[0.9448835,0.04460462,0.0029604952,0.0041570407,0.0029026165,0.00049171416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01142704,0.0027450167,0.003005436,0.003241337,0.0006039303,0.002662737,0.003490594,0.0012657112,0.14214267],"category_scores_gemma":[0.074330576,0.0018800114,0.0030601216,0.0032805637,0.0011296102,0.0023257711,0.0031077655,0.0035133827,0.054208558],"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":[0.00047759459,0.00008899772,0.0044069546,0.003352732,0.0014436989,0.0003773417,0.0003029585,0.011409685,0.00085613894,0.019029105,0.85916793,0.09908686],"study_design_scores_gemma":[0.00095060526,0.00019160031,0.011501579,0.0014828085,0.0008677906,0.0010874397,0.00011398188,0.07581811,0.0031628031,0.13249695,0.77200985,0.0003165031],"about_ca_topic_score_codex":0.0045905486,"about_ca_topic_score_gemma":0.006122534,"teacher_disagreement_score":0.14214267,"about_ca_system_score_codex":0.00096027926,"about_ca_system_score_gemma":0.0042409548,"threshold_uncertainty_score":0.4755146},"labels":[],"label_agreement":null},{"id":"W2946435821","doi":"10.1002/sim.8203","title":"Adjusting for differential misclassification in matched case‐control studies utilizing health administrative data","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","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":true,"ca_institutions":"University of Manitoba; Nova Scotia Health Authority; Dalhousie University; Vancouver Coastal Health; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Multiple Sclerosis Society","keywords":"Bayesian probability; Observational study; Bayes' theorem; Computer science; Disease; Leverage (statistics); Econometrics; Differential (mechanical device); Data mining; Medicine; Statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.3835311399523762,"score_gpt":0.535968901803228,"score_spread":0.15243776185085178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946435821","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":[0.06818583,0.0035387487,0.9235108,0.0018946478,0.00041535436,0.0005810394,0.00028329663,0.00025845767,0.0013318168],"genre_scores_gemma":[0.6930929,0.0013765673,0.3013871,0.0015890035,0.00025657463,0.0010438889,0.00047800737,0.00008960668,0.00068638835],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81690437,0.14668736,0.009840374,0.013514549,0.011596563,0.0014566664],"domain_scores_gemma":[0.71755934,0.21284547,0.029659273,0.0339266,0.005376654,0.0006326169],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19008614,0.0012524399,0.0019486888,0.0038860342,0.0014309998,0.002423414,0.0035649643,0.0021977718,0.0009820921],"category_scores_gemma":[0.42861068,0.0010784838,0.0032919091,0.0047238027,0.0028719476,0.002951716,0.0033491147,0.0020910664,0.0002109768],"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":[0.001516542,0.00034732668,0.39749214,0.0023441704,0.016814178,0.001001334,0.00574596,0.03765221,0.0024111895,0.1924227,0.0036084366,0.3386438],"study_design_scores_gemma":[0.00058367965,0.0010742865,0.18145615,0.00137509,0.006421447,0.0011823103,0.0010653207,0.16328652,0.006310513,0.61949766,0.017457293,0.00028967683],"about_ca_topic_score_codex":0.0072417436,"about_ca_topic_score_gemma":0.006132742,"teacher_disagreement_score":0.8099139,"about_ca_system_score_codex":0.001708,"about_ca_system_score_gemma":0.0027307787,"threshold_uncertainty_score":0.998768},"labels":[],"label_agreement":null},{"id":"W2948977041","doi":"10.1007/s13171-019-00170-7","title":"Two Stage Cluster Sampling Based Asymptotic Inferences in Survey Population Models for Longitudinal Count and Categorical Data","year":2019,"lang":"en","type":"article","venue":"Sankhya A","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland; Carleton University","funders":"","keywords":"Multinomial distribution; Statistics; Categorical variable; Mathematics; Estimator; Count data; Generalized estimating equation; Population; Poisson sampling; Cluster sampling; Marginal model; Generalized linear model; Estimating equations; Regression analysis; Sampling (signal processing); Econometrics; Importance sampling; Computer science; Poisson distribution; Monte Carlo method; Slice sampling","score_opus":0.31768935752834426,"score_gpt":0.45194382325553434,"score_spread":0.13425446572719008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948977041","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":[0.00979057,0.0001692067,0.989181,0.00013121685,0.0000350529,0.00010483923,0.00006714665,0.00013211377,0.00038895314],"genre_scores_gemma":[0.28184876,0.00063571084,0.7096303,0.00033333094,0.00022784913,0.0013755378,0.00085765455,0.00033115924,0.0047597904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9653945,0.02791949,0.00081415114,0.0029102121,0.0022866142,0.0006751095],"domain_scores_gemma":[0.7461544,0.2302833,0.004013992,0.012565042,0.005905272,0.0010779713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04857618,0.0013620541,0.0032984319,0.0032218285,0.002065414,0.0028591496,0.006895621,0.0025033487,0.0048060175],"category_scores_gemma":[0.20541246,0.0023087964,0.0036353027,0.0035484855,0.0048859366,0.00432758,0.004127695,0.005609855,0.0005998968],"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":[0.0010965498,0.00042301835,0.014168551,0.00081512996,0.0012723587,0.00054740714,0.0024509758,0.17815049,0.0016090564,0.6738903,0.004960995,0.12061517],"study_design_scores_gemma":[0.00013952106,0.0001249899,0.0022127794,0.000069152884,0.00019303692,0.00015229886,0.00015014359,0.7349447,0.00078793766,0.25974783,0.0014136325,0.00006389165],"about_ca_topic_score_codex":0.012698593,"about_ca_topic_score_gemma":0.01131529,"teacher_disagreement_score":0.04857618,"about_ca_system_score_codex":0.0024990612,"about_ca_system_score_gemma":0.0043901685,"threshold_uncertainty_score":0.25689846},"labels":[],"label_agreement":null},{"id":"W2950664191","doi":"10.2139/ssrn.2864820","title":"The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Bayesian Inference","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 Waterloo","funders":"Rheinische Friedrich-Wilhelms-Universität Bonn","keywords":"Regression discontinuity design; Variable (mathematics); Statistics; Discontinuity (linguistics); Regression; Mathematics; Observational error; Econometrics; Computer science; Mathematical analysis","score_opus":0.0836732141915292,"score_gpt":0.3420550317931291,"score_spread":0.25838181760159995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950664191","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":[0.12703222,0.0012126385,0.862375,0.0036699213,0.00048922206,0.00061757176,0.00053354714,0.0003664193,0.0037034578],"genre_scores_gemma":[0.8828518,0.00047933,0.10835628,0.0011123297,0.0003366913,0.0014874996,0.0003672812,0.000115284005,0.0048933965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.905692,0.07661026,0.0023768048,0.009113053,0.003942527,0.0022653635],"domain_scores_gemma":[0.64318854,0.3082115,0.017149016,0.025969757,0.003649071,0.0018321449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11140461,0.0015776017,0.0052746674,0.0023338299,0.0017400607,0.004499533,0.007984946,0.008350268,0.0066793854],"category_scores_gemma":[0.30615404,0.0018435393,0.0020472696,0.0030024198,0.008606848,0.0053358003,0.005305213,0.009585792,0.00091571716],"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":[0.0055514374,0.0014509817,0.05303546,0.0011110055,0.0022053996,0.0013744087,0.0029890363,0.041363213,0.001541175,0.78789055,0.0047163214,0.09677102],"study_design_scores_gemma":[0.0019416325,0.0018700376,0.013924422,0.00035471024,0.0012864494,0.00056928216,0.0006249366,0.30397958,0.0017302247,0.6698064,0.0036810813,0.00023124267],"about_ca_topic_score_codex":0.002071079,"about_ca_topic_score_gemma":0.001282639,"teacher_disagreement_score":0.11140461,"about_ca_system_score_codex":0.0015156391,"about_ca_system_score_gemma":0.0020983077,"threshold_uncertainty_score":0.58917093},"labels":[],"label_agreement":null},{"id":"W2953435350","doi":"10.1002/cjs.11512","title":"On the use of priors in goodness‐of‐fit tests","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of the Fraser Valley; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Mathematics; Test statistic; Goodness of fit; Statistics; Anderson–Darling test; Statistic; Null distribution; Statistical hypothesis testing; Sample size determination; Applied mathematics; Econometrics; Bayesian probability","score_opus":0.14863213683340357,"score_gpt":0.3412654381112434,"score_spread":0.1926333012778398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953435350","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":[0.0056162304,0.00071476377,0.9901219,0.0012222799,0.00007371628,0.00006705629,0.000053443575,0.0001216401,0.0020089252],"genre_scores_gemma":[0.3604548,0.001912675,0.6327556,0.001425224,0.00066525635,0.00071536493,0.00025550945,0.0004668911,0.0013487774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.88302624,0.10092008,0.002666195,0.0039504743,0.008422841,0.0010141216],"domain_scores_gemma":[0.4230015,0.54268706,0.00922588,0.016896453,0.0069447435,0.0012443992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11818279,0.0016201299,0.0026767268,0.005739036,0.0015048763,0.0058381394,0.00405454,0.0042046746,0.0031302006],"category_scores_gemma":[0.48947456,0.002058474,0.0018221759,0.0047523775,0.015111589,0.010625589,0.0073319455,0.0087240925,0.0007583307],"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":[0.00019041165,0.00005439758,0.003866576,0.0002541481,0.00017408603,0.00027890966,0.00046353313,0.06728661,0.00052427594,0.86907464,0.0020998165,0.05573262],"study_design_scores_gemma":[0.000048882634,0.00005883985,0.001082707,0.00031896375,0.000040460716,0.00019432408,0.00008681532,0.13519387,0.0007791502,0.8595592,0.0025669034,0.00007001693],"about_ca_topic_score_codex":0.0030114069,"about_ca_topic_score_gemma":0.0020223362,"teacher_disagreement_score":0.11818279,"about_ca_system_score_codex":0.0030780258,"about_ca_system_score_gemma":0.0031000623,"threshold_uncertainty_score":0.6250178},"labels":[],"label_agreement":null},{"id":"W2953859485","doi":"10.1002/cjs.11513","title":"Synthetic data method to incorporate external information into a current study","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Imputation (statistics); Missing data; Statistics; Context (archaeology); Computer science; Data set; Regression; Regression analysis; Data mining; Set (abstract data type); Mathematics; Geography","score_opus":0.08839897051169125,"score_gpt":0.3934734528448364,"score_spread":0.30507448233314516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953859485","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":[0.016863177,0.00054058566,0.9786531,0.0009774297,0.0002929027,0.00022332965,0.0009773215,0.0002234057,0.0012487979],"genre_scores_gemma":[0.47392276,0.00052905036,0.5164371,0.00094096776,0.0005089175,0.0020359408,0.003632116,0.00018648226,0.0018066632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9830216,0.014423521,0.0005137541,0.001126823,0.00077218044,0.00014218435],"domain_scores_gemma":[0.8375254,0.1399585,0.0057394644,0.010856533,0.00478756,0.001132497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042634003,0.0007860916,0.0013340261,0.0034124528,0.0007290606,0.0024220967,0.0027717932,0.0021418189,0.005320844],"category_scores_gemma":[0.1520304,0.000675092,0.0017102471,0.0027878403,0.0014590862,0.0021604071,0.0027361873,0.0022326112,0.00055512425],"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":[0.0010039634,0.00042112384,0.05547618,0.0008527702,0.0012771897,0.001169218,0.0009611512,0.47191605,0.0011348267,0.30173868,0.014705312,0.14934362],"study_design_scores_gemma":[0.00013435776,0.00016511322,0.0019482637,0.00016957706,0.0001357819,0.00022015147,0.00012291911,0.84512424,0.000425336,0.14285424,0.008652515,0.000047551788],"about_ca_topic_score_codex":0.003066841,"about_ca_topic_score_gemma":0.0023593004,"teacher_disagreement_score":0.042634003,"about_ca_system_score_codex":0.0012516679,"about_ca_system_score_gemma":0.0015935419,"threshold_uncertainty_score":0.22547281},"labels":[],"label_agreement":null},{"id":"W2953987477","doi":"10.22215/etd/2018-12930","title":"Sample Size Determination for Markovian Queueing Models","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Queueing theory; Layered queueing network; Sample size determination; Computer science; Sample (material); Mean value analysis; Inference; Applied mathematics; Algorithm; Statistics; Mathematics; Artificial intelligence; Physics; Computer network","score_opus":0.08602891992924673,"score_gpt":0.41641734262296903,"score_spread":0.3303884226937223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953987477","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":[0.006125672,0.00034532978,0.9920964,0.00029520047,0.0000500656,0.00008119756,0.00006462611,0.00018539165,0.00075615547],"genre_scores_gemma":[0.30995896,0.0010859113,0.68268055,0.00076891144,0.00050995336,0.0011684488,0.00072697154,0.0004450392,0.0026551932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9830442,0.010207506,0.00072540273,0.0030468826,0.0023351305,0.0006409283],"domain_scores_gemma":[0.85436416,0.12716193,0.0046345303,0.007926859,0.004754975,0.0011575143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027287474,0.0013921878,0.0027199748,0.0022324326,0.0012679832,0.0025286279,0.0038547446,0.0027314215,0.0028528136],"category_scores_gemma":[0.19111456,0.0013149844,0.0016381751,0.0015502004,0.0032157395,0.004781635,0.0034395077,0.0047891224,0.0005713018],"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":[0.00050434255,0.00023324178,0.0067879264,0.0005944291,0.00029775372,0.00036528546,0.0007549585,0.23280783,0.0040751337,0.6568531,0.0061379094,0.09058803],"study_design_scores_gemma":[0.000052754363,0.00008824782,0.0007404955,0.00008499613,0.000047001762,0.000121546684,0.00006236284,0.747919,0.0016877629,0.2472254,0.001929565,0.000040907133],"about_ca_topic_score_codex":0.0038436279,"about_ca_topic_score_gemma":0.0032122203,"teacher_disagreement_score":0.027287474,"about_ca_system_score_codex":0.0029720778,"about_ca_system_score_gemma":0.0029142546,"threshold_uncertainty_score":0.14431167},"labels":[],"label_agreement":null},{"id":"W295569394","doi":"10.1002/cjs.10115","title":"Optimal estimating functions in incomplete data and length biased sampling data problems","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Mathematics; Function (biology); Sampling (signal processing); Missing data; Score; Econometrics; Computer science","score_opus":0.42363944097747597,"score_gpt":0.3841857796871243,"score_spread":0.03945366129035166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W295569394","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":[0.013534375,0.0010173575,0.9837711,0.00062760536,0.000028948418,0.00008807051,0.00011445686,0.00009846306,0.0007197274],"genre_scores_gemma":[0.32671,0.0028407124,0.66455954,0.00039698725,0.00030535058,0.0010405834,0.0010582369,0.00028902196,0.0027996143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95983976,0.0333382,0.0012766733,0.0023123212,0.0024501143,0.000782837],"domain_scores_gemma":[0.7782448,0.20049042,0.007464461,0.006712587,0.006239516,0.00084820914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07070881,0.0016867797,0.0045751864,0.0052591306,0.0010018427,0.0028778187,0.003687562,0.0029626924,0.002901702],"category_scores_gemma":[0.21589951,0.0017250914,0.002348653,0.0045915726,0.005128848,0.005830539,0.00374079,0.0035949908,0.00056585437],"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":[0.00024283514,0.00008429739,0.007106389,0.0005404005,0.0004474864,0.00034818234,0.0005513107,0.3514116,0.0004275336,0.5367019,0.002763442,0.09937452],"study_design_scores_gemma":[0.000067562345,0.000065707565,0.0017212034,0.00017986666,0.00008874697,0.00011702553,0.000096731266,0.5707946,0.0004514893,0.42393363,0.0024285186,0.000054980574],"about_ca_topic_score_codex":0.0060015586,"about_ca_topic_score_gemma":0.0030195422,"teacher_disagreement_score":0.07070881,"about_ca_system_score_codex":0.003053755,"about_ca_system_score_gemma":0.0035952574,"threshold_uncertainty_score":0.3739484},"labels":[],"label_agreement":null},{"id":"W2960008033","doi":"10.1002/cjs.11517","title":"Instrumental variable estimation in ordinal probit models with mismeasured predictors","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Manitoba; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Tianjin University","keywords":"Covariate; Estimator; Ordinal data; Econometrics; Statistics; Instrumental variable; Probit model; Probit; Observational error; Normality; Errors-in-variables models; Ordinal regression; Variables; Ordered probit; Variable (mathematics); Polychoric correlation; Estimation; Mathematics; Economics; Correlation","score_opus":0.031578345626739786,"score_gpt":0.2670234926401559,"score_spread":0.23544514701341612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960008033","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":[0.010275843,0.00041378764,0.9877214,0.00044800565,0.000046498422,0.000058396778,0.00018776339,0.00016408735,0.0006842113],"genre_scores_gemma":[0.5915639,0.0013001837,0.39715976,0.0004311723,0.00027152128,0.0008363859,0.0010378927,0.00025272893,0.0071465517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9818071,0.015417449,0.00045322586,0.00085228943,0.001001116,0.00046885465],"domain_scores_gemma":[0.8300774,0.15550838,0.0072842212,0.0039248983,0.0025503158,0.0006548063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03317902,0.0011052849,0.0028426992,0.0023085184,0.0009789346,0.002771353,0.0043193717,0.0016974605,0.0050982507],"category_scores_gemma":[0.13586076,0.0010895173,0.001508476,0.0037934356,0.002974215,0.0021499353,0.0030315104,0.0031081145,0.0007179281],"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":[0.00030804955,0.0001585781,0.016867127,0.000387684,0.0005901752,0.0007142708,0.0004317876,0.4690953,0.0003118606,0.45001653,0.0043761525,0.0567425],"study_design_scores_gemma":[0.00004896773,0.000031898297,0.00080965995,0.00009292061,0.00006330117,0.000041119783,0.000062217165,0.8375104,0.00018223411,0.15962783,0.0014990857,0.000030425901],"about_ca_topic_score_codex":0.017372832,"about_ca_topic_score_gemma":0.015107914,"teacher_disagreement_score":0.03317902,"about_ca_system_score_codex":0.0020416607,"about_ca_system_score_gemma":0.0033482637,"threshold_uncertainty_score":0.17546952},"labels":[],"label_agreement":null},{"id":"W2963656411","doi":"10.1002/cjs.11341","title":"Approximate Bayesian estimation in large coloured graphical Gaussian models","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Graphical model; Gaussian; Rate of convergence; Bayesian probability; Applied mathematics; Matrix (chemical analysis); Bounded function; Statistics; Computer science; Mathematical analysis","score_opus":0.0542792138655318,"score_gpt":0.3402956228710384,"score_spread":0.28601640900550657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963656411","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":[0.008078086,0.0001773341,0.99093854,0.00015081702,0.000014265287,0.000016839902,0.00007367639,0.00013044883,0.00041996842],"genre_scores_gemma":[0.5025518,0.0012381058,0.48922333,0.00030084365,0.00019050142,0.0003127924,0.000899439,0.00030338715,0.004979779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945452,0.0032083418,0.0001776653,0.0008815606,0.00089334743,0.00029385506],"domain_scores_gemma":[0.9489337,0.042364053,0.0028992551,0.0034210086,0.0018654407,0.0005164931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013483483,0.0011449804,0.0021444885,0.0022168423,0.00070779474,0.0027667996,0.003902033,0.0021990852,0.0029316468],"category_scores_gemma":[0.08271977,0.0013433702,0.0015649855,0.0024981354,0.004364837,0.0047139153,0.0035882401,0.003851659,0.00069840706],"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":[0.00010054891,0.000030766514,0.0013039517,0.00014359955,0.000091782305,0.00011133093,0.00016690262,0.5950082,0.00087900524,0.37594125,0.0012338657,0.024988726],"study_design_scores_gemma":[0.000013745676,0.000012844934,0.00036222336,0.000025238465,0.000010903725,0.000023998982,0.000012873765,0.7714948,0.00027626785,0.22729877,0.0004469005,0.00002141824],"about_ca_topic_score_codex":0.009639,"about_ca_topic_score_gemma":0.007970866,"teacher_disagreement_score":0.013483483,"about_ca_system_score_codex":0.002584095,"about_ca_system_score_gemma":0.0019348733,"threshold_uncertainty_score":0.071308315},"labels":[],"label_agreement":null},{"id":"W2963777145","doi":"","title":"Multiple Imputation of Missing Values in Household Data with Structural Zeros","year":2017,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorical variable; Imputation (statistics); Multivariate statistics; Missing data; Statistics; Econometrics; Gibbs sampling; Multivariate normal distribution; Population; Mathematics; Latent variable; Computer science; Demography; Bayesian probability","score_opus":0.6315137670957544,"score_gpt":0.5150164246912262,"score_spread":0.11649734240452814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963777145","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":[0.012449163,0.0001423868,0.986629,0.00022018538,0.000032488253,0.000024723278,0.00013922816,0.00008972792,0.00027298703],"genre_scores_gemma":[0.3541175,0.00044591803,0.6421505,0.0002260018,0.00012815253,0.00037569442,0.0010254134,0.00010117581,0.0014295827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914289,0.0062615513,0.0003449132,0.0008373684,0.00086279336,0.00026448187],"domain_scores_gemma":[0.96647495,0.02468163,0.0023562242,0.0052316557,0.00090468087,0.00035087368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012823623,0.00054001855,0.0017050877,0.0019339214,0.0010804927,0.0013770767,0.003633284,0.0013981882,0.0030179122],"category_scores_gemma":[0.05255087,0.0009167857,0.001320699,0.0036124175,0.0015489085,0.0025307743,0.0037537212,0.0030746665,0.00046260224],"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":[0.0004109507,0.00030643825,0.03241187,0.0005025009,0.00061605085,0.00081395486,0.0016402826,0.19775207,0.0018851487,0.5129794,0.006169263,0.24451213],"study_design_scores_gemma":[0.00006227901,0.000077023666,0.002124882,0.000107201944,0.00006876114,0.00025520028,0.00013452306,0.47785014,0.0013368084,0.5144205,0.0035221323,0.000040487444],"about_ca_topic_score_codex":0.0013213067,"about_ca_topic_score_gemma":0.0027025794,"teacher_disagreement_score":0.012823623,"about_ca_system_score_codex":0.0006707234,"about_ca_system_score_gemma":0.001505869,"threshold_uncertainty_score":0.06781864},"labels":[],"label_agreement":null},{"id":"W2963805627","doi":"10.1002/cjs.11135","title":"Goodness‐of‐fit testing based on a weighted bootstrap: A fast large‐sample alternative to the parametric bootstrap","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Goodness of fit; Parametric statistics; Sample size determination; Resampling; Mathematics; Cumulative distribution function; Statistics; Empirical distribution function; Bivariate analysis; Univariate; Monte Carlo method; Econometrics; Multivariate statistics; Probability density function","score_opus":0.17588891536309534,"score_gpt":0.3725724726201711,"score_spread":0.1966835572570758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963805627","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":[0.0146603845,0.00019839981,0.98352736,0.00014715306,0.000046020123,0.00014507309,0.0000658278,0.0002760791,0.0009338399],"genre_scores_gemma":[0.31056955,0.00024864465,0.6866462,0.00015539823,0.0001405334,0.00085368427,0.00034320797,0.00027791472,0.0007648228],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96947587,0.024849419,0.00068609527,0.0012329116,0.003321075,0.0004346173],"domain_scores_gemma":[0.88680416,0.091961294,0.0040508024,0.010509692,0.0058920057,0.0007820291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02633741,0.0011108684,0.002360075,0.0038615658,0.000871484,0.0019024486,0.0026068948,0.0017551933,0.005771843],"category_scores_gemma":[0.15422873,0.00066801626,0.001748191,0.0042614224,0.0024895938,0.0034413484,0.003543017,0.0028546944,0.0009451714],"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":[0.0010750472,0.00031843683,0.016150735,0.00052720413,0.00082356954,0.0008923281,0.0006187959,0.24607523,0.0047488506,0.2634898,0.006966735,0.45831323],"study_design_scores_gemma":[0.00016377223,0.00043305583,0.005628892,0.0001197524,0.00007477787,0.00043263438,0.00014260321,0.82742053,0.0021144531,0.15890272,0.0044715935,0.00009523005],"about_ca_topic_score_codex":0.0018614838,"about_ca_topic_score_gemma":0.0016779054,"teacher_disagreement_score":0.02633741,"about_ca_system_score_codex":0.0008195993,"about_ca_system_score_gemma":0.0018426558,"threshold_uncertainty_score":0.13928723},"labels":[],"label_agreement":null},{"id":"W2964233174","doi":"10.1111/insr.12293","title":"Small Area Quantile Estimation","year":2018,"lang":"en","type":"article","venue":"International Statistical Review","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":"University of British Columbia","funders":"Project 211; Program of Shanghai Subject Chief Scientist; Yunnan University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Small area estimation; Quantile; Pooling; Statistics; Estimator; Computer science; Sample size determination; Resampling; Sample (material); Econometrics; Sampling (signal processing); Contrast (vision); Population; Mean squared error; Mathematics; Artificial intelligence","score_opus":0.1847528740441207,"score_gpt":0.46193052331383994,"score_spread":0.2771776492697192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964233174","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":[0.0038340902,0.0020557782,0.9914869,0.00020190905,0.000070469716,0.000050916016,0.00025013534,0.0002988448,0.0017509595],"genre_scores_gemma":[0.42277917,0.008137913,0.553285,0.0005329759,0.000744287,0.0006541682,0.0025968235,0.00038604593,0.01088359],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969446,0.0018508051,0.00011455221,0.0004998203,0.0004533895,0.00013679166],"domain_scores_gemma":[0.99218386,0.0055428245,0.0005776939,0.000783986,0.00080428866,0.00010737173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004740358,0.00064181327,0.0012708083,0.001890843,0.00030460255,0.0010981251,0.0018333872,0.00089866173,0.006959062],"category_scores_gemma":[0.023346692,0.0003632078,0.0011118635,0.00236475,0.0006702499,0.001363461,0.0011688926,0.0012499297,0.0015238277],"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":[0.00018861656,0.00011211379,0.013454801,0.0008551456,0.00055674714,0.00018442656,0.00025228638,0.12979606,0.001335253,0.22488889,0.017825603,0.61055005],"study_design_scores_gemma":[0.00007544416,0.00012566299,0.00969782,0.0002414457,0.00017623907,0.00021793132,0.00011796258,0.71814406,0.0012763247,0.23525725,0.03461658,0.00005321977],"about_ca_topic_score_codex":0.0030199026,"about_ca_topic_score_gemma":0.0018470935,"teacher_disagreement_score":0.006959062,"about_ca_system_score_codex":0.0005613225,"about_ca_system_score_gemma":0.0007974105,"threshold_uncertainty_score":0.025069714},"labels":[],"label_agreement":null},{"id":"W2964252496","doi":"10.1007/s13571-018-0152-7","title":"A Variant of AIC Based on the Bayesian Marginal Likelihood","year":2018,"lang":"en","type":"article","venue":"Sankhya B","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Japan Society for the Promotion of Science","keywords":"Frequentist inference; Marginal likelihood; Mathematics; Bayesian probability; Bayesian linear regression; Statistics; Econometrics; Bayes factor; Consistency (knowledge bases); Residual; Prior probability; Bayesian information criterion; Bayesian average; Bayesian inference; Algorithm","score_opus":0.04366308908866311,"score_gpt":0.33932216502564677,"score_spread":0.29565907593698365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964252496","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":[0.0015164271,0.0008092444,0.9952839,0.00022504356,0.00017205703,0.000016609305,0.0001593208,0.00022573309,0.0015916666],"genre_scores_gemma":[0.20807308,0.002268128,0.77892566,0.00071074424,0.0010968557,0.0002766501,0.0011597094,0.00092511246,0.0065641296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895041,0.0066111116,0.0004950826,0.001163032,0.001941282,0.0002854886],"domain_scores_gemma":[0.9688506,0.022770528,0.0009125639,0.0036500746,0.003435004,0.00038130782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077970484,0.0012737219,0.0029118366,0.0034841641,0.0012423333,0.0035279787,0.0040884367,0.0027493043,0.0097682],"category_scores_gemma":[0.049811397,0.0009722414,0.0015619039,0.0049223,0.0021388412,0.003825979,0.002191446,0.004661986,0.0027704479],"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":[0.00019229349,0.00008079942,0.0051774397,0.00067972904,0.00068838196,0.00034948866,0.00026087795,0.13118546,0.0018342861,0.66248584,0.012705239,0.1843602],"study_design_scores_gemma":[0.000041685144,0.000054291406,0.0017442828,0.00017217365,0.00016563812,0.00043421797,0.00005512035,0.5441432,0.0009905364,0.44339493,0.008700799,0.0001031855],"about_ca_topic_score_codex":0.0059809224,"about_ca_topic_score_gemma":0.007834663,"teacher_disagreement_score":0.0097682,"about_ca_system_score_codex":0.0013754161,"about_ca_system_score_gemma":0.0024064814,"threshold_uncertainty_score":0.04123521},"labels":[],"label_agreement":null},{"id":"W2964523523","doi":"10.1002/sim.8286","title":"Measuring variability between clusters by subgroup: An extension of the median odds ratio","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Institute for Clinical Evaluative Sciences; Mount Sinai Hospital; Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; Institute for Work & Health","funders":"Canadian Institutes of Health Research; Physicians' Services Incorporated Foundation; Heart and Stroke Foundation of Canada","keywords":"Extension (predicate logic); Odds; Odds ratio; Subgroup analysis; Statistics; Demography; Mathematics; Computer science; Confidence interval; Logistic regression; Sociology","score_opus":0.07676482540831311,"score_gpt":0.37053227655468124,"score_spread":0.29376745114636815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964523523","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":[0.029718624,0.0016466223,0.9647191,0.00092734623,0.00014929751,0.00027076833,0.0005276571,0.00022619551,0.0018143533],"genre_scores_gemma":[0.4356899,0.0013391711,0.5593248,0.0006366812,0.0004925866,0.0009892841,0.00060301163,0.00021520007,0.0007093814],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92853004,0.052333195,0.003483947,0.008834415,0.0061050346,0.00071334315],"domain_scores_gemma":[0.7446463,0.21398756,0.015517217,0.019256102,0.0054192822,0.0011736052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07108311,0.0012102972,0.0028954425,0.0057267677,0.0008494893,0.00366553,0.0035784931,0.001817372,0.002484937],"category_scores_gemma":[0.27709615,0.00074851926,0.0042869723,0.0060563623,0.0041579613,0.0069550457,0.004512013,0.0036944782,0.0003325564],"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":[0.0009870197,0.00031715765,0.23324826,0.0017986267,0.0061358237,0.0009926042,0.004251531,0.047502086,0.001969227,0.27400628,0.005170455,0.42362085],"study_design_scores_gemma":[0.00019407873,0.0013437116,0.07663964,0.000690343,0.001503241,0.0026016403,0.0014727373,0.24688397,0.0019258283,0.65134656,0.015009026,0.00038929872],"about_ca_topic_score_codex":0.0026451112,"about_ca_topic_score_gemma":0.0010976132,"teacher_disagreement_score":0.07108311,"about_ca_system_score_codex":0.0012383644,"about_ca_system_score_gemma":0.0015410417,"threshold_uncertainty_score":0.37592793},"labels":[],"label_agreement":null},{"id":"W2967839411","doi":"10.1002/sim.8941","title":"Bayesian design and analysis of external pilot trials for complex interventions","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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":"Programme Grants for Applied Research; Medical Research Council Canada; Department of Health and Social Care; Medical Research Council; National Institute for Health and Care Research","keywords":"Bayesian probability; Monte Carlo method; Piecewise; Markov chain Monte Carlo; Psychological intervention; Sample size determination; Design of experiments; Sensitivity (control systems); Research design","score_opus":0.45041322823576735,"score_gpt":0.5294635819590232,"score_spread":0.07905035372325586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967839411","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":[0.0078057405,0.00043245615,0.9880376,0.00043022764,0.00006060084,0.002113179,0.00010908369,0.00016972583,0.00084133644],"genre_scores_gemma":[0.16429354,0.0007698927,0.8192567,0.00047558857,0.00009867015,0.013644734,0.00024834598,0.000089074085,0.001123424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8733771,0.1165696,0.002151318,0.0031924644,0.0038105554,0.00089893665],"domain_scores_gemma":[0.82986546,0.14666985,0.00991096,0.008084574,0.004150953,0.0013181773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11567706,0.0021148087,0.003852372,0.0022367907,0.00083889544,0.0027141243,0.0030074122,0.0031397422,0.0047897655],"category_scores_gemma":[0.20391087,0.001767952,0.0028708186,0.0013345216,0.0037637667,0.0026835485,0.0037457033,0.0045742593,0.00063005363],"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":[0.007499036,0.00068438856,0.00559392,0.002397531,0.0019038501,0.00043517118,0.00096046535,0.33222923,0.0036945427,0.4517711,0.003504957,0.18932576],"study_design_scores_gemma":[0.0021796736,0.002215554,0.0020132402,0.0005042818,0.0006182595,0.00012860888,0.00006810632,0.70067644,0.0022791014,0.28387374,0.0053122914,0.00013073054],"about_ca_topic_score_codex":0.0012882787,"about_ca_topic_score_gemma":0.0013665643,"teacher_disagreement_score":0.11567706,"about_ca_system_score_codex":0.002538253,"about_ca_system_score_gemma":0.005413189,"threshold_uncertainty_score":0.6117661},"labels":[],"label_agreement":null},{"id":"W2968796615","doi":"10.1093/aje/kwz127","title":"Two-Phase, Generalized Case-Control Designs for the Study of Quantitative Longitudinal Outcomes","year":2019,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; University of Washington; Dell Medical School, University of Texas at Austin; National Institutes of Health; Vanderbilt University Medical Center; Johns Hopkins University; American Chemistry Council; Harvard University; Vanderbilt University; National Human Genome Research Institute; Massachusetts General Hospital","keywords":"Covariate; Statistics; Imputation (statistics); Sample size determination; Missing data; Regression analysis; Regression; Mathematics; Outcome (game theory); Econometrics; Sampling (signal processing); Research design; Computer science","score_opus":0.2619222887394497,"score_gpt":0.5162314750188502,"score_spread":0.2543091862794005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968796615","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":[0.004546548,0.0010275815,0.97937953,0.00039949128,0.00066015153,0.01252004,0.00063933723,0.000284053,0.0005432731],"genre_scores_gemma":[0.041686885,0.0010579274,0.8761804,0.0007383939,0.00036068147,0.07780316,0.000891327,0.000056009474,0.0012251855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8442262,0.134295,0.00462431,0.009069843,0.0066998983,0.0010848019],"domain_scores_gemma":[0.9097419,0.058121752,0.008663936,0.018232105,0.0045108725,0.00072949025],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14195657,0.0035967713,0.0046497993,0.0031177916,0.0016446235,0.0022993805,0.006825332,0.005265141,0.008372749],"category_scores_gemma":[0.22437443,0.0022906524,0.004016673,0.0038088034,0.0035081261,0.0036549985,0.003372711,0.0049119736,0.0014896883],"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":[0.0070530665,0.0021321464,0.019326951,0.0038876012,0.005901432,0.00058217195,0.0021087737,0.04260105,0.0025032181,0.697489,0.012748411,0.20366606],"study_design_scores_gemma":[0.012449184,0.0083817225,0.0065929582,0.000793447,0.0027859823,0.00073229487,0.00025478535,0.20246935,0.0016853168,0.72762805,0.03577176,0.00045522524],"about_ca_topic_score_codex":0.0013259491,"about_ca_topic_score_gemma":0.0015217817,"teacher_disagreement_score":0.85804343,"about_ca_system_score_codex":0.001732411,"about_ca_system_score_gemma":0.004421463,"threshold_uncertainty_score":0.7507471},"labels":[],"label_agreement":null},{"id":"W2969848951","doi":"10.1080/03610918.2019.1655574","title":"Bayesian methods for time series of count data","year":2019,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian 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 Saskatchewan","funders":"","keywords":"Count data; Bayesian probability; Series (stratigraphy); Computer science; Statistics; Latent variable; Mean squared error; Poisson regression; Time series; Mathematics; Poisson distribution; Algorithm","score_opus":0.2705509902636992,"score_gpt":0.545172728602905,"score_spread":0.2746217383392058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969848951","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":[0.0006927232,0.00042334254,0.9982815,0.00012492936,0.000030880805,0.000019436575,0.000036856884,0.00006217604,0.00032815302],"genre_scores_gemma":[0.0759663,0.0036002027,0.9139156,0.0003079139,0.00054447533,0.0007987517,0.0005904881,0.00023029928,0.004046011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98928845,0.0071793916,0.000377199,0.0009929069,0.0019695943,0.0001925473],"domain_scores_gemma":[0.9672394,0.028205913,0.0015385904,0.0012193576,0.001525034,0.00027173708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015882047,0.0015417344,0.002158805,0.0043810178,0.00082921854,0.0023333402,0.0041840593,0.0025823764,0.0042347633],"category_scores_gemma":[0.060187638,0.0012322788,0.0017485997,0.004105279,0.002297115,0.004783896,0.0024786792,0.003915346,0.0013502602],"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":[0.00008182816,0.000087815104,0.001373354,0.00040035817,0.00028209982,0.00010950937,0.00024788766,0.2704689,0.0008603907,0.6192493,0.0020997003,0.10473883],"study_design_scores_gemma":[0.00003060923,0.000026935046,0.0002723436,0.00006426708,0.000030649924,0.000057106336,0.00003049266,0.68521667,0.000325029,0.30962119,0.0042909235,0.000033852393],"about_ca_topic_score_codex":0.004318424,"about_ca_topic_score_gemma":0.0033367248,"teacher_disagreement_score":0.015882047,"about_ca_system_score_codex":0.0016763916,"about_ca_system_score_gemma":0.0017832351,"threshold_uncertainty_score":0.083993316},"labels":[],"label_agreement":null},{"id":"W2970007864","doi":"10.1002/sim.8344","title":"Overdispersion models for correlated multinomial data: Applications to blinding assessment","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"Institute for Work & Health; Public Health Ontario; University of Toronto","funders":"National Center for Advancing Translational Sciences","keywords":"Overdispersion; Statistics; Estimator; Generalized estimating equation; Multinomial distribution; Intraclass correlation; Mathematics; Gee; Econometrics; Estimating equations; Negative binomial distribution; Quasi-likelihood; Poisson distribution","score_opus":0.15022813602425147,"score_gpt":0.48922819164064546,"score_spread":0.339000055616394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970007864","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":[0.0057084747,0.000662816,0.99214,0.0005120473,0.00007169966,0.00022518083,0.00009181071,0.00013167228,0.0004563771],"genre_scores_gemma":[0.2767572,0.0021971052,0.7130499,0.0013978883,0.0004205744,0.002299663,0.00042296486,0.00027813492,0.003176552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.850364,0.12546031,0.0045128516,0.010793533,0.0076916236,0.0011776104],"domain_scores_gemma":[0.4756946,0.46102938,0.026501156,0.028774925,0.0069622835,0.0010376868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18340898,0.0021693478,0.0045217215,0.0035252406,0.0019821227,0.003565952,0.005347723,0.0044773,0.0045666536],"category_scores_gemma":[0.40271902,0.0017913431,0.0050949887,0.004925459,0.009416494,0.0062309047,0.0056231576,0.0059507303,0.00065391866],"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":[0.0004956335,0.0001490177,0.016772116,0.0013010644,0.0022817324,0.0008764765,0.002556316,0.14790703,0.0010530103,0.6962341,0.0030842912,0.12728932],"study_design_scores_gemma":[0.00022797621,0.00020972772,0.0030658718,0.00037953793,0.00044204903,0.00040053643,0.00019076576,0.2953544,0.0009481768,0.69394094,0.0046673613,0.00017265424],"about_ca_topic_score_codex":0.0054745083,"about_ca_topic_score_gemma":0.004000998,"teacher_disagreement_score":0.18340898,"about_ca_system_score_codex":0.0029882013,"about_ca_system_score_gemma":0.004144773,"threshold_uncertainty_score":0.96997094},"labels":[],"label_agreement":null},{"id":"W2970178973","doi":"10.1002/cjs.11501","title":"Goodness‐of‐fit tests for distributions estimated from complex survey data","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Statistics; Goodness of fit; National Health and Nutrition Examination Survey; Empirical distribution function; Mathematics; Parametric statistics; Survey data collection; Statistical hypothesis testing; Econometrics; Distribution (mathematics); Index (typography); Demography; Computer science; Sociology","score_opus":0.46385054591200153,"score_gpt":0.4370228011174592,"score_spread":0.026827744794542308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970178973","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":[0.2183717,0.0007027514,0.7760045,0.00095327216,0.00010486609,0.00024624352,0.00071484066,0.00056733884,0.0023345551],"genre_scores_gemma":[0.9229815,0.00018747471,0.07503208,0.00013572809,0.00008845524,0.0004901685,0.0006380042,0.00011647965,0.0003301105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9336313,0.055924233,0.0017919666,0.00334023,0.004647591,0.00066466775],"domain_scores_gemma":[0.4017624,0.5622982,0.014375631,0.015313586,0.004843015,0.0014071951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06907058,0.0009861012,0.0020167006,0.0064503145,0.00100496,0.0030844545,0.0032509505,0.0021112273,0.005666428],"category_scores_gemma":[0.4237811,0.0006225452,0.0020079499,0.004764984,0.0067573413,0.005275548,0.0031768463,0.0031076414,0.0006399259],"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":[0.002053937,0.0005095694,0.18083051,0.0009037586,0.00383967,0.0011217191,0.002266673,0.27123252,0.0016257372,0.32138792,0.0058965324,0.20833161],"study_design_scores_gemma":[0.0002774714,0.0008007368,0.050941717,0.0002690488,0.00018418096,0.0005673008,0.00077284465,0.5973936,0.0010625246,0.34405673,0.003490715,0.00018317165],"about_ca_topic_score_codex":0.002419713,"about_ca_topic_score_gemma":0.001316376,"teacher_disagreement_score":0.06907058,"about_ca_system_score_codex":0.001688953,"about_ca_system_score_gemma":0.0019990306,"threshold_uncertainty_score":0.3652845},"labels":[],"label_agreement":null},{"id":"W2970526340","doi":"10.1002/sim.8354","title":"A more intuitive and modern way to compute a small‐sample confidence interval for the mean of a Poisson distribution","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","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; McGill University Health Centre","funders":"","keywords":"Poisson distribution; Link (geometry); Confidence interval; Computer science; TRACE (psycholinguistics); Computation; Sample (material); Sample size determination; Statistics; Interval (graph theory); Distribution (mathematics); Mathematics; Algorithm; Mathematical analysis","score_opus":0.0681805430217062,"score_gpt":0.40129177527727117,"score_spread":0.33311123225556494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970526340","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":[0.00063284585,0.0015179076,0.982656,0.0040745083,0.0020787346,0.000053390417,0.00032997082,0.001002567,0.0076541137],"genre_scores_gemma":[0.044512525,0.002820748,0.92797387,0.00624267,0.0034773438,0.0005173983,0.0005841312,0.0021117902,0.011759519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98664993,0.007419617,0.00108468,0.0016094976,0.0029617124,0.00027459313],"domain_scores_gemma":[0.93446666,0.050294887,0.0034969284,0.006284676,0.0049631414,0.0004936623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017234376,0.0020862848,0.0014239027,0.0053022723,0.0015574085,0.0063498216,0.0033747116,0.0037208106,0.043331847],"category_scores_gemma":[0.16854797,0.0009570167,0.002573819,0.004904428,0.0048287245,0.009850107,0.0037620969,0.011588052,0.01848997],"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":[0.000110008245,0.00008900145,0.00079552655,0.00055672857,0.00014339776,0.0001919059,0.0007766788,0.006009776,0.0007536007,0.8274989,0.055149544,0.107925035],"study_design_scores_gemma":[0.00007472416,0.00014848703,0.0006406261,0.00079681457,0.00008652371,0.00081952417,0.00025338546,0.01895144,0.002267596,0.7963402,0.17941058,0.00021019678],"about_ca_topic_score_codex":0.0019452575,"about_ca_topic_score_gemma":0.0015559375,"teacher_disagreement_score":0.043331847,"about_ca_system_score_codex":0.0016408805,"about_ca_system_score_gemma":0.0015044858,"threshold_uncertainty_score":0.14495939},"labels":[],"label_agreement":null},{"id":"W2971556193","doi":"10.1017/asb.2019.25","title":"A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":43,"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":"Identifiability; Computer science; Inference; Class (philosophy); Expectation–maximization algorithm; Data mining; Econometrics; Logit; Multivariate statistics; Data set; Logistic regression; Set (abstract data type); Artificial intelligence; Machine learning; Mathematics; Statistics; Maximum likelihood","score_opus":0.03148779184123828,"score_gpt":0.317522757976733,"score_spread":0.28603496613549473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971556193","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":[0.032976024,0.00013251051,0.9652923,0.00025005703,0.000014395564,0.000056792018,0.000088791276,0.00014354978,0.0010454834],"genre_scores_gemma":[0.72692746,0.0004964521,0.2664056,0.00017805942,0.00009987009,0.00033619444,0.00039308262,0.0001008947,0.005062365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616015,0.0024210426,0.0001319554,0.00056198833,0.00049959944,0.00022531365],"domain_scores_gemma":[0.9825371,0.014177924,0.0013442078,0.0010210109,0.0006810934,0.00023866461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01113239,0.0009689434,0.0017455501,0.0019356294,0.0005451275,0.0016516667,0.0028569037,0.0024390332,0.0037148832],"category_scores_gemma":[0.033160377,0.0008589305,0.0019497613,0.0018227717,0.0018308868,0.002229311,0.0018664435,0.0025614665,0.00046024163],"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":[0.000095774696,0.00006411492,0.002774915,0.00007582835,0.00011268756,0.00022932031,0.00020413371,0.78442967,0.00051774323,0.18242197,0.00072523346,0.028348653],"study_design_scores_gemma":[0.000007360189,0.0000136966555,0.00031299007,0.000009599358,0.0000097439915,0.000038586815,0.000017510907,0.9660364,0.00009479595,0.033119496,0.0003259186,0.000013937299],"about_ca_topic_score_codex":0.006417574,"about_ca_topic_score_gemma":0.0047475873,"teacher_disagreement_score":0.01113239,"about_ca_system_score_codex":0.0011082398,"about_ca_system_score_gemma":0.0009505398,"threshold_uncertainty_score":0.05887443},"labels":[],"label_agreement":null},{"id":"W2971976077","doi":"10.1017/s0266466619000239","title":"ON EFFICIENCY GAINS FROM MULTIPLE INCOMPLETE SUBSAMPLES","year":2019,"lang":"en","type":"article","venue":"Econometric Theory","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":"Mathematics; Efficiency; Moment (physics); Generality; Sampling (signal processing); Monotonic function; Statistics; Function (biology); Selection (genetic algorithm); Population; Mathematical optimization; Econometrics; Estimator; Computer science","score_opus":0.07585946308862815,"score_gpt":0.3345326213294579,"score_spread":0.2586731582408297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971976077","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":[0.043648653,0.0018529113,0.9371585,0.002916382,0.000082018385,0.00044146364,0.0003205208,0.00019221319,0.013387246],"genre_scores_gemma":[0.66893566,0.0020486817,0.3179436,0.0017356388,0.000431301,0.0014441479,0.0009738735,0.0002814957,0.0062055746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9374398,0.051725578,0.0013033506,0.0033134483,0.004600436,0.0016173456],"domain_scores_gemma":[0.68715626,0.2730005,0.008885909,0.025383933,0.004345805,0.0012275705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07229654,0.0018690286,0.003144124,0.0018287384,0.0011876306,0.0031613458,0.0032044884,0.0018526053,0.0039985552],"category_scores_gemma":[0.2420991,0.0012567946,0.0016826751,0.002268808,0.005261818,0.006019341,0.007022285,0.0037085845,0.00074681925],"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":[0.00081623637,0.00030271194,0.010319804,0.0004855723,0.00058287627,0.0003624864,0.0008031397,0.15261321,0.0013883497,0.7331466,0.0029089001,0.096270025],"study_design_scores_gemma":[0.00026894428,0.00034061252,0.0042034253,0.00023521268,0.00024188937,0.00021423731,0.00028431273,0.29679725,0.0013935438,0.69090253,0.0050684176,0.000049692793],"about_ca_topic_score_codex":0.0024187118,"about_ca_topic_score_gemma":0.002394362,"teacher_disagreement_score":0.07229654,"about_ca_system_score_codex":0.0032790534,"about_ca_system_score_gemma":0.0038394984,"threshold_uncertainty_score":0.3823452},"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":"W2976405568","doi":"10.1002/bimj.201900036","title":"Meta‐analysis of the difference of medians","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":212,"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; Canadian Institutes of Health Research","keywords":"Median; Statistics; Meta-analysis; Weighting; Sample size determination; Mathematics; Standard deviation; Variance (accounting); Pooled variance; Outcome (game theory); Mean difference; Confidence interval; Medicine; Internal medicine","score_opus":0.18752076966507691,"score_gpt":0.3957336478869019,"score_spread":0.208212878221825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976405568","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":[0.027171057,0.3248095,0.6281469,0.0066977628,0.0032043613,0.0016708437,0.004497592,0.0008705914,0.002931386],"genre_scores_gemma":[0.53573096,0.06590536,0.3826263,0.004207314,0.0019657735,0.004999479,0.0023745645,0.00035963513,0.0018306798],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8703185,0.10939931,0.007059885,0.0071469527,0.0056253206,0.00045003078],"domain_scores_gemma":[0.7234815,0.25169954,0.009635523,0.011814802,0.0029117772,0.00045692406],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11121603,0.0022824677,0.0098268725,0.008761487,0.00066826335,0.004404958,0.0040233172,0.0027860196,0.0038775417],"category_scores_gemma":[0.28098002,0.0011494155,0.021407159,0.0076456717,0.0015226789,0.0034812682,0.0021827486,0.0042939004,0.00040820768],"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":[0.0044586165,0.00019518008,0.019095164,0.050038584,0.59228605,0.00084946415,0.0003654019,0.06548155,0.0013791412,0.041241962,0.008030414,0.21657836],"study_design_scores_gemma":[0.0042263777,0.0024657368,0.014956624,0.011864869,0.45901278,0.0014683666,0.00031551137,0.107985325,0.0040246043,0.35662642,0.03664395,0.0004095402],"about_ca_topic_score_codex":0.0015046785,"about_ca_topic_score_gemma":0.0013281924,"teacher_disagreement_score":0.888784,"about_ca_system_score_codex":0.0020043175,"about_ca_system_score_gemma":0.002252429,"threshold_uncertainty_score":0.5881736},"labels":[],"label_agreement":null},{"id":"W2977609233","doi":"10.1002/bimj.201800146","title":"Latent variable models for harmonization of test scores: A case study on memory","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","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":true,"ca_institutions":"McGill University Health Centre; McMaster University; Impact","funders":"FP7 Health","keywords":"Equating; Harmonization; Test (biology); Observational study; Latent variable; Econometrics; Reliability (semiconductor); Statistics; Variable (mathematics); Computer science; Mathematics; Data mining","score_opus":0.16124854860980803,"score_gpt":0.39609553401830894,"score_spread":0.2348469854085009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977609233","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":[0.09170463,0.0012727099,0.8933064,0.0052239485,0.000056603792,0.00045945833,0.00034779357,0.00015793022,0.0074705007],"genre_scores_gemma":[0.6099134,0.00071428326,0.38554153,0.00040837104,0.000056141085,0.0010319807,0.00036271164,0.00010108769,0.0018705534],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9277104,0.06602583,0.0010743574,0.0021291296,0.0022651316,0.00079505524],"domain_scores_gemma":[0.82115674,0.16063198,0.0047622216,0.009559055,0.0033454914,0.00054453465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06508822,0.0007061721,0.0013986515,0.0031488622,0.001821561,0.0038466984,0.0031972362,0.003224643,0.004047616],"category_scores_gemma":[0.13687472,0.00056975364,0.0032341995,0.0073901345,0.00416127,0.0050293994,0.0039977636,0.0040034465,0.00036925805],"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":[0.0003996467,0.00051170075,0.045384347,0.00046849326,0.00085697713,0.0022003264,0.016872939,0.0549752,0.00045348378,0.69686943,0.004088025,0.17691945],"study_design_scores_gemma":[0.00025602902,0.00029155423,0.016014364,0.00038430953,0.00033244598,0.0009979368,0.005194293,0.2449356,0.0008083597,0.7139614,0.016689802,0.00013387234],"about_ca_topic_score_codex":0.018905636,"about_ca_topic_score_gemma":0.022519585,"teacher_disagreement_score":0.06508822,"about_ca_system_score_codex":0.004312935,"about_ca_system_score_gemma":0.0028264094,"threshold_uncertainty_score":0.3442235},"labels":[],"label_agreement":null},{"id":"W2980332746","doi":"10.1177/0165025419880609","title":"Missing data treatments in intervention studies: What was, what is, and what should be","year":2019,"lang":"en","type":"article","venue":"International Journal of Behavioral Development","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Missing data; Psychological intervention; Intervention (counseling); Data collection; Psychology; Imputation (statistics); Data mining; Data science; Computer science; Statistics; Machine learning; Mathematics","score_opus":0.3270462062986678,"score_gpt":0.5109850988590352,"score_spread":0.18393889256036738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980332746","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":[0.004986559,0.4276789,0.4283509,0.12286678,0.0074015106,0.0022152045,0.0016849608,0.00021992959,0.0045952727],"genre_scores_gemma":[0.09542169,0.38692322,0.44225514,0.049321115,0.011348149,0.011627627,0.001209043,0.00029120257,0.0016028481],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6572077,0.29516596,0.023158895,0.008300984,0.014717566,0.0014489585],"domain_scores_gemma":[0.48173934,0.47462037,0.022082668,0.010234299,0.01009031,0.0012330908],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25403035,0.0019226025,0.007186256,0.004597642,0.0031777236,0.0077322773,0.0069909235,0.009634166,0.0053393138],"category_scores_gemma":[0.42705238,0.001885821,0.006615634,0.0070159836,0.010472392,0.013646049,0.004927556,0.014203792,0.0010099525],"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":[0.00090418756,0.00031542487,0.007319712,0.08345426,0.004223702,0.00038279305,0.0052748816,0.005201717,0.0008220585,0.39839786,0.024763001,0.46894038],"study_design_scores_gemma":[0.00044819585,0.0008285838,0.0040556323,0.097616285,0.0025506583,0.0007393858,0.0022388338,0.0070397956,0.001959522,0.76750964,0.11462388,0.00038963562],"about_ca_topic_score_codex":0.0023447839,"about_ca_topic_score_gemma":0.0031243507,"teacher_disagreement_score":0.74596965,"about_ca_system_score_codex":0.0064592133,"about_ca_system_score_gemma":0.011597121,"threshold_uncertainty_score":0.9199134},"labels":[],"label_agreement":null},{"id":"W2980548856","doi":"10.1080/01621459.2019.1677241","title":"Doubly Robust Inference With Nonprobability Survey Samples","year":2019,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":154,"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":"Estimator; Nonprobability sampling; Robustness (evolution); Statistics; Sample (material); Survey sampling; Population; Inference; Computer science; Variance (accounting); Statistical inference; Sampling (signal processing); Econometrics; Mathematics; Artificial intelligence","score_opus":0.060624572119190485,"score_gpt":0.3571311995777698,"score_spread":0.2965066274585793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980548856","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":[0.0026864535,0.000083310704,0.99632794,0.00020380228,0.00002652409,0.00005486577,0.0001008151,0.00006402321,0.00045227713],"genre_scores_gemma":[0.3332241,0.00058815366,0.66013604,0.0007089384,0.00042131392,0.0013175047,0.0008799592,0.00013582545,0.0025881266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9539194,0.035359196,0.0013129248,0.003554276,0.005180284,0.0006738003],"domain_scores_gemma":[0.81988513,0.1490728,0.009512129,0.016539434,0.004349695,0.0006407982],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.052416135,0.0013097485,0.0029685292,0.0029304803,0.00096904545,0.0025021145,0.0058797533,0.0022365141,0.0056180917],"category_scores_gemma":[0.21499816,0.0011202168,0.002855327,0.0023754644,0.004123888,0.0042394972,0.0039197113,0.0040010475,0.00063520577],"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":[0.00012706083,0.00014656445,0.0042755976,0.0003165128,0.00040683922,0.00031352823,0.00026006575,0.12118249,0.0006304672,0.82389003,0.002234903,0.046215996],"study_design_scores_gemma":[0.00006823135,0.000063116975,0.0012773258,0.00008055712,0.0000694194,0.00007730851,0.00003563828,0.5070355,0.0005782461,0.4885443,0.002139626,0.000030705014],"about_ca_topic_score_codex":0.0037045255,"about_ca_topic_score_gemma":0.002771025,"teacher_disagreement_score":0.94758385,"about_ca_system_score_codex":0.0020474927,"about_ca_system_score_gemma":0.0024875258,"threshold_uncertainty_score":0.2772063},"labels":[],"label_agreement":null},{"id":"W2981024151","doi":"10.1002/cjs.11523","title":"Validity and efficiency in analyzing ordinal responses with missing observations","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Imputation (statistics); Ordinal data; Covariate; Computer science; Ordinal regression; Statistics; Data set; Data mining; Econometrics; Mathematics","score_opus":0.11401097624796401,"score_gpt":0.34243754533414783,"score_spread":0.2284265690861838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981024151","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":[0.06299309,0.0008029045,0.9281045,0.0018525682,0.00007588712,0.00034142498,0.0003959615,0.0003148722,0.005118784],"genre_scores_gemma":[0.54998827,0.00048255347,0.44634005,0.00043617075,0.00011074248,0.0007039734,0.00063346454,0.00023143478,0.0010733218],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.77743596,0.19907922,0.0046808533,0.0054657515,0.012215914,0.0011222479],"domain_scores_gemma":[0.21801512,0.7319864,0.0118504055,0.027771708,0.009872805,0.0005036943],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22020201,0.00062093546,0.0022146082,0.0043856087,0.0012806711,0.004433759,0.0030693451,0.0016489901,0.0034186598],"category_scores_gemma":[0.6197456,0.0009388859,0.0016632797,0.006069704,0.0060508302,0.005434206,0.0041769645,0.0025776264,0.0007767762],"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":[0.0020302834,0.00042552184,0.089822724,0.001276582,0.0012697352,0.0006349091,0.0044590374,0.09671009,0.0013839562,0.35842317,0.0065359836,0.43702784],"study_design_scores_gemma":[0.00025936033,0.0002801051,0.024223648,0.0008424618,0.00028834646,0.00043605175,0.001106881,0.43185642,0.0030767082,0.5306963,0.006794767,0.00013886449],"about_ca_topic_score_codex":0.0036561708,"about_ca_topic_score_gemma":0.0022162294,"teacher_disagreement_score":0.22020201,"about_ca_system_score_codex":0.0017718568,"about_ca_system_score_gemma":0.004353878,"threshold_uncertainty_score":0.96162975},"labels":[],"label_agreement":null},{"id":"W2981977985","doi":"10.1111/rssb.12342","title":"Bayesian Empirical Likelihood Inference with Complex Survey Data","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Frequentist inference; Computer science; Markov chain Monte Carlo; Prior probability; Sampling (signal processing); Mathematics; Sampling design; Importance sampling; Estimator; Bayesian probability; Bayesian inference; Statistics; Population; Monte Carlo method","score_opus":0.264996072021531,"score_gpt":0.4441774908476976,"score_spread":0.17918141882616656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981977985","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":[0.002667014,0.00014020246,0.996418,0.00018214055,0.000010511846,0.000027934555,0.00006202489,0.00005110146,0.00044109114],"genre_scores_gemma":[0.25032535,0.00083707203,0.74528855,0.00030499292,0.00011722617,0.00060395483,0.00039115341,0.000114611365,0.0020170705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98493123,0.011939817,0.00043210457,0.0009830049,0.0015100029,0.00020383252],"domain_scores_gemma":[0.9319998,0.058967408,0.002813028,0.0036620232,0.002205713,0.0003519744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024476493,0.0007579381,0.0018545116,0.0025222783,0.00052837183,0.002463363,0.002585703,0.0013622085,0.004073514],"category_scores_gemma":[0.09623756,0.00087904447,0.0011498678,0.0028367671,0.002383475,0.0030717112,0.002381519,0.0029593755,0.0006822694],"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":[0.000069213245,0.00008086833,0.003472008,0.00025466224,0.00017764086,0.00013274091,0.0002827986,0.4164663,0.00040599782,0.49357992,0.002020669,0.08305717],"study_design_scores_gemma":[0.000030003,0.00001710299,0.0005995761,0.000057967085,0.000017178938,0.00003677489,0.000034176846,0.6676141,0.00017167153,0.32948443,0.0019122784,0.000024713148],"about_ca_topic_score_codex":0.0035930306,"about_ca_topic_score_gemma":0.0027632732,"teacher_disagreement_score":0.024476493,"about_ca_system_score_codex":0.0015054725,"about_ca_system_score_gemma":0.0018232985,"threshold_uncertainty_score":0.12944567},"labels":[],"label_agreement":null},{"id":"W2982189774","doi":"10.3389/fevo.2019.00372","title":"Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC","year":2019,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":83,"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; Japan Society for the Promotion of Science","keywords":"Statistics; Frequentist inference; Type I and type II errors; Statistical hypothesis testing; Econometrics; Sample size determination; Statistical inference; Mathematics; Inference; Contrast (vision); Sample (material); Computer science; Bayesian inference; Bayesian probability; Artificial intelligence","score_opus":0.09091726407699352,"score_gpt":0.33861403767434634,"score_spread":0.24769677359735282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982189774","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":[0.015883591,0.021526178,0.9459297,0.006875542,0.0008565388,0.000119815835,0.00039120458,0.00033059224,0.008086791],"genre_scores_gemma":[0.6273337,0.016255002,0.34759957,0.002943248,0.0022581026,0.0006866104,0.0006058559,0.00038596356,0.0019319946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8833985,0.077833064,0.007301376,0.0069561405,0.023188738,0.0013222218],"domain_scores_gemma":[0.5131935,0.43127656,0.01771082,0.022516256,0.014320341,0.0009826085],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.097986355,0.0026105014,0.0051346933,0.010331593,0.0018035218,0.008899377,0.0064459094,0.005313412,0.0024761586],"category_scores_gemma":[0.42187312,0.0014507911,0.0032188625,0.010835842,0.012301366,0.010538375,0.0061807553,0.010396259,0.0006605381],"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":[0.0003275158,0.00008996032,0.018958515,0.0020103953,0.0013535239,0.0008442471,0.0012228857,0.14530343,0.00029395503,0.7020096,0.006836628,0.12074934],"study_design_scores_gemma":[0.000046909692,0.00007632334,0.0034308021,0.0008496975,0.00023334792,0.00031837475,0.00020720476,0.11287467,0.0004008152,0.8775771,0.003885603,0.00009906642],"about_ca_topic_score_codex":0.008382776,"about_ca_topic_score_gemma":0.0060518053,"teacher_disagreement_score":0.90201366,"about_ca_system_score_codex":0.004684459,"about_ca_system_score_gemma":0.004389143,"threshold_uncertainty_score":0.51820755},"labels":[],"label_agreement":null},{"id":"W2982955940","doi":"10.1111/2041-210x.13559","title":"The consequences of checking for zero‐inflation and overdispersion in the analysis of count data","year":2021,"lang":"en","type":"preprint","venue":"Methods in Ecology and Evolution","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overdispersion; Count data; Zero-inflated model; Econometrics; Poisson distribution; Model selection; Generalized linear model; Inflation (cosmology); Quasi-likelihood; Statistics; Poisson regression; Computer science; Mathematics","score_opus":0.15372952043200264,"score_gpt":0.4799882579713001,"score_spread":0.32625873753929746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982955940","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":[0.47725362,0.0010950266,0.511166,0.0044074734,0.00040302094,0.00045138955,0.00043443733,0.0006840824,0.0041049216],"genre_scores_gemma":[0.91423607,0.00010841093,0.08379185,0.000851081,0.00006327523,0.0002494725,0.00021279824,0.000118664924,0.00036828723],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6966537,0.25877568,0.0101304995,0.01221081,0.020004896,0.002224392],"domain_scores_gemma":[0.10889869,0.82968515,0.021122664,0.029216295,0.009729866,0.001347325],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.25512657,0.0010335817,0.0019873495,0.0027181983,0.0029366824,0.0033987083,0.0040464476,0.0031541171,0.0024664328],"category_scores_gemma":[0.69089264,0.000988483,0.00322711,0.004181519,0.0100631835,0.004663843,0.003574073,0.0054265778,0.000424519],"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":[0.0026650878,0.0006539006,0.46250522,0.0014854287,0.0037151515,0.0033252214,0.008486769,0.21997888,0.0054339515,0.1359991,0.0076619852,0.14808935],"study_design_scores_gemma":[0.00037042546,0.0017769209,0.07937823,0.0009840983,0.0010379846,0.0015112701,0.0022823678,0.63791454,0.012897568,0.25601903,0.005420858,0.00040673034],"about_ca_topic_score_codex":0.006221776,"about_ca_topic_score_gemma":0.005142174,"teacher_disagreement_score":0.25512657,"about_ca_system_score_codex":0.0030474104,"about_ca_system_score_gemma":0.0046408325,"threshold_uncertainty_score":0.9185616},"labels":[],"label_agreement":null},{"id":"W2987365550","doi":"10.1161/circoutcomes.119.005927","title":"Effect of Variable Selection Strategy on the Performance of Prognostic Models When Using Multiple Imputation","year":2019,"lang":"en","type":"article","venue":"Circulation Cardiovascular Quality and Outcomes","topic":"Statistical Methods and Bayesian Inference","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 Health Network; University of Toronto; Sunnybrook Hospital; TD Bank Group; Institute of Health Services and Policy Research; Toronto Rehabilitation Institute; Sunnybrook Health Science Centre","funders":"Medical Research Council; Canadian Institutes of Health Research","keywords":"Missing data; Imputation (statistics); Statistics; Logistic regression; Feature selection; Sample size determination; Sample (material); Variables; Regression analysis; Regression; Computer science; Data mining; Mathematics; Artificial intelligence","score_opus":0.0995815895126265,"score_gpt":0.360958675853795,"score_spread":0.2613770863411685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987365550","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":[0.611006,0.0042727375,0.37558073,0.0035997338,0.00044067716,0.00069904514,0.0008918527,0.0009183335,0.0025909098],"genre_scores_gemma":[0.9154653,0.00047163197,0.0817106,0.0005330527,0.000096194985,0.0004070789,0.00085265207,0.00017725916,0.00028623338],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7904421,0.19192858,0.006779746,0.0043660677,0.005556195,0.00092736963],"domain_scores_gemma":[0.4277693,0.5302129,0.0149299875,0.017285978,0.00873726,0.0010645272],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.22593386,0.0018158902,0.0018988666,0.001675113,0.0011510273,0.0018150598,0.0019361087,0.0018669715,0.00137089],"category_scores_gemma":[0.34023708,0.00058878836,0.0033739258,0.0030290917,0.0015224136,0.0019475799,0.0018099291,0.002519554,0.00048306357],"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":[0.008941718,0.00075263064,0.58141917,0.0009535062,0.010674169,0.0010811093,0.0016247531,0.15161067,0.0028807267,0.0038595225,0.005335503,0.2308665],"study_design_scores_gemma":[0.001858252,0.00607548,0.1410334,0.00095774146,0.004358613,0.0017176076,0.0004822801,0.8137043,0.010408636,0.016035076,0.0031283912,0.00024019799],"about_ca_topic_score_codex":0.0019708194,"about_ca_topic_score_gemma":0.001502825,"teacher_disagreement_score":0.77406615,"about_ca_system_score_codex":0.0008351452,"about_ca_system_score_gemma":0.0019271299,"threshold_uncertainty_score":0.95456135},"labels":[],"label_agreement":null},{"id":"W2988372504","doi":"10.1002/bimj.201900046","title":"Berkson's paradox and weighted distributions: An application to Alzheimer's disease","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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 on Aging; Canadian Institutes of Health Research","keywords":"Mathematics; Statistics; Inference; Population; Statistical inference; Sample (material); Correlation; Econometrics; Demography; Computer science; Artificial intelligence","score_opus":0.04853374036936612,"score_gpt":0.3790857080481019,"score_spread":0.33055196767873574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988372504","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":[0.040449128,0.0043739816,0.9413598,0.0052677207,0.00032008806,0.000072059964,0.00011022268,0.00007830589,0.007968614],"genre_scores_gemma":[0.5865219,0.0046937144,0.39979434,0.0022713493,0.0008700534,0.0002664746,0.00010787417,0.00009629011,0.005377989],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99236476,0.0053695757,0.00027487846,0.00069175946,0.0011245665,0.00017443585],"domain_scores_gemma":[0.9618514,0.0315072,0.0021997506,0.0019201869,0.0018398475,0.00068155077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02059319,0.00070971,0.0013318121,0.002856909,0.0016098452,0.002155781,0.0015770197,0.002187499,0.001385193],"category_scores_gemma":[0.07022115,0.00045985112,0.0011919383,0.003302468,0.0050293184,0.0040839734,0.0035774629,0.0035330092,0.00020229776],"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":[0.000069222064,0.000046377245,0.0038399512,0.000099149605,0.000084007814,0.00072380976,0.00084255135,0.016751645,0.0003930481,0.9335677,0.0022411947,0.041341297],"study_design_scores_gemma":[0.000024263236,0.00002892194,0.0011364323,0.000041213287,0.0000206201,0.0005400226,0.0001561574,0.07948238,0.00019512436,0.9140884,0.0042494847,0.000037008587],"about_ca_topic_score_codex":0.0030533618,"about_ca_topic_score_gemma":0.002437121,"teacher_disagreement_score":0.02059319,"about_ca_system_score_codex":0.0018161321,"about_ca_system_score_gemma":0.0015279345,"threshold_uncertainty_score":0.108908534},"labels":[],"label_agreement":null},{"id":"W2990278103","doi":"10.1002/sim.8403","title":"Joint modeling of binary response and survival for clustered data in clinical trials","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","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":true,"ca_institutions":"Population Health Research Institute; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Jackknife resampling; Covariate; Inference; Resampling; Sample size determination; Outcome (game theory); Statistics; Statistical inference; Computer science; Multivariate statistics; Statistical model; Survival analysis; Random effects model; Econometrics; Mathematics; Artificial intelligence; Medicine; Estimator; Meta-analysis; Internal medicine","score_opus":0.5370167228892887,"score_gpt":0.5753942456398025,"score_spread":0.03837752275051376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990278103","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":[0.0070418348,0.00075611396,0.99004245,0.0008508744,0.00008382354,0.00044142784,0.00022679286,0.00015611465,0.00040046612],"genre_scores_gemma":[0.32113895,0.0016605856,0.66610765,0.0013562668,0.00037613817,0.005645552,0.0010912312,0.000165734,0.0024579319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8785556,0.10611914,0.0030436555,0.0071240775,0.004242615,0.00091494335],"domain_scores_gemma":[0.7702827,0.20042993,0.015698463,0.009323486,0.0033345679,0.00093087245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12879993,0.0014598832,0.004132553,0.0029526805,0.0009331066,0.0027845467,0.005501695,0.0041925376,0.0031214694],"category_scores_gemma":[0.22767122,0.0014012834,0.0039164377,0.003950762,0.0036698251,0.0033960296,0.002812895,0.005330079,0.00074095774],"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":[0.00120468,0.00030949674,0.019811621,0.002132837,0.0023025745,0.00080518593,0.0013536569,0.44241253,0.0011497142,0.42084762,0.004524798,0.10314525],"study_design_scores_gemma":[0.00034787762,0.00035893824,0.0032202827,0.0002776437,0.0004486771,0.00015303104,0.000082292616,0.72936165,0.0005151824,0.26231638,0.00284073,0.00007729482],"about_ca_topic_score_codex":0.0042694644,"about_ca_topic_score_gemma":0.0043638716,"teacher_disagreement_score":0.12879993,"about_ca_system_score_codex":0.0028111327,"about_ca_system_score_gemma":0.0038195946,"threshold_uncertainty_score":0.68116724},"labels":[],"label_agreement":null},{"id":"W2990428857","doi":"10.1214/19-aoas1274","title":"Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS","year":2019,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian 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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; City University of New York; National Science Foundation","keywords":"Covariate; Proportional hazards model; Accelerated failure time model; Econometrics; Inference; Statistics; Survival analysis; Computer science; Outcome (game theory); Mathematics; Artificial intelligence","score_opus":0.08529347782029319,"score_gpt":0.3467134122240529,"score_spread":0.2614199344037597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990428857","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":[0.012961065,0.00040069496,0.9847964,0.00041841465,0.00005612848,0.000058614274,0.00020792578,0.00013881722,0.00096196873],"genre_scores_gemma":[0.59703386,0.0021041387,0.37603536,0.00057553535,0.00039414564,0.0010905692,0.0010808178,0.00023100156,0.02145456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958144,0.0025965986,0.00013986348,0.00070694933,0.00046115855,0.00028101212],"domain_scores_gemma":[0.98737144,0.009418635,0.0014240822,0.0007392115,0.0007432006,0.00030344687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011805109,0.0013448892,0.0020195607,0.0014676207,0.000756833,0.0016077422,0.0036634754,0.0022722073,0.0041671745],"category_scores_gemma":[0.024891395,0.0010412749,0.002600108,0.0017877469,0.0023425182,0.002873084,0.0021917892,0.0032355986,0.00067431194],"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":[0.00018491603,0.00008512332,0.005823607,0.0002305283,0.0002268632,0.00046554214,0.00042476816,0.52689517,0.0007841106,0.43772626,0.0014197372,0.025733437],"study_design_scores_gemma":[0.000033730892,0.000069814756,0.00081253634,0.000023349226,0.00006567872,0.00011951901,0.000031832322,0.8978777,0.00015952998,0.09891262,0.0018644186,0.000029323968],"about_ca_topic_score_codex":0.010161136,"about_ca_topic_score_gemma":0.007418862,"teacher_disagreement_score":0.011805109,"about_ca_system_score_codex":0.0017029664,"about_ca_system_score_gemma":0.0022243455,"threshold_uncertainty_score":0.06243211},"labels":[],"label_agreement":null},{"id":"W2990549853","doi":"","title":"Estimation for Zero-Inflated Beta-Binomial Regression Model with Missing Response and Covariate Measurement Error","year":2019,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"University of Windsor","keywords":"Covariate; Statistics; Mathematics; Observational error; Negative binomial distribution; Zero (linguistics); Errors-in-variables models; Econometrics; Beta-binomial distribution; Non-sampling error; Regression analysis; Poisson distribution","score_opus":0.11413565403722685,"score_gpt":0.3246377051946198,"score_spread":0.21050205115739296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990549853","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":[0.0079209665,0.00023029209,0.9911885,0.00011564054,0.000017857195,0.000054471886,0.00012235166,0.0001334184,0.00021652144],"genre_scores_gemma":[0.19894697,0.0013590191,0.79236317,0.000301197,0.00012798687,0.0008682336,0.0017769091,0.00021950237,0.004037005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9810951,0.014174725,0.0006527973,0.002294633,0.0012534288,0.00052930444],"domain_scores_gemma":[0.92454576,0.06580884,0.0036997925,0.003379853,0.0021776215,0.00038812653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045186967,0.0012892522,0.0037565092,0.0017757136,0.0008057791,0.0022112664,0.005247576,0.0028016008,0.0034454623],"category_scores_gemma":[0.089156955,0.0016321751,0.0027402565,0.0027821285,0.0019230272,0.0034124237,0.002333881,0.004233495,0.0010913429],"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":[0.000562703,0.00019254444,0.019244505,0.0010732673,0.0010025996,0.0007752337,0.00095459644,0.5907133,0.0029125642,0.225204,0.0037811883,0.15358347],"study_design_scores_gemma":[0.000058947546,0.000112689624,0.0021849247,0.00012125904,0.00011492899,0.00023200047,0.00006737872,0.9285169,0.00069172547,0.065976664,0.0018731948,0.000049302897],"about_ca_topic_score_codex":0.006346026,"about_ca_topic_score_gemma":0.0056117256,"teacher_disagreement_score":0.045186967,"about_ca_system_score_codex":0.0014976163,"about_ca_system_score_gemma":0.0019781035,"threshold_uncertainty_score":0.23897439},"labels":[],"label_agreement":null},{"id":"W2991299027","doi":"10.4236/ojs.2019.96040","title":"Likelihood Methods for Basic Stratified Sampling, with Application to Von Bertalanffy Growth Model Estimation","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute; Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Statistics; Estimator; Stratified sampling; Sampling (signal processing); Maximum likelihood; Mathematics; Computer science; Marginal likelihood","score_opus":0.06901775248005243,"score_gpt":0.4414852496311544,"score_spread":0.37246749715110195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991299027","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":[0.0007827894,0.00013006684,0.99868304,0.000060050512,0.000011423074,0.000031392843,0.000036800608,0.00006153721,0.00020289289],"genre_scores_gemma":[0.04479411,0.00059922395,0.9521514,0.00010435438,0.00010042144,0.0005821014,0.0003829158,0.00015631987,0.0011290913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99372524,0.004904042,0.00022262578,0.0003569897,0.000670813,0.000120276054],"domain_scores_gemma":[0.9805064,0.016158286,0.0007323807,0.0012300655,0.0011915645,0.0001812842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015622493,0.0010548267,0.0012723458,0.0019279729,0.0006677305,0.0011740308,0.0023996537,0.0012112446,0.0029954396],"category_scores_gemma":[0.059910234,0.0009791562,0.001472191,0.0021661567,0.0015970112,0.0018400642,0.0024841204,0.0024206871,0.00081659533],"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":[0.00014088067,0.00007182317,0.003945285,0.00042475795,0.0002485152,0.00023429003,0.00046025045,0.26615694,0.0020013503,0.55740845,0.0032679343,0.16563958],"study_design_scores_gemma":[0.000033534652,0.00003304505,0.00079460867,0.00005657492,0.0000298324,0.00008652533,0.000029991432,0.7309111,0.00057643413,0.262421,0.004996399,0.000031001444],"about_ca_topic_score_codex":0.005342014,"about_ca_topic_score_gemma":0.004787584,"teacher_disagreement_score":0.015622493,"about_ca_system_score_codex":0.0013303651,"about_ca_system_score_gemma":0.0025016551,"threshold_uncertainty_score":0.08262062},"labels":[],"label_agreement":null},{"id":"W2991920218","doi":"10.1002/sim.8435","title":"Assessing the prior event rate ratio method via probabilistic bias analysis on a Bayesian network","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Ontario Drug Policy Research Network; University of Toronto; University of Manitoba; Manitoba Health; Sanofi (Canada); University of Guelph","funders":"","keywords":"Confounding; Observational study; Bayesian probability; Econometrics; Robustness (evolution); Statistics; Computer science; Population; Probabilistic logic; Medicine; Mathematics; Environmental health","score_opus":0.07427757909138852,"score_gpt":0.44618420140823795,"score_spread":0.37190662231684946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991920218","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":[0.0044648913,0.00029629204,0.99360484,0.00038432254,0.000025519848,0.00028949275,0.00015202563,0.00014429355,0.0006385051],"genre_scores_gemma":[0.20973104,0.0009865108,0.7849427,0.00042928176,0.00020164308,0.0019702907,0.0005305418,0.00012596606,0.0010820463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9367964,0.053003613,0.0017845707,0.0034633412,0.0044061746,0.0005459521],"domain_scores_gemma":[0.67337465,0.30207017,0.010777045,0.006733049,0.0061416733,0.000903416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09197383,0.0019421912,0.0028743423,0.0048409007,0.0010389071,0.003034869,0.0042136186,0.002842353,0.0048770905],"category_scores_gemma":[0.26085702,0.0014856268,0.003681451,0.002683339,0.0027017747,0.0038619363,0.0037932934,0.0046466757,0.0005875676],"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":[0.0005700093,0.00015444764,0.020234268,0.0009981947,0.001688926,0.0003735666,0.0005084165,0.63636434,0.00090543507,0.1865128,0.002989314,0.14870039],"study_design_scores_gemma":[0.00011646959,0.00012017626,0.0012848763,0.0001911026,0.00026476034,0.00014867041,0.000038659913,0.8669629,0.0004313259,0.12828301,0.0021132997,0.000044842607],"about_ca_topic_score_codex":0.0095567,"about_ca_topic_score_gemma":0.0046149706,"teacher_disagreement_score":0.09197383,"about_ca_system_score_codex":0.0026412068,"about_ca_system_score_gemma":0.0042448873,"threshold_uncertainty_score":0.4864099},"labels":[],"label_agreement":null},{"id":"W2994289034","doi":"10.1093/ajhp/zxz245","title":"Missing data reporting in clinical pharmacy research","year":2019,"lang":"en","type":"article","venue":"American Journal of Health-System Pharmacy","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"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":"Pharmacy; Research data; Missing data; Data science; Medicine; Computer science; Family medicine; Data curation","score_opus":0.5973067412134718,"score_gpt":0.6476696383502688,"score_spread":0.05036289713679698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994289034","genre_codex":"review","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14604019,0.5096368,0.25296006,0.05116608,0.0064960406,0.0066103367,0.009484225,0.0006135017,0.016992826],"genre_scores_gemma":[0.7432107,0.07256854,0.14595743,0.019848319,0.0046763127,0.008014416,0.00442504,0.00023817985,0.0010610337],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.25500268,0.55678886,0.11957511,0.018030902,0.048472725,0.002129842],"domain_scores_gemma":[0.09428878,0.71009946,0.12603723,0.0367382,0.031444263,0.0013920909],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.47995713,0.0013511871,0.0038206398,0.017177176,0.0031335053,0.010018011,0.0065124677,0.0040247464,0.0028755001],"category_scores_gemma":[0.7304248,0.001875125,0.0042004143,0.023742542,0.007426225,0.011561975,0.008248598,0.004228097,0.00051495904],"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":[0.0017283106,0.00038516097,0.32821694,0.12956007,0.012023235,0.001048986,0.021947816,0.004349467,0.00089187064,0.042832028,0.018973364,0.43804276],"study_design_scores_gemma":[0.0011797284,0.0029721044,0.19243096,0.36686456,0.016981052,0.008120096,0.01979482,0.013435661,0.009265474,0.18317474,0.18468192,0.0010988782],"about_ca_topic_score_codex":0.0030586338,"about_ca_topic_score_gemma":0.0022122767,"teacher_disagreement_score":0.5200429,"about_ca_system_score_codex":0.005621518,"about_ca_system_score_gemma":0.014506518,"threshold_uncertainty_score":0.64130545},"labels":[],"label_agreement":null},{"id":"W2995208797","doi":"10.1002/ecy.2960","title":"Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs","year":2019,"lang":"en","type":"article","venue":"Ecology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":44,"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 Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Mathematics; Path (computing); Ecology; Econometrics; Directed acyclic graph; Combinatorics; Biology; Computer science","score_opus":0.04413909132856765,"score_gpt":0.323906192495782,"score_spread":0.27976710116721437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995208797","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":[0.026744358,0.00065500627,0.96779346,0.0006184108,0.000080749254,0.00013089472,0.0013041147,0.0005231527,0.0021497926],"genre_scores_gemma":[0.5443875,0.0014228246,0.44492438,0.0004666012,0.00028531288,0.0016226574,0.0042219446,0.000597695,0.002071089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9773358,0.016302325,0.0009868678,0.0021196862,0.0026664408,0.0005889312],"domain_scores_gemma":[0.6476175,0.32536116,0.007278675,0.010666979,0.007975762,0.0010998995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03006415,0.0011840223,0.0023439676,0.0067519676,0.00084057095,0.0022970976,0.003819237,0.0022595827,0.010177879],"category_scores_gemma":[0.21887213,0.00096344313,0.0020245353,0.0071296934,0.003729895,0.004339618,0.0019041699,0.0038395717,0.0012524984],"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":[0.00019390664,0.0000920103,0.020906826,0.00058798736,0.0008027524,0.0005495444,0.0004649403,0.34042957,0.00045997914,0.5556873,0.0071459454,0.07267932],"study_design_scores_gemma":[0.000035010453,0.000050157523,0.002862689,0.0001418942,0.00007980559,0.00016458852,0.00009519741,0.43647096,0.00015393717,0.55842716,0.0014672071,0.00005144216],"about_ca_topic_score_codex":0.0054993657,"about_ca_topic_score_gemma":0.0051981416,"teacher_disagreement_score":0.03006415,"about_ca_system_score_codex":0.0019896992,"about_ca_system_score_gemma":0.00307969,"threshold_uncertainty_score":0.15899634},"labels":[],"label_agreement":null},{"id":"W2995378146","doi":"10.1093/ectj/utz025","title":"Partial identification in nonseparable count data instrumental variable models","year":2019,"lang":"en","type":"article","venue":"Econometrics Journal","topic":"Statistical Methods and Bayesian Inference","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":"Instrumental variable; Identification (biology); Inference; Outcome (game theory); Moment (physics); Count data; Variable (mathematics); Set (abstract data type); Econometrics; Computer science; Data set; Estimation; Mathematics; Mathematical optimization; Statistics; Artificial intelligence; Economics; Poisson distribution; Mathematical economics","score_opus":0.1913697430092952,"score_gpt":0.3752746082601343,"score_spread":0.1839048652508391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995378146","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":[0.015848402,0.0002071483,0.9823255,0.00032622117,0.000016749218,0.000039707473,0.0001466512,0.00008724876,0.0010023856],"genre_scores_gemma":[0.7138823,0.00076554314,0.27758303,0.0003348824,0.000110097884,0.0007394969,0.00074274745,0.0000969834,0.0057448163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913209,0.005768724,0.00038920672,0.0010370179,0.0010592588,0.000424912],"domain_scores_gemma":[0.9157896,0.07425034,0.003933842,0.0042183623,0.0014306837,0.00037719996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015070653,0.0007292604,0.0021251582,0.0015592396,0.0005910472,0.0021229016,0.0026503408,0.0015328878,0.0039149686],"category_scores_gemma":[0.06585144,0.00092379306,0.0016761674,0.001664352,0.0025858127,0.0028931636,0.0035955955,0.0030230857,0.00045131618],"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":[0.00009430707,0.000055781864,0.004140949,0.00023941368,0.00018235098,0.0002469008,0.00035366285,0.277947,0.00048659305,0.6787608,0.0010135282,0.03647885],"study_design_scores_gemma":[0.000019385681,0.000030122876,0.0006687094,0.000043567816,0.000023483492,0.00003081776,0.00005278261,0.60502243,0.0003204141,0.39262378,0.001145733,0.000018801455],"about_ca_topic_score_codex":0.0025379306,"about_ca_topic_score_gemma":0.0020399245,"teacher_disagreement_score":0.015070653,"about_ca_system_score_codex":0.0010183404,"about_ca_system_score_gemma":0.0015211847,"threshold_uncertainty_score":0.07970214},"labels":[],"label_agreement":null},{"id":"W3003321657","doi":"10.1111/rssb.12358","title":"Sumca: Simple, Unified, Monte-Carlo-assisted Approach to Second-Order Unbiased Mean-Squared Prediction Error Estimation","year":2020,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","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 Manitoba","funders":"","keywords":"Monte Carlo method; Estimator; Mean squared error; Jackknife resampling; Best linear unbiased prediction; Statistics; Mathematics; Computer science; Bias of an estimator; Algorithm; Minimum-variance unbiased estimator; Selection (genetic algorithm); Artificial intelligence","score_opus":0.16877973629719845,"score_gpt":0.3750817467668877,"score_spread":0.20630201046968927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003321657","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":[0.0011145909,0.000105879706,0.9981306,0.000055399727,0.000028766255,0.00001828775,0.000018767054,0.00024292475,0.00028471646],"genre_scores_gemma":[0.14614953,0.00032543522,0.8494209,0.00025384137,0.0002322569,0.00033244086,0.00024953653,0.0003724244,0.0026636224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955924,0.0021266956,0.00018731947,0.00051457126,0.0013815874,0.00019743599],"domain_scores_gemma":[0.99128693,0.004815054,0.00043834126,0.0011597896,0.002108048,0.0001919445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064003994,0.0009955073,0.0019458326,0.001859394,0.0008175572,0.0017225213,0.0026414355,0.0015467397,0.00354214],"category_scores_gemma":[0.02185988,0.00086361566,0.0013786703,0.0013518783,0.0014279513,0.0017969897,0.002346692,0.002315709,0.001249992],"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":[0.00017296705,0.00009370769,0.0022324738,0.00023448796,0.00030726127,0.00015378873,0.00013834791,0.66477877,0.0053490107,0.10664808,0.0060252137,0.21386582],"study_design_scores_gemma":[0.000009089571,0.000018490877,0.00017976039,0.000015847807,0.00001365467,0.0000351819,0.000004685572,0.98048097,0.0010604918,0.016749503,0.0014128183,0.000019552657],"about_ca_topic_score_codex":0.004618602,"about_ca_topic_score_gemma":0.0059937807,"teacher_disagreement_score":0.0064003994,"about_ca_system_score_codex":0.00085044006,"about_ca_system_score_gemma":0.0030055302,"threshold_uncertainty_score":0.033849},"labels":[],"label_agreement":null},{"id":"W3003574023","doi":"10.1080/00273171.2019.1709405","title":"Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables","year":2020,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McGill University","funders":"National Institute on Drug Abuse","keywords":"Prior probability; Frequentist inference; Bayesian probability; Latent variable; Conjugate prior; Econometrics; Mathematics; Statistics; Bayesian inference; Bayesian statistics; Computer science","score_opus":0.42997985970240477,"score_gpt":0.49058741574929887,"score_spread":0.06060755604689411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003574023","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":[0.28533402,0.004646276,0.6344218,0.020243775,0.00080222555,0.0009760854,0.001630208,0.0010095958,0.050936066],"genre_scores_gemma":[0.9088994,0.0015693434,0.080300376,0.0009613591,0.00019364551,0.00081406755,0.0007521693,0.0004568692,0.0060528456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97008455,0.024100808,0.00060699147,0.0026765035,0.0014993313,0.0010318451],"domain_scores_gemma":[0.68064654,0.3016038,0.0034715286,0.009575905,0.0024686975,0.0022335423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07249422,0.0012770413,0.0017660513,0.0021739574,0.0017952943,0.007268649,0.0038366618,0.004816061,0.017209407],"category_scores_gemma":[0.29302642,0.0011211572,0.0024908646,0.0020345266,0.0048815194,0.012835326,0.0045586578,0.0076053403,0.0017241586],"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":[0.0023364907,0.0005714203,0.012203257,0.0008608085,0.000597593,0.0006360632,0.003969743,0.04738749,0.000808644,0.83402884,0.00896279,0.08763692],"study_design_scores_gemma":[0.00067174813,0.00018412886,0.009233231,0.0006842861,0.00042716324,0.00031343874,0.001971969,0.15198794,0.000856356,0.8299783,0.0034252817,0.0002661635],"about_ca_topic_score_codex":0.007685305,"about_ca_topic_score_gemma":0.008875373,"teacher_disagreement_score":0.07249422,"about_ca_system_score_codex":0.002448746,"about_ca_system_score_gemma":0.0024671755,"threshold_uncertainty_score":0.38339067},"labels":[],"label_agreement":null},{"id":"W3003667353","doi":"10.1002/sim.8468","title":"A fair comparison of tree‐based and parametric methods in multiple imputation by chained equations","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":102,"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":"Norman Cousins Center for Psychoneuroimmunology; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; National Institute of Environmental Health Sciences; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Alzheimer's Association; Fujirebio US; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Roche; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; AbbVie; Foundation for the National Institutes of Health","keywords":"Imputation (statistics); Parametric statistics; Computer science; Nonparametric statistics; Inference; Missing data; Statistics; Econometrics; Data mining; Mathematics; Machine learning; Artificial intelligence","score_opus":0.172531786709154,"score_gpt":0.4936504161418888,"score_spread":0.32111862943273484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003667353","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":[0.008792108,0.002814976,0.98497945,0.0008993193,0.00022679327,0.00018615162,0.0003181238,0.00026501546,0.0015181045],"genre_scores_gemma":[0.22451434,0.0029856602,0.7660345,0.00096447393,0.0003830031,0.0013067544,0.0014699383,0.0004446188,0.001896738],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8841032,0.09839463,0.003044368,0.0042037107,0.009333769,0.0009202937],"domain_scores_gemma":[0.62584394,0.3244499,0.0076512275,0.029048214,0.011991602,0.0010150406],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12852415,0.0011722852,0.0025894335,0.0024966756,0.0012512052,0.0032859144,0.0034578685,0.002768196,0.004561874],"category_scores_gemma":[0.35220265,0.0008893479,0.0025766238,0.0030283302,0.0032992517,0.006616035,0.004790709,0.0037752108,0.0009589983],"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":[0.002369834,0.00023748216,0.014880997,0.0015077221,0.0022687595,0.00019898999,0.001056365,0.2382236,0.000966165,0.38761473,0.012436934,0.33823845],"study_design_scores_gemma":[0.00048497543,0.0006678093,0.0042704935,0.0009288207,0.0004645045,0.00030909668,0.0002252754,0.61648786,0.0020012842,0.35678336,0.017201062,0.00017545794],"about_ca_topic_score_codex":0.0035227959,"about_ca_topic_score_gemma":0.002768308,"teacher_disagreement_score":0.8714758,"about_ca_system_score_codex":0.0019483004,"about_ca_system_score_gemma":0.0038672136,"threshold_uncertainty_score":0.67970884},"labels":[],"label_agreement":null},{"id":"W3004845713","doi":"10.1002/sim.8479","title":"Ordinal outcomes: A cumulative probability model with the log link and an assumption of proportionality","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ordinal regression; Logit; Mathematics; Link (geometry); Ordered logit; Odds; Statistics; Logistic regression; Ordinal data; Log-linear model; Econometrics; Uniqueness; Linear model; Combinatorics","score_opus":0.1775003639773146,"score_gpt":0.44978809162532396,"score_spread":0.27228772764800935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004845713","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":[0.019171402,0.0022350915,0.9546183,0.00644634,0.00029471883,0.00018169971,0.0010261761,0.0002476757,0.015778651],"genre_scores_gemma":[0.6672083,0.005579244,0.27621368,0.0034994131,0.0024194024,0.002018898,0.0017507282,0.00041808345,0.04089222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9895476,0.0063200225,0.00030931013,0.0016382366,0.0014634754,0.0007213402],"domain_scores_gemma":[0.94258314,0.04620459,0.004368607,0.0039027887,0.0019366515,0.001004246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019354831,0.0013217485,0.0021835952,0.0029202271,0.0013184563,0.0053155925,0.004374247,0.0031955529,0.017208405],"category_scores_gemma":[0.07413495,0.0008362046,0.0024488461,0.0042685284,0.004797079,0.010475737,0.0035902779,0.0065141344,0.0026785554],"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":[0.000073698786,0.00007815564,0.0038591311,0.00018484211,0.00009862241,0.00027887762,0.00044524542,0.022370301,0.00014829989,0.94475317,0.002930135,0.02477952],"study_design_scores_gemma":[0.000047641333,0.00010944926,0.0015470558,0.00013471776,0.00008527828,0.00040826912,0.00012063567,0.108262785,0.000109755114,0.87923926,0.009885971,0.00004924151],"about_ca_topic_score_codex":0.005717457,"about_ca_topic_score_gemma":0.0027025884,"teacher_disagreement_score":0.019354831,"about_ca_system_score_codex":0.0023446109,"about_ca_system_score_gemma":0.0021466836,"threshold_uncertainty_score":0.102359354},"labels":[],"label_agreement":null},{"id":"W3005050419","doi":"10.1002/bimj.201900184","title":"Skew‐normal random‐effects model for meta‐analysis of diagnostic test accuracy (DTA) studies","year":2020,"lang":"en","type":"review","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian 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":"Impact; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bivariate analysis; Mathematics; Statistics; Random effects model; Logit; Sensitivity (control systems); Econometrics; Bivariate data; Multivariate normal distribution; Linear model; Meta-analysis; Multivariate statistics","score_opus":0.4388444531037595,"score_gpt":0.5271458066450686,"score_spread":0.08830135354130908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005050419","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":[0.0026433165,0.07960251,0.89892536,0.00190183,0.001103138,0.0062969383,0.0063169105,0.0015274064,0.0016826446],"genre_scores_gemma":[0.11690032,0.05263446,0.7574679,0.003561731,0.0009010543,0.05348862,0.009198145,0.00079143525,0.0050562816],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8972752,0.07886055,0.008972746,0.008375628,0.0059068683,0.0006090058],"domain_scores_gemma":[0.9100761,0.07367862,0.00658494,0.006976041,0.0024482554,0.00023603794],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10588949,0.004118837,0.009124399,0.007878011,0.00092293264,0.003312053,0.0062934286,0.0037862149,0.0179552],"category_scores_gemma":[0.19463952,0.0020487255,0.02445418,0.010700252,0.0017722254,0.0039680176,0.002208327,0.005649739,0.0033531873],"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":[0.002569896,0.00030425916,0.018251546,0.107220374,0.15965284,0.0010052614,0.0011670471,0.09143603,0.001700309,0.13611934,0.035699766,0.44487327],"study_design_scores_gemma":[0.0042419382,0.0017734166,0.016416563,0.022837693,0.16996178,0.0014191353,0.0003110305,0.1969705,0.0024976754,0.453328,0.1296193,0.0006229726],"about_ca_topic_score_codex":0.0071214857,"about_ca_topic_score_gemma":0.0071554272,"teacher_disagreement_score":0.8941105,"about_ca_system_score_codex":0.0029746706,"about_ca_system_score_gemma":0.0060090297,"threshold_uncertainty_score":0.5600039},"labels":[],"label_agreement":null},{"id":"W3005360419","doi":"10.1111/sjos.12448","title":"Inference for longitudinal data from complex sampling surveys: An approach based on quadratic inference functions","year":2020,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Statistics; Statistical inference; Inference; Goodness of fit; Statistic; Test statistic; Asymptotic distribution; Sampling distribution; Applied mathematics; Fiducial inference; Statistical hypothesis testing; Frequentist inference; Bayesian inference; Computer science; Artificial intelligence","score_opus":0.48576614394923917,"score_gpt":0.460891759868534,"score_spread":0.024874384080705192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005360419","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":[0.0021932516,0.00006327921,0.9974796,0.00006564541,0.000007586082,0.000021695527,0.000018374812,0.000027329032,0.00012306777],"genre_scores_gemma":[0.17623807,0.0005442175,0.81957567,0.00027218123,0.0001265531,0.0005526398,0.00032580507,0.00013904722,0.0022256798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9846972,0.011927734,0.00044669662,0.0013139878,0.0013845586,0.00022987042],"domain_scores_gemma":[0.930346,0.061521906,0.0023922625,0.0024754875,0.002865384,0.0003990419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040821213,0.0009632183,0.0018582137,0.0026228365,0.00064838096,0.001657534,0.0031411245,0.0011787399,0.0033848223],"category_scores_gemma":[0.1021768,0.0008615091,0.0015049716,0.0024070079,0.0020076232,0.0031726256,0.0027749056,0.002238018,0.00041721313],"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":[0.00024633954,0.00020662996,0.007240588,0.00039319284,0.00047427908,0.00025365772,0.0004701932,0.47811615,0.0016831188,0.35838738,0.0022060275,0.15032251],"study_design_scores_gemma":[0.000023948078,0.00005063725,0.00070108456,0.000021220423,0.000032093994,0.000043079996,0.000020269033,0.9236195,0.00026338277,0.07434635,0.00085998804,0.000018529056],"about_ca_topic_score_codex":0.004637582,"about_ca_topic_score_gemma":0.0030478002,"teacher_disagreement_score":0.040821213,"about_ca_system_score_codex":0.001373008,"about_ca_system_score_gemma":0.0018637469,"threshold_uncertainty_score":0.21588576},"labels":[],"label_agreement":null},{"id":"W3005876327","doi":"10.1002/sim.8484","title":"Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"Sunnybrook Hospital; Institute of Health Services and Policy Research; University Health Network; University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Economic and Social Research Council; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Logistic regression; Statistics; Multivariate statistics; Bayesian probability; Medicine; Multivariate analysis; Econometrics; Mathematics","score_opus":0.08760402899998009,"score_gpt":0.3791329511887469,"score_spread":0.2915289221887668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005876327","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":[0.1379022,0.00044466977,0.85483015,0.0027146847,0.0001781374,0.0008107678,0.00078100944,0.0005420093,0.0017965026],"genre_scores_gemma":[0.641859,0.0005318937,0.34614852,0.0011471298,0.0002976338,0.0032069448,0.0022354748,0.00024792977,0.0043254737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9026798,0.08584392,0.0014117322,0.0055808425,0.0027796535,0.0017041184],"domain_scores_gemma":[0.8079376,0.1723882,0.00846473,0.0047305226,0.0054557994,0.0010231133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.085547715,0.002433722,0.0034589285,0.003931355,0.0010910601,0.0036832548,0.006556328,0.003094847,0.005568454],"category_scores_gemma":[0.15881415,0.0017823091,0.004829217,0.004149464,0.0023755806,0.0044311327,0.003695396,0.0057490445,0.0014328511],"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":[0.0024974768,0.0011071193,0.059275754,0.00046232482,0.002953263,0.00049030344,0.0018906994,0.7694158,0.0005026353,0.07747348,0.003002611,0.08092848],"study_design_scores_gemma":[0.00029755034,0.0003352953,0.0048686382,0.00007492369,0.00015674463,0.000046426732,0.00018491255,0.970311,0.00010353274,0.02272364,0.00080774643,0.000089683635],"about_ca_topic_score_codex":0.031272087,"about_ca_topic_score_gemma":0.0135876965,"teacher_disagreement_score":0.085547715,"about_ca_system_score_codex":0.004153105,"about_ca_system_score_gemma":0.0038538347,"threshold_uncertainty_score":0.45242494},"labels":[],"label_agreement":null},{"id":"W3006196254","doi":"10.1093/ije/dyz277","title":"Power calculations for cluster randomized trials (CRTs) with right-truncated Poisson-distributed outcomes: a motivating example from a malaria vector control trial","year":2019,"lang":"en","type":"article","venue":"International Journal of Epidemiology","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":"Université de Montréal","funders":"Medical Research Council; Department for International Development; Bill and Melinda Gates Foundation","keywords":"Truncation (statistics); CRTS; Sample size determination; Statistics; Cluster randomised controlled trial; Poisson distribution; Population; Type I and type II errors; Mathematics; Medicine; Randomized controlled trial; Computer science; Surgery; Environmental health","score_opus":0.1383667190110728,"score_gpt":0.44195265306120224,"score_spread":0.30358593405012946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006196254","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":[0.017679207,0.0036938714,0.95910394,0.008548024,0.00038829775,0.0022804306,0.00057418057,0.00035267157,0.0073793675],"genre_scores_gemma":[0.2509172,0.0015884798,0.7391029,0.0024569058,0.00027249244,0.00435933,0.00027946086,0.00018875774,0.0008345388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81847626,0.1662863,0.004156991,0.0033382438,0.00707279,0.00066953455],"domain_scores_gemma":[0.4072252,0.5651888,0.00880215,0.010424833,0.007723358,0.0006356258],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.16071562,0.0013918563,0.002578459,0.0020771904,0.0011100417,0.0023595924,0.0028467933,0.004380785,0.0042738984],"category_scores_gemma":[0.40404564,0.00072172954,0.0032891522,0.0025588402,0.003156439,0.002232811,0.0024702942,0.0059142555,0.0006232529],"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":[0.0050110035,0.0003557945,0.010386606,0.0055852034,0.00266817,0.0022183347,0.0021717115,0.30689797,0.0016413963,0.45633414,0.021209033,0.18552065],"study_design_scores_gemma":[0.0026775182,0.0013966843,0.0028284772,0.0017973008,0.0008645555,0.0012528647,0.00020641764,0.3633951,0.0018362746,0.6024883,0.021109397,0.0001472159],"about_ca_topic_score_codex":0.0027142626,"about_ca_topic_score_gemma":0.0017111828,"teacher_disagreement_score":0.83928436,"about_ca_system_score_codex":0.002144806,"about_ca_system_score_gemma":0.0041410252,"threshold_uncertainty_score":0.8499556},"labels":[],"label_agreement":null},{"id":"W3010247233","doi":"10.1007/s12561-020-09270-7","title":"A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility","year":2020,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Statistical Methods and Bayesian 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":"University of Waterloo","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Copula (linguistics); Statistics; Econometrics; Mathematics; Inference; Likelihood function; Accelerated failure time model; Confidence interval; Population; Survival analysis; Maximum likelihood; Computer science; Medicine","score_opus":0.06900862316380572,"score_gpt":0.36422775930383877,"score_spread":0.29521913614003303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010247233","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":[0.022942796,0.00085035013,0.97331494,0.0007677919,0.00009104429,0.00006949549,0.00046309605,0.00036296673,0.0011375812],"genre_scores_gemma":[0.7277022,0.0036464287,0.23667492,0.00076510455,0.000750153,0.0011491441,0.0026331102,0.00057948154,0.026099484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99487585,0.0024990572,0.00028221993,0.0011292114,0.00072204904,0.000491553],"domain_scores_gemma":[0.9616149,0.030281324,0.0029482557,0.0023836265,0.001921913,0.0008499641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013950063,0.0022586156,0.005075927,0.004472289,0.0010187645,0.0040478986,0.007529517,0.0051000626,0.006743441],"category_scores_gemma":[0.03917533,0.002420177,0.0041152514,0.003725949,0.0038482903,0.007000086,0.0030605101,0.0052793855,0.0017683703],"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":[0.0005015739,0.00017413835,0.0058814,0.00039357648,0.0004999797,0.00054728263,0.00065525365,0.42701104,0.0022861648,0.5345489,0.0032649743,0.02423576],"study_design_scores_gemma":[0.000070713504,0.00006159353,0.0011811301,0.000068996094,0.00017551318,0.00022412692,0.000046563793,0.85129136,0.00025155855,0.14558429,0.0009539285,0.00009019588],"about_ca_topic_score_codex":0.0074591283,"about_ca_topic_score_gemma":0.0051343013,"teacher_disagreement_score":0.013950063,"about_ca_system_score_codex":0.0019291696,"about_ca_system_score_gemma":0.0016789832,"threshold_uncertainty_score":0.07377589},"labels":[],"label_agreement":null},{"id":"W3010609735","doi":"10.1002/cjs.11540","title":"Empirical likelihood for nonlinear regression models with nonignorable missing responses","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Statistics; Estimator; Missing data; Logistic regression; Propensity score matching; Empirical likelihood; Inverse probability; Mathematics; Econometrics; Inverse probability weighting; Regression analysis; Regression; Computer science; Bayesian probability","score_opus":0.140628118886543,"score_gpt":0.3763695566115431,"score_spread":0.23574143772500009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010609735","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":[0.008765178,0.0002654897,0.99002516,0.00036176585,0.0000092529635,0.000028137083,0.00004557953,0.000084252126,0.00041520037],"genre_scores_gemma":[0.42954955,0.0011413061,0.5644816,0.00022026805,0.00013410943,0.00046654078,0.00061699166,0.00015166012,0.0032380347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98757637,0.0106235165,0.0003070114,0.0006143335,0.0006680207,0.00021073193],"domain_scores_gemma":[0.9096226,0.08190182,0.0034349211,0.0031266245,0.0016084879,0.00030550253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02684034,0.0007802667,0.0019366881,0.0019170484,0.000504228,0.0017934027,0.0034166556,0.0014492901,0.0028787397],"category_scores_gemma":[0.12652588,0.00091969303,0.0013246634,0.0026593213,0.0025864006,0.0034086152,0.002601632,0.0025751512,0.00062274456],"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":[0.00021487637,0.00015632603,0.013199921,0.00046575893,0.00039233163,0.0006456508,0.0006658849,0.4231313,0.00057681254,0.38630643,0.0023403598,0.17190436],"study_design_scores_gemma":[0.000037330177,0.000025706939,0.0013907732,0.00005201906,0.000029211982,0.00008117474,0.00006557063,0.8554945,0.00022458081,0.1415793,0.0009959172,0.00002397732],"about_ca_topic_score_codex":0.0046840315,"about_ca_topic_score_gemma":0.0035708856,"teacher_disagreement_score":0.02684034,"about_ca_system_score_codex":0.0013409678,"about_ca_system_score_gemma":0.001710502,"threshold_uncertainty_score":0.14194703},"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":"W3013921428","doi":"10.1186/s12874-020-0900-z","title":"Methods of competing risks flexible parametric modeling for estimation of the risk of the first disease among HIV infected men","year":2020,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","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":"McMaster University; Impact","funders":"National Institute of Allergy and Infectious Diseases; National Cancer Institute; National Institute on Deafness and Other Communication Disorders; National Institute on Drug Abuse; Northwestern University; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Center for Advancing Translational Sciences; Johns Hopkins Bloomberg School of Public Health; University of California, Los Angeles; National Institutes of Health; Johns Hopkins University; Tehran University of Medical Sciences and Health Services; University of Pittsburgh; McMaster University","keywords":"Estimation; Human immunodeficiency virus (HIV); Disease; Parametric statistics; Medicine; Environmental health; Econometrics; Computer science; Statistics; Risk analysis (engineering); Family medicine; Mathematics; Internal medicine; Engineering","score_opus":0.6493900727292393,"score_gpt":0.595324126106831,"score_spread":0.05406594662240827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013921428","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":[0.0034128267,0.000264422,0.9953147,0.00016280303,0.00003681305,0.00023782134,0.00014030763,0.00012452924,0.00030569878],"genre_scores_gemma":[0.22032589,0.0010673095,0.7707893,0.00028071267,0.00024432776,0.0041028424,0.0008320261,0.00024973377,0.0021079117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9343352,0.059590604,0.0011342925,0.0020454505,0.0023145785,0.00057981216],"domain_scores_gemma":[0.7756071,0.20985776,0.004982837,0.0058288807,0.0030876196,0.00063580385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06923937,0.002008789,0.0019930871,0.0037323923,0.00077049795,0.0022675898,0.004736531,0.0020066088,0.0062785954],"category_scores_gemma":[0.15335678,0.0012244205,0.0055759866,0.0027491956,0.0022105195,0.0018073941,0.0031792088,0.0043453416,0.00059887447],"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":[0.00065229804,0.0002751422,0.028121604,0.001202569,0.0033719037,0.0010669448,0.0018400692,0.41469097,0.0012129381,0.34604698,0.005281576,0.19623697],"study_design_scores_gemma":[0.00012945659,0.0003633478,0.0033001075,0.0002749092,0.00040638575,0.00040730994,0.00023583726,0.81189775,0.0005790615,0.17717876,0.005107776,0.000119331766],"about_ca_topic_score_codex":0.0048409603,"about_ca_topic_score_gemma":0.0028486687,"teacher_disagreement_score":0.06923937,"about_ca_system_score_codex":0.0013915309,"about_ca_system_score_gemma":0.0031176184,"threshold_uncertainty_score":0.36617714},"labels":[],"label_agreement":null},{"id":"W3014919713","doi":"10.1007/s11222-021-10012-y","title":"A robust and efficient algorithm to find profile likelihood confidence intervals","year":2021,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","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":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada; University of Alberta","keywords":"Likelihood function; Confidence interval; Estimator; Confidence distribution; Range (aeronautics); Function (biology); Confidence region; Benchmark (surveying); Expectation–maximization algorithm","score_opus":0.049899599753010795,"score_gpt":0.342029639112509,"score_spread":0.2921300393594982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014919713","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":[0.0016506113,0.00006551189,0.9970632,0.00003698881,0.000012092379,0.000038499562,0.00003167371,0.0007085947,0.00039287124],"genre_scores_gemma":[0.06661337,0.00009060287,0.9317889,0.00005567473,0.000033077886,0.00014971732,0.00023626594,0.00028817102,0.0007442302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728787,0.00077913253,0.00025634322,0.00044588384,0.0010360291,0.00019481471],"domain_scores_gemma":[0.98471314,0.0099670915,0.0010477053,0.0011383877,0.0028208303,0.00031286024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004512341,0.0011854147,0.0016327648,0.0036853012,0.00080996775,0.0024453471,0.0028924593,0.0017544498,0.007279347],"category_scores_gemma":[0.034979187,0.000869599,0.0013401442,0.0023561956,0.0010249302,0.0023732563,0.002654252,0.0028590723,0.0029259487],"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":[0.000255595,0.00011854145,0.0019570694,0.00021191026,0.00009546826,0.00022794322,0.00019431362,0.45019922,0.004563121,0.03326355,0.005137412,0.5037758],"study_design_scores_gemma":[0.00003142319,0.00002707467,0.00014759313,0.00002401734,0.000011181527,0.000085754844,0.000017022661,0.98521924,0.0021336747,0.010916901,0.0013714057,0.000014720115],"about_ca_topic_score_codex":0.005629011,"about_ca_topic_score_gemma":0.0030181776,"teacher_disagreement_score":0.007279347,"about_ca_system_score_codex":0.001213963,"about_ca_system_score_gemma":0.0030658308,"threshold_uncertainty_score":0.024351835},"labels":[],"label_agreement":null},{"id":"W3014964148","doi":"10.1002/sim.8531","title":"STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":126,"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; University of British Columbia","funders":"National Institutes of Health; National Cancer Institute; Medical Research Council; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; Patient-Centered Outcomes Research Institute","keywords":"Categorical variable; Covariate; Observational error; Computer science; Statistics; Imputation (statistics); Bayesian probability; Errors-in-variables models; Data mining; Bayes' theorem; Missing data; Standard error; Econometrics; Mathematics","score_opus":0.4407366589677345,"score_gpt":0.5053344638457099,"score_spread":0.0645978048779754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014964148","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":[0.0022542158,0.07428978,0.4628504,0.18517363,0.037224423,0.004842915,0.05860157,0.0072121383,0.16755089],"genre_scores_gemma":[0.010752948,0.0876628,0.652219,0.07025423,0.014564453,0.007183733,0.039025437,0.005332122,0.11300527],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9688326,0.014464184,0.0029490262,0.0014651293,0.011442187,0.0008469036],"domain_scores_gemma":[0.91655034,0.05060347,0.0040500863,0.0074723046,0.019882921,0.0014407736],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.034636084,0.0026271923,0.0028987993,0.006447534,0.0015467509,0.004830299,0.005774295,0.009141687,0.046246335],"category_scores_gemma":[0.09947564,0.0016554579,0.0034114106,0.00685479,0.0027789965,0.0039660656,0.004099357,0.009065861,0.028180419],"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":[0.000046952588,0.00006307361,0.0006322987,0.0014178671,0.00004945773,0.00009703802,0.00027200326,0.0023466032,0.00034222042,0.066271715,0.8309164,0.097544424],"study_design_scores_gemma":[0.000042887834,0.00004872251,0.0012152109,0.002413619,0.000035134428,0.00018939067,0.000077295634,0.001231326,0.0003842606,0.04383164,0.95047176,0.000058812846],"about_ca_topic_score_codex":0.037,"about_ca_topic_score_gemma":0.03298335,"teacher_disagreement_score":0.9653639,"about_ca_system_score_codex":0.0042822342,"about_ca_system_score_gemma":0.016216498,"threshold_uncertainty_score":0.18317527},"labels":[],"label_agreement":null},{"id":"W3015042751","doi":"10.1037/met0000265","title":"Partitioning variation in multilevel models for count data.","year":2020,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":67,"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":"Economic and Social Research Council","keywords":"Overdispersion; Categorical variable; Intraclass correlation; Statistics; Count data; Multilevel model; Mathematics; Cluster analysis; Negative binomial distribution; Poisson distribution; Item response theory; Variance (accounting); Binary data; Econometrics; Correlation; Binary number; Psychometrics","score_opus":0.6438736940352537,"score_gpt":0.5849986390061367,"score_spread":0.05887505502911705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015042751","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":[0.0015669089,0.0010638121,0.9928732,0.00069407537,0.0001526909,0.00021781497,0.00035406416,0.00027036743,0.002807109],"genre_scores_gemma":[0.1296848,0.0027389554,0.85379833,0.0014202826,0.0005400441,0.0030980804,0.0017560761,0.0008089494,0.0061544534],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9376839,0.04519148,0.0021045667,0.006140953,0.007735788,0.0011432568],"domain_scores_gemma":[0.9300846,0.05235997,0.0053360052,0.008387857,0.0033043234,0.00052736053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04017115,0.0018408552,0.0029104634,0.0040908023,0.0016448833,0.0058139013,0.0068141343,0.0033279306,0.011714089],"category_scores_gemma":[0.14277917,0.0017341427,0.0063726767,0.0072244653,0.0041843834,0.007236723,0.007154553,0.0077463174,0.003491536],"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":[0.000030178095,0.000030359955,0.0026820612,0.00054318423,0.0005326602,0.00020955381,0.0013496962,0.03314344,0.00021945623,0.91411555,0.0065630525,0.040580746],"study_design_scores_gemma":[0.000019232055,0.00005860321,0.0014869476,0.00032195915,0.00013942528,0.0002327171,0.00024812468,0.1557351,0.0001819373,0.8233667,0.018137285,0.00007197286],"about_ca_topic_score_codex":0.009594896,"about_ca_topic_score_gemma":0.011835363,"teacher_disagreement_score":0.04017115,"about_ca_system_score_codex":0.00493773,"about_ca_system_score_gemma":0.0034013677,"threshold_uncertainty_score":0.21244788},"labels":[],"label_agreement":null},{"id":"W3018909183","doi":"10.1093/jssam/smaa004","title":"Multiply Robust Bootstrap Variance Estimation in the Presence of Singly Imputed Survey Data","year":2020,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Statistical Methods and Bayesian 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":"University of Winnipeg; Université de Montréal","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Estimator; Quantile; Imputation (statistics); Statistics; Robustness (evolution); Variance (accounting); Point estimation; Econometrics; Mathematics; Robust statistics; Population; Computer science; Missing data","score_opus":0.6810327651611238,"score_gpt":0.4918226381347552,"score_spread":0.18921012702636858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018909183","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":[0.006676668,0.00026870723,0.9924636,0.00007617315,0.000017295873,0.000037489644,0.000026676506,0.00017921095,0.00025415057],"genre_scores_gemma":[0.19025904,0.00044308355,0.80771524,0.000117566466,0.00011931449,0.00034171614,0.00024430902,0.00012262355,0.00063719385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9813107,0.014436008,0.0005640582,0.0011343688,0.002280458,0.00027441754],"domain_scores_gemma":[0.95577806,0.03187373,0.0030469242,0.0069419728,0.0021288672,0.00023053377],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01627766,0.0008134197,0.0015357053,0.0019625057,0.0005729265,0.0010883838,0.002615853,0.0019276455,0.0016099841],"category_scores_gemma":[0.09556707,0.0006758928,0.0014850604,0.0018985629,0.0014114045,0.0015463239,0.0025308717,0.001773666,0.0006660212],"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":[0.00053356186,0.00021563582,0.014459787,0.00070427207,0.0010782507,0.0005529055,0.0007916652,0.11118942,0.008747135,0.17594434,0.0037114783,0.68207145],"study_design_scores_gemma":[0.0001286178,0.00048623665,0.008841,0.00022992212,0.00027560728,0.0010069092,0.00015203869,0.7462594,0.012778971,0.21960746,0.0100933295,0.00014055046],"about_ca_topic_score_codex":0.00071819715,"about_ca_topic_score_gemma":0.00075066945,"teacher_disagreement_score":0.9837223,"about_ca_system_score_codex":0.00043249375,"about_ca_system_score_gemma":0.00085875863,"threshold_uncertainty_score":0.0860855},"labels":[],"label_agreement":null},{"id":"W3021389985","doi":"10.1002/sim.8560","title":"A tractable method to account for high‐dimensional nonignorable missing data in intensive longitudinal data","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Simon Fraser University","funders":"National Cancer Institute","keywords":"Missing data; Covariate; Computer science; Curse of dimensionality; Multinomial distribution; Outcome (game theory); Econometrics; Data mining; Statistics; Mathematics; Machine learning","score_opus":0.2896584364384498,"score_gpt":0.49417319198457804,"score_spread":0.20451475554612825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021389985","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":[0.0006790777,0.000056446974,0.998814,0.000117793,0.000014224172,0.0000365319,0.000036421556,0.000072238174,0.00017332062],"genre_scores_gemma":[0.0572943,0.00046490025,0.93792015,0.00036645454,0.00020192467,0.00085511664,0.00027569523,0.00020277395,0.002418668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99505544,0.0031022436,0.00025484493,0.0005042788,0.0009248609,0.00015830851],"domain_scores_gemma":[0.97244656,0.022500986,0.0012781061,0.0020699436,0.0013140263,0.00039043205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018664353,0.0012640205,0.0019524689,0.002401417,0.0009802646,0.0014667497,0.0033942726,0.0018456578,0.0058637927],"category_scores_gemma":[0.06359266,0.001154318,0.0023014655,0.0023202754,0.0017090419,0.0027033852,0.0033906903,0.0036173519,0.0009859562],"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":[0.00010669386,0.00014505119,0.005459231,0.0005165899,0.00047678532,0.0006165167,0.0006184344,0.4220672,0.0029212087,0.3454357,0.0071484526,0.2144881],"study_design_scores_gemma":[0.000044152683,0.00004115074,0.0006539859,0.00007434955,0.00006475094,0.00027559526,0.000041814397,0.83903986,0.0006755718,0.15400003,0.005052742,0.000036052043],"about_ca_topic_score_codex":0.00610754,"about_ca_topic_score_gemma":0.009347152,"teacher_disagreement_score":0.018664353,"about_ca_system_score_codex":0.0015436729,"about_ca_system_score_gemma":0.005455103,"threshold_uncertainty_score":0.098707676},"labels":[],"label_agreement":null},{"id":"W3021448502","doi":"10.1093/ije/dyaa077","title":"Reflection on modern methods: when is a stepped-wedge cluster randomized trial a good study design choice?","year":2020,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":157,"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":"Collaboration for Leadership in Applied Health Research and Care - Greater Manchester; National Institute for Health and Care Research","keywords":"Randomized controlled trial; Cluster (spacecraft); Cluster randomised controlled trial; Computer science; CRTS; Actuarial science; Statistics; Medicine; Business; Mathematics; Surgery","score_opus":0.39176948390202776,"score_gpt":0.5397261085584006,"score_spread":0.14795662465637288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021448502","genre_codex":"commentary","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013596585,0.02417754,0.27069563,0.6606694,0.03343436,0.0028862257,0.00023593828,0.00041305734,0.0061281044],"genre_scores_gemma":[0.032411356,0.010115795,0.5230962,0.39300162,0.02323825,0.015471591,0.00012018005,0.00055138394,0.0019936978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.25657058,0.64290714,0.034139603,0.018113216,0.045789484,0.0024799847],"domain_scores_gemma":[0.14872079,0.7614846,0.022643315,0.036989357,0.02642183,0.003740149],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.664008,0.0031912543,0.011147346,0.0046555786,0.0044135544,0.018547988,0.0117749125,0.032278568,0.008119],"category_scores_gemma":[0.82564884,0.0035368504,0.006969942,0.00432147,0.051853012,0.023956861,0.010822154,0.06505222,0.004779473],"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":[0.0025409157,0.00030620326,0.0016233055,0.013878667,0.0027568836,0.0005580592,0.009447821,0.0050165397,0.00077514845,0.567558,0.1931777,0.20236081],"study_design_scores_gemma":[0.0027641293,0.000753889,0.0006238918,0.021022867,0.00076287595,0.0005544145,0.0009354956,0.008714001,0.001095779,0.8221167,0.14019741,0.00045866703],"about_ca_topic_score_codex":0.0031582324,"about_ca_topic_score_gemma":0.0020951629,"teacher_disagreement_score":0.33599198,"about_ca_system_score_codex":0.015357594,"about_ca_system_score_gemma":0.03193238,"threshold_uncertainty_score":0.414338},"labels":[],"label_agreement":null},{"id":"W3022398729","doi":"10.1371/journal.pone.0232822","title":"SimSurvey: An R package for comparing the design and analysis of surveys by simulating spatially-correlated populations","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","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":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Sampling design; Computer science; Stratified sampling; Sample size determination; Population; Range (aeronautics); Sample (material); Statistics; Variance (accounting); Data mining; R package; Sampling bias; Mathematics; Engineering","score_opus":0.3798373053768187,"score_gpt":0.38186651729222254,"score_spread":0.002029211915403839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022398729","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014733752,0.00092687603,0.70273733,0.0006979647,0.00057529425,0.0010707433,0.07144299,0.20037381,0.007441198],"genre_scores_gemma":[0.08171192,0.001206344,0.72297317,0.0015583235,0.00023329933,0.008218423,0.064047575,0.11546308,0.0045878417],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940726,0.0033118436,0.0005721665,0.0008623125,0.00090641197,0.00027465523],"domain_scores_gemma":[0.9559165,0.037120998,0.002100272,0.0027946949,0.001660679,0.00040681555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011960247,0.0033041164,0.0027168102,0.0027001104,0.0006199119,0.0024655273,0.0053107166,0.0015038456,0.048926026],"category_scores_gemma":[0.06625721,0.0020664847,0.0036409132,0.0020768642,0.0012368007,0.002644768,0.0033431263,0.0041889716,0.018012347],"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":[0.001223187,0.0003800722,0.027446168,0.0076961173,0.004804803,0.0009707261,0.001289657,0.16971411,0.005379611,0.036948234,0.6100685,0.13407879],"study_design_scores_gemma":[0.0010205881,0.000590799,0.011382294,0.0012437084,0.0013024348,0.0007853957,0.00028586696,0.48184472,0.008194628,0.07389214,0.4189964,0.0004611365],"about_ca_topic_score_codex":0.0058017564,"about_ca_topic_score_gemma":0.006108269,"teacher_disagreement_score":0.048926026,"about_ca_system_score_codex":0.000999744,"about_ca_system_score_gemma":0.003752088,"threshold_uncertainty_score":0.16367388},"labels":[],"label_agreement":null},{"id":"W3023241358","doi":"10.1093/jaoacint/qsaa005","title":"Interpretation and Implications of Lognormal Linear Regression Used for Bacterial Enumeration","year":2020,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Statistical Methods and Bayesian 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":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Agri-Food Innovation Alliance; University of Guelph","keywords":"Log-normal distribution; Mathematics; Logarithm; Statistics; Linear regression; Enumeration; Multiplicative function; Regression; Regression analysis; Nonlinear regression; Log-linear model; Distribution (mathematics); Linear model; Combinatorics; Mathematical analysis","score_opus":0.07340118082217764,"score_gpt":0.4098052458474797,"score_spread":0.33640406502530207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023241358","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":[0.12565835,0.0074217673,0.830581,0.012803106,0.0025750424,0.0006666173,0.0019766896,0.0034074003,0.014910039],"genre_scores_gemma":[0.7771811,0.0026752593,0.20919308,0.0030519802,0.0005763801,0.0008116805,0.0012293786,0.001157075,0.00412413],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9504233,0.031822447,0.0030249283,0.0052537806,0.008674639,0.00080087484],"domain_scores_gemma":[0.847252,0.11695245,0.0145944245,0.007135378,0.013322039,0.0007437271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042092152,0.0015243836,0.0012189576,0.0024831484,0.0009413231,0.003605898,0.0034265055,0.00163853,0.0050477535],"category_scores_gemma":[0.17993169,0.0004725212,0.0019373721,0.0032073902,0.0028910253,0.0029132504,0.0020036001,0.0035322742,0.0015522807],"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":[0.0021122333,0.00054040697,0.25403106,0.006382269,0.00158527,0.0042712726,0.0056423913,0.08520644,0.018508568,0.103790484,0.036629345,0.48130023],"study_design_scores_gemma":[0.00017069823,0.0017445243,0.15521534,0.0048171533,0.00092685595,0.00696783,0.010095175,0.40701917,0.028082717,0.3012778,0.08299547,0.000687194],"about_ca_topic_score_codex":0.0064916946,"about_ca_topic_score_gemma":0.005774191,"teacher_disagreement_score":0.042092152,"about_ca_system_score_codex":0.00297359,"about_ca_system_score_gemma":0.00369929,"threshold_uncertainty_score":0.22260725},"labels":[],"label_agreement":null},{"id":"W3023705342","doi":"10.1093/ije/dyaa042","title":"Reflection on modern methods: planned missing data designs for epidemiological research","year":2020,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":28,"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 Sherbrooke; Héma-Québec","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Missing data; Imputation (statistics); Data collection; Research design; Computer science; Epidemiology; Clinical study design; Statistical power; Sample size determination; External validity; Statistics; Data mining; Data science; Econometrics; Medicine; Mathematics; Machine learning; Clinical trial","score_opus":0.8970950348652054,"score_gpt":0.6862177658914597,"score_spread":0.2108772689737457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023705342","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":[0.00027944756,0.00093229074,0.99404633,0.0022654007,0.0005169805,0.00043509633,0.0001271271,0.00013893761,0.0012583664],"genre_scores_gemma":[0.010032719,0.0020172058,0.97968227,0.0022530043,0.00083619944,0.0043340726,0.00014633893,0.00014750242,0.00055061915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6864506,0.28289333,0.0071076024,0.0066360035,0.016097702,0.0008148027],"domain_scores_gemma":[0.47695568,0.44984472,0.012957631,0.04343891,0.015507713,0.0012953435],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.26515964,0.0029308703,0.003844296,0.004146163,0.002006161,0.0062822737,0.0058937753,0.007071907,0.008705729],"category_scores_gemma":[0.4739821,0.0027331365,0.004953111,0.006502433,0.010920324,0.009890939,0.0072284676,0.01929586,0.0035356614],"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":[0.00033944202,0.00016153869,0.0015934549,0.0031647652,0.0006783168,0.00015666784,0.002534153,0.0071811504,0.0005516807,0.80396754,0.016513333,0.16315798],"study_design_scores_gemma":[0.00023071379,0.000333658,0.00057829975,0.0014570928,0.00017337043,0.00023924503,0.00017476536,0.014561184,0.0007132701,0.94423187,0.037181556,0.00012501262],"about_ca_topic_score_codex":0.001497774,"about_ca_topic_score_gemma":0.0014409971,"teacher_disagreement_score":0.7348404,"about_ca_system_score_codex":0.0026152546,"about_ca_system_score_gemma":0.009555838,"threshold_uncertainty_score":0.90618896},"labels":[],"label_agreement":null},{"id":"W3024447182","doi":"10.1007/978-3-030-44246-0_11","title":"Bayesian Empirical Likelihood Methods","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Frequentist inference; Bayesian probability; Estimator; Bayesian inference; Point estimation; Computer science; Econometrics; Population; Bayesian statistics; Inference; Statistics; Mathematics; Artificial intelligence","score_opus":0.08940799383601485,"score_gpt":0.42335294135512913,"score_spread":0.3339449475191143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024447182","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":[0.0004957966,0.015529773,0.8066681,0.0022751442,0.001179954,0.000053609598,0.0006061901,0.0013447339,0.17184669],"genre_scores_gemma":[0.030618114,0.02334029,0.5015276,0.0023637267,0.002475495,0.00038939243,0.002418534,0.002552354,0.4343145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855274,0.00056276005,0.00004544018,0.00017347849,0.00062659057,0.000038897713],"domain_scores_gemma":[0.99814105,0.0011670179,0.000053133954,0.00028850726,0.00030825578,0.00004197161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019891076,0.0013573581,0.0013580472,0.0017439639,0.00056806696,0.0026432627,0.0015595728,0.0019810419,0.052607782],"category_scores_gemma":[0.007478312,0.0010687592,0.0008392672,0.0018747252,0.0011799285,0.0024019044,0.0016377984,0.0036516832,0.03495575],"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":[0.00001566083,0.00005222652,0.00015741502,0.00022714668,0.00005122013,0.00005215336,0.0000998427,0.008592892,0.00046261997,0.47333813,0.18497747,0.3319732],"study_design_scores_gemma":[0.0000068580484,0.000010235199,0.00022032521,0.00016404127,0.000018861549,0.00015856948,0.000024893092,0.026062418,0.0004786033,0.6144261,0.35840264,0.000026419832],"about_ca_topic_score_codex":0.001207949,"about_ca_topic_score_gemma":0.0022617306,"teacher_disagreement_score":0.052607782,"about_ca_system_score_codex":0.00089822727,"about_ca_system_score_gemma":0.0010607459,"threshold_uncertainty_score":0.17599052},"labels":[],"label_agreement":null},{"id":"W3024649583","doi":"10.1111/insr.12380","title":"Benchmarked Estimators for a Small Area Mean Under a Onefold Nested Regression Model","year":2020,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian 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":"Statistics Canada","funders":"","keywords":"Estimator; Extremum estimator; Mathematics; Small area estimation; M-estimator; Statistics; Mean squared error; Benchmarking; Population; Regression; Variable (mathematics); Regression analysis","score_opus":0.25715017163748316,"score_gpt":0.4505042942402393,"score_spread":0.19335412260275614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024649583","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":[0.039047696,0.00029053903,0.9597772,0.00008944877,0.000037651418,0.000027226532,0.00005527169,0.00007673106,0.0005982307],"genre_scores_gemma":[0.63281435,0.00035247102,0.36484095,0.00009655671,0.000097010554,0.00020336952,0.00025717277,0.00006071854,0.0012773467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98875123,0.008586953,0.00035742106,0.0011899592,0.00094518083,0.0001692676],"domain_scores_gemma":[0.95463187,0.033065367,0.0035335459,0.0049088704,0.0035157676,0.0003446146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023568833,0.00047661265,0.0013681588,0.0008732146,0.00022059162,0.0010703253,0.002195298,0.0009284728,0.0016157897],"category_scores_gemma":[0.06564516,0.00034213992,0.00084015256,0.00084481423,0.0011311204,0.0020434852,0.0015020095,0.0013361705,0.00019944481],"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":[0.00019905105,0.00012354176,0.014596827,0.0003038537,0.0005538902,0.00013393282,0.00031544198,0.63091147,0.0026100457,0.26058376,0.0011796411,0.08848856],"study_design_scores_gemma":[0.000021323849,0.00012582279,0.004194801,0.000056372683,0.00006727966,0.000035833888,0.000036396523,0.91181,0.001134619,0.081303276,0.0011838769,0.000030472735],"about_ca_topic_score_codex":0.0017982159,"about_ca_topic_score_gemma":0.0010635853,"teacher_disagreement_score":0.023568833,"about_ca_system_score_codex":0.0007402393,"about_ca_system_score_gemma":0.0006760281,"threshold_uncertainty_score":0.12464541},"labels":[],"label_agreement":null},{"id":"W3024827624","doi":"10.1080/08982112.2020.1741619","title":"Bayesian probability of agreement for comparing survival or reliability functions with parametric lifetime regression models","year":2020,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Covariate; Bayesian probability; Reliability (semiconductor); Weibull distribution; Parametric statistics; Similarity (geometry); Computer science; Statistics; Econometrics; Data mining; Mathematics; Artificial intelligence","score_opus":0.1977749977517763,"score_gpt":0.3806717568118881,"score_spread":0.1828967590601118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024827624","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":[0.0030075544,0.00045560446,0.9943738,0.0001911645,0.000062590014,0.000080156904,0.00013987388,0.00019439058,0.0014949184],"genre_scores_gemma":[0.3181706,0.0013296191,0.67383856,0.0006015131,0.00047031345,0.0022235971,0.0011749452,0.00074240763,0.0014484852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8807805,0.08789946,0.004625909,0.009418338,0.016180776,0.0010949917],"domain_scores_gemma":[0.6042699,0.34007877,0.018869625,0.024260167,0.011411063,0.0011104407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13590702,0.0019412136,0.0025862358,0.008094971,0.0014076995,0.005034673,0.004216637,0.004030596,0.005377478],"category_scores_gemma":[0.40431413,0.0012050627,0.0037423861,0.005228926,0.0071820854,0.0076613277,0.0058004307,0.0063883057,0.001313388],"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":[0.00046002734,0.00015195619,0.012572616,0.0012000771,0.0015766557,0.00024239482,0.001805552,0.20289415,0.0016459702,0.64098066,0.005540153,0.13092978],"study_design_scores_gemma":[0.000067508,0.00025709163,0.004875442,0.0004543237,0.00022497283,0.0003853156,0.0003441892,0.33808365,0.002012792,0.64485013,0.008251557,0.00019306861],"about_ca_topic_score_codex":0.002218946,"about_ca_topic_score_gemma":0.0011398596,"teacher_disagreement_score":0.13590702,"about_ca_system_score_codex":0.0025849505,"about_ca_system_score_gemma":0.002657819,"threshold_uncertainty_score":0.71875364},"labels":[],"label_agreement":null},{"id":"W3025212448","doi":"10.1007/978-3-030-44246-0_16","title":"Dual Frame and Multiple Frame Surveys","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Sampling frame; Frame (networking); Statistics; Population; Computer science; Dual (grammatical number); Confidence interval; Sampling (signal processing); Estimation; Econometrics; Geography; Mathematics; Medicine; Engineering; Computer vision; Telecommunications; Environmental health","score_opus":0.06505049951236007,"score_gpt":0.33589761594120576,"score_spread":0.2708471164288457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025212448","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":[0.0039902735,0.025155665,0.7840291,0.006052945,0.0017635372,0.00006248476,0.0008465095,0.0004114644,0.17768806],"genre_scores_gemma":[0.21173151,0.045915447,0.39770433,0.00497394,0.008306329,0.0008420608,0.0028016036,0.000886406,0.3268383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99875367,0.0007017241,0.000034456338,0.00021037212,0.00023319115,0.00006659664],"domain_scores_gemma":[0.9973381,0.001818172,0.00012330216,0.00036802134,0.00027277993,0.00007970823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023605083,0.0008671796,0.0016009863,0.001932075,0.0008636934,0.0023418898,0.0010644656,0.0017360144,0.021353103],"category_scores_gemma":[0.009775892,0.00085646077,0.0007028064,0.0037766546,0.0013783448,0.0035176254,0.0012853318,0.0023807022,0.0047227577],"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":[0.000017092982,0.000014034246,0.00011941925,0.00006787752,0.00001009401,0.0000149338775,0.000065111104,0.0011973885,0.00009864664,0.9082272,0.030373514,0.059794668],"study_design_scores_gemma":[0.0000049646455,0.000011138157,0.00021703602,0.00003542495,0.000008934342,0.00004985938,0.000029083134,0.005471156,0.00007520834,0.9294897,0.06459834,0.000009130387],"about_ca_topic_score_codex":0.0020385087,"about_ca_topic_score_gemma":0.0023442504,"teacher_disagreement_score":0.021353103,"about_ca_system_score_codex":0.00141538,"about_ca_system_score_gemma":0.0010571858,"threshold_uncertainty_score":0.071433246},"labels":[],"label_agreement":null},{"id":"W3025239458","doi":"10.1007/978-3-030-44246-0_5","title":"Model-Based Prediction and Model-Assisted Estimation","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"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 Waterloo","funders":"","keywords":"Estimation; Computer science; Data mining; Model validation; Sampling (signal processing); Survey sampling; Sampling design; Population; Machine learning; Artificial intelligence; Engineering; Data science","score_opus":0.07511977745115439,"score_gpt":0.3384333039895078,"score_spread":0.26331352653835344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025239458","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":[0.0007856914,0.00975684,0.97383195,0.0008207923,0.00067954825,0.000019754407,0.0002717445,0.0007432919,0.013090356],"genre_scores_gemma":[0.11611884,0.03511116,0.72670573,0.0017047139,0.003246918,0.00031671513,0.002237428,0.0017015596,0.112856895],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99924445,0.00024396545,0.00003141564,0.0001689316,0.00028409166,0.000027131246],"domain_scores_gemma":[0.9976514,0.0017755652,0.00007767426,0.0002648298,0.00020762697,0.000022867747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011893536,0.0013149519,0.0017803811,0.00086454314,0.00027647614,0.0020246047,0.0017015678,0.0016381628,0.011458633],"category_scores_gemma":[0.005777433,0.00095749693,0.0010727124,0.0018710473,0.0009303196,0.0024664633,0.00094499934,0.002864597,0.006701358],"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":[0.00007251816,0.00011989976,0.0005391783,0.00058063323,0.00014832667,0.00013031912,0.00008882832,0.1642851,0.0018705855,0.24278404,0.09066849,0.49871206],"study_design_scores_gemma":[0.000011836944,0.000042006057,0.0006050069,0.00011395324,0.000050984185,0.00018213237,0.000018350685,0.5574168,0.001493453,0.36998096,0.07003439,0.000050155566],"about_ca_topic_score_codex":0.0026473023,"about_ca_topic_score_gemma":0.0026293083,"teacher_disagreement_score":0.011458633,"about_ca_system_score_codex":0.00065217906,"about_ca_system_score_gemma":0.00087467185,"threshold_uncertainty_score":0.03833294},"labels":[],"label_agreement":null},{"id":"W3025512758","doi":"10.1007/978-3-030-44246-0_7","title":"Regression Analysis and Estimating Equations","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Waterloo","funders":"","keywords":"Regression analysis; Statistics; Mathematics; Applied mathematics; Econometrics","score_opus":0.07434739647276818,"score_gpt":0.3781793320178126,"score_spread":0.30383193554504445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025512758","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":[0.0010073996,0.10751492,0.7073488,0.0058266437,0.0033883015,0.00008096022,0.0011684803,0.001433469,0.17223105],"genre_scores_gemma":[0.028000142,0.12860572,0.3053847,0.0040784692,0.0071114264,0.0005627477,0.0027853835,0.0019370706,0.5215343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99873346,0.0005872645,0.000056931887,0.00019017026,0.00039984967,0.000032443484],"domain_scores_gemma":[0.99724305,0.002183227,0.00008130104,0.00021810512,0.000253702,0.00002058987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015988467,0.00165056,0.0020784193,0.0019070621,0.00042437034,0.0023838028,0.0010801851,0.0017173077,0.029112691],"category_scores_gemma":[0.006178415,0.0010414881,0.0010187018,0.0037994047,0.001314811,0.0024980928,0.00081284944,0.003376593,0.023219647],"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":[0.000014905042,0.0000517588,0.00027945475,0.0006066856,0.00007451306,0.000074547745,0.0001697967,0.008541727,0.00044896518,0.4690727,0.22472265,0.2959423],"study_design_scores_gemma":[0.000007657206,0.000023598544,0.0006818749,0.0003149889,0.00004643349,0.00018698712,0.00005360797,0.014971226,0.00038157994,0.5012487,0.4820489,0.00003436074],"about_ca_topic_score_codex":0.002582245,"about_ca_topic_score_gemma":0.0037327094,"teacher_disagreement_score":0.029112691,"about_ca_system_score_codex":0.00094647537,"about_ca_system_score_gemma":0.0012547182,"threshold_uncertainty_score":0.097391665},"labels":[],"label_agreement":null},{"id":"W3026934696","doi":"10.1002/9781118445112.stat08246","title":"Negative Binomial Regression","year":2020,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Count data; Negative binomial distribution; Poisson regression; Poisson distribution; Quasi-likelihood; Overdispersion; Statistics; Zero-inflated model; Econometrics; Generalization; Binomial distribution; Generalized linear model; Mathematics; Medicine; Population","score_opus":0.10850202850818269,"score_gpt":0.4044765539488858,"score_spread":0.29597452544070313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026934696","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":[0.0068807965,0.004389923,0.9639185,0.002337778,0.00050520303,0.000099981706,0.001622382,0.0009158747,0.019329542],"genre_scores_gemma":[0.46708643,0.01291654,0.3981653,0.0026209617,0.003112352,0.00095169904,0.006169799,0.0018276804,0.107149296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99425346,0.0031723315,0.00019539126,0.0010501067,0.0010583423,0.00027037537],"domain_scores_gemma":[0.97954834,0.015659861,0.0015028522,0.001371838,0.0016817203,0.00023542013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0091484,0.0012102454,0.0019507785,0.0025249333,0.000725257,0.0024491455,0.003335884,0.0018604333,0.018596176],"category_scores_gemma":[0.04552758,0.0007306722,0.0014615476,0.0038612832,0.0016981006,0.0031477774,0.0016786908,0.003627358,0.00786705],"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":[0.00013094084,0.000093409435,0.012451623,0.0005744962,0.00022815002,0.0005881932,0.00022264931,0.10065042,0.00062845455,0.61689144,0.05308859,0.2144517],"study_design_scores_gemma":[0.000030879255,0.00004119416,0.0030470658,0.00030522456,0.00007233021,0.00067046256,0.000078694284,0.53063715,0.00042361443,0.41963723,0.04499074,0.00006531773],"about_ca_topic_score_codex":0.008015277,"about_ca_topic_score_gemma":0.0052597667,"teacher_disagreement_score":0.018596176,"about_ca_system_score_codex":0.0013037688,"about_ca_system_score_gemma":0.0012955744,"threshold_uncertainty_score":0.06221038},"labels":[],"label_agreement":null},{"id":"W3030594160","doi":"10.22215/etd/2014-10562","title":"Statistical Inference in the Presence of Missing Data","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Missing data; Estimator; Statistics; Goodness of fit; Imputation (statistics); Inference; Binary data; Statistic; Mathematics; Statistical inference; Test statistic; Population; Econometrics; Computer science; Statistical hypothesis testing; Binary number; Artificial intelligence","score_opus":0.16764771206877946,"score_gpt":0.47628942146419245,"score_spread":0.308641709395413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030594160","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":[0.004716066,0.0045577246,0.98551744,0.0029546393,0.0003637174,0.000072555435,0.00015564109,0.00012973617,0.0015325214],"genre_scores_gemma":[0.20021942,0.014567483,0.775349,0.0026496833,0.0024426025,0.0011290608,0.00078473834,0.00027793756,0.0025800746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93416655,0.051602192,0.0025157605,0.004510087,0.0065035955,0.00070187706],"domain_scores_gemma":[0.60444933,0.36695683,0.011266824,0.011366297,0.0053418186,0.000618858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09041137,0.0017126708,0.004417623,0.004014318,0.0014747657,0.0043087862,0.0043649073,0.0038261681,0.0027110016],"category_scores_gemma":[0.31664628,0.0019654704,0.0023190305,0.0057222135,0.0072229197,0.006880186,0.003757525,0.008521864,0.00069172285],"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":[0.00012125374,0.00015965827,0.009452095,0.0018993288,0.0015865642,0.0008661644,0.0016335074,0.096044466,0.0004945193,0.74361247,0.0068753227,0.13725474],"study_design_scores_gemma":[0.00004204666,0.00006851192,0.00089218497,0.0004286371,0.000102406,0.00017184616,0.0001438925,0.10441487,0.0003763462,0.8882878,0.005037433,0.000033974615],"about_ca_topic_score_codex":0.002272696,"about_ca_topic_score_gemma":0.0013955861,"teacher_disagreement_score":0.09041137,"about_ca_system_score_codex":0.0023911132,"about_ca_system_score_gemma":0.0046002916,"threshold_uncertainty_score":0.47814673},"labels":[],"label_agreement":null},{"id":"W3031396609","doi":"10.1177/0962280220925840","title":"Robust bivariate random-effects model for accommodating outlying and influential studies in meta-analysis of diagnostic test accuracy studies","year":2020,"lang":"en","type":"review","venue":"Statistical Methods in Medical Research","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":"Impact; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bivariate analysis; Random effects model; Statistics; Weighting; Point estimation; Inference; Mathematics; Confidence interval; Econometrics; Meta-analysis; Computer science; Artificial intelligence; Medicine","score_opus":0.8395150209186724,"score_gpt":0.7124022221931562,"score_spread":0.12711279872551617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031396609","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":[0.0014925266,0.002400368,0.99484766,0.0003095563,0.00007548059,0.00022537565,0.00016601368,0.00022810974,0.00025487653],"genre_scores_gemma":[0.13457295,0.0057071312,0.8503318,0.0011526627,0.00032626244,0.004373314,0.0010599042,0.00038869563,0.00208733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.940826,0.048048344,0.0025735113,0.004450158,0.0034770546,0.00062497216],"domain_scores_gemma":[0.92465687,0.06582517,0.0033656168,0.0035907258,0.002266763,0.00029497838],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.089153774,0.0028435234,0.0053007356,0.0060771215,0.00083758147,0.0035236878,0.0063478663,0.003794111,0.0035402388],"category_scores_gemma":[0.14509451,0.001888156,0.009216029,0.0066366256,0.002214781,0.0036162261,0.0031747737,0.004483425,0.00078950706],"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":[0.0010568192,0.00018147806,0.010193564,0.006067735,0.013456574,0.0015073986,0.0010870912,0.5034568,0.0023188698,0.23614016,0.007163773,0.21736974],"study_design_scores_gemma":[0.0006314749,0.00035402804,0.0026798411,0.0010774498,0.0074165193,0.0006746017,0.0000871061,0.72803485,0.0015560308,0.2445798,0.012671095,0.000237233],"about_ca_topic_score_codex":0.008055938,"about_ca_topic_score_gemma":0.0058830874,"teacher_disagreement_score":0.91084623,"about_ca_system_score_codex":0.0021495486,"about_ca_system_score_gemma":0.0046761353,"threshold_uncertainty_score":0.47149587},"labels":[],"label_agreement":null},{"id":"W3032026822","doi":"10.1002/cjs.11551","title":"Correlated and misclassified binary observations in complex surveys","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Generalized estimating equation; Binary data; Binary number; Gee; Computer science; Sampling (signal processing); Data mining; Focus (optics); Statistics; Survey data collection; Econometrics; Mathematics; Machine learning","score_opus":0.25472017973863065,"score_gpt":0.33681342960655886,"score_spread":0.08209324986792821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032026822","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":[0.08400597,0.0014865983,0.91214824,0.0006964838,0.00010864325,0.00017914003,0.00026683032,0.00017380658,0.00093421777],"genre_scores_gemma":[0.7190569,0.0018134783,0.27443105,0.0007394727,0.00019258342,0.0007410495,0.0008700758,0.000084069376,0.0020711955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.956576,0.03034225,0.0019570522,0.006457472,0.004001756,0.00066554657],"domain_scores_gemma":[0.7608643,0.19061793,0.025905993,0.017804276,0.0040373523,0.0007701366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06334173,0.0008802539,0.0019321662,0.0028031787,0.0010934705,0.0026337963,0.003154696,0.0021592386,0.0017256852],"category_scores_gemma":[0.21052323,0.0011791874,0.0016721379,0.0038228363,0.0035446435,0.0030296347,0.0031776542,0.0024254657,0.00033515293],"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":[0.0005583164,0.00026379866,0.19147192,0.0013307817,0.001145604,0.0018108645,0.004052169,0.21571381,0.0013256238,0.33538243,0.0044437684,0.24250105],"study_design_scores_gemma":[0.00010782061,0.000212289,0.05295298,0.00067463896,0.00038178294,0.0010552288,0.00085382926,0.54030216,0.0018815316,0.3943955,0.007037875,0.00014445091],"about_ca_topic_score_codex":0.004717666,"about_ca_topic_score_gemma":0.0042392192,"teacher_disagreement_score":0.06334173,"about_ca_system_score_codex":0.001836082,"about_ca_system_score_gemma":0.0015312706,"threshold_uncertainty_score":0.33498704},"labels":[],"label_agreement":null},{"id":"W303262662","doi":"10.22237/jmasm/1130803620","title":"Testing Goodness Of Fit Of The Geometric Distribution: An Application To Human Fecundability Data","year":2005,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor","funders":"","keywords":"Mathematics; Statistics; Likelihood-ratio test; Goodness of fit; Statistic; Pearson's chi-squared test; Test statistic; Score test; Statistical hypothesis testing; Econometrics","score_opus":0.23636901583072445,"score_gpt":0.493781108541738,"score_spread":0.25741209271101356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W303262662","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":[0.21706167,0.00082750263,0.77589446,0.0014924387,0.00007591027,0.0003326783,0.00076311704,0.000424077,0.003128125],"genre_scores_gemma":[0.7940554,0.00050138764,0.20253652,0.00030904537,0.00012804156,0.0005333364,0.0011658601,0.00014823193,0.0006220521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97663,0.019164063,0.0005384748,0.0012112726,0.0022480357,0.00020820725],"domain_scores_gemma":[0.6631875,0.31656662,0.0074673467,0.007989785,0.003830137,0.00095870515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039316684,0.0008194564,0.0015345124,0.0036656621,0.00088644825,0.001450436,0.0019616978,0.0019542295,0.0037881848],"category_scores_gemma":[0.29260716,0.00032534773,0.0016936492,0.0046475646,0.0034638185,0.0027462896,0.002782183,0.0024670428,0.000509347],"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":[0.0012234646,0.000405915,0.19980338,0.00055455946,0.0015594012,0.0024499635,0.0023534591,0.22285268,0.0025407532,0.22269565,0.0069954726,0.33656532],"study_design_scores_gemma":[0.00019338637,0.0008688949,0.04057992,0.00014908396,0.00013329776,0.0018570838,0.00076799357,0.6040658,0.0013027478,0.34543726,0.0044849464,0.00015956743],"about_ca_topic_score_codex":0.0021680293,"about_ca_topic_score_gemma":0.001076614,"teacher_disagreement_score":0.039316684,"about_ca_system_score_codex":0.000938209,"about_ca_system_score_gemma":0.0011686416,"threshold_uncertainty_score":0.20792896},"labels":[],"label_agreement":null},{"id":"W3036120216","doi":"10.1002/cjs.11556","title":"Inference for misclassified multinomial data with covariates","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Multinomial distribution; Inference; Bayesian probability; Computer science; Classifier (UML); Subject (documents); Multinomial logistic regression; Statistics; Bayesian inference; Artificial intelligence; Econometrics; Mathematics; Machine learning","score_opus":0.19360667528887185,"score_gpt":0.3686499324939307,"score_spread":0.17504325720505884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036120216","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":[0.02641646,0.0008624124,0.97063893,0.0010092492,0.00012529222,0.000054333108,0.00025726933,0.00015865071,0.0004775351],"genre_scores_gemma":[0.5128446,0.0021741926,0.4749008,0.0011223871,0.00066502625,0.00039872382,0.002180298,0.00021922524,0.0054946463],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9746045,0.015576123,0.0016742221,0.004328954,0.003039919,0.00077635417],"domain_scores_gemma":[0.84973186,0.12721841,0.009349817,0.009862788,0.0029170418,0.00092013774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052863732,0.0015042273,0.003385686,0.0030881723,0.0012082793,0.004226987,0.0052050375,0.0032597245,0.0029844418],"category_scores_gemma":[0.20488264,0.0015740824,0.0023678604,0.003363545,0.0036953993,0.0057768687,0.0035301333,0.0059921504,0.0005121617],"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":[0.00048465253,0.00018184804,0.03566318,0.0006364759,0.001113195,0.001012736,0.0010106047,0.34918138,0.00074843754,0.49956408,0.0035710845,0.10683228],"study_design_scores_gemma":[0.00005195446,0.00004989488,0.0024598818,0.00014681727,0.000117720374,0.00020928863,0.000095765325,0.5112012,0.00067976146,0.48342514,0.001525288,0.000037303096],"about_ca_topic_score_codex":0.008576537,"about_ca_topic_score_gemma":0.0061667818,"teacher_disagreement_score":0.052863732,"about_ca_system_score_codex":0.0030418034,"about_ca_system_score_gemma":0.0026017062,"threshold_uncertainty_score":0.2795735},"labels":[],"label_agreement":null},{"id":"W3037512058","doi":"10.1214/20-aoas1331","title":"Focused model selection for linear mixed models with an application to whale ecology","year":2020,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","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":"Toronto Metropolitan University","funders":"Norges Forskningsråd","keywords":"Whaling; Generalized linear mixed model; Model selection; Mixed model; Estimator; Linear model; Selection (genetic algorithm); Computer science; Whale; Information Criteria; Ecology; Econometrics; Mathematics; Statistics; Artificial intelligence; Machine learning; Biology","score_opus":0.19487171623464805,"score_gpt":0.40556105054850106,"score_spread":0.210689334313853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037512058","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":[0.0006595029,0.00042713585,0.9981927,0.00027140413,0.000026559726,0.00002590506,0.000033303637,0.00006351927,0.0002999003],"genre_scores_gemma":[0.05881895,0.001814671,0.93515515,0.00064173125,0.0005088179,0.00085013837,0.00041119868,0.000309546,0.0014897602],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97538495,0.021030663,0.0006109961,0.0011889193,0.0015172403,0.00026716533],"domain_scores_gemma":[0.88856083,0.10335989,0.0023438435,0.0026627511,0.0026608284,0.0004119197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03598071,0.0018901376,0.0023356748,0.0040169097,0.0011163338,0.0023754882,0.003364263,0.0025275575,0.0040904097],"category_scores_gemma":[0.104575515,0.0011931816,0.0034391545,0.0031166049,0.0039742473,0.003305654,0.0043372433,0.004864979,0.00083234115],"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":[0.000077141216,0.0000626704,0.0015718366,0.0006238105,0.0004832358,0.0003498229,0.0005830312,0.09062429,0.0010275358,0.824905,0.0038133264,0.075878434],"study_design_scores_gemma":[0.000024143681,0.000086115244,0.0003880698,0.00014208595,0.000069295456,0.0001212413,0.000052806216,0.3555411,0.0005164742,0.63701326,0.006000423,0.000044924185],"about_ca_topic_score_codex":0.0029494548,"about_ca_topic_score_gemma":0.0029938307,"teacher_disagreement_score":0.03598071,"about_ca_system_score_codex":0.0019647942,"about_ca_system_score_gemma":0.0022316247,"threshold_uncertainty_score":0.19028652},"labels":[],"label_agreement":null},{"id":"W3038734982","doi":"10.1017/asb.2020.19","title":"TESTING FOR RANDOM EFFECTS IN COMPOUND RISK MODELS VIA BREGMAN DIVERGENCE","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"","keywords":"Econometrics; Bregman divergence; Random effects model; Portfolio; Divergence (linguistics); Statistical hypothesis testing; Robustness (evolution); Marginal distribution; Random variable; Computer science; Mathematics; Statistics; Economics; Financial economics","score_opus":0.07780423729120785,"score_gpt":0.3231394561443849,"score_spread":0.24533521885317705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038734982","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":[0.11402308,0.00047348134,0.8819445,0.0010238249,0.00005722481,0.00016879325,0.0003184514,0.0004347068,0.0015558945],"genre_scores_gemma":[0.81541127,0.00029072454,0.18096153,0.00053603895,0.00010715371,0.00046919126,0.00081173616,0.0001881898,0.0012241803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9387998,0.046305332,0.0019747135,0.0068785064,0.0047480906,0.0012936587],"domain_scores_gemma":[0.50763905,0.462654,0.011334731,0.011824462,0.0043937983,0.002154023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07563185,0.0017161065,0.0041787303,0.004851999,0.002265671,0.0036929443,0.004838723,0.003793852,0.005088846],"category_scores_gemma":[0.26653913,0.0012996583,0.0045945244,0.003151436,0.0068061356,0.0054356134,0.0052321316,0.0060105766,0.00047602557],"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":[0.0006139939,0.00051494484,0.05292436,0.00041137656,0.0021221766,0.0013031168,0.001371683,0.4229722,0.0012820754,0.44650766,0.0032208879,0.06675556],"study_design_scores_gemma":[0.00008138867,0.00026254077,0.0048062364,0.00009846697,0.00010237773,0.000150235,0.00019662963,0.695654,0.0005174023,0.29709908,0.0009643458,0.00006734951],"about_ca_topic_score_codex":0.006984892,"about_ca_topic_score_gemma":0.0035747665,"teacher_disagreement_score":0.07563185,"about_ca_system_score_codex":0.0027623866,"about_ca_system_score_gemma":0.0035808894,"threshold_uncertainty_score":0.39998424},"labels":[],"label_agreement":null},{"id":"W3041151490","doi":"10.1093/jssam/smab004","title":"Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison","year":2021,"lang":"en","type":"preprint","venue":"Journal of Survey Statistics and Methodology","topic":"Statistical Methods and Bayesian 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 Ottawa","funders":"","keywords":"Imputation (statistics); Nonparametric statistics; Computer science; Machine learning; Artificial intelligence; Data mining; Data set; Econometrics; Missing data; Mathematics","score_opus":0.5742043220523099,"score_gpt":0.5636730961020205,"score_spread":0.010531225950289325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041151490","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":[0.32929245,0.017117992,0.6415478,0.002442937,0.00022136835,0.0005806891,0.00070205843,0.00036012125,0.0077345585],"genre_scores_gemma":[0.85371083,0.0028980179,0.14080372,0.00033790054,0.00018740511,0.00040797144,0.0007185184,0.00013200227,0.00080369663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8072861,0.17839079,0.0026522358,0.002510478,0.008475523,0.00068490766],"domain_scores_gemma":[0.3414253,0.60348046,0.018713519,0.02414095,0.01141555,0.00082414865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13889143,0.0005960376,0.0013421898,0.0034564063,0.0007205585,0.0024127583,0.001953996,0.0017580244,0.0030771128],"category_scores_gemma":[0.39221826,0.00049115246,0.0017220769,0.0065948335,0.0025340288,0.004810079,0.0025196846,0.0021493388,0.00046337876],"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":[0.0024492415,0.0011714072,0.15394971,0.0025342675,0.003943099,0.00021432852,0.0027624548,0.122623086,0.0004608427,0.18345319,0.009550793,0.5168876],"study_design_scores_gemma":[0.0007252959,0.0024201537,0.18940221,0.0026082778,0.0012730506,0.0010992073,0.0032979806,0.51567876,0.001814469,0.26231688,0.019024592,0.0003392057],"about_ca_topic_score_codex":0.0013993017,"about_ca_topic_score_gemma":0.001115916,"teacher_disagreement_score":0.13889143,"about_ca_system_score_codex":0.0011207627,"about_ca_system_score_gemma":0.0015827714,"threshold_uncertainty_score":0.7345369},"labels":[],"label_agreement":null},{"id":"W3041871434","doi":"10.1002/cjs.11558","title":"Copula‐based predictions in small area estimation","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Manitoba Health; University of Manitoba; Hospital for Sick Children; Statistics Canada","funders":"","keywords":"Small area estimation; Copula (linguistics); Estimator; Best linear unbiased prediction; Econometrics; Statistics; Parametric statistics; Multivariate statistics; Mean squared error; Unbiased Estimation; Mathematics; Computer science; Artificial intelligence","score_opus":0.12049928959919744,"score_gpt":0.3237608917039106,"score_spread":0.20326160210471317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041871434","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":[0.021951044,0.00017964219,0.9767928,0.00010178563,0.000011762366,0.000017128968,0.000056948753,0.00014214093,0.00074668246],"genre_scores_gemma":[0.8473916,0.00044430423,0.14936003,0.00009549637,0.00005541066,0.00012705031,0.00032538772,0.00017356891,0.0020271076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982539,0.0011318857,0.000050599258,0.00025185238,0.00023443476,0.000077405835],"domain_scores_gemma":[0.987097,0.010329052,0.0009215485,0.0006971428,0.0008214186,0.00013375124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052392446,0.0006647842,0.0010159144,0.0012887655,0.00036505488,0.0010651398,0.0012062944,0.0007076029,0.0020624893],"category_scores_gemma":[0.026395107,0.00042635942,0.0006759024,0.001336947,0.000788802,0.0015909608,0.00108571,0.0013997563,0.0004892225],"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":[0.000026417623,0.000027652499,0.0038469466,0.00004060249,0.0000622027,0.00006634641,0.00006888429,0.9415674,0.00045331396,0.025096431,0.0010765826,0.027667116],"study_design_scores_gemma":[0.0000012177338,0.000004305031,0.0005322418,0.0000053292215,0.000002878735,0.000006227254,0.000007078341,0.9929374,0.00010496192,0.0062499396,0.00014474125,0.0000037396048],"about_ca_topic_score_codex":0.008790399,"about_ca_topic_score_gemma":0.005358473,"teacher_disagreement_score":0.008790399,"about_ca_system_score_codex":0.00063260365,"about_ca_system_score_gemma":0.0007528924,"threshold_uncertainty_score":0.027708113},"labels":[],"label_agreement":null},{"id":"W3042519163","doi":"10.3390/stats3030016","title":"Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method","year":2020,"lang":"en","type":"article","venue":"Stats","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":"Public Health Ontario; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Mixed model; Pairwise comparison; Bivariate analysis; Quasi-maximum likelihood; Computer science; Statistics; Statistic; Inference; Multivariate analysis; Generalized linear mixed model; Statistical inference; Mathematics; Maximum likelihood; Artificial intelligence; Expectation–maximization algorithm","score_opus":0.10336816615645723,"score_gpt":0.3912805883882589,"score_spread":0.2879124222318017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042519163","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":[0.0012545242,0.00019132676,0.9973062,0.00021841368,0.00004387906,0.00016305706,0.00017988544,0.00012185908,0.00052077917],"genre_scores_gemma":[0.09257094,0.0006945604,0.89916724,0.00050839316,0.00026451517,0.0027573076,0.00080868526,0.00019504738,0.003033374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9697628,0.025569476,0.00057662517,0.0016830923,0.0019697752,0.00043821963],"domain_scores_gemma":[0.97512674,0.02075968,0.0011540386,0.001248831,0.0013012063,0.0004094328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032983955,0.0019300816,0.0034693717,0.00236999,0.0007851753,0.0019489727,0.005550975,0.0021677497,0.01051724],"category_scores_gemma":[0.049275443,0.000879874,0.003694908,0.0029320645,0.0016879628,0.0031253207,0.0031052686,0.0043298425,0.0020150987],"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":[0.000691281,0.00026883144,0.0047687152,0.0009021943,0.00081874063,0.00042650654,0.00060986256,0.14836797,0.0009002995,0.6853284,0.0073105865,0.1496065],"study_design_scores_gemma":[0.00022726302,0.00023778922,0.0008075779,0.00010706854,0.00019029326,0.00021105305,0.000069909234,0.59333134,0.000502772,0.3979844,0.0062458753,0.00008459986],"about_ca_topic_score_codex":0.0021006903,"about_ca_topic_score_gemma":0.0017323227,"teacher_disagreement_score":0.032983955,"about_ca_system_score_codex":0.0016179834,"about_ca_system_score_gemma":0.0029994778,"threshold_uncertainty_score":0.17443794},"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,"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":"Point estimation; Statistics; Computer science; Population; Bayesian probability; Mathematics; Econometrics; Statistical inference; Statistical hypothesis testing; Empirical likelihood; Confidence interval; Medicine","score_opus":0.3675017885248815,"score_gpt":0.4945654689663485,"score_spread":0.127063680441467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042907151","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":[0.0015116791,0.00089686294,0.9962627,0.00050063344,0.000038593113,0.00003188087,0.00020526373,0.00010948881,0.0004429852],"genre_scores_gemma":[0.11651087,0.006121821,0.8642263,0.0005607504,0.0007670231,0.0015694204,0.002361622,0.0005120656,0.007370202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9789216,0.016712096,0.0010969564,0.0017585927,0.001249056,0.00026181483],"domain_scores_gemma":[0.845294,0.14316729,0.0029595818,0.005452703,0.0027414109,0.0003849874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028073404,0.0015793925,0.0030256375,0.0037153545,0.00095017935,0.0033740443,0.0042484864,0.002576239,0.0053339787],"category_scores_gemma":[0.15335134,0.0019811569,0.0029636556,0.0058149872,0.0034118176,0.0060467883,0.0036982517,0.006225415,0.0013156722],"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":[0.00003458593,0.00004537866,0.002912691,0.0003676038,0.00025039128,0.00016141782,0.00046563963,0.053081803,0.00020927282,0.87905693,0.004302085,0.059112247],"study_design_scores_gemma":[0.000020345726,0.000014332654,0.0007942125,0.0000672892,0.00008205069,0.00012903857,0.000060920145,0.19405061,0.00010757111,0.7991706,0.005469924,0.00003307373],"about_ca_topic_score_codex":0.007780139,"about_ca_topic_score_gemma":0.004198556,"teacher_disagreement_score":0.028073404,"about_ca_system_score_codex":0.0023066676,"about_ca_system_score_gemma":0.0040164096,"threshold_uncertainty_score":0.14846808},"labels":[],"label_agreement":null},{"id":"W3043139790","doi":"10.1002/sim.8584","title":"A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"NIHR School for Primary Care Research; Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research","keywords":"Pooling; Missing data; Imputation (statistics); Expert opinion; Expert elicitation; Statistics; Computer science; Econometrics; Medicine; Artificial intelligence; Mathematics","score_opus":0.16647558434536588,"score_gpt":0.4701183694083897,"score_spread":0.3036427850630238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043139790","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":[0.00016441381,0.00005503445,0.9983222,0.00010245982,0.000023140125,0.000625804,0.00016803209,0.00022107083,0.00031793694],"genre_scores_gemma":[0.009281385,0.00019082295,0.9824572,0.00019012853,0.000060425216,0.0069128578,0.00027798626,0.00018405633,0.00044501474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8196725,0.15964685,0.005511345,0.004502681,0.009835472,0.0008311364],"domain_scores_gemma":[0.66240865,0.2942905,0.009775685,0.024804233,0.008117535,0.0006034103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13609527,0.0033646487,0.0044271736,0.008036437,0.0017540633,0.0036578388,0.0047253068,0.0035821656,0.015952181],"category_scores_gemma":[0.33274195,0.003126297,0.008016099,0.006983652,0.0034857607,0.0040248935,0.007394305,0.008117261,0.0029867569],"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":[0.0004831387,0.00028260704,0.003362135,0.0038347566,0.0028300164,0.0006203878,0.0018859418,0.13729607,0.0027345163,0.52627164,0.01787045,0.30252832],"study_design_scores_gemma":[0.0003697047,0.00030707027,0.001098001,0.001001936,0.0005254502,0.00059670897,0.00018095439,0.33710673,0.002902771,0.61623275,0.039359033,0.0003188683],"about_ca_topic_score_codex":0.0026954776,"about_ca_topic_score_gemma":0.0025793812,"teacher_disagreement_score":0.13609527,"about_ca_system_score_codex":0.0025303192,"about_ca_system_score_gemma":0.006834042,"threshold_uncertainty_score":0.7197492},"labels":[],"label_agreement":null},{"id":"W3043148834","doi":"10.1080/03610918.2020.1775849","title":"Graphical analysis of residuals in multivariate growth curve models and applications in the analysis of longitudinal data","year":2020,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normality; Multivariate statistics; Multivariate normal distribution; Econometrics; Transformation (genetics); Mathematics; Statistics; Growth curve (statistics); Computer science","score_opus":0.38522557342222585,"score_gpt":0.5177550500803529,"score_spread":0.1325294766581271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043148834","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":[0.0044531347,0.00031178878,0.99404067,0.00017231968,0.000019507514,0.000019606512,0.00011364474,0.00042656483,0.0004426998],"genre_scores_gemma":[0.3206089,0.002247294,0.6717261,0.0002632326,0.00014161675,0.00031578713,0.0011392805,0.00089651183,0.002661329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945914,0.0040670033,0.00018743941,0.00047546843,0.00055629655,0.00012227487],"domain_scores_gemma":[0.9693049,0.025015082,0.0020512107,0.002035092,0.0013483613,0.0002453362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009831123,0.0011881912,0.0012510093,0.002612532,0.00045960548,0.0016207194,0.0017245715,0.0012741581,0.004509425],"category_scores_gemma":[0.048834756,0.00062695856,0.0019792684,0.0028859563,0.0023173024,0.0022639364,0.0023155601,0.0026595425,0.0009088078],"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":[0.00013663726,0.00006189572,0.0062613115,0.00044828665,0.00019001872,0.00030922427,0.0004964239,0.5436471,0.002845319,0.33306128,0.0028720761,0.10967043],"study_design_scores_gemma":[0.0000149571915,0.000051948515,0.0011489418,0.00006187121,0.00003079724,0.00009982075,0.000058011865,0.8251239,0.0007534302,0.16967683,0.0029389693,0.000040646],"about_ca_topic_score_codex":0.005537475,"about_ca_topic_score_gemma":0.0033887767,"teacher_disagreement_score":0.009831123,"about_ca_system_score_codex":0.00096078985,"about_ca_system_score_gemma":0.0013403925,"threshold_uncertainty_score":0.051992536},"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":"W3043998930","doi":"10.1007/s11749-023-00912-8","title":"A generalized Hosmer–Lemeshow goodness-of-fit test for a family of generalized linear models","year":2023,"lang":"en","type":"article","venue":"Test","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":31,"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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Goodness of fit; Generalized linear model; Test statistic; Mathematics; Statistics; Consistency (knowledge bases); Statistic; Statistical hypothesis testing; Applied mathematics; Discrete mathematics","score_opus":0.24363673780766584,"score_gpt":0.42204993750778497,"score_spread":0.17841319970011912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043998930","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":[0.042500746,0.0010093739,0.9441554,0.0011943012,0.00025665044,0.0008205924,0.0033990173,0.0020676106,0.004596316],"genre_scores_gemma":[0.4910094,0.0007589042,0.49105838,0.0014318851,0.00054744794,0.0034935721,0.0074339826,0.0011115288,0.0031548687],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9704305,0.020329656,0.001469599,0.0034512647,0.0036773523,0.0006415722],"domain_scores_gemma":[0.84619546,0.12748931,0.007980063,0.011255194,0.0057567377,0.001323272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031381372,0.0015864598,0.0029284998,0.00628871,0.0013999916,0.0025422948,0.00379849,0.002610186,0.022052854],"category_scores_gemma":[0.1698452,0.00056159945,0.0033490146,0.005544564,0.0031303791,0.0039027857,0.0032939482,0.0033899476,0.0038145448],"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":[0.0016772157,0.00060502475,0.13888139,0.0021399616,0.0044290083,0.0017310652,0.0015214994,0.07190965,0.004424082,0.15343909,0.052120525,0.5671216],"study_design_scores_gemma":[0.0006274547,0.002305438,0.06979836,0.00073534803,0.0012199477,0.0031895337,0.0011936936,0.5060087,0.0053092446,0.3709513,0.03802877,0.0006322665],"about_ca_topic_score_codex":0.002401522,"about_ca_topic_score_gemma":0.0022994657,"teacher_disagreement_score":0.031381372,"about_ca_system_score_codex":0.0009919386,"about_ca_system_score_gemma":0.0036047196,"threshold_uncertainty_score":0.16596252},"labels":[],"label_agreement":null},{"id":"W3044882181","doi":"10.15446/rce.v43n2.81979","title":"On Predictive Distribution of K-Inﬂated Poisson Models with and Without Additional Information","year":2020,"lang":"en","type":"article","venue":"Revista Colombiana de Estadística","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":"Brock University","funders":"","keywords":"Poisson distribution; Estimator; Random variable; Mathematics; Statistics; Bayesian probability; Variable (mathematics); Observable; Random effects model; Applied mathematics; Computer science; Algorithm; Mathematical analysis; Physics","score_opus":0.03176814174912603,"score_gpt":0.2936605017613618,"score_spread":0.2618923600122358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044882181","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":[0.028926935,0.0013350795,0.96544725,0.0009490123,0.00007654801,0.000055543995,0.00021514166,0.00020049812,0.0027939882],"genre_scores_gemma":[0.6825475,0.005918216,0.29654142,0.0007417373,0.0007249881,0.00053113187,0.0016419019,0.00038845063,0.010964625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99600005,0.002449127,0.0001281343,0.0006174895,0.0005539824,0.00025120325],"domain_scores_gemma":[0.9458335,0.04693897,0.0025163419,0.0022029285,0.0019838586,0.00052444666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01667445,0.0011572024,0.0023869288,0.0025723677,0.0010949064,0.0026618722,0.004266705,0.002004635,0.0033938824],"category_scores_gemma":[0.06418267,0.001193513,0.002093679,0.0022295804,0.0034355693,0.006209673,0.0027813232,0.004396678,0.00053393096],"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":[0.00016259491,0.000058694426,0.006250576,0.00026168948,0.0001680765,0.00028515726,0.0005217964,0.4458835,0.00058057246,0.50360245,0.0027405086,0.03948431],"study_design_scores_gemma":[0.000012424914,0.00002182854,0.0006903882,0.000063453204,0.000037980513,0.00007771432,0.00005670598,0.8891284,0.00026178677,0.10855233,0.0010635953,0.000033330925],"about_ca_topic_score_codex":0.013261915,"about_ca_topic_score_gemma":0.008033659,"teacher_disagreement_score":0.01667445,"about_ca_system_score_codex":0.0025532478,"about_ca_system_score_gemma":0.0020203018,"threshold_uncertainty_score":0.08818394},"labels":[],"label_agreement":null},{"id":"W3045509375","doi":"10.1002/cjs.11563","title":"Regression modelling with the tilted beta distribution: A Bayesian approach","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Beta distribution; Bayesian linear regression; Bayesian probability; BETA (programming language); Regression analysis; Regression; Mathematics; Statistics; Econometrics; Bayesian inference; Computer science","score_opus":0.08379535248473838,"score_gpt":0.2941001364617345,"score_spread":0.21030478397699612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045509375","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":[0.0066207545,0.00055932207,0.9906431,0.00061710365,0.00003612698,0.000025823641,0.000105401006,0.000097806005,0.0012945493],"genre_scores_gemma":[0.51163185,0.0037767226,0.47747716,0.00065646187,0.00041891087,0.00045732153,0.0005168638,0.000238603,0.004826049],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99180084,0.0059331153,0.00024390023,0.00077572814,0.00093296554,0.00031347468],"domain_scores_gemma":[0.96724933,0.0272973,0.002111594,0.0012783096,0.0016857915,0.00037768672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015497579,0.0010685046,0.0020503777,0.0027053375,0.00074971473,0.0036429653,0.0031826044,0.0024961627,0.0043889554],"category_scores_gemma":[0.059024006,0.0012205696,0.0016079764,0.0029981493,0.0021146093,0.004070965,0.0024921172,0.0043595354,0.00086005084],"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":[0.00008566564,0.00007804783,0.0042869053,0.00019137847,0.0002131186,0.00020632104,0.00031238442,0.4384296,0.00065270724,0.5045976,0.0029382177,0.04800811],"study_design_scores_gemma":[0.000026503058,0.000029248662,0.00086932385,0.00010099169,0.000047615784,0.00009027507,0.000045998804,0.7367026,0.00015471257,0.25975528,0.0021396657,0.00003780076],"about_ca_topic_score_codex":0.010919469,"about_ca_topic_score_gemma":0.007575796,"teacher_disagreement_score":0.015497579,"about_ca_system_score_codex":0.0018840608,"about_ca_system_score_gemma":0.0017165019,"threshold_uncertainty_score":0.08196002},"labels":[],"label_agreement":null},{"id":"W3048472775","doi":"10.1080/00949655.2020.1797738","title":"Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","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":"University of Toronto; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Economic and Social Research Council; Canadian Institutes of Health Research; Institute of Chemical and Engineering Sciences; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Statistics; Mathematics; Random effects model; Parametric statistics; Multilevel model; Nonparametric statistics; Monte Carlo method; Econometrics; Intraclass correlation; Logistic regression","score_opus":0.22818581758207412,"score_gpt":0.4372503342114528,"score_spread":0.2090645166293787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048472775","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":[0.028549386,0.00015099316,0.9701635,0.00015323308,0.000024803314,0.0001811467,0.00012073037,0.00017790543,0.0004783121],"genre_scores_gemma":[0.4068965,0.00021437948,0.59031844,0.00013710842,0.000040031562,0.0015005388,0.0003452383,0.00022716774,0.0003206352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9559355,0.038563702,0.0011013324,0.001837762,0.0021231107,0.0004385519],"domain_scores_gemma":[0.7636009,0.21558493,0.0042474465,0.011944902,0.0041758195,0.00044592115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.080392115,0.0009406332,0.0020100991,0.0030618561,0.0010457424,0.0022389828,0.0036251906,0.0019070375,0.0023515767],"category_scores_gemma":[0.3082855,0.0009620595,0.002537209,0.0024223502,0.0023217362,0.0028820718,0.0027830987,0.0039875335,0.0002896915],"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":[0.00039570444,0.0002008791,0.014713954,0.000379963,0.0009189766,0.00046893736,0.00086843694,0.6972919,0.0014385923,0.22408964,0.0014848663,0.057748165],"study_design_scores_gemma":[0.00007905173,0.000083477986,0.0015600851,0.00014467939,0.000106226,0.000083538274,0.00008537786,0.86771196,0.0007980973,0.12851055,0.00079464546,0.000042255313],"about_ca_topic_score_codex":0.0053189876,"about_ca_topic_score_gemma":0.0060352753,"teacher_disagreement_score":0.080392115,"about_ca_system_score_codex":0.0019152301,"about_ca_system_score_gemma":0.0025802143,"threshold_uncertainty_score":0.42515922},"labels":[],"label_agreement":null},{"id":"W30694091","doi":"10.1007/978-1-4939-2428-8_6","title":"Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions","year":2015,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Foothills Medical Centre; University of Calgary","funders":"","keywords":"Longitudinal data; Outcome (game theory); Generalized linear model; Linear regression; Regression analysis; Statistics; Linear model; Generalized linear mixed model; Econometrics; Mixed model; Computer science; Mathematics; Data mining","score_opus":0.5966843994995419,"score_gpt":0.6003483513581748,"score_spread":0.0036639518586328856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30694091","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":[0.008292967,0.004523488,0.9818678,0.0025550066,0.0002889091,0.00014436469,0.00089371885,0.00049623876,0.00093750027],"genre_scores_gemma":[0.16604558,0.010980875,0.80036134,0.0021138585,0.0017932112,0.0022471156,0.0026422504,0.0009471687,0.012868662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9744892,0.020262817,0.0009576834,0.0028095625,0.0011590153,0.00032162998],"domain_scores_gemma":[0.887658,0.09088485,0.004986342,0.012357622,0.0032912449,0.0008219525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.065154634,0.0018148973,0.0023831192,0.0021751567,0.000804402,0.003385445,0.004263185,0.0029763978,0.009293715],"category_scores_gemma":[0.13021414,0.0016920908,0.005699516,0.0033206758,0.002020001,0.0053294976,0.0031594788,0.0047906814,0.0016367198],"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":[0.0007168463,0.0003643478,0.019405793,0.0018610157,0.0050719553,0.0005781157,0.0012134745,0.08384312,0.0007346164,0.5869007,0.024063578,0.2752465],"study_design_scores_gemma":[0.00020247544,0.00020048444,0.00457927,0.0004747893,0.00108467,0.00032297743,0.00014415434,0.21717808,0.00030203053,0.75939536,0.016025998,0.0000895423],"about_ca_topic_score_codex":0.0066732764,"about_ca_topic_score_gemma":0.006564321,"teacher_disagreement_score":0.065154634,"about_ca_system_score_codex":0.0011205258,"about_ca_system_score_gemma":0.0034967728,"threshold_uncertainty_score":0.34457475},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.296091431730889,"score_gpt":0.505309739435327,"score_spread":0.209218307704438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3077243495","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":[0.00008214651,0.9892579,0.0083063245,0.0016515168,0.0004556967,0.00001075455,0.000035928002,0.000017146627,0.00018255232],"genre_scores_gemma":[0.0024255489,0.98153657,0.012611889,0.0013108054,0.0018315283,0.00005549273,0.00006971654,0.000036960908,0.000121467405],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98721075,0.0072959983,0.0016599137,0.0014590089,0.0022036808,0.00017066955],"domain_scores_gemma":[0.8365298,0.14890616,0.002762776,0.002596044,0.008814667,0.00039057108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039973494,0.0019574892,0.0059354655,0.0067437883,0.00060001976,0.0028915587,0.00528192,0.004813004,0.0022503638],"category_scores_gemma":[0.09963667,0.0018179397,0.002759481,0.008995136,0.0033317842,0.0053931475,0.0020453103,0.005936256,0.0012301116],"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":[0.00016186843,0.000079493235,0.0011440275,0.037550125,0.0013069223,0.00020359564,0.00022558238,0.0024508007,0.00025635364,0.037952706,0.0283415,0.89032716],"study_design_scores_gemma":[0.00023928467,0.0004167068,0.008293395,0.07669947,0.0060650213,0.002913393,0.0005006967,0.010723571,0.0016834817,0.19799168,0.69386935,0.0006039584],"about_ca_topic_score_codex":0.005136842,"about_ca_topic_score_gemma":0.0052079894,"teacher_disagreement_score":0.039973494,"about_ca_system_score_codex":0.0024392176,"about_ca_system_score_gemma":0.007676492,"threshold_uncertainty_score":0.2114026},"labels":[],"label_agreement":null},{"id":"W3081542665","doi":"10.47302/jsr.2020540102","title":"Marginal models for longitudinal count data with dropouts","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Research","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":"Carleton 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":"Estimator; Generalized estimating equation; Count data; Estimating equations; Mathematics; Statistics; Longitudinal data; Missing data; Dropout (neural networks); Generalized linear model; Marginal model; Monotone polygon; Applied mathematics; Series (stratigraphy); Sample (material); Econometrics; Regression analysis; Computer science; Data mining","score_opus":0.5825319721491508,"score_gpt":0.5531447952059968,"score_spread":0.02938717694315396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081542665","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":[0.018439243,0.00083241053,0.97809803,0.0008547262,0.00006066433,0.00013708558,0.00054050505,0.00027163373,0.00076568767],"genre_scores_gemma":[0.6183503,0.0039330455,0.35687986,0.0008823039,0.0005433999,0.002635362,0.0030630035,0.00037207987,0.013340587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.987405,0.008929457,0.000518082,0.0014405324,0.0011558881,0.00055108167],"domain_scores_gemma":[0.9419946,0.04645452,0.004375952,0.0038488815,0.0026650508,0.00066088705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033809498,0.0014541384,0.0026776665,0.0025946938,0.00082099385,0.002774982,0.0068034097,0.0023502223,0.0077046608],"category_scores_gemma":[0.10111293,0.0013469568,0.0031118118,0.0030417023,0.0032213696,0.0063706203,0.003572613,0.004250664,0.0010957746],"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":[0.0002212571,0.000104998486,0.009820037,0.0004091399,0.00042256483,0.0005116249,0.0014103128,0.17857857,0.00043902133,0.7662071,0.003076587,0.03879875],"study_design_scores_gemma":[0.00007096768,0.00009019875,0.0017745027,0.000102150596,0.000123932,0.00016080907,0.00018929588,0.545153,0.00016521422,0.4489013,0.0032150685,0.000053446798],"about_ca_topic_score_codex":0.008698279,"about_ca_topic_score_gemma":0.0068729077,"teacher_disagreement_score":0.033809498,"about_ca_system_score_codex":0.002118375,"about_ca_system_score_gemma":0.0020206259,"threshold_uncertainty_score":0.17880386},"labels":[],"label_agreement":null},{"id":"W3082132378","doi":"10.1002/cjs.11567","title":"Robust estimation of mean squared prediction error in small‐area estimation","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Jackknife resampling; Mean squared error; Small area estimation; Statistics; Estimation; Mean squared prediction error; Mathematics; Moment (physics); Regression; Computer science; Measure (data warehouse); Data mining; Estimator","score_opus":0.1341427918307235,"score_gpt":0.3137459272865134,"score_spread":0.1796031354557899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082132378","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":[0.0055490243,0.00027972035,0.99366075,0.000082497645,0.000031573934,0.000025134192,0.000045861492,0.00012366046,0.00020183253],"genre_scores_gemma":[0.4061618,0.00078614,0.5894269,0.00017053238,0.00019735107,0.0003890738,0.00068889355,0.0002951019,0.0018842921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9791408,0.015151625,0.00079076266,0.0024836601,0.0020633186,0.00036981888],"domain_scores_gemma":[0.84778965,0.13321663,0.005200048,0.008067814,0.0052351602,0.000490746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034348015,0.0012953503,0.0027197602,0.0020624667,0.00070927554,0.0016469597,0.003670484,0.0016975881,0.0020170144],"category_scores_gemma":[0.15770684,0.0008571474,0.0017086301,0.002313958,0.0021210003,0.0022185883,0.0023852536,0.002532901,0.00053195114],"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":[0.00034712275,0.00015432402,0.010267689,0.00051401934,0.0013183772,0.0002518431,0.0003035265,0.69265383,0.0018943198,0.12735963,0.0031840228,0.16175126],"study_design_scores_gemma":[0.000025856876,0.000074608164,0.0018329826,0.00007118291,0.00007357963,0.00006177193,0.00003832596,0.93607825,0.0010710327,0.059250306,0.0013855008,0.00003655973],"about_ca_topic_score_codex":0.006269284,"about_ca_topic_score_gemma":0.0039321636,"teacher_disagreement_score":0.034348015,"about_ca_system_score_codex":0.0011052749,"about_ca_system_score_gemma":0.002152485,"threshold_uncertainty_score":0.18165183},"labels":[],"label_agreement":null},{"id":"W3082605128","doi":"10.1016/j.sste.2022.100497","title":"Computationally efficient parameter estimation for spatial individual-level models of infectious disease transmission","year":2022,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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 Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Approximate Bayesian computation; Computation; Markov chain Monte Carlo; Computer science; Bayesian probability; Aggregate (composite); Statistics; Set (abstract data type); CAD; Data set; Population; Data mining; Algorithm; Artificial intelligence; Mathematics; Machine learning; Inference; Medicine; Engineering","score_opus":0.14158897307282373,"score_gpt":0.3784635775609124,"score_spread":0.2368746044880887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082605128","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":[0.0065747085,0.00022751084,0.9921401,0.00027458125,0.00001438604,0.000025059593,0.00013189638,0.00017244928,0.00043934086],"genre_scores_gemma":[0.38274774,0.0011418118,0.60771996,0.00029514433,0.0001940017,0.00064492726,0.0014864728,0.00036962522,0.0054003396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976864,0.0014534098,0.00011835169,0.00028144964,0.0003154829,0.00014486432],"domain_scores_gemma":[0.9754269,0.022211766,0.0007398114,0.0009245919,0.00045844403,0.00023848722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006085529,0.0010145787,0.002470379,0.0015382394,0.00075175107,0.0021502676,0.003634472,0.0020687564,0.004026083],"category_scores_gemma":[0.036779743,0.0016971341,0.0015600533,0.0020521826,0.0016626985,0.0031424176,0.002597285,0.0033902184,0.0008721706],"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":[0.00007711567,0.00005763361,0.0010241065,0.0000904643,0.00008346617,0.00007581331,0.00008361356,0.9121194,0.00034630956,0.060491312,0.0010316003,0.024519151],"study_design_scores_gemma":[0.000010633823,0.000004620538,0.000111783454,0.000006834303,0.000008544973,0.000018826238,0.000008613364,0.9488533,0.00007590332,0.050673794,0.00022033532,0.0000069068096],"about_ca_topic_score_codex":0.014809954,"about_ca_topic_score_gemma":0.015402343,"teacher_disagreement_score":0.014809954,"about_ca_system_score_codex":0.0017839923,"about_ca_system_score_gemma":0.0034959652,"threshold_uncertainty_score":0.032183766},"labels":[],"label_agreement":null},{"id":"W3082894259","doi":"10.47302/jsr.2020540101","title":"A comparison of statistical methods for the analysis of binary repeated measures data with additional hierarchical structure","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Statistical Methods and Bayesian Inference","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":"University of Prince Edward Island; University of Ottawa","funders":"","keywords":"Statistics; Mathematics; Random effects model; Autocorrelation; Marginal model; Binary data; Markov chain Monte Carlo; Marginal likelihood; Quasi-likelihood; Count data; Overdispersion; Negative binomial distribution; Bayesian probability; Regression analysis; Poisson distribution; Binary number","score_opus":0.4562199842346878,"score_gpt":0.6012562082949747,"score_spread":0.14503622406028693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082894259","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":[0.037270725,0.0017663751,0.9563243,0.00039372526,0.00036930142,0.0016740297,0.00038559124,0.00087335234,0.00094260584],"genre_scores_gemma":[0.11193161,0.0010946905,0.8773463,0.00025245373,0.00012433455,0.0072889985,0.00053975516,0.00060481427,0.00081704103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.85168123,0.12696198,0.0043223794,0.0051588784,0.011048281,0.00082724844],"domain_scores_gemma":[0.60947156,0.3442991,0.011848148,0.020892782,0.012374775,0.0011136791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.117315695,0.0011054071,0.0022459358,0.0027515,0.0007480027,0.0018455304,0.002540676,0.0017256989,0.0035199127],"category_scores_gemma":[0.28888428,0.00076026947,0.0028585491,0.0033426208,0.0018156819,0.0024263726,0.0019234959,0.003267965,0.0007049791],"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":[0.011812716,0.0018804822,0.044714708,0.006021776,0.009167526,0.00085938926,0.0034193648,0.058512975,0.018662846,0.06848545,0.009182787,0.7672799],"study_design_scores_gemma":[0.0034158265,0.017206464,0.11760832,0.0036105495,0.0034743033,0.0027902196,0.0021421707,0.67493784,0.01873856,0.12045694,0.034536723,0.0010820869],"about_ca_topic_score_codex":0.0015211377,"about_ca_topic_score_gemma":0.0018667164,"teacher_disagreement_score":0.117315695,"about_ca_system_score_codex":0.0014617543,"about_ca_system_score_gemma":0.0036546565,"threshold_uncertainty_score":0.62043214},"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,"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","keywords":"Predictability; Estimator; Nonparametric statistics; Gaussian process; Event (particle physics); Mathematics; Computer science; Event data; Statistics; Algorithm; Applied mathematics; Gaussian","score_opus":0.1266690097974926,"score_gpt":0.4154319901281967,"score_spread":0.28876298033070413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084811620","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":[0.10053897,0.00042419974,0.897095,0.00022182992,0.000083420266,0.00009304419,0.0005716199,0.00032520288,0.0006467712],"genre_scores_gemma":[0.88821757,0.00040607018,0.10606793,0.00011896378,0.00033652812,0.00028530552,0.0028683592,0.00011331658,0.0015860136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944596,0.002479909,0.00044860685,0.0015234379,0.0007620648,0.00032637714],"domain_scores_gemma":[0.94453806,0.045326665,0.0035776738,0.0042486633,0.0018184693,0.0004904188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013127764,0.00095565635,0.0017205243,0.002242407,0.0005880206,0.0016550181,0.0027144162,0.0011700077,0.0025645336],"category_scores_gemma":[0.04222147,0.00055979576,0.0019424036,0.0024886832,0.0010114209,0.0023246019,0.001358581,0.0016903944,0.000354076],"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":[0.0025714876,0.00059688947,0.100826316,0.0009071461,0.0023080385,0.0023000645,0.00062528043,0.41111115,0.01116236,0.13645406,0.0038913824,0.32724577],"study_design_scores_gemma":[0.000028492303,0.00010508001,0.0062578516,0.00002469828,0.00018987255,0.0002545466,0.000055955938,0.9617962,0.0011974432,0.029284816,0.00077033805,0.000034747347],"about_ca_topic_score_codex":0.0022439652,"about_ca_topic_score_gemma":0.0016780787,"teacher_disagreement_score":0.013127764,"about_ca_system_score_codex":0.00064143044,"about_ca_system_score_gemma":0.0012132885,"threshold_uncertainty_score":0.06942707},"labels":[],"label_agreement":null},{"id":"W3086774075","doi":"10.1002/sim.8738","title":"Meta‐analysis of quantile intervals from different studies with an application to a pulmonary tuberculosis data","year":2020,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University","funders":"","keywords":"Quantile; Confidence interval; Statistics; Coverage probability; CDF-based nonparametric confidence interval; Meta-analysis; Random effects model; Estimator; Mathematics; Robust confidence intervals; Confidence distribution; Econometrics; Medicine; Internal medicine","score_opus":0.3857157534275251,"score_gpt":0.5229027888145859,"score_spread":0.13718703538706073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086774075","genre_codex":"review","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":[0.00831725,0.8963886,0.08913118,0.001195957,0.0004918307,0.0010031657,0.0018669105,0.00022386586,0.0013812043],"genre_scores_gemma":[0.3295629,0.50401014,0.15405329,0.0019579015,0.00071056955,0.005054165,0.0034229222,0.00021691047,0.001011142],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95395225,0.035191383,0.004836248,0.0023218328,0.0034300608,0.0002681934],"domain_scores_gemma":[0.9106427,0.07861858,0.004709466,0.003860101,0.0020284331,0.00014084305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053913992,0.001774208,0.0065828986,0.007511768,0.0004318225,0.002833066,0.0019965854,0.0015208372,0.0025720275],"category_scores_gemma":[0.13726908,0.00093818665,0.019866563,0.009208939,0.00066688244,0.0014408441,0.001488931,0.002448655,0.00025949697],"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":[0.002782547,0.00010403603,0.008714574,0.18661648,0.51545906,0.00039676426,0.00026570843,0.011579098,0.0013202691,0.007882759,0.003471043,0.26140767],"study_design_scores_gemma":[0.0025603655,0.0011143858,0.016352514,0.030526374,0.86260325,0.00080020854,0.00023660506,0.010168588,0.0032049501,0.03879046,0.033439226,0.00020308605],"about_ca_topic_score_codex":0.0019944638,"about_ca_topic_score_gemma":0.002540218,"teacher_disagreement_score":0.053913992,"about_ca_system_score_codex":0.0015748738,"about_ca_system_score_gemma":0.002235382,"threshold_uncertainty_score":0.28512788},"labels":[],"label_agreement":null},{"id":"W3088631473","doi":"10.1214/23-sts885","title":"Parameter Restrictions for the Sake of Identification: Is There Utility in Asserting That Perhaps a Restriction Holds?","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identification (biology); Statistical model; Key (lock); Context (archaeology); Computer science; Bayesian probability; Value (mathematics); Mathematical economics; Econometrics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.2103441734683346,"score_gpt":0.45051168967869337,"score_spread":0.24016751621035878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088631473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011834566,0.0036294884,0.91083014,0.049791846,0.0012951166,0.00012055908,0.000384239,0.00042125146,0.02169284],"genre_scores_gemma":[0.63124835,0.0047405427,0.32802048,0.02359824,0.0056294315,0.00095347327,0.0006964406,0.00064277253,0.004470327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9475793,0.029624859,0.0040607993,0.008026798,0.009234868,0.0014734039],"domain_scores_gemma":[0.6328414,0.26583594,0.028358402,0.05878408,0.011457303,0.0027227926],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08633738,0.0015542087,0.0045938287,0.0033553697,0.0039787595,0.008874421,0.0071757874,0.008885055,0.007948688],"category_scores_gemma":[0.34262544,0.0019954483,0.003470199,0.0037394487,0.04112069,0.039427105,0.007271381,0.022867199,0.0035444836],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007545683,0.000041411175,0.0016161838,0.00029330925,0.00013397235,0.00025861556,0.0009326436,0.0032149532,0.00034835225,0.96921957,0.0033914018,0.020474263],"study_design_scores_gemma":[0.000013846824,0.000018347455,0.0002701879,0.00018637213,0.000022419012,0.00013004868,0.00014239177,0.0039280555,0.00030435572,0.99040246,0.0045545665,0.000027090193],"about_ca_topic_score_codex":0.002582122,"about_ca_topic_score_gemma":0.001436902,"teacher_disagreement_score":0.9136626,"about_ca_system_score_codex":0.0030255413,"about_ca_system_score_gemma":0.005205589,"threshold_uncertainty_score":0.45660114},"labels":[],"label_agreement":null},{"id":"W3089031369","doi":"10.1002/sim.8744","title":"Comparing Kaplan‐Meier curves with the probability of agreement","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Similarity (geometry); Statistics; Confidence interval; Computer science; Reliability (semiconductor); Coverage probability; Point estimation; Mathematics; Sample size determination; Contrast (vision); Econometrics; Artificial intelligence","score_opus":0.1459445148099727,"score_gpt":0.39587051930710915,"score_spread":0.24992600449713645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089031369","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":[0.023200952,0.00515184,0.9611376,0.0011497968,0.0003993016,0.0006587475,0.0010801356,0.001428422,0.00579317],"genre_scores_gemma":[0.5780479,0.0036433763,0.40992105,0.00064672006,0.00055979285,0.0028488648,0.0016015439,0.00092145253,0.0018093598],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9261653,0.053487815,0.0047975983,0.0057589826,0.008990276,0.0007999876],"domain_scores_gemma":[0.5825381,0.3578052,0.026674861,0.019041134,0.012422038,0.0015186083],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09638296,0.0015251007,0.0017313475,0.00953615,0.00088445377,0.0047741323,0.0024272916,0.002828636,0.008524261],"category_scores_gemma":[0.33320358,0.0007972973,0.0024972588,0.0048460006,0.004299488,0.008416049,0.004961409,0.004761392,0.0011963921],"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":[0.0030750881,0.00021749471,0.0528262,0.0039645047,0.0039678887,0.00067890494,0.004436661,0.15837546,0.002060551,0.30354014,0.017645063,0.44921204],"study_design_scores_gemma":[0.00037366853,0.0018503007,0.03357028,0.0021306672,0.001057276,0.0022573555,0.0021549822,0.33322248,0.0056611015,0.5616445,0.05530655,0.0007708046],"about_ca_topic_score_codex":0.0014857379,"about_ca_topic_score_gemma":0.0006665435,"teacher_disagreement_score":0.903617,"about_ca_system_score_codex":0.0019800013,"about_ca_system_score_gemma":0.0019897893,"threshold_uncertainty_score":0.5097279},"labels":[],"label_agreement":null},{"id":"W3090201104","doi":"10.1371/journal.pone.0239821","title":"Measurement protocols, random-variable-valued measurements, and response process error: Estimation and inference when sample data are not deterministic","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"Computer science; Inference; Process (computing); Sample (material); Observational error; Sample size determination; Data mining; Random variable; Variable (mathematics); Statistics; Random error; Mathematics; Artificial intelligence","score_opus":0.5544942425360624,"score_gpt":0.42711713201185075,"score_spread":0.12737711052421163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090201104","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":[0.0013124302,0.0003123695,0.99683845,0.0005961935,0.00007148841,0.00012352702,0.00005248712,0.000049732065,0.00064328837],"genre_scores_gemma":[0.13383436,0.0013955419,0.8581577,0.0010458417,0.0004178652,0.00297608,0.0002942565,0.000117167874,0.0017612001],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81077987,0.15487026,0.006742601,0.012651694,0.013816302,0.001139353],"domain_scores_gemma":[0.58494115,0.33572373,0.025772078,0.04144702,0.011158436,0.00095766975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18532906,0.0024476477,0.0036895615,0.0038477273,0.0018604615,0.0062267515,0.007046548,0.006760799,0.0029186846],"category_scores_gemma":[0.4404812,0.0020356993,0.0031689403,0.0052691763,0.013938295,0.011734034,0.006537128,0.010176433,0.00078975275],"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":[0.00007124334,0.000045560613,0.0031362113,0.00032018422,0.0003157314,0.00011706722,0.0005170408,0.028351467,0.00034158895,0.92502594,0.0007847414,0.04097314],"study_design_scores_gemma":[0.000040089973,0.00007612466,0.00085654453,0.00019235299,0.000075594864,0.00010414338,0.00007941761,0.08827392,0.00053712435,0.9067295,0.002976518,0.000058747093],"about_ca_topic_score_codex":0.0031969096,"about_ca_topic_score_gemma":0.0018084223,"teacher_disagreement_score":0.18532906,"about_ca_system_score_codex":0.0048054704,"about_ca_system_score_gemma":0.0053363196,"threshold_uncertainty_score":0.9801255},"labels":[],"label_agreement":null},{"id":"W3102763196","doi":"10.7916/d8v99mjx","title":"The Prior Can Often Only Be Understood in the Context of the Likelihood","year":2017,"lang":"en","type":"article","venue":"Columbia Academic Commons (Columbia University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":464,"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":"Context (archaeology); Computer science; Econometrics; Statistical physics; Mathematics; Biology; Physics; Paleontology","score_opus":0.0614308441815082,"score_gpt":0.3068775392655041,"score_spread":0.24544669508399589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102763196","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":[0.0016968255,0.0050171684,0.9556523,0.01607543,0.0008386613,0.000033869517,0.00039046383,0.0002097457,0.020085558],"genre_scores_gemma":[0.27898836,0.02343107,0.6551577,0.012854157,0.006505447,0.00060719746,0.0009748757,0.0011956216,0.020285629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908552,0.004938421,0.000537922,0.0017792655,0.0016778648,0.00021131951],"domain_scores_gemma":[0.96989655,0.02418455,0.0013135243,0.003251272,0.0009903185,0.0003637635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014814686,0.0015476384,0.0020766684,0.0034452097,0.0018110245,0.0063886507,0.0037537087,0.0052226963,0.012091428],"category_scores_gemma":[0.06463864,0.0011919695,0.0013042227,0.0039927047,0.014433478,0.018338272,0.0043932707,0.013560019,0.0052152327],"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":[0.00000786895,0.000007726927,0.00014102654,0.00009576803,0.00001557893,0.000043711447,0.0001608411,0.0011500195,0.00013022973,0.98495704,0.0028114833,0.010478744],"study_design_scores_gemma":[0.000003345181,0.0000069568,0.00011568369,0.000099356104,0.000010304788,0.00009531784,0.000037808048,0.0023876356,0.00011658744,0.9836543,0.013458159,0.000014545833],"about_ca_topic_score_codex":0.002742766,"about_ca_topic_score_gemma":0.0024795095,"teacher_disagreement_score":0.014814686,"about_ca_system_score_codex":0.0025284474,"about_ca_system_score_gemma":0.0021990058,"threshold_uncertainty_score":0.07834852},"labels":[],"label_agreement":null},{"id":"W3103564249","doi":"10.1186/s12874-020-01055-2","title":"A comparison of residual diagnosis tools for diagnosing regression models for count data","year":2020,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":113,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Statistics; Overdispersion; Covariate; Deviance (statistics); Studentized residual; Goodness of fit; Regression analysis; Residual; Generalized linear model; Normality; Econometrics; Linear regression; Nominal level; Regression; Statistic; Computer science; Mathematics; Poisson distribution; Confidence interval","score_opus":0.9407488530482577,"score_gpt":0.6995752683244929,"score_spread":0.24117358472376482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103564249","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":[0.08093826,0.0015964278,0.91013926,0.0003464353,0.0001734863,0.00040250827,0.0006272726,0.0042248294,0.0015515976],"genre_scores_gemma":[0.2859722,0.0008581508,0.71,0.00013864237,0.00006815394,0.00047143415,0.0012640723,0.00056688266,0.0006604371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9719524,0.018441066,0.0026178532,0.0021628423,0.004323633,0.0005022102],"domain_scores_gemma":[0.75448143,0.21030872,0.010174897,0.007306103,0.01652115,0.0012077988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047733203,0.0019963668,0.0016323809,0.0060263174,0.0004434078,0.0021406107,0.0027066062,0.0019073099,0.0033078275],"category_scores_gemma":[0.16174793,0.00058460166,0.0027276403,0.0029659872,0.0011155141,0.0033650144,0.0023469834,0.002183753,0.00081466354],"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":[0.0035315836,0.000683478,0.0792289,0.0025783,0.0014043677,0.0005239822,0.0016779237,0.18839182,0.0053331177,0.03723123,0.007835863,0.67157936],"study_design_scores_gemma":[0.00021142536,0.0009913887,0.014949401,0.00028785126,0.00027487922,0.000370545,0.00060462614,0.96538645,0.004366425,0.008473811,0.0038902694,0.00019300886],"about_ca_topic_score_codex":0.0045796405,"about_ca_topic_score_gemma":0.0036718792,"teacher_disagreement_score":0.047733203,"about_ca_system_score_codex":0.0013375131,"about_ca_system_score_gemma":0.0020522284,"threshold_uncertainty_score":0.25244033},"labels":[],"label_agreement":null},{"id":"W3106418448","doi":"","title":"RELATIVE ERRORS FOR BOOTSTRAP APPROXIMATIONS OF THE SERIAL CORRELATION COEFFICIENT","year":2016,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Dalhousie University","funders":"","keywords":"Mathematics; Statistic; Gaussian; Statistics; Saddle point; Correlation coefficient; Applied mathematics","score_opus":0.09872771381957815,"score_gpt":0.3786233022975727,"score_spread":0.27989558847799456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106418448","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":[0.0040230327,0.00027826166,0.994304,0.00015742384,0.000044500302,0.000019626037,0.000040016006,0.00012216611,0.0010109398],"genre_scores_gemma":[0.34686142,0.0016489492,0.6436202,0.0006556527,0.0003345703,0.0006867363,0.0007389619,0.00075411756,0.0046994984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908636,0.0056834803,0.0003630087,0.00070887164,0.0021135546,0.00026753772],"domain_scores_gemma":[0.9109149,0.075577766,0.003654262,0.005614274,0.0037359595,0.00050284696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02796052,0.0012446411,0.0013117159,0.0030785115,0.00073684443,0.0018435576,0.0028870308,0.0023809886,0.0032280535],"category_scores_gemma":[0.17064218,0.0008117866,0.0014841555,0.0024114558,0.0032678002,0.003867456,0.0031485688,0.0041327826,0.0012059847],"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":[0.0001509295,0.00004936996,0.002644671,0.00025345595,0.00013550332,0.00028196335,0.000347336,0.24622193,0.001624077,0.70697767,0.0021520695,0.03916096],"study_design_scores_gemma":[0.000021128399,0.000044544246,0.0007510536,0.00017955147,0.000028990551,0.0001371421,0.000060801558,0.6751759,0.0017002099,0.3187011,0.0031623945,0.00003727672],"about_ca_topic_score_codex":0.0018707009,"about_ca_topic_score_gemma":0.0013943067,"teacher_disagreement_score":0.02796052,"about_ca_system_score_codex":0.0014667559,"about_ca_system_score_gemma":0.0011191778,"threshold_uncertainty_score":0.14787114},"labels":[],"label_agreement":null},{"id":"W3107982686","doi":"10.1080/01621459.2020.1846975","title":"The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach.","year":2020,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multivariate statistics; Statistics; Marginal model; Multivariate analysis; Econometrics; Computer science; Mathematics; Regression analysis","score_opus":0.06808856179599139,"score_gpt":0.3688075242345694,"score_spread":0.30071896243857804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107982686","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":[0.0042183995,0.0034114872,0.98801416,0.0023457075,0.0002753326,0.00013039919,0.00061726384,0.00020787673,0.0007793315],"genre_scores_gemma":[0.32972968,0.013892901,0.64256215,0.0017545255,0.0024659666,0.0021021704,0.0024112742,0.00029537955,0.004785891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9779668,0.018016022,0.00062075397,0.0015010275,0.0015944068,0.0003009357],"domain_scores_gemma":[0.8977566,0.0897814,0.0051879575,0.004866042,0.0017025687,0.0007054583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04090419,0.0020372372,0.0032120799,0.0032627396,0.0009543648,0.0023063286,0.004069009,0.0021365576,0.0045908988],"category_scores_gemma":[0.1107931,0.0010526116,0.0028830597,0.0032226574,0.0041142744,0.0035849086,0.003096395,0.005562437,0.0009709775],"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":[0.0005172849,0.00026947394,0.015231136,0.0016934748,0.002779751,0.00046746415,0.001107416,0.093751624,0.0008961759,0.6752725,0.01824513,0.18976854],"study_design_scores_gemma":[0.00007279643,0.00021757031,0.0031961163,0.00025056227,0.00033048223,0.00023273271,0.00011385924,0.25316963,0.00035107994,0.73414564,0.007844568,0.000075007236],"about_ca_topic_score_codex":0.0064631375,"about_ca_topic_score_gemma":0.004771532,"teacher_disagreement_score":0.04090419,"about_ca_system_score_codex":0.0014403571,"about_ca_system_score_gemma":0.0030031393,"threshold_uncertainty_score":0.21632463},"labels":[],"label_agreement":null},{"id":"W3108212320","doi":"10.1002/cjs.11580","title":"A semiparametric regression model under biased sampling and random censoring: A local pseudo‐likelihood approach","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Censoring (clinical trials); Estimator; Covariate; Truncation (statistics); Mathematics; Statistics; Robustness (evolution); Regression analysis; Likelihood function; Econometrics; Maximum likelihood","score_opus":0.14459337898977087,"score_gpt":0.3367661875600452,"score_spread":0.19217280857027436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108212320","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":[0.014055309,0.00019423722,0.98448074,0.00043474822,0.000013771306,0.000045687164,0.00008429547,0.00010730061,0.00058396417],"genre_scores_gemma":[0.73878425,0.00090003444,0.25141293,0.00051650265,0.00017857182,0.0006521473,0.0005182327,0.00016080563,0.0068765227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99330103,0.005378803,0.0001448882,0.00045958397,0.0005230852,0.00019250701],"domain_scores_gemma":[0.95704144,0.036648687,0.0027827434,0.0017099523,0.0014188453,0.00039842472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015635617,0.00066016364,0.00141622,0.0012348855,0.0003999984,0.0015440659,0.0033535776,0.0016564889,0.002890396],"category_scores_gemma":[0.03990515,0.00078944536,0.0012927739,0.0013827829,0.0020953065,0.0018686178,0.0018288549,0.0019973894,0.00053203147],"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":[0.00016968888,0.00010137843,0.005958702,0.00035386573,0.0002670519,0.00063344365,0.00050827686,0.64257926,0.0013401131,0.3018818,0.00199972,0.044206813],"study_design_scores_gemma":[0.00002066491,0.00004084109,0.0008029224,0.000026703286,0.000030632673,0.00007099821,0.000027901768,0.95346415,0.00019233554,0.044712376,0.0005923397,0.000018106297],"about_ca_topic_score_codex":0.0032816727,"about_ca_topic_score_gemma":0.0024180652,"teacher_disagreement_score":0.015635617,"about_ca_system_score_codex":0.0011618005,"about_ca_system_score_gemma":0.0011592093,"threshold_uncertainty_score":0.08269006},"labels":[],"label_agreement":null},{"id":"W3109682927","doi":"10.1016/j.cjca.2020.11.010","title":"Missing Data in Clinical Research: A Tutorial on Multiple Imputation","year":2020,"lang":"en","type":"review","venue":"Canadian Journal of Cardiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":956,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University Health Network; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Hospital","funders":"Medical Research Council; Canadian Institutes of Health Research; Chest Heart and Stroke Scotland; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Missing data; Imputation (statistics); Statistics; Medicine; Regression analysis; Sample size determination; Data mining; Confidence interval; Computer science; Mathematics","score_opus":0.6879125250333257,"score_gpt":0.5908263245184462,"score_spread":0.09708620051487948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109682927","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":[0.0000914157,0.91884464,0.06280868,0.010086535,0.0023193574,0.00015680774,0.00033779486,0.0002593182,0.0050953166],"genre_scores_gemma":[0.0015104042,0.9260646,0.056596097,0.005757747,0.006436139,0.0005718121,0.0004168425,0.00021617729,0.00243009],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9847777,0.00951088,0.0016661173,0.0008510088,0.0029931995,0.00020112234],"domain_scores_gemma":[0.9377195,0.056016628,0.0019150256,0.0012681494,0.0025653995,0.00051540736],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024246132,0.002135329,0.004339639,0.0072955186,0.00056269404,0.0030251804,0.00392184,0.0047507472,0.0138012115],"category_scores_gemma":[0.044497114,0.0016309128,0.0036349022,0.011406816,0.0033061458,0.005089846,0.002815198,0.009583461,0.010730499],"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":[0.000059107853,0.00009127012,0.0004254116,0.016696481,0.00045092127,0.00019307865,0.0004024124,0.0021419127,0.00027161688,0.065146945,0.17781243,0.73630834],"study_design_scores_gemma":[0.00004198255,0.000093085604,0.0009318078,0.01352464,0.00013868645,0.0013041762,0.000109475885,0.001688623,0.00017458698,0.12610187,0.8557926,0.00009856076],"about_ca_topic_score_codex":0.0022095093,"about_ca_topic_score_gemma":0.002192539,"teacher_disagreement_score":0.97575384,"about_ca_system_score_codex":0.0020873286,"about_ca_system_score_gemma":0.00413198,"threshold_uncertainty_score":0.1282273},"labels":[],"label_agreement":null},{"id":"W3112354172","doi":"10.1136/bmjopen-2020-039921","title":"Introduction to statistical simulations in health research","year":2020,"lang":"en","type":"article","venue":"BMJ Open","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McMaster University; Impact; McGill University","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Deutsche Forschungsgemeinschaft","keywords":"Data science; Relevance (law); Interpretation (philosophy); Management science; Variety (cybernetics); Computer science; Simple (philosophy); Medicine; Data mining; Artificial intelligence; Epistemology","score_opus":0.6015976294718749,"score_gpt":0.6350166459467407,"score_spread":0.03341901647486578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112354172","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":[0.0004313451,0.0056828847,0.9632712,0.008163442,0.0012770734,0.00022965748,0.0010161307,0.0013697091,0.018558588],"genre_scores_gemma":[0.021089002,0.0119581325,0.9487856,0.004328595,0.0030731447,0.00163593,0.0009880863,0.0013416243,0.0067998385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9820458,0.013528935,0.0010782129,0.000934913,0.00217884,0.00023324639],"domain_scores_gemma":[0.88320583,0.10686907,0.0017382314,0.0038830987,0.003580005,0.00072366965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017998641,0.0020225567,0.0014035105,0.0027958148,0.0009982736,0.003830807,0.0030527464,0.0049249143,0.032478005],"category_scores_gemma":[0.08014911,0.0011717052,0.0031868645,0.0039010323,0.004058258,0.003988317,0.0037377349,0.009200515,0.011742647],"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":[0.00007525027,0.00005820699,0.0007743913,0.001184696,0.00012574579,0.00022082381,0.00054398034,0.03195497,0.0004288463,0.836535,0.045896135,0.08220198],"study_design_scores_gemma":[0.000051836116,0.000049136554,0.0002660716,0.0009715561,0.000029486668,0.00024834683,0.000052837782,0.03591562,0.00029898607,0.75518,0.20686957,0.000066488785],"about_ca_topic_score_codex":0.0024626907,"about_ca_topic_score_gemma":0.0015262935,"teacher_disagreement_score":0.032478005,"about_ca_system_score_codex":0.0017926755,"about_ca_system_score_gemma":0.0032807977,"threshold_uncertainty_score":0.10864973},"labels":[],"label_agreement":null},{"id":"W3112661912","doi":"10.5539/ijsp.v9n6p56","title":"New Test Statistics for One and Two Mean Vectors with Two-step Monotone Missing Data","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Mathematics; Statistics; Test statistic; Statistic; Percentile; Monotone polygon; Order statistic; Sample size determination; Missing data; Monte Carlo method; Asymptotic distribution; Sample (material); Statistical hypothesis testing; Applied mathematics; Estimator","score_opus":0.12593724391944713,"score_gpt":0.3970332308904559,"score_spread":0.2710959869710088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112661912","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":[0.017840242,0.00031583433,0.98040533,0.0001821076,0.000085637075,0.00013368689,0.00019845717,0.00022041747,0.0006183647],"genre_scores_gemma":[0.2575735,0.0004457316,0.7378157,0.0004097957,0.0002474782,0.001306164,0.0009435594,0.00015657018,0.0011014623],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9872069,0.0070018126,0.00077758014,0.0013857555,0.0032627103,0.00036527467],"domain_scores_gemma":[0.91847897,0.06567532,0.0047264746,0.005047009,0.0053071976,0.0007650418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020310385,0.0008989803,0.0017461984,0.0025205154,0.00039224591,0.0013744137,0.0028267913,0.0017919248,0.003354032],"category_scores_gemma":[0.103312105,0.0004483034,0.0015939607,0.0022272884,0.0020373478,0.0029311476,0.0015378417,0.002664822,0.00055173086],"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":[0.001431517,0.0005248966,0.029386537,0.001582033,0.0014096891,0.0011731711,0.00057174405,0.15345065,0.013972552,0.33378965,0.0053565903,0.45735106],"study_design_scores_gemma":[0.0006659647,0.00167144,0.0130114285,0.00024471674,0.0003265601,0.0012650909,0.00016766101,0.7223821,0.010103315,0.24308929,0.006869681,0.00020285843],"about_ca_topic_score_codex":0.0004737658,"about_ca_topic_score_gemma":0.00046230425,"teacher_disagreement_score":0.020310385,"about_ca_system_score_codex":0.00072513946,"about_ca_system_score_gemma":0.0020691848,"threshold_uncertainty_score":0.107412875},"labels":[],"label_agreement":null},{"id":"W3114447885","doi":"10.5539/ijsp.v10n1p85","title":"An Empirical Evaluation of a Test Procedure for the Median of Symmetrical and Asymmetrical Populations Using an Interpolated Nonparametric Confidence Interval","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Confidence interval; Mathematics; Statistics; CDF-based nonparametric confidence interval; Interval (graph theory); Nominal level; Population; Test (biology); Econometrics; Combinatorics; Medicine","score_opus":0.2697663713855408,"score_gpt":0.48500253367143786,"score_spread":0.21523616228589704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114447885","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":[0.14646144,0.00037359097,0.85041577,0.00015300051,0.000049785533,0.00013735952,0.00012643657,0.00030026695,0.0019824086],"genre_scores_gemma":[0.6304107,0.00017868285,0.36831516,0.000058420326,0.000040631236,0.00020227503,0.0002928652,0.00007204789,0.00042928464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98631835,0.010066087,0.00031157778,0.0009474923,0.0021643261,0.00019208973],"domain_scores_gemma":[0.77672774,0.20611604,0.004328737,0.006076814,0.005988364,0.0007623376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032945186,0.0005288908,0.0007642203,0.00221214,0.00060843537,0.0010847745,0.0020439725,0.0012541363,0.00245082],"category_scores_gemma":[0.20624101,0.00017265485,0.0007899646,0.0014702359,0.0016703026,0.0018376261,0.0017356524,0.00111247,0.00023916842],"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":[0.003172445,0.0004871149,0.082096845,0.00074811105,0.0007691399,0.00095559197,0.0014876106,0.3400519,0.010656681,0.12498365,0.002022857,0.43256804],"study_design_scores_gemma":[0.00018786789,0.0022642368,0.019440267,0.00018561295,0.00017381385,0.0011010914,0.00048582687,0.92997646,0.010665328,0.033063598,0.002311261,0.00014464364],"about_ca_topic_score_codex":0.0011575327,"about_ca_topic_score_gemma":0.00071752083,"teacher_disagreement_score":0.032945186,"about_ca_system_score_codex":0.00068712584,"about_ca_system_score_gemma":0.0012466653,"threshold_uncertainty_score":0.1742329},"labels":[],"label_agreement":null},{"id":"W3118559993","doi":"10.1007/s13171-020-00234-z","title":"A Weighted Composite Likelihood Approach to Inference from Clustered Survey Data Under a Two-Level Model","year":2021,"lang":"en","type":"article","venue":"Sankhya A","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Carleton University; Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Likelihood-ratio test; Mathematics; Statistic; Estimator; Quasi-maximum likelihood; Test statistic; Likelihood principle; Statistical hypothesis testing; Asymptotic distribution; Inference; Matching (statistics); Null hypothesis; Quasi-likelihood; Score test; Likelihood function; Econometrics; Maximum likelihood; Computer science; Artificial intelligence; Count data","score_opus":0.3301234954727942,"score_gpt":0.42739641603763967,"score_spread":0.09727292056484549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118559993","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":[0.0029301,0.00012275166,0.9963695,0.00010978924,0.00001877479,0.000027744167,0.00008296468,0.00007886351,0.00025962526],"genre_scores_gemma":[0.16229117,0.00061798235,0.83123314,0.0002516134,0.00023343916,0.00064555823,0.0008903777,0.00023151874,0.0036052852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9759804,0.017793832,0.00077853457,0.0030235294,0.0018172648,0.00060644594],"domain_scores_gemma":[0.87761533,0.10717475,0.0036082475,0.007899256,0.0029236032,0.0007788145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0302971,0.001396939,0.004146655,0.0039240946,0.0013504904,0.003970727,0.0068553677,0.0031179623,0.0056703924],"category_scores_gemma":[0.103390165,0.0023839239,0.0042303964,0.006061198,0.003281091,0.006501315,0.0042829076,0.0047942046,0.0009168813],"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":[0.00025543958,0.00018924194,0.006258285,0.0005920091,0.0011570991,0.00041331514,0.0006532356,0.22555874,0.0008918502,0.67779815,0.0029159363,0.083316796],"study_design_scores_gemma":[0.00003808795,0.000053963133,0.0011117082,0.00004741827,0.0001183458,0.000115040566,0.000060023427,0.62336457,0.00027138312,0.37327006,0.0014993306,0.0000501067],"about_ca_topic_score_codex":0.006279735,"about_ca_topic_score_gemma":0.005794088,"teacher_disagreement_score":0.0302971,"about_ca_system_score_codex":0.0019391977,"about_ca_system_score_gemma":0.0027189236,"threshold_uncertainty_score":0.16022831},"labels":[],"label_agreement":null},{"id":"W3120513924","doi":"10.3390/risks9010019","title":"An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Bayesian Inference","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":"Simon Fraser University","funders":"","keywords":"Expectation–maximization algorithm; Inverse Gaussian distribution; Exponential family; Exponential function; Gaussian; Inverse; Dispersion (optics); Maximization; Mathematics; Applied mathematics; Mixture model; Generalized linear model; Algorithm; Aggregate (composite); Flexibility (engineering); Mathematical optimization; Distribution (mathematics); Maximum likelihood; Statistics; Mathematical analysis","score_opus":0.12189029564465068,"score_gpt":0.37926580771480695,"score_spread":0.2573755120701563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120513924","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":[0.00051124505,0.00006365626,0.9989586,0.000060961705,0.000007846138,0.00001844338,0.00001762698,0.000072257106,0.00028932432],"genre_scores_gemma":[0.036102425,0.00043282786,0.96021533,0.00011141962,0.000059367045,0.0002935516,0.00024514948,0.00016873432,0.0023712488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894804,0.0005972216,0.000048441038,0.00018092473,0.00017348197,0.000051859144],"domain_scores_gemma":[0.9977633,0.0017967497,0.0001259967,0.00008829782,0.00018397337,0.000041708503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039624227,0.0012979142,0.0011642377,0.0008791638,0.00058243406,0.0011377243,0.0026237757,0.0018479079,0.0043273354],"category_scores_gemma":[0.009381172,0.0008959276,0.0015479376,0.0015110644,0.0009825493,0.0018273671,0.0018447847,0.002852845,0.0016836838],"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":[0.00007929282,0.00007812397,0.0010766503,0.00020533931,0.00010805317,0.00020849914,0.00018629098,0.6905723,0.002229218,0.16666369,0.0044208462,0.13417163],"study_design_scores_gemma":[0.000010416223,0.000017951063,0.00011251839,0.000022219454,0.000011375415,0.000064079075,0.00001181235,0.9637647,0.00031813316,0.033436853,0.0022149698,0.0000149879215],"about_ca_topic_score_codex":0.005962343,"about_ca_topic_score_gemma":0.0062762694,"teacher_disagreement_score":0.005962343,"about_ca_system_score_codex":0.0010551217,"about_ca_system_score_gemma":0.0019877467,"threshold_uncertainty_score":0.020955563},"labels":[],"label_agreement":null},{"id":"W3121222928","doi":"","title":"Finite-Sample Properties of the Maximum Likelihood Estimator for the Poisson Regression Model With Random Covariates","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian Inference","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":"University of Victoria","funders":"","keywords":"Covariate; Mathematics; Statistics; Estimator; Mean squared error; Poisson distribution; Poisson regression; Bias of an estimator; Restricted maximum likelihood; Consistent estimator; Regression analysis; Minimum-variance unbiased estimator; Maximum likelihood","score_opus":0.0936711146407538,"score_gpt":0.3706179157122402,"score_spread":0.2769468010714864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121222928","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":[0.033302248,0.0008493033,0.96053976,0.0013918104,0.000055511875,0.0001538643,0.00019010287,0.00030601144,0.0032114575],"genre_scores_gemma":[0.63885653,0.0014156358,0.35119167,0.00070670433,0.00025855209,0.0010817469,0.00085250905,0.00059101946,0.0050456673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912879,0.0060415394,0.0002621697,0.000773456,0.0013567081,0.00027821324],"domain_scores_gemma":[0.66271913,0.31141156,0.010390786,0.0074958093,0.007163248,0.00081954175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04328919,0.0008568558,0.0016223073,0.0017403208,0.0008636465,0.0022261685,0.0033103263,0.002192104,0.0076591526],"category_scores_gemma":[0.30217502,0.000739053,0.0010632257,0.0019579066,0.0032712468,0.0060977815,0.0022774064,0.0031348134,0.0010832027],"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":[0.0004638308,0.00018338095,0.02383205,0.0010190323,0.000370707,0.0009132595,0.001275443,0.29518706,0.0038878713,0.6002046,0.0052589206,0.06740395],"study_design_scores_gemma":[0.00010407996,0.00011396628,0.0039648362,0.00023097712,0.00006493745,0.00046720105,0.00016108002,0.761886,0.0018523603,0.22928059,0.0018077367,0.00006627394],"about_ca_topic_score_codex":0.002628853,"about_ca_topic_score_gemma":0.0014845774,"teacher_disagreement_score":0.04328919,"about_ca_system_score_codex":0.0017526144,"about_ca_system_score_gemma":0.0017967606,"threshold_uncertainty_score":0.2289378},"labels":[],"label_agreement":null},{"id":"W3122337097","doi":"10.48550/arxiv.2001.09295","title":"Bayesian Panel Quantile Regression for Binary Outcomes with Correlated\\n Random Effects: An Application on Crime Recidivism in Canada","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","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":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Markov chain Monte Carlo; Econometrics; Bayesian probability; Quantile; Recidivism; Random effects model; Bayesian inference; Statistics; Quantile regression; Computer science; Mathematics; Psychology; Criminology","score_opus":0.10341753095004878,"score_gpt":0.25637912991378714,"score_spread":0.15296159896373834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122337097","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":[0.5326786,0.0018703724,0.44596207,0.003757148,0.000058738202,0.0003744669,0.0037572894,0.0010685185,0.010472805],"genre_scores_gemma":[0.93936,0.0009238968,0.053909075,0.00013784526,0.000021072148,0.00009398903,0.0010943884,0.00005676132,0.0044029434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99853647,0.0006975466,0.00003767831,0.00020307448,0.0002639387,0.00026128054],"domain_scores_gemma":[0.99609864,0.0024950209,0.00030911103,0.000282666,0.00068237365,0.00013216917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004478735,0.00045647795,0.0008741228,0.0012119809,0.0011263507,0.0011110589,0.0019684958,0.000885469,0.0031311468],"category_scores_gemma":[0.015755149,0.00035397612,0.00078944914,0.00273055,0.00096081005,0.00059361284,0.001072059,0.001487664,0.00021214734],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027281273,0.00016109351,0.1270351,0.00014498671,0.00035004498,0.0005592661,0.00088193116,0.643538,0.00060744706,0.11120649,0.00652533,0.10871754],"study_design_scores_gemma":[0.00004331389,0.000022043467,0.028652055,0.00003663948,0.00006948292,0.000042162454,0.00033303126,0.9452761,0.00026693175,0.022294108,0.0029168674,0.000047342266],"about_ca_topic_score_codex":0.9395931,"about_ca_topic_score_gemma":0.90489626,"teacher_disagreement_score":0.060406923,"about_ca_system_score_codex":0.009248672,"about_ca_system_score_gemma":0.012451588,"threshold_uncertainty_score":0.12152529},"labels":[],"label_agreement":null},{"id":"W3122538495","doi":"","title":"NSE: Computation of Numerical Standard Errors in R","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Bayesian 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":"HEC Montréal","funders":"","keywords":"Estimator; Heteroscedasticity; Mathematics; Standard error; Standard deviation; Statistics; Kernel (algebra); Econometrics; Kernel density estimation; Applied mathematics; Combinatorics","score_opus":0.03946022179448175,"score_gpt":0.39322501227914464,"score_spread":0.3537647904846629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122538495","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025558954,0.0007125121,0.9070974,0.0007907053,0.0007635957,0.00059034216,0.033211645,0.048067715,0.0062101297],"genre_scores_gemma":[0.028684065,0.00050888513,0.9119895,0.00056901464,0.00025572884,0.0036185556,0.016103787,0.03327277,0.0049976944],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9748014,0.013677333,0.003032493,0.0033348135,0.004722677,0.00043124284],"domain_scores_gemma":[0.868782,0.10185535,0.0052694934,0.014378679,0.009039072,0.00067532284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022680702,0.0020689948,0.0031248531,0.0053885807,0.0008711478,0.004638331,0.004429482,0.0015518286,0.10602169],"category_scores_gemma":[0.24107972,0.0016450298,0.0031502694,0.0057104295,0.0016499001,0.004037138,0.0029702517,0.005915433,0.05614965],"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":[0.00042193767,0.00016308519,0.008012313,0.0040286887,0.002114328,0.00052008027,0.001126197,0.04402692,0.0020638749,0.11077548,0.5352264,0.2915207],"study_design_scores_gemma":[0.0004604821,0.00017713619,0.006902527,0.0016594416,0.0005991446,0.0007394984,0.0003760513,0.14788084,0.005648664,0.36942744,0.4657494,0.000379437],"about_ca_topic_score_codex":0.0031317289,"about_ca_topic_score_gemma":0.0036296109,"teacher_disagreement_score":0.10602169,"about_ca_system_score_codex":0.0013563004,"about_ca_system_score_gemma":0.003914011,"threshold_uncertainty_score":0.35467786},"labels":[],"label_agreement":null},{"id":"W3122731439","doi":"","title":"General Saddlepoint Approximations: Application to the Anderson-Darling Test Statistic","year":2007,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian 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 Victoria","funders":"","keywords":"Mathematics; Test statistic; Statistic; Approximations of π; Limit (mathematics); Distribution (mathematics); Normal distribution; Applied mathematics; Edgeworth series; Approximation error; Central limit theorem; Statistics; Mathematical analysis; Statistical hypothesis testing","score_opus":0.09264336963606834,"score_gpt":0.4194469804108377,"score_spread":0.3268036107747694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122731439","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":[0.007620014,0.00094499555,0.98795474,0.0004004098,0.000059249207,0.000036466892,0.00003697297,0.00010801145,0.0028392388],"genre_scores_gemma":[0.37686333,0.0041320673,0.6094711,0.00066249125,0.00042280863,0.00037791015,0.0002960511,0.00046708042,0.0073071606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99711007,0.0018236886,0.00010684662,0.00025362222,0.0005862308,0.00011952487],"domain_scores_gemma":[0.9693971,0.02663128,0.0010776069,0.0011776168,0.0014417913,0.00027468248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015755992,0.0011297615,0.0014891841,0.002720997,0.0006902257,0.0018874076,0.002517532,0.002043919,0.0035349205],"category_scores_gemma":[0.07122114,0.0004890348,0.0015009571,0.002105033,0.0019933376,0.0028577596,0.0023833027,0.0026582982,0.0008234607],"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":[0.000082747676,0.00003569879,0.002034698,0.00016361977,0.00007923515,0.00041792615,0.00036390527,0.28605756,0.0009483699,0.6694725,0.0028058009,0.037537944],"study_design_scores_gemma":[0.000015428883,0.00003918884,0.000294504,0.000063431005,0.000019630266,0.00017917379,0.000057549176,0.74446917,0.00042497666,0.25180116,0.002609492,0.000026303143],"about_ca_topic_score_codex":0.0031714013,"about_ca_topic_score_gemma":0.0024280874,"teacher_disagreement_score":0.015755992,"about_ca_system_score_codex":0.0014303316,"about_ca_system_score_gemma":0.0010635362,"threshold_uncertainty_score":0.0833267},"labels":[],"label_agreement":null},{"id":"W3123149727","doi":"10.6084/m9.figshare.12292412","title":"Kernel smoothed probability mass functions for ordered datatypes","year":2020,"lang":"en","type":"preprint","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"McMaster University","funders":"","keywords":"Smoothing; Kernel smoother; Estimator; Categorical variable; Kernel (algebra); Mathematics; Computer science; Probability mass function; Kernel embedding of distributions; Applied mathematics; Kernel method; Mathematical optimization; Algorithm; Statistics; Random variable; Machine learning; Radial basis function kernel; Support vector machine; Discrete mathematics","score_opus":0.3148869667627392,"score_gpt":0.41065954247628794,"score_spread":0.09577257571354875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123149727","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":[0.003634768,0.00007071824,0.9957125,0.00008292239,0.000011373837,0.000027937203,0.000055911743,0.00015314494,0.00025072915],"genre_scores_gemma":[0.2209699,0.00042222493,0.7728031,0.0003224253,0.000116478186,0.00059319666,0.00089734775,0.00033883896,0.0035363585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99288416,0.0032449004,0.00046931193,0.000983123,0.0019945821,0.00042393358],"domain_scores_gemma":[0.94429886,0.035216462,0.0039078943,0.0103947725,0.005513196,0.00066884956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01964883,0.0008117843,0.0015930178,0.0036030258,0.00085911265,0.0029777125,0.004542421,0.002114358,0.0039805076],"category_scores_gemma":[0.0889989,0.00079287525,0.0019464209,0.0030941542,0.0029621879,0.00651589,0.0029275056,0.0036872432,0.0014916756],"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":[0.0001253039,0.00010710719,0.0038189827,0.00024472296,0.000112775815,0.00024778733,0.00044948666,0.14951316,0.0020293877,0.73520255,0.0025277468,0.10562105],"study_design_scores_gemma":[0.000014250336,0.000028152075,0.0007670834,0.000047072757,0.00001737768,0.00010112816,0.000050714538,0.70702744,0.00084159063,0.2888451,0.0022309716,0.000029117993],"about_ca_topic_score_codex":0.0027552736,"about_ca_topic_score_gemma":0.0017826741,"teacher_disagreement_score":0.01964883,"about_ca_system_score_codex":0.0018848869,"about_ca_system_score_gemma":0.0019028897,"threshold_uncertainty_score":0.1039142},"labels":[],"label_agreement":null},{"id":"W3123907890","doi":"","title":"A simple consistent test of conditional symmetry in symmetrically trimmed tobit models","year":2014,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian 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":"","keywords":"Mathematics; Nonparametric statistics; Test statistic; Kolmogorov–Smirnov test; Smoothing; Statistic; Statistics; Statistical hypothesis testing; Econometrics","score_opus":0.09939235925845602,"score_gpt":0.3958624445531058,"score_spread":0.2964700852946498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123907890","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":[0.137619,0.0004224947,0.8523957,0.0016946065,0.00021216698,0.00049122056,0.002013739,0.0006954479,0.004455498],"genre_scores_gemma":[0.8006968,0.00022182346,0.19322796,0.00064550276,0.0002351673,0.00089177635,0.0027940623,0.00018082546,0.0011060563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96873325,0.020195512,0.0018149693,0.003751022,0.004742949,0.000762347],"domain_scores_gemma":[0.77097684,0.17625004,0.023115156,0.018661428,0.00938603,0.0016105317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03192157,0.0009526777,0.002672143,0.0043642153,0.0012832363,0.0029804164,0.004720999,0.0030044443,0.011079436],"category_scores_gemma":[0.2165167,0.00066077046,0.0029577664,0.006325765,0.0031258096,0.004002477,0.0026734956,0.003364586,0.002015016],"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":[0.001540537,0.0013089719,0.16374022,0.0011506975,0.005297455,0.0017130843,0.0017484654,0.10135638,0.0047602085,0.40045646,0.02062954,0.29629803],"study_design_scores_gemma":[0.0009592435,0.0022161682,0.0627852,0.00045562998,0.00083936995,0.0007570943,0.0011246825,0.51156026,0.006019495,0.4012725,0.011662903,0.0003474143],"about_ca_topic_score_codex":0.0022159372,"about_ca_topic_score_gemma":0.0014892769,"teacher_disagreement_score":0.03192157,"about_ca_system_score_codex":0.0009693533,"about_ca_system_score_gemma":0.003928378,"threshold_uncertainty_score":0.16881937},"labels":[],"label_agreement":null},{"id":"W3124622365","doi":"","title":"Biased-Reduced Maximum Likelihood Estimation for the Zero-Inflated Poisson Distribution","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Bayesian Inference","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":"University of Victoria; University of British Columbia","funders":"","keywords":"Mathematics; Estimator; Statistics; Poisson distribution; Mean squared error; Zero-inflated model; Monte Carlo method; Parametric statistics; Distribution (mathematics); Maximum likelihood; Poisson regression; Mathematical analysis; Population","score_opus":0.10256387646734542,"score_gpt":0.3987105891324568,"score_spread":0.2961467126651114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124622365","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":[0.031767998,0.00075789954,0.96520853,0.00042072995,0.00002949013,0.00003500053,0.000060040355,0.00012494739,0.0015952578],"genre_scores_gemma":[0.6641756,0.0017604859,0.33080375,0.00032129698,0.00021459546,0.00028281522,0.0003435635,0.00018374519,0.0019141356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916932,0.006203496,0.00017044628,0.0005309196,0.001139855,0.0002620046],"domain_scores_gemma":[0.88279206,0.10468515,0.004693013,0.0042368276,0.0032047792,0.00038809777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019547526,0.00063771126,0.0013199622,0.0021209104,0.00047738856,0.0018311186,0.0027561581,0.0015453664,0.0023267372],"category_scores_gemma":[0.17521238,0.0005695533,0.0007928764,0.0017265191,0.0024256813,0.0022145947,0.002143184,0.0017145453,0.0006447346],"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":[0.0002285869,0.0000868957,0.011654243,0.000542774,0.00026477152,0.0004920747,0.0006508519,0.37857234,0.003184189,0.5314423,0.0026404136,0.07024058],"study_design_scores_gemma":[0.000033654287,0.00003660186,0.0014996018,0.0000761346,0.000035721976,0.00015711931,0.00005550517,0.71543276,0.0011610414,0.28029323,0.0011879475,0.00003074157],"about_ca_topic_score_codex":0.0014452203,"about_ca_topic_score_gemma":0.00091032713,"teacher_disagreement_score":0.019547526,"about_ca_system_score_codex":0.001233062,"about_ca_system_score_gemma":0.0009273323,"threshold_uncertainty_score":0.103378415},"labels":[],"label_agreement":null},{"id":"W3125937508","doi":"10.1002/sim.8879","title":"Multiple imputation strategies for a bounded outcome variable in a competing risks analysis","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"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":"University of Bristol; Medical Research Council; Royal Society; NHS Blood and Transplant; Medical Research Council Canada; Wellcome Trust","keywords":"Imputation (statistics); Statistics; Missing data; Cumulative distribution function; Econometrics; Cumulative incidence; Mathematics; Regression analysis; Confidence interval; Computer science; Probability density function","score_opus":0.14089669959972412,"score_gpt":0.4712305057681186,"score_spread":0.3303338061683945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125937508","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":[0.0011359547,0.00032741931,0.9971614,0.0004012991,0.000057092348,0.0003032127,0.00012042637,0.00015062529,0.0003426044],"genre_scores_gemma":[0.03690368,0.0004280283,0.9578379,0.00050809263,0.00011648299,0.0027131746,0.00040991278,0.00019308779,0.0008896866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.87961257,0.105937086,0.0051313504,0.0042468724,0.0043692146,0.0007029042],"domain_scores_gemma":[0.7772918,0.19092794,0.008292589,0.015469544,0.0070174984,0.0010006555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1473591,0.0019231079,0.0038078243,0.0032647,0.0012592071,0.0034487792,0.008249113,0.002914939,0.010096566],"category_scores_gemma":[0.24219783,0.0014843125,0.005410573,0.0042516124,0.0014348569,0.0033448269,0.003895263,0.0061120973,0.0019803452],"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":[0.0011197388,0.0004075268,0.01686902,0.001939188,0.0053031845,0.0011220264,0.0024932767,0.13403724,0.0010453999,0.41536352,0.018321373,0.40197843],"study_design_scores_gemma":[0.0005742048,0.0005179828,0.0023848086,0.00084755087,0.0009824722,0.00070468866,0.00024367281,0.49818155,0.0014642235,0.47551095,0.018380895,0.00020701831],"about_ca_topic_score_codex":0.0023613821,"about_ca_topic_score_gemma":0.002735399,"teacher_disagreement_score":0.1473591,"about_ca_system_score_codex":0.0013823997,"about_ca_system_score_gemma":0.0037211338,"threshold_uncertainty_score":0.77931875},"labels":[],"label_agreement":null},{"id":"W3126006076","doi":"","title":"How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Missing data; Categorical variable; Imputation (statistics); Bayesian probability; Computer science; Statistics; Data mining; Regression; Mathematics","score_opus":0.04866115049043223,"score_gpt":0.3706269176177021,"score_spread":0.3219657671272699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126006076","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":[0.08358679,0.00068782514,0.90777314,0.0025275783,0.00014606315,0.00074412074,0.00032900303,0.00048346844,0.0037220356],"genre_scores_gemma":[0.53700936,0.0006043268,0.456214,0.0011822387,0.0002724621,0.001664912,0.0009115051,0.00048029117,0.001660971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7999559,0.17346503,0.0044238046,0.008233927,0.012323518,0.001597748],"domain_scores_gemma":[0.117270835,0.8515968,0.0077101863,0.016794447,0.0054635927,0.0011640878],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.26296285,0.0015863705,0.004013374,0.003456311,0.0015560307,0.0034926645,0.004582939,0.0043292716,0.008951067],"category_scores_gemma":[0.67689157,0.0009774808,0.004410842,0.0036689355,0.0057371487,0.009177403,0.0039819297,0.0047990484,0.0014211432],"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":[0.0083183935,0.0014838639,0.069592044,0.0020100547,0.009222347,0.0009257392,0.0030632934,0.18167174,0.00199965,0.26497036,0.011444671,0.4452979],"study_design_scores_gemma":[0.0013610314,0.0020096141,0.017618205,0.0004914543,0.001095371,0.0005398965,0.0007776011,0.68367726,0.002298862,0.28525853,0.0046568876,0.00021526801],"about_ca_topic_score_codex":0.0021398214,"about_ca_topic_score_gemma":0.0009447828,"teacher_disagreement_score":0.26296285,"about_ca_system_score_codex":0.0013736836,"about_ca_system_score_gemma":0.004273352,"threshold_uncertainty_score":0.908898},"labels":[],"label_agreement":null},{"id":"W3126231708","doi":"10.22215/etd/2018-13385","title":"Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Missing data; Estimator; Generalized linear mixed model; Statistics; Random effects model; Mixed model; Computer science; Longitudinal data; Generalized linear model; Econometrics; Mathematics; Data mining; Meta-analysis; Medicine","score_opus":0.11699977621375485,"score_gpt":0.4142581151764946,"score_spread":0.29725833896273973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126231708","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":[0.0037432981,0.0062653064,0.98233277,0.0043464634,0.0005633379,0.00009994802,0.0003980449,0.0001481248,0.0021027797],"genre_scores_gemma":[0.14817826,0.021615723,0.8069156,0.0051216614,0.0034744772,0.0021996105,0.0019165393,0.00046437304,0.010113769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9509188,0.03886913,0.0017114535,0.005021916,0.0027324066,0.00074626075],"domain_scores_gemma":[0.8142906,0.16643785,0.008041654,0.0073997886,0.0030886456,0.000741573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052847873,0.002603922,0.003667662,0.0022733654,0.0016695824,0.0045808624,0.0057332795,0.005305819,0.006814738],"category_scores_gemma":[0.18847874,0.0021822792,0.004370025,0.0046544936,0.006034952,0.007875484,0.004112182,0.008889284,0.0019564647],"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":[0.00017391573,0.00010017331,0.0045968173,0.0020998619,0.0009457966,0.0009073761,0.0021671066,0.033748314,0.0004867286,0.82800347,0.010080606,0.116689846],"study_design_scores_gemma":[0.00004084515,0.00008419045,0.00085685763,0.00060014584,0.00022241934,0.0002756615,0.00020543608,0.04860856,0.0002956249,0.9371224,0.011604472,0.00008332815],"about_ca_topic_score_codex":0.0033805075,"about_ca_topic_score_gemma":0.0034432346,"teacher_disagreement_score":0.052847873,"about_ca_system_score_codex":0.0025671097,"about_ca_system_score_gemma":0.0035911126,"threshold_uncertainty_score":0.27948964},"labels":[],"label_agreement":null},{"id":"W3131090517","doi":"10.5539/ijsp.v10n2p81","title":"Large Sample Problems","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sample size determination; Statistics; Mathematics; Z-test; Sample (material); Variance (accounting); Test statistic; Statistic; Population variance; Statistical hypothesis testing; Econometrics; Sample variance; Population; Sampling (signal processing); F-test of equality of variances; Computer science; Demography","score_opus":0.06104389126143896,"score_gpt":0.3756108241307883,"score_spread":0.31456693286934934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131090517","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":[0.0024489593,0.0052624173,0.9664955,0.013537044,0.0021532997,0.000735509,0.00070419285,0.0004567565,0.008206386],"genre_scores_gemma":[0.16164477,0.0069598537,0.77907956,0.017249886,0.008738853,0.0072802426,0.0016886336,0.00092550315,0.016432736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.891933,0.06949609,0.0057915603,0.014884694,0.016730746,0.0011639495],"domain_scores_gemma":[0.437548,0.5252516,0.012322512,0.014758,0.008892988,0.001226825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1000124,0.003109193,0.0067026853,0.0040276395,0.003438114,0.006517048,0.0077035413,0.0086067505,0.017050326],"category_scores_gemma":[0.42770922,0.0021312665,0.0029207172,0.006212045,0.009642933,0.011418914,0.006966764,0.011368245,0.0027923738],"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":[0.0003126973,0.0001370454,0.0041650296,0.0019772109,0.0008776431,0.0020237963,0.00087712106,0.018465258,0.00032618723,0.80889964,0.051570363,0.11036798],"study_design_scores_gemma":[0.000158999,0.00004316774,0.0004656886,0.00039305986,0.00011284506,0.0005990262,0.00014868299,0.040150084,0.00027124546,0.93960834,0.017997984,0.00005100621],"about_ca_topic_score_codex":0.0041479887,"about_ca_topic_score_gemma":0.0032797377,"teacher_disagreement_score":0.1000124,"about_ca_system_score_codex":0.004584001,"about_ca_system_score_gemma":0.0042564035,"threshold_uncertainty_score":0.52892244},"labels":[],"label_agreement":null},{"id":"W3134466506","doi":"10.1002/cjs.11612","title":"Perturbation‐based null hypothesis tests with an application to Clayton models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Null hypothesis; Estimator; Resampling; Statistical hypothesis testing; Mathematics; Alternative hypothesis; Null (SQL); Statistics; Econometrics; Covariance matrix; Multivariate statistics; Applied mathematics; Computer science; Data mining","score_opus":0.0708541344264414,"score_gpt":0.31413040709494655,"score_spread":0.24327627266850516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134466506","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":[0.010217727,0.0003318832,0.9876308,0.0003768193,0.000078579935,0.00015210487,0.00009441155,0.00027347225,0.0008442244],"genre_scores_gemma":[0.40286836,0.00042597242,0.59347695,0.00048490765,0.00029884768,0.0009193124,0.0003853333,0.00016295753,0.0009772725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.963749,0.030274082,0.0008019664,0.0016113579,0.0032204331,0.0003431833],"domain_scores_gemma":[0.71191776,0.26799673,0.006041656,0.0073342156,0.005315045,0.0013945937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03555687,0.0012148601,0.0022703314,0.0043513137,0.0011127006,0.0019087865,0.0027784044,0.0023834184,0.0042217895],"category_scores_gemma":[0.21316697,0.000692542,0.0020416745,0.0029930011,0.003949372,0.0031213167,0.003706737,0.004319119,0.00054146553],"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":[0.0010519354,0.00030126568,0.008121965,0.0006425463,0.00086246966,0.001302776,0.0004957349,0.43075195,0.004420591,0.35053688,0.006044021,0.19546784],"study_design_scores_gemma":[0.00008139239,0.00019283124,0.00079562084,0.000051537063,0.000045534936,0.00018422665,0.00004371354,0.8151297,0.001643754,0.1804479,0.0013294333,0.000054367938],"about_ca_topic_score_codex":0.0011548003,"about_ca_topic_score_gemma":0.000676456,"teacher_disagreement_score":0.03555687,"about_ca_system_score_codex":0.0014190105,"about_ca_system_score_gemma":0.0016080858,"threshold_uncertainty_score":0.18804497},"labels":[],"label_agreement":null},{"id":"W3135534839","doi":"10.47302/jsr.2020540203","title":"Using external data to incorporate unmeasured confounders: A plasmode simulation study comparing alternative approaches to impute body mass index in a study of the relationship between osteoarthritis and cardiovascular disease","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Research","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":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Imputation (statistics); Body mass index; Missing data; Confounding; Medicine; Logistic regression; Overweight; Context (archaeology); Statistics; Odds ratio; Demography; Mathematics; Internal medicine","score_opus":0.8048200240118021,"score_gpt":0.5299953276986183,"score_spread":0.27482469631318374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135534839","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":[0.9712713,0.00035723727,0.021533616,0.001070173,0.000064929685,0.00054580386,0.0018592051,0.00011856241,0.003179045],"genre_scores_gemma":[0.96899307,0.00022485005,0.026090628,0.0003520678,0.000026460037,0.0008969222,0.0024159967,0.00004195225,0.000958098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931471,0.005654408,0.00013842419,0.0004849755,0.00024748567,0.00032769612],"domain_scores_gemma":[0.91610205,0.073565766,0.002114317,0.003606148,0.0031587286,0.0014531099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018249573,0.0008332331,0.0012444147,0.0009427089,0.00096454215,0.0013851527,0.0025123092,0.002091181,0.0036065183],"category_scores_gemma":[0.05331567,0.0006561689,0.0021824122,0.0013237105,0.0009768876,0.0011748094,0.0018916108,0.0024713192,0.0002606157],"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":[0.0058547654,0.005575452,0.16377537,0.0006003947,0.0022543282,0.0010167892,0.0011388063,0.7697912,0.00055006513,0.019375324,0.0059538875,0.024113676],"study_design_scores_gemma":[0.0019213572,0.0016878607,0.014878843,0.00012038697,0.00048235338,0.00016750458,0.00057546416,0.973504,0.00040011026,0.004657858,0.0015320266,0.000072266885],"about_ca_topic_score_codex":0.051709775,"about_ca_topic_score_gemma":0.03251148,"teacher_disagreement_score":0.051709775,"about_ca_system_score_codex":0.0027381657,"about_ca_system_score_gemma":0.0027458419,"threshold_uncertainty_score":0.102817595},"labels":[],"label_agreement":null},{"id":"W3137145701","doi":"10.1515/ijb-2020-0130","title":"Bayesian approaches to variable selection: a comparative study from practical perspectives","year":2021,"lang":"en","type":"review","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","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":"Public Health Ontario; University of Toronto; Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Prior probability; Bayesian probability; Feature selection; Selection (genetic algorithm); Machine learning; Variable (mathematics); Bayesian inference; Artificial intelligence; Inference; Data mining; Mathematics","score_opus":0.4570566698120438,"score_gpt":0.5017153012643956,"score_spread":0.04465863145235177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137145701","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011551636,0.9138982,0.07627455,0.003828641,0.0002527409,0.00006720813,0.00006121909,0.000042572898,0.004419584],"genre_scores_gemma":[0.01782504,0.9260655,0.05319681,0.0009672341,0.0007101876,0.00020231756,0.00013832616,0.000056228404,0.00083823554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9874943,0.008593378,0.0007566944,0.00069048116,0.0023318913,0.00013331455],"domain_scores_gemma":[0.9353842,0.059603367,0.000944735,0.00065654196,0.00314586,0.00026539687],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026649216,0.0012452151,0.0024161588,0.00465585,0.0004722083,0.0024167427,0.002034972,0.0019378536,0.0042605265],"category_scores_gemma":[0.053299267,0.0006577546,0.0016420488,0.006462244,0.0016498278,0.002407204,0.001266924,0.0028102493,0.0012238631],"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":[0.000118141885,0.000060553102,0.0014400411,0.0076314877,0.0004791139,0.00009647629,0.00021720293,0.005258452,0.00016626962,0.057364825,0.0066542807,0.9205131],"study_design_scores_gemma":[0.0002962355,0.0009905811,0.01014088,0.027179793,0.0020196103,0.0034113848,0.00083199394,0.03561178,0.0018789967,0.3198092,0.5975172,0.00031244228],"about_ca_topic_score_codex":0.003152308,"about_ca_topic_score_gemma":0.003950321,"teacher_disagreement_score":0.97335076,"about_ca_system_score_codex":0.0016057915,"about_ca_system_score_gemma":0.0028505616,"threshold_uncertainty_score":0.14093626},"labels":[],"label_agreement":null},{"id":"W3137397187","doi":"10.1007/s13171-021-00250-7","title":"Behaviour of the Monotone Single Index Model Under Repeated Measurements","year":2021,"lang":"en","type":"article","venue":"Sankhya A","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":"York University","funders":"Centre National de la Recherche Scientifique; Eidgenössische Technische Hochschule Zürich; Agence Nationale de la Recherche; York University","keywords":"Mathematics; Monotone polygon; Applied mathematics; Generalization; Isotonic regression; Estimator; Model selection; Inference; Nonparametric statistics; Goodness of fit; Statistical inference; Monotonic function; Function (biology); Linear model; Generalized linear model; Statistics; Computer science; Artificial intelligence","score_opus":0.1899152790747429,"score_gpt":0.3765147406021283,"score_spread":0.1865994615273854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137397187","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":[0.39262584,0.0008142115,0.598372,0.0023985635,0.00009236274,0.0002957869,0.0006557521,0.0005024425,0.004243137],"genre_scores_gemma":[0.9567921,0.00034765166,0.03764821,0.00032518193,0.00009868287,0.00032205152,0.00046346107,0.000110821,0.00389192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913195,0.005623772,0.0002396737,0.0013465565,0.00092513935,0.0005453798],"domain_scores_gemma":[0.90226036,0.07822448,0.009851131,0.0046438086,0.0038809057,0.0011393293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033500023,0.0010857546,0.003067577,0.0017298055,0.0010172292,0.0020693787,0.003701004,0.0031098435,0.003463667],"category_scores_gemma":[0.09306795,0.0009806228,0.0015365478,0.0015063193,0.0045085596,0.0037117924,0.0018699121,0.0029013625,0.0005440758],"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":[0.00064122805,0.00026536675,0.027790831,0.00045037334,0.00046746215,0.0017116748,0.0013910397,0.6349094,0.0042243265,0.28717113,0.0030896121,0.03788746],"study_design_scores_gemma":[0.000054063537,0.00017933313,0.0045204577,0.000042772754,0.000047844864,0.00028752812,0.000100411875,0.91677535,0.0005459213,0.076966316,0.00041662776,0.00006344347],"about_ca_topic_score_codex":0.00832702,"about_ca_topic_score_gemma":0.0038005258,"teacher_disagreement_score":0.033500023,"about_ca_system_score_codex":0.001698614,"about_ca_system_score_gemma":0.001381045,"threshold_uncertainty_score":0.17716724},"labels":[],"label_agreement":null},{"id":"W3137498593","doi":"10.1007/s00184-023-00897-2","title":"A refined continuity correction for the negative binomial distribution and asymptotics of the median","year":2023,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Centre de Recherches Mathématiques","keywords":"Mathematics; Negative binomial distribution; Binomial distribution; Continuity correction; Binomial (polynomial); Estimator; Distribution (mathematics); Limit (mathematics); Upper and lower bounds; Random variable; Combinatorics; Statistics; Beta-binomial distribution; Mathematical analysis; Poisson distribution","score_opus":0.05968265485861679,"score_gpt":0.358064667786666,"score_spread":0.2983820129280492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137498593","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":[0.0018401158,0.000496417,0.9947241,0.0005416502,0.00020350468,0.000021841226,0.000068361354,0.0001915412,0.0019124042],"genre_scores_gemma":[0.15376769,0.0016692192,0.82447577,0.0010523569,0.0015301775,0.00037407837,0.0004120969,0.0012365566,0.015482093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9873562,0.0071433065,0.00058221177,0.0020234003,0.002430411,0.00046449082],"domain_scores_gemma":[0.9323976,0.04930873,0.00257543,0.009280904,0.0055122836,0.0009249373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029151019,0.0010140674,0.0017508803,0.0042028045,0.0018081564,0.003203727,0.005641146,0.0032653443,0.0119046075],"category_scores_gemma":[0.1436618,0.0011801701,0.0025163684,0.0036975632,0.006152849,0.008015208,0.0041545094,0.008751579,0.0031488743],"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":[0.00009962676,0.00003746379,0.0016439514,0.0002099714,0.000063323845,0.00028133343,0.00030046084,0.011455832,0.0019151034,0.92138743,0.0055419905,0.05706354],"study_design_scores_gemma":[0.000045689383,0.0000673715,0.0014898961,0.0001683964,0.000072830546,0.0007460963,0.00008338126,0.16984788,0.0021751418,0.8074272,0.01780565,0.00007049883],"about_ca_topic_score_codex":0.0035186452,"about_ca_topic_score_gemma":0.0030242743,"teacher_disagreement_score":0.029151019,"about_ca_system_score_codex":0.002192005,"about_ca_system_score_gemma":0.0029239808,"threshold_uncertainty_score":0.15416718},"labels":[],"label_agreement":null},{"id":"W3141196614","doi":"10.1002/sim.8966","title":"Multiparameter one‐sided tests for nonlinear mixed effects models with censored responses","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"","keywords":"Nonlinear system; Statistical hypothesis testing; Computer science; Econometrics; Mathematics; Statistics; Physics","score_opus":0.10619714876143097,"score_gpt":0.42130522307059237,"score_spread":0.31510807430916143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141196614","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":[0.036108635,0.00074657565,0.959401,0.0006906942,0.00010113646,0.00044541716,0.00074298796,0.00049567455,0.0012679626],"genre_scores_gemma":[0.5464087,0.00047257406,0.4469264,0.0006059246,0.0002095777,0.0025818862,0.0014451543,0.00015877538,0.0011910596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89299023,0.0919538,0.002959647,0.006682328,0.0045755506,0.0008385054],"domain_scores_gemma":[0.46850267,0.5015132,0.011384711,0.014904425,0.0026145747,0.0010805016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.075111546,0.0017264271,0.0035953955,0.0036253678,0.0014372173,0.0030820067,0.005045234,0.0031964642,0.008827999],"category_scores_gemma":[0.3037856,0.0008288737,0.0037931958,0.004432917,0.0049627563,0.0055370205,0.0034719687,0.0047944593,0.000787081],"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":[0.0032687525,0.00058219954,0.042800155,0.001864513,0.0052283807,0.001426651,0.0010020718,0.28001562,0.0024688102,0.3603823,0.0052364343,0.2957241],"study_design_scores_gemma":[0.0005201539,0.00096886436,0.00919352,0.00025161638,0.0003579469,0.00038788328,0.00023691064,0.7081185,0.0018536431,0.2738297,0.004138643,0.00014261667],"about_ca_topic_score_codex":0.0014230282,"about_ca_topic_score_gemma":0.0013719401,"teacher_disagreement_score":0.075111546,"about_ca_system_score_codex":0.0018334987,"about_ca_system_score_gemma":0.002971267,"threshold_uncertainty_score":0.3972326},"labels":[],"label_agreement":null},{"id":"W3141775592","doi":"10.1007/s10463-021-00793-4","title":"Empirical likelihood meta-analysis with publication bias correction under Copas-like selection model","year":2021,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Restricted maximum likelihood; Statistics; Mathematics; Likelihood principle; Marginal likelihood; Empirical likelihood; Estimator; Likelihood function; Maximum likelihood sequence estimation; Likelihood-ratio test; Maximum likelihood; Inference; Conditional probability distribution; Econometrics; Model selection; Parametric statistics; Selection (genetic algorithm); Quasi-maximum likelihood; Computer science; Artificial intelligence","score_opus":0.3490179428316027,"score_gpt":0.4380967730010622,"score_spread":0.0890788301694595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141775592","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":[0.016825028,0.022107048,0.94782484,0.0050635277,0.0013702866,0.0013324312,0.002777213,0.0012794993,0.0014202117],"genre_scores_gemma":[0.53718233,0.008782831,0.4234999,0.0073346808,0.0024751076,0.008544629,0.0038538552,0.0009139316,0.0074127824],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7092766,0.25117263,0.011394182,0.020360954,0.006019199,0.001776403],"domain_scores_gemma":[0.54198337,0.39602306,0.015966654,0.039570954,0.005196305,0.0012596447],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20477524,0.0033596568,0.014862574,0.005262617,0.0014620374,0.006358299,0.008798233,0.008580645,0.009340853],"category_scores_gemma":[0.3641804,0.0027201327,0.018865274,0.008809115,0.0033490371,0.005562513,0.0044940193,0.0072909514,0.0016911735],"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":[0.015932405,0.00064952986,0.045212798,0.028784256,0.35847795,0.0048615607,0.0012115493,0.10971804,0.0022175766,0.18737559,0.02418791,0.22137086],"study_design_scores_gemma":[0.009203972,0.0023041815,0.012026945,0.0028247177,0.14189278,0.0028848178,0.00023464196,0.4204898,0.0016727116,0.39172366,0.014338406,0.00040343514],"about_ca_topic_score_codex":0.0029985355,"about_ca_topic_score_gemma":0.0024338157,"teacher_disagreement_score":0.20477524,"about_ca_system_score_codex":0.001690652,"about_ca_system_score_gemma":0.0056560426,"threshold_uncertainty_score":0.9806537},"labels":[],"label_agreement":null},{"id":"W3142873646","doi":"10.1007/978-3-030-44246-0_9","title":"Methods for Handling Missing Data","year":2020,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Waterloo","funders":"","keywords":"Missing data; Sample (material); Survey sampling; Statistics; Computer science; Sampling (signal processing); Survey data collection; Sampling design; Population; Survey methodology; Ideal (ethics); Sampling frame; Data mining; Data science; Mathematics; Medicine","score_opus":0.23699416276483037,"score_gpt":0.47565453478512637,"score_spread":0.238660372020296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142873646","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":[0.00013028481,0.0025981737,0.99065673,0.00029141738,0.00038089545,0.000028931794,0.00022724933,0.0005787496,0.0051075937],"genre_scores_gemma":[0.008886293,0.0065234452,0.9408457,0.0009044137,0.0012794711,0.0004912806,0.0013342666,0.0011126426,0.038622465],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99626786,0.0016928468,0.0002065626,0.0005252438,0.0012108872,0.000096638396],"domain_scores_gemma":[0.9913328,0.006196184,0.00021891849,0.0015022792,0.0006671783,0.00008258569],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005026806,0.0019137093,0.0020855186,0.0026859972,0.00076701277,0.0024804627,0.003291751,0.0024232296,0.035777964],"category_scores_gemma":[0.018533668,0.0017024413,0.0019026055,0.0032986812,0.001701935,0.0032903403,0.0025921292,0.004044991,0.023512824],"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":[0.000052596257,0.00006789644,0.00041980628,0.00087357993,0.00022458314,0.0001589077,0.00025000083,0.01240292,0.0012998453,0.32273507,0.08985008,0.5716648],"study_design_scores_gemma":[0.000033765464,0.000035784655,0.00038555582,0.00029279164,0.00009875977,0.00052870385,0.00005890245,0.080610774,0.0015281477,0.7212697,0.19508773,0.000069307745],"about_ca_topic_score_codex":0.0010627451,"about_ca_topic_score_gemma":0.0016387961,"teacher_disagreement_score":0.9949732,"about_ca_system_score_codex":0.0005929687,"about_ca_system_score_gemma":0.0011751077,"threshold_uncertainty_score":0.11968917},"labels":[],"label_agreement":null},{"id":"W3144823608","doi":"10.1108/dta-12-2020-0298","title":"A systematic review of machine learning-based missing value imputation techniques","year":2021,"lang":"en","type":"review","venue":"Data Technologies and Applications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"","keywords":"Imputation (statistics); Missing data; Computer science; Data mining; Cluster analysis; Mean squared error; Machine learning; Artificial intelligence; Information retrieval; Statistics; Mathematics","score_opus":0.12576228665755548,"score_gpt":0.4470710314993138,"score_spread":0.3213087448417583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144823608","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053892285,0.9939814,0.002698899,0.00081213977,0.00031260413,0.0007547534,0.00046412484,0.000029562192,0.00040751635],"genre_scores_gemma":[0.008408738,0.97840893,0.008586988,0.0010426048,0.00032068216,0.0024746307,0.0005205294,0.000024073306,0.00021291502],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9464516,0.025842387,0.015203165,0.0027410141,0.009190319,0.0005714336],"domain_scores_gemma":[0.7656234,0.18661489,0.024660429,0.0046410724,0.017622115,0.0008380163],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.049265645,0.0023366371,0.0087211775,0.016604682,0.0012101694,0.0042430977,0.004275272,0.0030432194,0.0055108783],"category_scores_gemma":[0.22835205,0.0015577838,0.011449362,0.015868548,0.0013584147,0.0052753254,0.002377307,0.003031545,0.0008889621],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019279624,0.00003082014,0.0008002931,0.8626257,0.0062096487,0.00009915729,0.0003401432,0.0004244931,0.00014071417,0.0010485115,0.004323514,0.123764284],"study_design_scores_gemma":[0.00014106026,0.000231802,0.0020086502,0.93693125,0.02187002,0.00038647358,0.00025881612,0.00036717614,0.00027421577,0.0010680768,0.036411524,0.000051041305],"about_ca_topic_score_codex":0.0046133236,"about_ca_topic_score_gemma":0.011824046,"teacher_disagreement_score":0.9507344,"about_ca_system_score_codex":0.0054141567,"about_ca_system_score_gemma":0.025857022,"threshold_uncertainty_score":0.26054472},"labels":[],"label_agreement":null},{"id":"W3144928113","doi":"10.1111/sjos.12523","title":"Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty","year":2021,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","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 Calgary; University of Winnipeg","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Inference; Bayesian inference; Covariate; Likelihood function; Gaussian process; Sampling (signal processing); Importance sampling; Data mining; Machine learning; Artificial intelligence; Statistics; Algorithm; Gaussian; Monte Carlo method; Mathematics; Estimation theory","score_opus":0.04461313327371366,"score_gpt":0.3582277068474163,"score_spread":0.3136145735737026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144928113","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":[0.014964142,0.0001125538,0.9843509,0.00016216822,0.000010278467,0.000019501513,0.00003312305,0.00006518203,0.00028213163],"genre_scores_gemma":[0.62299144,0.0006027805,0.37317702,0.00022779123,0.00010112533,0.00034702296,0.0006177887,0.00015389302,0.0017810912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651235,0.0027184526,0.00012445703,0.00026928622,0.0002690906,0.000106328036],"domain_scores_gemma":[0.95482343,0.040918663,0.0015456532,0.0012998908,0.0011122406,0.00030013244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010900251,0.0007114283,0.0011922035,0.0016833713,0.0004834344,0.0009831374,0.002204343,0.0013594403,0.0015054016],"category_scores_gemma":[0.05574169,0.0008866125,0.0012693711,0.0011952313,0.0017816459,0.0022817291,0.0019378518,0.002234509,0.00028522027],"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":[0.000059543134,0.00004470591,0.0025314305,0.00005312402,0.000065482775,0.00007061407,0.00007243597,0.9088333,0.00038633237,0.07484925,0.00044855656,0.012585204],"study_design_scores_gemma":[0.000004429836,0.000005483306,0.00013521929,0.0000065347035,0.0000032529952,0.000010701016,0.0000049694013,0.9860173,0.00013926807,0.013543928,0.00012518831,0.000003632933],"about_ca_topic_score_codex":0.0049292957,"about_ca_topic_score_gemma":0.0035272276,"teacher_disagreement_score":0.010900251,"about_ca_system_score_codex":0.0012541129,"about_ca_system_score_gemma":0.0012900695,"threshold_uncertainty_score":0.05764669},"labels":[],"label_agreement":null},{"id":"W3145123286","doi":"10.1002/cam4.3826","title":"Clinical research associates experience with missing patient reported outcomes data in cancer randomized controlled trials","year":2021,"lang":"en","type":"article","venue":"Cancer Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Missing data; Descriptive statistics; Data quality; Data collection; Psychology; Research design; Randomized controlled trial; Clinical trial; Quality (philosophy); Data science; Applied psychology; Medicine; Computer science; Statistics; Engineering; Operations management; Pathology","score_opus":0.601876104890742,"score_gpt":0.6398374643147249,"score_spread":0.03796135942398293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145123286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14028619,0.05219717,0.4021059,0.35274324,0.003561083,0.008628356,0.0013478007,0.00097164355,0.038158637],"genre_scores_gemma":[0.68789566,0.016630173,0.22039789,0.051772486,0.0028587175,0.017083073,0.00055055757,0.00050401123,0.0023074974],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.063570604,0.855119,0.04175905,0.0064735166,0.031353217,0.0017245605],"domain_scores_gemma":[0.014452675,0.91195023,0.041837577,0.020101834,0.009810443,0.0018472114],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.7720637,0.0010184437,0.0030089414,0.006200925,0.007627033,0.0138731245,0.0073604514,0.0070434297,0.0097859455],"category_scores_gemma":[0.9067055,0.003042956,0.0030661456,0.009224763,0.028308682,0.018862419,0.015286609,0.00929159,0.0015721372],"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":[0.0034324154,0.0005857371,0.05736234,0.03759685,0.0017638188,0.003553175,0.39632678,0.005061729,0.001389174,0.11513033,0.03701917,0.34077847],"study_design_scores_gemma":[0.0016145206,0.0050634025,0.019399818,0.09733613,0.0017517634,0.017070781,0.19044113,0.01677889,0.0045853946,0.31828552,0.32661372,0.0010588536],"about_ca_topic_score_codex":0.0021319268,"about_ca_topic_score_gemma":0.0023053412,"teacher_disagreement_score":0.22793633,"about_ca_system_score_codex":0.012708341,"about_ca_system_score_gemma":0.042984385,"threshold_uncertainty_score":0.28108615},"labels":[],"label_agreement":null},{"id":"W3149712772","doi":"10.1002/0470856289.ch4","title":"Cost‐Effectiveness Analysis","year":2006,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Toronto","funders":"","keywords":"Computer science","score_opus":0.09307497340847272,"score_gpt":0.4228866253687913,"score_spread":0.3298116519603186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149712772","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":[0.025437923,0.2397475,0.5031834,0.024880407,0.0061391047,0.008553602,0.024612572,0.0012438482,0.16620158],"genre_scores_gemma":[0.48378772,0.17976138,0.24802575,0.011855019,0.004525479,0.010296334,0.016080445,0.0011495294,0.044518355],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9891155,0.006817665,0.00057973637,0.0005338358,0.002727148,0.00022611232],"domain_scores_gemma":[0.98201823,0.015466346,0.00052179856,0.0005259841,0.0013403771,0.0001272327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011573996,0.0010490862,0.002681214,0.0023103873,0.00017841654,0.0024789788,0.0012448137,0.0010809565,0.033993144],"category_scores_gemma":[0.055107914,0.000396606,0.0033500958,0.0019232149,0.00029753635,0.0018749795,0.0006976248,0.0025514208,0.0033214442],"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":[0.00191654,0.00034600944,0.0021911422,0.009387067,0.0039933305,0.00017270866,0.0000908055,0.04939711,0.00094862113,0.070774294,0.047532324,0.81325],"study_design_scores_gemma":[0.0022280444,0.003756974,0.02430368,0.018192625,0.014659224,0.0020837174,0.0004042898,0.16728668,0.007447689,0.3627738,0.39653188,0.00033142598],"about_ca_topic_score_codex":0.0011650271,"about_ca_topic_score_gemma":0.0010220108,"teacher_disagreement_score":0.033993144,"about_ca_system_score_codex":0.002503263,"about_ca_system_score_gemma":0.0022758534,"threshold_uncertainty_score":0.11371845},"labels":[],"label_agreement":null},{"id":"W3151993145","doi":"10.1177/1740774520980052","title":"Improving efficiency in the stepped-wedge trial design via Bayesian modeling with an informative prior for the time effects","year":2021,"lang":"en","type":"article","venue":"Clinical Trials","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":"St. Paul's Hospital; University of British Columbia","funders":"","keywords":"Sample size determination; Randomized controlled trial; Frequentist inference; Statistics; Bayesian probability; Statistical power; Medicine; Econometrics; Computer science; Mathematics; Bayesian inference","score_opus":0.34031754041561096,"score_gpt":0.510608425108701,"score_spread":0.17029088469309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151993145","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":[0.0060423594,0.00043010566,0.9898783,0.00054087915,0.0000509808,0.0018032832,0.000100101555,0.00024588546,0.00090814015],"genre_scores_gemma":[0.1559059,0.00058606075,0.83330446,0.0007722597,0.00006666619,0.00820621,0.0001858847,0.00011585382,0.0008566877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8619464,0.12684624,0.0025582928,0.0034013784,0.0044175754,0.0008300216],"domain_scores_gemma":[0.7540006,0.22164214,0.00862728,0.009410986,0.005003574,0.0013153971],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1661395,0.0023307807,0.004712061,0.002144147,0.0009885074,0.0027680695,0.0050982945,0.0042061764,0.006530776],"category_scores_gemma":[0.28633884,0.0027005034,0.004250498,0.00211841,0.0029483219,0.004412767,0.004672386,0.006400594,0.0009920733],"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":[0.0060218894,0.0008064044,0.007615322,0.0035490966,0.0027278643,0.00064787763,0.0024527882,0.47563058,0.0021722552,0.29034305,0.005529339,0.20250346],"study_design_scores_gemma":[0.0028478876,0.001394706,0.001302106,0.0010210999,0.00094834977,0.00019838115,0.00010606525,0.7609085,0.0012964823,0.22423553,0.0055965986,0.00014442547],"about_ca_topic_score_codex":0.0033354957,"about_ca_topic_score_gemma":0.003429811,"teacher_disagreement_score":0.8338605,"about_ca_system_score_codex":0.0022690778,"about_ca_system_score_gemma":0.0074865413,"threshold_uncertainty_score":0.8786402},"labels":[],"label_agreement":null},{"id":"W3154533874","doi":"10.3390/stats4020021","title":"A Flexible Multivariate Distribution for Correlated Count Data","year":2021,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Count data; Multivariate statistics; Negative binomial distribution; Poisson distribution; Statistics; Dispersion (optics); Mathematics; Multivariate analysis of variance; Multivariate normal distribution; Multivariate analysis; Overdispersion; Computer science; Physics","score_opus":0.23436907771996443,"score_gpt":0.46162506249675983,"score_spread":0.2272559847767954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154533874","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":[0.007226772,0.00017764358,0.988664,0.0006385245,0.00005173422,0.00011801809,0.0004966099,0.0004265935,0.002200075],"genre_scores_gemma":[0.38606197,0.0015170531,0.5975069,0.001252305,0.00039806293,0.0016325208,0.0027175683,0.0006698325,0.008243702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930698,0.0034288752,0.00040021184,0.0012403745,0.0014919795,0.00036867204],"domain_scores_gemma":[0.97772115,0.013195509,0.0027058555,0.0034934087,0.002299556,0.0005843728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012322194,0.0010989693,0.0011671938,0.0029793072,0.0012302082,0.0029351441,0.003558848,0.0023769327,0.0081312135],"category_scores_gemma":[0.05229576,0.00074854534,0.0016967818,0.0051649986,0.0034683808,0.0056933933,0.0031131892,0.0051818117,0.0022306724],"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":[0.000072636685,0.000056866782,0.0070310305,0.00015491997,0.00005717392,0.00051504985,0.000547325,0.076062895,0.001230856,0.8495249,0.0066914232,0.058054898],"study_design_scores_gemma":[0.000041003284,0.000077999866,0.003117938,0.00015739496,0.000030854295,0.00078662444,0.00023814278,0.50223434,0.0008703627,0.4742177,0.01811187,0.000115717135],"about_ca_topic_score_codex":0.0053679175,"about_ca_topic_score_gemma":0.0043217633,"teacher_disagreement_score":0.012322194,"about_ca_system_score_codex":0.002206049,"about_ca_system_score_gemma":0.0024427953,"threshold_uncertainty_score":0.06516671},"labels":[],"label_agreement":null},{"id":"W3154562935","doi":"10.1080/10485252.2021.1914337","title":"Composite empirical likelihood for multisample clustered data","year":2021,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Statistical Methods and Bayesian 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":"University of Waterloo; University of British Columbia","funders":"Higher Education Discipline Innovation Project; Yunnan University; FPInnovations; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Estimator; Quantile; Cluster (spacecraft); Mathematics; Statistics; Parametric statistics; Variance (accounting); Covariance; Population; Econometrics; Data mining; Computer science; Algorithm","score_opus":0.23150292683023807,"score_gpt":0.4725785605815837,"score_spread":0.24107563375134564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154562935","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":[0.003108959,0.00012259217,0.9958609,0.00013937653,0.000020061787,0.00005536803,0.00006688922,0.00014276449,0.0004831766],"genre_scores_gemma":[0.1617649,0.00041720676,0.83268315,0.00033400423,0.00014766015,0.00095224293,0.0008834509,0.0002700575,0.002547349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97976375,0.014478566,0.0006653597,0.0019882747,0.0027913873,0.00031270852],"domain_scores_gemma":[0.8847554,0.09401993,0.0049066893,0.009326075,0.005915172,0.0010767546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040398553,0.00081820716,0.0021784916,0.0030756316,0.000932868,0.0024932732,0.004180342,0.0023620585,0.0042184554],"category_scores_gemma":[0.15394121,0.0007732287,0.0015451515,0.0034456062,0.0027867588,0.0036714275,0.003528411,0.0037093563,0.0012805308],"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":[0.00045000052,0.00015517957,0.011038954,0.0006330647,0.00032230487,0.0008329081,0.0013231715,0.2018758,0.001370863,0.62714255,0.005826951,0.14902824],"study_design_scores_gemma":[0.00005750862,0.000080279315,0.0017835171,0.00008113582,0.00003254354,0.0003183571,0.00013045938,0.6879647,0.00069601735,0.30426386,0.004540744,0.00005086393],"about_ca_topic_score_codex":0.0014374885,"about_ca_topic_score_gemma":0.0012536513,"teacher_disagreement_score":0.040398553,"about_ca_system_score_codex":0.0016299821,"about_ca_system_score_gemma":0.001956556,"threshold_uncertainty_score":0.21365052},"labels":[],"label_agreement":null},{"id":"W3155520879","doi":"10.1186/s12874-021-01276-z","title":"Growth mixture models: a case example of the longitudinal analysis of patient‐reported outcomes data captured by a clinical registry","year":2021,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian 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":"Providence Health Care Research Institute; Providence Health Care; St. Paul's Hospital; Trinity Western University; University of British Columbia; Western University","funders":"School of Nursing, University of British Columbia; Canadian Institutes of Health Research; University of British Columbia; Canadian Nurses Foundation","keywords":"Metric (unit); Population; Identification (biology); Computer science; Outcome (game theory); Longitudinal study; Econometrics; Statistics; Medicine; Mathematics","score_opus":0.7970365415584607,"score_gpt":0.6160857039973919,"score_spread":0.18095083756106878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155520879","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":[0.08441767,0.0015053792,0.8874733,0.013428295,0.00023508891,0.00041408432,0.0025005676,0.0006084997,0.009417158],"genre_scores_gemma":[0.5641711,0.0012677624,0.42447075,0.0013583502,0.00030488934,0.0013482313,0.002394538,0.00019868971,0.0044858055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98420054,0.012406435,0.00052748795,0.0012384518,0.0012163427,0.0004107669],"domain_scores_gemma":[0.9259873,0.06151116,0.0038229062,0.005086913,0.0029529692,0.0006388141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031390127,0.000843569,0.0009150107,0.0026171308,0.0014519494,0.0021716766,0.0022680925,0.002765312,0.0025850786],"category_scores_gemma":[0.05942504,0.0005853695,0.0024868464,0.0061178734,0.002481395,0.0021754515,0.0037594922,0.004281133,0.0005686485],"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":[0.00046290868,0.00034224437,0.14186518,0.0005068399,0.0007283215,0.0034761073,0.007197797,0.17368309,0.00082377484,0.5518969,0.016220694,0.10279618],"study_design_scores_gemma":[0.000101706064,0.00014463413,0.02364698,0.00018860663,0.0001606155,0.0013426834,0.0012975768,0.6470433,0.00056977256,0.29807556,0.027271215,0.00015744193],"about_ca_topic_score_codex":0.028431574,"about_ca_topic_score_gemma":0.026298985,"teacher_disagreement_score":0.031390127,"about_ca_system_score_codex":0.002816,"about_ca_system_score_gemma":0.0030638638,"threshold_uncertainty_score":0.16600889},"labels":[],"label_agreement":null},{"id":"W3158192678","doi":"10.1080/03610918.2021.1914090","title":"Logarithmic confidence intervals for the cross-product ratio of binomial proportions under different sampling schemes","year":2021,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Regina","funders":"Kazan Federal University","keywords":"Confidence interval; Mathematics; Statistics; Coverage probability; Estimator; Logarithm; CDF-based nonparametric confidence interval; Binomial proportion confidence interval; Sampling (signal processing); Binomial distribution; Tolerance interval; Monte Carlo method; Applied mathematics; Negative binomial distribution; Poisson distribution; Computer science; Mathematical analysis","score_opus":0.39434160221835873,"score_gpt":0.5489182486201566,"score_spread":0.15457664640179786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158192678","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":[0.029387884,0.001191165,0.9660688,0.0003196775,0.000051755018,0.000093508315,0.00024404211,0.0003760512,0.0022671956],"genre_scores_gemma":[0.640684,0.0012720061,0.35439354,0.0002546466,0.00020071815,0.0006382433,0.0011269479,0.00030949866,0.0011204387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9751785,0.01459559,0.0012759166,0.0032117236,0.005108163,0.0006301568],"domain_scores_gemma":[0.61786467,0.3424403,0.011569858,0.016215313,0.01073404,0.0011758631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052244455,0.0012185995,0.0014327733,0.0041742027,0.00065978785,0.0032537004,0.0045074043,0.002603558,0.0043629785],"category_scores_gemma":[0.3869929,0.0005900861,0.0015645288,0.0036334207,0.0034839227,0.005852607,0.0033580463,0.0027562482,0.00070368836],"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":[0.0016420288,0.00013035233,0.023582526,0.0010534829,0.00038089068,0.000639797,0.0009551193,0.47286153,0.0039506224,0.3446126,0.0026164844,0.14757462],"study_design_scores_gemma":[0.00009471767,0.00025051663,0.004753549,0.00045441554,0.00012021261,0.0006224192,0.00021061061,0.81192374,0.0050374987,0.17365628,0.002745533,0.00013044526],"about_ca_topic_score_codex":0.0013568702,"about_ca_topic_score_gemma":0.00061320333,"teacher_disagreement_score":0.052244455,"about_ca_system_score_codex":0.0015431363,"about_ca_system_score_gemma":0.00083861843,"threshold_uncertainty_score":0.2762984},"labels":[],"label_agreement":null},{"id":"W3158999510","doi":"10.1002/sim.8918","title":"Confidence interval estimation for treatment effects in cluster randomization trials based on ranks","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confidence interval; Statistics; Estimator; Restricted randomization; Coverage probability; Randomization; Cluster randomised controlled trial; Mathematics; Interval estimation; Cluster (spacecraft); Computer science; Randomized controlled trial; Medicine","score_opus":0.09668797906989537,"score_gpt":0.46362505929020326,"score_spread":0.3669370802203079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158999510","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":[0.0031282762,0.0023496589,0.99100643,0.0003243672,0.00017036138,0.001018393,0.000177062,0.0004961396,0.0013292689],"genre_scores_gemma":[0.12714928,0.0020651163,0.8612091,0.00054221804,0.00024702196,0.0073004556,0.0005911031,0.00035240463,0.0005433149],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.61765444,0.3309839,0.014474756,0.011547473,0.023838202,0.0015013051],"domain_scores_gemma":[0.27160916,0.66083133,0.0291577,0.023391012,0.014036732,0.0009740868],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2832662,0.0022307723,0.006153129,0.009404305,0.0011328042,0.005388231,0.006482997,0.004413148,0.005726664],"category_scores_gemma":[0.71740794,0.0015234287,0.0062238486,0.00742477,0.005176414,0.0052828044,0.0045972993,0.008438302,0.0010068988],"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":[0.0047789807,0.00034670366,0.013134026,0.0086527215,0.006650596,0.00036073243,0.00266379,0.094253026,0.0014033451,0.40315956,0.010590365,0.45400625],"study_design_scores_gemma":[0.0028267228,0.002671106,0.009572246,0.005083777,0.0040388247,0.0007947035,0.00046837676,0.4796988,0.006919869,0.4645078,0.02291457,0.0005031456],"about_ca_topic_score_codex":0.001984963,"about_ca_topic_score_gemma":0.0009595416,"teacher_disagreement_score":0.2832662,"about_ca_system_score_codex":0.002843725,"about_ca_system_score_gemma":0.004878449,"threshold_uncertainty_score":0.8838604},"labels":[],"label_agreement":null},{"id":"W3159624973","doi":"10.1186/s12859-021-04138-z","title":"Dynamic model updating (DMU) approach for statistical learning model building with missing data","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Statistical Methods and Bayesian Inference","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":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","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; Prostate Cancer Canada","keywords":"Missing data; Categorical variable; Data mining; Imputation (statistics); Computer science; Cluster analysis; Closeness; Statistical model; Bayesian probability; Machine learning; Artificial intelligence; Mathematics","score_opus":0.14795907018902008,"score_gpt":0.39605897055970263,"score_spread":0.24809990037068255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159624973","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":[0.0020131024,0.00047241003,0.9964032,0.00024806464,0.00004228607,0.000049458973,0.00010963799,0.0003494126,0.0003123382],"genre_scores_gemma":[0.19016217,0.0012255795,0.8028681,0.0006843036,0.00034035422,0.0011785396,0.001379967,0.0003242573,0.0018367099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923396,0.005276808,0.0003556369,0.0010637906,0.00071980734,0.00024430142],"domain_scores_gemma":[0.9796569,0.016776726,0.00094937504,0.0011066612,0.0012216949,0.0002886993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010199861,0.0015956705,0.0030580056,0.002257555,0.0010221389,0.0020722235,0.0043137684,0.0020544366,0.0029674259],"category_scores_gemma":[0.027879445,0.0013708737,0.0028955953,0.0024661266,0.0010757073,0.0017658604,0.0031196377,0.0042150156,0.0008396507],"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":[0.00020046298,0.00014934754,0.0041711754,0.00057848665,0.0009634132,0.0002733419,0.0004063421,0.7068472,0.0008093618,0.04867849,0.0046189157,0.23230347],"study_design_scores_gemma":[0.000023005117,0.000045100805,0.00020918642,0.00003707048,0.00006295421,0.00004767644,0.000022598919,0.9703658,0.00035111065,0.026295507,0.0025216474,0.000018300761],"about_ca_topic_score_codex":0.00822877,"about_ca_topic_score_gemma":0.008570992,"teacher_disagreement_score":0.010199861,"about_ca_system_score_codex":0.0016468859,"about_ca_system_score_gemma":0.0029974196,"threshold_uncertainty_score":0.05394268},"labels":[],"label_agreement":null},{"id":"W3160435793","doi":"10.1016/j.spasta.2021.100509","title":"The root-Gaussian Cox process and a generalized EMS algorithm","year":2021,"lang":"en","type":"article","venue":"Spatial Statistics","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":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Algorithm; Gaussian process; Gaussian; Process (computing); Plasmodium falciparum; Computer science; Square root; Cox process; Mathematics; Statistics; Data mining; Malaria; Artificial intelligence; Biology","score_opus":0.02917966577671349,"score_gpt":0.3594704571164984,"score_spread":0.3302907913397849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160435793","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":[0.002255911,0.0001108693,0.99684286,0.0001919772,0.000031240346,0.00002805547,0.000045551253,0.000064588814,0.00042903447],"genre_scores_gemma":[0.16079213,0.0008548106,0.8289294,0.00025669983,0.00024618165,0.00042066505,0.00044077626,0.00015129367,0.007908088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956546,0.0027429555,0.00014265273,0.0005854189,0.0006790301,0.00019532263],"domain_scores_gemma":[0.99071175,0.0063375914,0.00064027554,0.00092221744,0.0011412762,0.00024698995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008884123,0.0007202753,0.0012366953,0.0015369422,0.0005835192,0.0014010526,0.0027400309,0.0014632521,0.004158058],"category_scores_gemma":[0.019399917,0.0007575676,0.0015614905,0.001764222,0.00209624,0.0020098018,0.0021198522,0.0023445997,0.00087555783],"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":[0.00011035837,0.00004016391,0.0028186818,0.00010415551,0.00014852396,0.00018766738,0.0001622966,0.332635,0.0010388821,0.58760446,0.002998383,0.07215154],"study_design_scores_gemma":[0.000028868792,0.00004450496,0.00044267066,0.000016614276,0.00002483498,0.00007069325,0.000023604125,0.8585153,0.00038129275,0.13654938,0.0038697068,0.00003251962],"about_ca_topic_score_codex":0.0054272725,"about_ca_topic_score_gemma":0.0055271913,"teacher_disagreement_score":0.008884123,"about_ca_system_score_codex":0.0010398151,"about_ca_system_score_gemma":0.0027663368,"threshold_uncertainty_score":0.046984255},"labels":[],"label_agreement":null},{"id":"W3165356593","doi":"10.1002/cjs.11615","title":"New semiparametric regression method with applications in selection‐biased sampling and missing data problems","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Missing data; Statistics; Semiparametric regression; Computer science; Sampling (signal processing); Regression analysis; Regression; Sampling distribution; Econometrics; Generalized linear model; Mathematics","score_opus":0.1524018724888086,"score_gpt":0.3992928038424844,"score_spread":0.2468909313536758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165356593","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":[0.00083267555,0.00017116974,0.9986683,0.00007047796,0.00001588099,0.000010045804,0.000018738916,0.00008516349,0.00012757699],"genre_scores_gemma":[0.09573082,0.0009303943,0.89953774,0.0003106952,0.00022973651,0.00040490157,0.00028899848,0.00027516138,0.0022914694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99357694,0.004648639,0.00020452889,0.00049066736,0.00093511416,0.00014411443],"domain_scores_gemma":[0.9827503,0.012992786,0.0010077363,0.0013337776,0.0016824092,0.00023291871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0106657455,0.0009753519,0.0020494752,0.0020773427,0.00043438884,0.0011647585,0.003442181,0.0015859726,0.0024674735],"category_scores_gemma":[0.03321104,0.0009988367,0.0016517941,0.0020290124,0.0011707296,0.0022343763,0.0028544934,0.0025628642,0.0007974439],"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":[0.00021741848,0.00021269424,0.0042267647,0.00072949973,0.0006102987,0.0005169185,0.00045547518,0.3749856,0.0059739165,0.28177977,0.005846602,0.32444498],"study_design_scores_gemma":[0.000036411384,0.00004304075,0.00040344853,0.000034171484,0.00004445069,0.00017327681,0.000019489014,0.94932306,0.00073647493,0.04595557,0.0031969494,0.000033573593],"about_ca_topic_score_codex":0.0017319147,"about_ca_topic_score_gemma":0.0011797139,"teacher_disagreement_score":0.0106657455,"about_ca_system_score_codex":0.0006150567,"about_ca_system_score_gemma":0.0014698082,"threshold_uncertainty_score":0.056406498},"labels":[],"label_agreement":null},{"id":"W3165766469","doi":"10.31234/osf.io/smcdv","title":"Dealing with multivariate missing data in principal component analyses and subsequent model estimation: a two-step worked example using data from the Canadian Longitudinal Study of Aging","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian 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":"University of Saskatchewan; Université de Montréal","funders":"Canadian Institutes of Health Research; Consortium canadien en neurodégénérescence associée au vieillissement; University of British Columbia; Government of Canada; Réseau québécois de recherche sur le vieillissement","keywords":"Missing data; Imputation (statistics); Principal component analysis; Raw data; Inference; Statistics; Computer science; Multivariate statistics; Data mining; Statistical inference; Econometrics; Mathematics; Artificial intelligence","score_opus":0.6496341280986746,"score_gpt":0.5106114991789917,"score_spread":0.13902262891968287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165766469","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":[0.024075285,0.0019203513,0.95796233,0.005383211,0.0003818377,0.00072325225,0.0023094707,0.0016587474,0.0055854875],"genre_scores_gemma":[0.08868024,0.0012897494,0.90410703,0.0005324866,0.000098122466,0.00043979337,0.0012029172,0.000402593,0.0032470417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9837781,0.011015836,0.0006336867,0.0009372618,0.0031417054,0.000493411],"domain_scores_gemma":[0.9427739,0.04002396,0.0015090278,0.0045360615,0.010585157,0.00057184295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044844985,0.0018504056,0.0017663774,0.0031758272,0.0039301305,0.0028232571,0.0031700807,0.0024341028,0.0061837174],"category_scores_gemma":[0.115842134,0.0007388417,0.0030331006,0.008834101,0.0014520737,0.0020405322,0.00285789,0.0045708627,0.0013074043],"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":[0.00063248666,0.00040392863,0.051662367,0.0020951785,0.0012383808,0.003113966,0.01390405,0.07217304,0.0023656639,0.103085466,0.07375677,0.67556876],"study_design_scores_gemma":[0.0004366732,0.0004503693,0.050909176,0.0018153831,0.0009736767,0.0022962822,0.008835687,0.46411183,0.007578968,0.25351,0.20811416,0.0009677417],"about_ca_topic_score_codex":0.39418787,"about_ca_topic_score_gemma":0.5823256,"teacher_disagreement_score":0.39418787,"about_ca_system_score_codex":0.004329339,"about_ca_system_score_gemma":0.016291922,"threshold_uncertainty_score":0.7837869},"labels":[],"label_agreement":null},{"id":"W3165880537","doi":"10.1007/s10985-021-09524-6","title":"Augmented likelihood for incorporating auxiliary information into left-truncated data","year":2021,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truncation (statistics); Computer science; Maximization; Importance sampling; Monte Carlo method; Sampling (signal processing); Event (particle physics); Sample (material); Statistics; Expectation–maximization algorithm; Data mining; Algorithm; Mathematics; Mathematical optimization; Maximum likelihood; Machine learning","score_opus":0.08406282603851854,"score_gpt":0.3943989918236715,"score_spread":0.31033616578515294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165880537","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":[0.0015073491,0.00013763609,0.99737275,0.00019827519,0.000025538106,0.000022175704,0.0002385166,0.00013091126,0.0003668352],"genre_scores_gemma":[0.13968715,0.0010172883,0.8460875,0.00051217293,0.00043874068,0.00074300484,0.0027357566,0.0004921213,0.008286273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99010766,0.007108025,0.00042799267,0.0010349025,0.0010052281,0.000316145],"domain_scores_gemma":[0.9177417,0.06924277,0.0028679015,0.0068868604,0.0025706647,0.0006901565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024195673,0.0017317721,0.0032652265,0.0022331965,0.0010542861,0.0035590017,0.005858508,0.0031798917,0.008906092],"category_scores_gemma":[0.09733266,0.0021442724,0.002220988,0.0034381172,0.003411798,0.007257392,0.004348623,0.0068514603,0.0020220021],"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":[0.00034599268,0.00014179929,0.0019919146,0.00042820818,0.00031137202,0.00045012028,0.00041844122,0.2880641,0.0010238958,0.6271554,0.0056159217,0.07405282],"study_design_scores_gemma":[0.000029848106,0.000030107007,0.0003297414,0.00006396463,0.000039329752,0.00007584768,0.000022887823,0.6922087,0.0003369809,0.30459142,0.002230439,0.000040719628],"about_ca_topic_score_codex":0.005337866,"about_ca_topic_score_gemma":0.0053896904,"teacher_disagreement_score":0.024195673,"about_ca_system_score_codex":0.0020855821,"about_ca_system_score_gemma":0.0038986688,"threshold_uncertainty_score":0.12796044},"labels":[],"label_agreement":null},{"id":"W3168316256","doi":"10.1111/stan.12250","title":"Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis","year":2021,"lang":"en","type":"article","venue":"Statistica Neerlandica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"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":"Medical Research Council; Medical Research Council Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; London School of Hygiene and Tropical Medicine","keywords":"Censoring (clinical trials); Covariate; Inference; Imputation (statistics); Econometrics; Statistics; Computer science; Tobit model; Conditional probability distribution; Mathematics; Missing data; Robustness (evolution); Statistical inference; Data mining; Artificial intelligence","score_opus":0.06785236872376028,"score_gpt":0.3731215192547555,"score_spread":0.30526915053099524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168316256","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":[0.0024512953,0.00089865667,0.9941273,0.0005839704,0.000074281,0.00029644754,0.000121110796,0.00013206758,0.0013148917],"genre_scores_gemma":[0.25428224,0.00238267,0.7350048,0.0012218844,0.00028466858,0.0033794176,0.00042990668,0.00038926804,0.002625111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85647255,0.13201366,0.0025024184,0.0030954392,0.0050729695,0.0008428572],"domain_scores_gemma":[0.51551926,0.45699966,0.009047811,0.0120424945,0.0055588717,0.0008319176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18208206,0.0021770296,0.0038799183,0.004961506,0.0011228267,0.0036707562,0.0039946907,0.003295129,0.009276884],"category_scores_gemma":[0.40959203,0.0015909116,0.0061411033,0.0033974422,0.0040915078,0.0046935515,0.006233595,0.008449301,0.00067436305],"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":[0.0007273956,0.00019077293,0.0025898619,0.0017595564,0.0014566954,0.0010913057,0.0011152478,0.3039723,0.0011194932,0.62051743,0.0030519713,0.062408056],"study_design_scores_gemma":[0.00015930642,0.0002351331,0.0005426888,0.00039217077,0.00028106503,0.00026604207,0.00008594887,0.4748475,0.00069598475,0.51813525,0.0042779595,0.00008101377],"about_ca_topic_score_codex":0.0027768894,"about_ca_topic_score_gemma":0.001575422,"teacher_disagreement_score":0.18208206,"about_ca_system_score_codex":0.00358846,"about_ca_system_score_gemma":0.0037572177,"threshold_uncertainty_score":0.96295345},"labels":[],"label_agreement":null},{"id":"W3168352277","doi":"10.1002/cjs.11622","title":"Estimation of design‐based mean squared error of a small area mean model‐based estimator under a nested error linear regression model","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Aluminerie Alouette (Canada); Statistics Canada","funders":"","keywords":"Mean squared error; Estimator; Mathematics; Statistics; Bias of an estimator; Population; Small area estimation; Efficient estimator; Minimum-variance unbiased estimator","score_opus":0.1874498084875181,"score_gpt":0.3650237628846509,"score_spread":0.17757395439713278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168352277","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":[0.012024281,0.00019079063,0.98717135,0.000088225526,0.000017500846,0.000027624828,0.000056732766,0.00009216939,0.0003313407],"genre_scores_gemma":[0.6193941,0.00045664783,0.37658328,0.00027194206,0.00009545441,0.00039428586,0.00075015007,0.00013029674,0.0019237493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9847147,0.0091957785,0.00056915276,0.0028875265,0.002322615,0.0003101405],"domain_scores_gemma":[0.9216999,0.06381317,0.0043260995,0.0048552337,0.0048684105,0.00043713354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022947142,0.000946768,0.00210433,0.0010261402,0.00032543272,0.0013096961,0.0019256364,0.0012892368,0.0021195002],"category_scores_gemma":[0.08240572,0.0007107198,0.0013192758,0.00087014027,0.0015926076,0.001820696,0.0018576663,0.0016197715,0.00038875762],"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":[0.0002227919,0.00014319526,0.026616147,0.0006047039,0.00085844425,0.00014219042,0.00025176984,0.7806217,0.004287042,0.08941584,0.0016308487,0.09520533],"study_design_scores_gemma":[0.000028605857,0.00017660882,0.0035018213,0.000054001834,0.00009277406,0.000065819615,0.000023629573,0.96849203,0.0015227895,0.024958773,0.0010545528,0.000028491344],"about_ca_topic_score_codex":0.0027111846,"about_ca_topic_score_gemma":0.0020167755,"teacher_disagreement_score":0.022947142,"about_ca_system_score_codex":0.0013055732,"about_ca_system_score_gemma":0.0022429416,"threshold_uncertainty_score":0.12135756},"labels":[],"label_agreement":null},{"id":"W3173462899","doi":"10.5705/ss.202019.0243","title":"FULL-SEMIPARAMETRIC-LIKELIHOOD-BASED INFERENCE FOR NON-IGNORABLE MISSING DATA","year":2020,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"Inference; Missing data; Semiparametric model; Econometrics; Computer science; Semiparametric regression; Statistics; Mathematics; Artificial intelligence; Nonparametric statistics","score_opus":0.21381248393687877,"score_gpt":0.4448338917614719,"score_spread":0.23102140782459316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173462899","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":[0.0014514902,0.00025226775,0.9979074,0.000107220745,0.000008381415,0.000012286571,0.00004191659,0.000057231857,0.00016186853],"genre_scores_gemma":[0.26455715,0.0018754856,0.7299717,0.0003594965,0.00019360823,0.00037005945,0.00076799444,0.00019749835,0.001706915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919552,0.006347958,0.00029898138,0.00053855154,0.0007190579,0.00014028752],"domain_scores_gemma":[0.9692797,0.026124705,0.0012414684,0.0022835708,0.0008296453,0.0002409013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016946945,0.0010115549,0.0022710552,0.001921741,0.00049327716,0.0014981013,0.0033717363,0.0014664724,0.0027588212],"category_scores_gemma":[0.05321547,0.00097600115,0.0021223652,0.0020157027,0.002031743,0.0028137553,0.003444933,0.0028414691,0.00066870236],"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":[0.00021616624,0.00015053728,0.0040761735,0.0012894348,0.0006905277,0.00045624276,0.00040419347,0.38235247,0.002212196,0.43827266,0.0026860011,0.16719332],"study_design_scores_gemma":[0.00003262871,0.000031882657,0.00043799749,0.0000693575,0.00004393274,0.00015878498,0.00003070151,0.7086712,0.00069115445,0.28825182,0.001553841,0.000026621534],"about_ca_topic_score_codex":0.0012534139,"about_ca_topic_score_gemma":0.0015588744,"teacher_disagreement_score":0.016946945,"about_ca_system_score_codex":0.00080089335,"about_ca_system_score_gemma":0.0021616933,"threshold_uncertainty_score":0.08962506},"labels":[],"label_agreement":null},{"id":"W3174289447","doi":"10.1201/9780429341731-10","title":"The Tangent Exponential Model","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Tangent; Exponential function; Mathematics; Mathematical analysis; Geometry","score_opus":0.11437047040112859,"score_gpt":0.3711520085378132,"score_spread":0.25678153813668464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174289447","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":[0.0041643176,0.0013506712,0.97046643,0.0011327158,0.00019838958,0.00003406413,0.00032631008,0.0004045488,0.02192257],"genre_scores_gemma":[0.45866668,0.009056515,0.42565408,0.0026425722,0.0010904172,0.0004721288,0.0020155509,0.0015947741,0.09880732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99760604,0.0009335281,0.000111354886,0.00042586846,0.00070779957,0.0002154393],"domain_scores_gemma":[0.9931371,0.0040648095,0.00051103765,0.00097433804,0.001082235,0.00023044046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043325513,0.0011235854,0.0012055804,0.0016475212,0.0010437722,0.0037992245,0.0034366257,0.0021866134,0.019276865],"category_scores_gemma":[0.031771712,0.0007363478,0.0016437428,0.002500419,0.0027901626,0.008219806,0.002545547,0.004105414,0.008231441],"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":[0.000021185318,0.00001332202,0.0004804958,0.00006412524,0.000019702346,0.00006388371,0.00015670188,0.017180538,0.00017689158,0.9572046,0.0055026268,0.01911586],"study_design_scores_gemma":[0.000008194216,0.000019914898,0.00028567872,0.00007806976,0.000018637867,0.00021664631,0.00005933663,0.17119592,0.00022264218,0.80833626,0.019529438,0.00002934747],"about_ca_topic_score_codex":0.008328033,"about_ca_topic_score_gemma":0.0054351543,"teacher_disagreement_score":0.019276865,"about_ca_system_score_codex":0.0023530582,"about_ca_system_score_gemma":0.002008532,"threshold_uncertainty_score":0.06448752},"labels":[],"label_agreement":null},{"id":"W3174651326","doi":"10.5539/ijsp.v10n4p157","title":"A Weighted Poisson Distribution for Underdispersed Count Data","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Poisson distribution; Mathematics; Compound Poisson distribution; Count data; Exponential distribution; Exponential family; Random variable; Zero-inflated model; Statistics; Compound probability distribution; Compound Poisson process; Distribution (mathematics); Poisson regression; Applied mathematics; Logarithmic distribution; Negative binomial distribution; Distribution fitting; Mathematical analysis; Poisson process","score_opus":0.09732130617071327,"score_gpt":0.4051110483413699,"score_spread":0.30778974217065663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174651326","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":[0.02252019,0.00040993348,0.9740383,0.00046570643,0.00013330903,0.00019414893,0.0005680618,0.00023498452,0.0014353094],"genre_scores_gemma":[0.56692094,0.002266421,0.4113475,0.0012654226,0.0010247368,0.0021091695,0.003161613,0.0004832532,0.011420955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99094814,0.0033899015,0.00055323984,0.0026897187,0.0019005653,0.0005183958],"domain_scores_gemma":[0.9723282,0.016310178,0.0039142375,0.0036161405,0.0033367437,0.000494604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015403168,0.0011398043,0.0019241422,0.002797433,0.001143291,0.0029998117,0.005734062,0.0025916288,0.0059920535],"category_scores_gemma":[0.056141607,0.00094301614,0.0020041363,0.0036987686,0.0024801088,0.009287548,0.0029389327,0.0036764245,0.0015863796],"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":[0.00039930383,0.00014873478,0.028163267,0.00049647904,0.00035551767,0.0015270581,0.0016131978,0.13218664,0.004273263,0.71804065,0.0072636637,0.105532244],"study_design_scores_gemma":[0.000052023275,0.0001388117,0.0057293484,0.0001463695,0.00011258319,0.0012963284,0.00041366613,0.55757934,0.0015676684,0.41837847,0.014447273,0.00013818814],"about_ca_topic_score_codex":0.003277902,"about_ca_topic_score_gemma":0.0021645257,"teacher_disagreement_score":0.015403168,"about_ca_system_score_codex":0.002054593,"about_ca_system_score_gemma":0.0012476594,"threshold_uncertainty_score":0.081460714},"labels":[],"label_agreement":null},{"id":"W3175648361","doi":"10.1186/s40488-021-00121-4","title":"A comparison of zero-inflated and hurdle models for modeling zero-inflated count data","year":2021,"lang":"en","type":"review","venue":"Journal of Statistical Distributions and Applications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":296,"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; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Zero (linguistics); Count data; Mathematics; Statistics; Computer science; Applied mathematics; Poisson distribution; Philosophy","score_opus":0.29473010906368335,"score_gpt":0.5048017762537285,"score_spread":0.2100716671900451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175648361","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":[0.0058351466,0.15408829,0.8305911,0.002629679,0.0005436865,0.00013546545,0.0004425068,0.00036820132,0.0053659366],"genre_scores_gemma":[0.20200671,0.33528742,0.44563764,0.0021198087,0.0019045565,0.0014171499,0.0024121802,0.0005293796,0.008685164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924299,0.00472512,0.00045079307,0.0007842149,0.0013998994,0.0002100597],"domain_scores_gemma":[0.97044814,0.025531532,0.0011007177,0.0009201363,0.0017765461,0.00022287024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013455884,0.0015028121,0.0024550369,0.0035774473,0.00046165974,0.0027842526,0.0042592073,0.0022811259,0.003206518],"category_scores_gemma":[0.03430404,0.0008240521,0.0027119685,0.0043993983,0.0013867941,0.0036888164,0.0014580959,0.0032036463,0.0014136309],"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":[0.00020701026,0.00014197767,0.006630419,0.00429004,0.00090702815,0.00036903727,0.00066971395,0.11260841,0.0004886871,0.47904158,0.013828368,0.38081786],"study_design_scores_gemma":[0.00008984728,0.00028576155,0.003822619,0.003261508,0.00062077807,0.0009740216,0.00034688337,0.4189813,0.00085187267,0.47099498,0.09949327,0.00027711227],"about_ca_topic_score_codex":0.0057113525,"about_ca_topic_score_gemma":0.0041049477,"teacher_disagreement_score":0.013455884,"about_ca_system_score_codex":0.0018236325,"about_ca_system_score_gemma":0.0024351706,"threshold_uncertainty_score":0.07116234},"labels":[],"label_agreement":null},{"id":"W3175869339","doi":"10.4236/ojs.2021.113026","title":"Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data","year":2021,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Count data; Mathematics; Negative binomial distribution; Poisson distribution; Statistics; Nonparametric statistics; Analysis of covariance; Zero-inflated model; Quasi-likelihood; Covariate; Goodness of fit; Poisson regression; Population","score_opus":0.28560320261456945,"score_gpt":0.4368913635636237,"score_spread":0.15128816094905423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175869339","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":[0.0043046656,0.000099228644,0.99496174,0.00007442081,0.000033474713,0.000117196665,0.00005154055,0.00013050326,0.00022721331],"genre_scores_gemma":[0.08382257,0.00028864297,0.91346675,0.00014844244,0.00009864315,0.0013588233,0.00022689384,0.00019867261,0.00039054453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9242648,0.065018356,0.0015145353,0.004040689,0.0046118023,0.0005497525],"domain_scores_gemma":[0.793277,0.18263723,0.006998538,0.012561157,0.0039264266,0.000599592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.082293086,0.0018729441,0.0027256163,0.003482994,0.0017792688,0.0023412157,0.0046829935,0.002402412,0.0035099487],"category_scores_gemma":[0.24541973,0.0009904088,0.0036480643,0.0035488515,0.004955662,0.0042990614,0.00358854,0.005128052,0.00056993286],"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":[0.00071054103,0.0003331022,0.014984908,0.0012011852,0.0021326074,0.000710706,0.0019845734,0.1465698,0.0039647147,0.5575985,0.0038703708,0.26593915],"study_design_scores_gemma":[0.00015382643,0.00048768596,0.004284829,0.00020513585,0.0002583043,0.00048954965,0.00033754305,0.51515585,0.0027711212,0.47092423,0.004793683,0.00013826994],"about_ca_topic_score_codex":0.0032141416,"about_ca_topic_score_gemma":0.0027126574,"teacher_disagreement_score":0.082293086,"about_ca_system_score_codex":0.0015648641,"about_ca_system_score_gemma":0.0039190412,"threshold_uncertainty_score":0.4352126},"labels":[],"label_agreement":null},{"id":"W3176979971","doi":"10.1002/sim.9097","title":"Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University; Impact","funders":"","keywords":"Multinomial distribution; Statistics; Computer science; Goodness of fit; Gold standard (test); Degrees of freedom (physics and chemistry); Test (biology); Mathematics","score_opus":0.1483904432595583,"score_gpt":0.4300721741808364,"score_spread":0.2816817309212781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176979971","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":[0.007512024,0.0026335898,0.9846795,0.0025697309,0.00045772523,0.0003019015,0.00019938168,0.0003160366,0.0013300241],"genre_scores_gemma":[0.18718146,0.0010978093,0.8051366,0.0033143696,0.00078353885,0.001103933,0.0004590146,0.00030461056,0.0006187695],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5454236,0.3434803,0.030833583,0.029456118,0.04836409,0.0024422912],"domain_scores_gemma":[0.25025406,0.6548974,0.02388303,0.053423055,0.016119534,0.0014230117],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.36545005,0.002946964,0.008780839,0.007854655,0.0030982709,0.008818237,0.007837966,0.010528138,0.0011172509],"category_scores_gemma":[0.7013905,0.0020446524,0.0074823084,0.004918231,0.017364,0.008910326,0.008612079,0.014820624,0.001090484],"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":[0.0020832894,0.00035609456,0.12603267,0.003844402,0.0070017916,0.0022503445,0.004404429,0.059472445,0.0027931703,0.3536062,0.016891865,0.42126337],"study_design_scores_gemma":[0.0003338548,0.0009822726,0.011775108,0.0026039302,0.0011350284,0.00305182,0.0005142202,0.20449264,0.005095776,0.7494755,0.020084405,0.00045543353],"about_ca_topic_score_codex":0.0029847964,"about_ca_topic_score_gemma":0.0017872824,"teacher_disagreement_score":0.63455,"about_ca_system_score_codex":0.0035791798,"about_ca_system_score_gemma":0.0046223737,"threshold_uncertainty_score":0.7825131},"labels":[],"label_agreement":null},{"id":"W3179091049","doi":"10.31234/osf.io/rq6yb","title":"Tutorial: How to Generate Missing Data For Simulation Studies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia","funders":"","keywords":"Missing data; Computer science; Data mining; Data science; Machine learning","score_opus":0.5334281666007765,"score_gpt":0.5366739157454582,"score_spread":0.0032457491446816755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179091049","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":[0.0002806335,0.0022265848,0.97814494,0.0028343562,0.0012836,0.00032904043,0.0016896644,0.00614407,0.0070671155],"genre_scores_gemma":[0.0046510133,0.0044751186,0.9693634,0.0018946768,0.001229413,0.0011993634,0.0020932364,0.0033941849,0.01169964],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99507785,0.0031478324,0.00043483064,0.0003222195,0.0009149805,0.00010221328],"domain_scores_gemma":[0.9562868,0.036766924,0.00092999695,0.0016397835,0.0037304147,0.0006461546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009989675,0.0032543377,0.0019263532,0.002717344,0.0007617899,0.0029974552,0.002828083,0.0030801087,0.10848398],"category_scores_gemma":[0.06875793,0.0019951027,0.0030522863,0.0026421943,0.00085948437,0.005083468,0.002106127,0.0061127697,0.040126074],"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":[0.00019430651,0.00026581256,0.0008180571,0.0039511127,0.00032020942,0.00068618945,0.0004460978,0.024404148,0.0020183402,0.13533232,0.43301398,0.39854953],"study_design_scores_gemma":[0.00021803382,0.000096000316,0.00052190956,0.0011958285,0.000119914264,0.0010262402,0.00011069503,0.06023925,0.0021892767,0.3316241,0.60250014,0.00015866345],"about_ca_topic_score_codex":0.0012750414,"about_ca_topic_score_gemma":0.0021521305,"teacher_disagreement_score":0.10848398,"about_ca_system_score_codex":0.0010273722,"about_ca_system_score_gemma":0.0018282519,"threshold_uncertainty_score":0.36291504},"labels":[],"label_agreement":null},{"id":"W3179584355","doi":"10.1002/sim.9126","title":"Fitting marginal models in small samples: A simulation study of marginalized multilevel models and generalized estimating equations","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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 Heart, Lung, and Blood Institute","keywords":"Marginal model; Generalized estimating equation; Gee; Inference; Econometrics; Estimating equations; Sample size determination; Statistics; Multilevel model; Statistical inference; Cluster (spacecraft); Random effects model; Sample (material); Population; Mathematics; Computer science; Regression analysis; Meta-analysis; Estimator; Physics; Demography; Artificial intelligence","score_opus":0.3026635934558983,"score_gpt":0.45129308017905495,"score_spread":0.14862948672315662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179584355","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":[0.23654427,0.0036105583,0.74177676,0.005603147,0.00013761531,0.00046123125,0.0005336464,0.00032685767,0.0110059865],"genre_scores_gemma":[0.7508728,0.0018836089,0.24270926,0.0008176817,0.0000973007,0.00079096504,0.0005038746,0.00018994877,0.0021345825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9754843,0.021940645,0.00036519064,0.00094198907,0.00090189616,0.0003660065],"domain_scores_gemma":[0.6876401,0.29509816,0.005030215,0.0065835263,0.004628503,0.0010194761],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.044569477,0.00091238855,0.0019158253,0.0014894096,0.0010289429,0.002016785,0.002444625,0.0020660441,0.004227848],"category_scores_gemma":[0.19953063,0.00067193713,0.0022373493,0.0027776384,0.00228566,0.004200985,0.0023317747,0.0047431956,0.00036948625],"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":[0.0004179898,0.0002864962,0.026696445,0.00040595088,0.00062165415,0.00066005316,0.001952369,0.5202715,0.0002460942,0.40656763,0.004366571,0.03750721],"study_design_scores_gemma":[0.00013397337,0.0001631768,0.0024227921,0.00015888715,0.00012736663,0.0001384351,0.00043505468,0.8448356,0.00016705501,0.14891657,0.0024548452,0.000046268444],"about_ca_topic_score_codex":0.019649081,"about_ca_topic_score_gemma":0.018256776,"teacher_disagreement_score":0.9554305,"about_ca_system_score_codex":0.0025088033,"about_ca_system_score_gemma":0.0021663578,"threshold_uncertainty_score":0.23570871},"labels":[],"label_agreement":null},{"id":"W3183460754","doi":"10.1371/journal.pone.0255389","title":"Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review","year":2021,"lang":"en","type":"review","venue":"PLoS ONE","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Trinity Western University; Western University; Centre for Advancing Health Outcomes; University of British Columbia","funders":"","keywords":"Sample size determination; CRTS; Cluster (spacecraft); Type I and type II errors; Statistics; Statistical power; Computer science; Contrast (vision); Data mining; Mathematics; Artificial intelligence","score_opus":0.5379822485880341,"score_gpt":0.5581104941484647,"score_spread":0.02012824556043058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183460754","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007057203,0.7996349,0.12679069,0.009985926,0.0049841874,0.051237687,0.0014178407,0.000433757,0.0048093502],"genre_scores_gemma":[0.012865407,0.48535967,0.3060868,0.0054542013,0.0013857095,0.18622991,0.0011533096,0.00030322943,0.0011618031],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.4604648,0.3455023,0.12449723,0.013798413,0.053929012,0.0018082205],"domain_scores_gemma":[0.17127758,0.7314555,0.038461648,0.020366875,0.03763448,0.0008039068],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.46326151,0.0056563807,0.019226506,0.045594305,0.0047813854,0.0152875,0.011165826,0.012084857,0.011757503],"category_scores_gemma":[0.72195196,0.005263825,0.022751125,0.03381777,0.009207416,0.016936336,0.010009218,0.00957601,0.002896361],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003356962,0.000077289355,0.0007385958,0.66514915,0.0069493786,0.00015619313,0.002738624,0.0013956355,0.0002394919,0.022829518,0.011283915,0.28810647],"study_design_scores_gemma":[0.00036743734,0.00017371663,0.0007333086,0.9148308,0.008551521,0.00019121992,0.00063891575,0.0011333107,0.00035505477,0.024805393,0.0480356,0.00018378149],"about_ca_topic_score_codex":0.0067588077,"about_ca_topic_score_gemma":0.0113785565,"teacher_disagreement_score":0.5367385,"about_ca_system_score_codex":0.020843236,"about_ca_system_score_gemma":0.05535323,"threshold_uncertainty_score":0.6618941},"labels":[],"label_agreement":null},{"id":"W3186374827","doi":"10.6000/1929-6029.2020.09.03","title":"Analysis of Recurrent Events with Associated Informative Censoring: Application to HIV Data","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Medicine; Proportional hazards model; Psychological intervention; Hazard ratio; Human immunodeficiency virus (HIV); Statistics; Internal medicine; Family medicine; Nursing; Mathematics","score_opus":0.22995770666512147,"score_gpt":0.549643455015053,"score_spread":0.31968574834993146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186374827","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":[0.103727035,0.002062811,0.8893495,0.0010335773,0.00014361784,0.0005252166,0.0017075018,0.0008644447,0.0005861941],"genre_scores_gemma":[0.6458811,0.0012696659,0.34683165,0.00031625453,0.00028336738,0.0010961975,0.002525615,0.0004298281,0.0013663751],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98334783,0.012957086,0.0008295956,0.0014415805,0.0009851118,0.00043870264],"domain_scores_gemma":[0.87783986,0.10929707,0.0046778354,0.0054852874,0.0017902678,0.0009096283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038911875,0.0010840895,0.0017559739,0.0017814059,0.0007965451,0.00212354,0.0034640455,0.0017730489,0.003768506],"category_scores_gemma":[0.09636191,0.00062076416,0.0033636892,0.0026521701,0.0008011135,0.00158781,0.002422121,0.0034113813,0.0005042146],"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":[0.001682122,0.00045544788,0.25354275,0.0018436961,0.004190756,0.0032718887,0.0019246138,0.3295628,0.0014313076,0.03690578,0.0062738387,0.358915],"study_design_scores_gemma":[0.00012656236,0.0005490419,0.020990802,0.00021071339,0.0004967295,0.0008981007,0.0003293546,0.9347499,0.0006146442,0.036889955,0.00404802,0.00009616684],"about_ca_topic_score_codex":0.0052591236,"about_ca_topic_score_gemma":0.0045312657,"teacher_disagreement_score":0.038911875,"about_ca_system_score_codex":0.0008166361,"about_ca_system_score_gemma":0.001921675,"threshold_uncertainty_score":0.20578814},"labels":[],"label_agreement":null},{"id":"W3193090929","doi":"10.1002/cjs.11637","title":"Avoiding prior–data conflict in regression models via mixture priors","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prior probability; Bayesian probability; Component (thermodynamics); Regression; Computer science; Bayesian inference; Artificial intelligence; Econometrics; Statistics; Mathematics","score_opus":0.12671721036991787,"score_gpt":0.3632595157545536,"score_spread":0.23654230538463575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193090929","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":[0.0065406286,0.00022747983,0.9920134,0.00040053387,0.000010760435,0.000031816355,0.000028463533,0.0001442508,0.0006027386],"genre_scores_gemma":[0.2872398,0.00059432554,0.7084239,0.00043660688,0.00012564077,0.00046951327,0.00026912938,0.00030288074,0.0021380717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97932476,0.01562407,0.00075678754,0.0018303312,0.0019927823,0.0004712911],"domain_scores_gemma":[0.9073346,0.08273588,0.0033688955,0.004437021,0.0014462359,0.0006773095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029623158,0.0017534816,0.0021742594,0.0026222267,0.0015726225,0.004512302,0.003834605,0.0044882204,0.0031384544],"category_scores_gemma":[0.13157666,0.0026402448,0.0027983645,0.0025453279,0.0045487126,0.008475709,0.0066084242,0.006949292,0.0008575198],"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":[0.0005411473,0.00012517178,0.0035171125,0.00034971331,0.00036553302,0.00040313357,0.0015907312,0.3147015,0.0033420501,0.5686663,0.0017997718,0.10459796],"study_design_scores_gemma":[0.0000555019,0.000047779147,0.0004559699,0.00006841737,0.000053857275,0.000101831974,0.000058792677,0.5698524,0.0009983861,0.426723,0.0015137395,0.000070319395],"about_ca_topic_score_codex":0.0044172416,"about_ca_topic_score_gemma":0.005364629,"teacher_disagreement_score":0.029623158,"about_ca_system_score_codex":0.002422076,"about_ca_system_score_gemma":0.0019487963,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W3193380525","doi":"10.1002/sim.9167","title":"A Bayesian nonparametric approach to dynamic item‐response modeling: An application to the GUSTO cohort study","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McGill University; Douglas Mental Health University Institute","funders":"Medical Research Council; National Medical Research Council; National Research Foundation Singapore; Singapore Institute for Clinical Sciences; National University Health System; National Research Foundation","keywords":"Nonparametric statistics; Respondent; Cluster analysis; Item response theory; Bayesian probability; Cohort; Computer science; Econometrics; Statistics; Psychology; Machine learning; Psychometrics; Mathematics; Artificial intelligence","score_opus":0.05233594052253809,"score_gpt":0.4136245242099736,"score_spread":0.3612885836874355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193380525","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":[0.0361959,0.00046516256,0.96009177,0.0014625603,0.000055114968,0.00028057734,0.00042197283,0.00014076395,0.00088623026],"genre_scores_gemma":[0.43792167,0.0011031829,0.5544958,0.00053111155,0.00018754431,0.0018363637,0.0008279105,0.00011804527,0.0029783596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9789596,0.019060802,0.00025417228,0.0007752831,0.0007345698,0.00021558408],"domain_scores_gemma":[0.94414675,0.049817506,0.0015041281,0.0030645172,0.0010710879,0.00039606754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03674323,0.0007093144,0.0014015196,0.0016164082,0.00093675306,0.0013057988,0.0024710053,0.0016263206,0.0023489224],"category_scores_gemma":[0.07849805,0.00063195796,0.0016274707,0.0020040113,0.0012888083,0.0009057326,0.0021872835,0.0025089646,0.0003544727],"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":[0.0010795388,0.0005743452,0.05706485,0.00042261768,0.0015317983,0.0016119834,0.0030668706,0.26979458,0.0011629976,0.43800002,0.008109298,0.21758111],"study_design_scores_gemma":[0.00017044821,0.00018621252,0.0101700695,0.00009276871,0.00012325973,0.0003237356,0.00030404868,0.7743108,0.00018107299,0.20815934,0.0058934772,0.00008471728],"about_ca_topic_score_codex":0.016189385,"about_ca_topic_score_gemma":0.0122537175,"teacher_disagreement_score":0.03674323,"about_ca_system_score_codex":0.0012727306,"about_ca_system_score_gemma":0.001960493,"threshold_uncertainty_score":0.19431913},"labels":[],"label_agreement":null},{"id":"W3193938275","doi":"10.1093/jssam/smab029","title":"Bootstrap Estimation of the Conditional Bias for Measuring Influence in Complex Surveys","year":2021,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Estimator; Statistics; Sampling (signal processing); Variance (accounting); Sampling design; Conditional variance; Sample (material); Econometrics; Mathematics; Estimation; Population; Computer science; Autoregressive conditional heteroskedasticity","score_opus":0.7007546710550773,"score_gpt":0.5016242371275665,"score_spread":0.19913043392751073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193938275","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":[0.017763639,0.00034683806,0.9798472,0.00015333094,0.000054448403,0.00012931612,0.00010062826,0.00017490715,0.0014296452],"genre_scores_gemma":[0.5966239,0.00055994536,0.39989507,0.00031583544,0.00020345509,0.0009868597,0.0005149719,0.00021105743,0.00068895484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94924015,0.04064842,0.0015248432,0.002259632,0.0058214166,0.0005055041],"domain_scores_gemma":[0.6821668,0.26756537,0.015250076,0.022962946,0.011129583,0.0009252589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05431816,0.00086077565,0.0015883732,0.0040486157,0.0008987564,0.0016876743,0.0023569355,0.0017582857,0.003384317],"category_scores_gemma":[0.33559042,0.00063483353,0.0017277359,0.0040812264,0.0034372313,0.0028433844,0.003249537,0.0023842654,0.00062098727],"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":[0.0005819841,0.00024137019,0.09318882,0.00086350663,0.0014370348,0.0005838199,0.0014381452,0.17717451,0.0037367167,0.4577108,0.0049423003,0.25810093],"study_design_scores_gemma":[0.00010831399,0.0003053213,0.026085285,0.00053756306,0.0002754661,0.0004495257,0.00030290624,0.6222063,0.005314244,0.33653164,0.007760551,0.00012284076],"about_ca_topic_score_codex":0.0028535163,"about_ca_topic_score_gemma":0.001819096,"teacher_disagreement_score":0.05431816,"about_ca_system_score_codex":0.0013807681,"about_ca_system_score_gemma":0.0011854781,"threshold_uncertainty_score":0.2872653},"labels":[],"label_agreement":null},{"id":"W3194587742","doi":"10.3389/fpsyg.2021.667802","title":"Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood","year":2021,"lang":"en","type":"article","venue":"Frontiers in Psychology","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; Imputation (statistics); Sample (material); Fraction (chemistry); Statistics; Econometrics; Sample size determination; Maximum likelihood; Regression analysis; Regression; Computer science; Mathematics","score_opus":0.033847661278348234,"score_gpt":0.3541757619031275,"score_spread":0.32032810062477923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194587742","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":[0.0031749122,0.00066048565,0.99363977,0.00042805757,0.000074434145,0.00022658124,0.0003107996,0.00052606355,0.0009588643],"genre_scores_gemma":[0.0472931,0.0005672217,0.9482488,0.00040032138,0.000101988095,0.0011942805,0.0010320077,0.00033218763,0.00083007157],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9830586,0.012160321,0.00077029865,0.0013371741,0.0023538799,0.00031973986],"domain_scores_gemma":[0.9077454,0.07486064,0.0030481783,0.0085791685,0.0050212084,0.0007452972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0350447,0.0017325338,0.0022793403,0.003964209,0.0013249813,0.0031433634,0.005242247,0.003356966,0.009363509],"category_scores_gemma":[0.20336221,0.0010568051,0.0036601527,0.003843234,0.0028311454,0.005776414,0.005245217,0.005828591,0.001605531],"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":[0.0012559593,0.00023574596,0.0074768523,0.0016649695,0.0014672398,0.0003077227,0.0020150098,0.07002738,0.0018118218,0.27888185,0.015686382,0.61916906],"study_design_scores_gemma":[0.0004065984,0.000380014,0.005125507,0.0008206565,0.00047833606,0.0005887673,0.0003525993,0.38583755,0.0032731884,0.5829917,0.019383224,0.00036187432],"about_ca_topic_score_codex":0.0035234406,"about_ca_topic_score_gemma":0.003993551,"teacher_disagreement_score":0.0350447,"about_ca_system_score_codex":0.001992944,"about_ca_system_score_gemma":0.002895061,"threshold_uncertainty_score":0.18533629},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W3196973804","doi":"10.1002/sim.3967","title":"Correction","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Regret; Statistics; Confidence interval; Proofreading; Estimator; Mathematics; Computer science; Medicine","score_opus":0.04686030747778479,"score_gpt":0.41881745912163143,"score_spread":0.37195715164384663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196973804","genre_codex":"editorial","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":[0.00052110094,0.0012775238,0.006334702,0.0751095,0.8921367,0.00016440573,0.011394729,0.002832376,0.0102289375],"genre_scores_gemma":[0.036192913,0.004004,0.03194685,0.09575424,0.24521688,0.0012111743,0.035989985,0.01356767,0.5361163],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9776774,0.0047504855,0.0039121807,0.0031457925,0.00906611,0.0014481475],"domain_scores_gemma":[0.77332,0.042212814,0.00828737,0.028448118,0.14119236,0.006539377],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.014211453,0.001980152,0.0020594874,0.008229463,0.004094911,0.005394864,0.005077888,0.004916943,0.2821386],"category_scores_gemma":[0.25789562,0.0010224676,0.00263372,0.004637699,0.0025290234,0.004646779,0.004841323,0.009409727,0.13699856],"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":[0.000043789976,0.000005292666,0.00014457482,0.00014707369,0.000017869925,0.00012971017,0.0000788467,0.000028460085,0.00008746364,0.0015165906,0.9869732,0.010827143],"study_design_scores_gemma":[0.00002641079,0.000013167975,0.00060170214,0.0002808738,0.000034195575,0.00037527672,0.00009968905,0.00013683017,0.00034885423,0.003056287,0.99498737,0.000039402094],"about_ca_topic_score_codex":0.005535771,"about_ca_topic_score_gemma":0.0074233646,"teacher_disagreement_score":0.7178614,"about_ca_system_score_codex":0.00398777,"about_ca_system_score_gemma":0.0075442847,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3198791580","doi":"10.82308/509","title":"Comparison of prior distributions for bayesian inference for small proportions","year":2011,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"McGill University","keywords":"Bayesian probability; Bayesian inference; Inference; Statistics; Frequentist inference; Artificial intelligence; Prior probability; Computer science; Fiducial inference; Mathematics; Econometrics","score_opus":0.16069528724835777,"score_gpt":0.38689449497329587,"score_spread":0.2261992077249381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198791580","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":[0.020259691,0.0016397798,0.9737104,0.00049055705,0.000081983824,0.00022282604,0.00026517405,0.00040117447,0.0029284472],"genre_scores_gemma":[0.3423028,0.0028940265,0.6478391,0.00042898368,0.00024557454,0.0015585495,0.0015001801,0.0005194224,0.0027113454],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9532401,0.03540312,0.0016783664,0.004029926,0.0049159545,0.0007324961],"domain_scores_gemma":[0.5139122,0.46059754,0.0047664684,0.012116964,0.007619549,0.0009872506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07662774,0.0013541882,0.0025437074,0.0047745043,0.0015254036,0.005460634,0.0035356763,0.0029364144,0.0072515435],"category_scores_gemma":[0.3408568,0.0013128351,0.003139748,0.002636453,0.0033086857,0.0068722344,0.0034645924,0.004890694,0.0010630545],"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":[0.002178039,0.00024017142,0.016587032,0.0014696508,0.0013406466,0.00035059566,0.0019041942,0.32958826,0.001876631,0.30755404,0.0041487436,0.3327619],"study_design_scores_gemma":[0.00034004133,0.00035571866,0.007657029,0.0010463832,0.00035506338,0.0004647362,0.00047626233,0.6582963,0.0022822428,0.3211748,0.0073563545,0.00019507518],"about_ca_topic_score_codex":0.0049289824,"about_ca_topic_score_gemma":0.0030698502,"teacher_disagreement_score":0.07662774,"about_ca_system_score_codex":0.0023607924,"about_ca_system_score_gemma":0.0024926874,"threshold_uncertainty_score":0.4052511},"labels":[],"label_agreement":null},{"id":"W3200455051","doi":"10.1097/pcc.0000000000002835","title":"Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry","year":2021,"lang":"en","type":"article","venue":"Pediatric Critical Care Medicine","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":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Missing data; Imputation (statistics); Medicine; Statistics; Logistic regression; Regression; Mean squared error; Data mining; Computer science; Mathematics","score_opus":0.07993944460828359,"score_gpt":0.40497496330382093,"score_spread":0.3250355186955374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200455051","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":[0.9894526,0.00018345416,0.00834104,0.0003991098,0.000015477151,0.00022272344,0.0005469288,0.00009043171,0.0007482824],"genre_scores_gemma":[0.9848475,0.00014894837,0.013347406,0.00011200575,0.000012510818,0.00037967451,0.0008911935,0.000019598834,0.00024107244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915583,0.0070509687,0.00028753866,0.000534163,0.0002846238,0.0002843606],"domain_scores_gemma":[0.89130634,0.09255455,0.004901233,0.004942956,0.0050350516,0.0012598786],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031012315,0.0010712995,0.0013243456,0.0010631315,0.0009384736,0.0011639706,0.0022066599,0.002092661,0.0013304397],"category_scores_gemma":[0.05627919,0.00083142606,0.0020378162,0.0017438766,0.000988815,0.0014958036,0.0012910381,0.0022950592,0.00017816357],"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":[0.002200646,0.0018940745,0.122648425,0.000161167,0.0007385677,0.00033780956,0.00047543357,0.85774857,0.0002845791,0.0017130306,0.0016174326,0.010180231],"study_design_scores_gemma":[0.00061719125,0.0009136592,0.012236751,0.000045860488,0.00016739417,0.00008377395,0.00022273262,0.9841564,0.00037014813,0.0008774659,0.0002692157,0.00003934885],"about_ca_topic_score_codex":0.0350054,"about_ca_topic_score_gemma":0.019674731,"teacher_disagreement_score":0.9689877,"about_ca_system_score_codex":0.0027095347,"about_ca_system_score_gemma":0.0029080757,"threshold_uncertainty_score":0.16401076},"labels":[],"label_agreement":null},{"id":"W3200775649","doi":"10.1002/sim.9562","title":"Network meta‐analysis of rare events using penalized likelihood regression","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"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":"Medical Research Council; Horizon 2020 Framework Programme; Medical Research Council Canada","keywords":"Overdispersion; Rare events; Statistics; Computer science; Econometrics; Multiplicative function; Likelihood function; Meta-analysis; Mathematics; Maximum likelihood; Count data; Poisson distribution; Medicine","score_opus":0.1664385312629363,"score_gpt":0.44900875208886887,"score_spread":0.2825702208259325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200775649","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":[0.0077766753,0.026718369,0.96102244,0.0012619931,0.00025323743,0.0007295738,0.0007375359,0.0007136111,0.0007866044],"genre_scores_gemma":[0.3744614,0.01460777,0.6004192,0.0012120908,0.00050989015,0.0048290235,0.0017029566,0.0005314335,0.0017262099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8947655,0.09701447,0.0024510024,0.003270259,0.0021741574,0.00032464144],"domain_scores_gemma":[0.87254614,0.117873445,0.003916375,0.00411222,0.0013468998,0.00020489984],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06026303,0.0021458361,0.0066769845,0.00545672,0.0006563854,0.0028985417,0.0031064984,0.0018507537,0.0037659325],"category_scores_gemma":[0.13311048,0.0010703132,0.01283436,0.004821256,0.0008121255,0.0023359125,0.0023354825,0.0035677787,0.00038849513],"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":[0.004771101,0.00020297122,0.013145803,0.027455486,0.18424912,0.0010913899,0.00041761735,0.41801208,0.0015266836,0.06222798,0.008963211,0.27793658],"study_design_scores_gemma":[0.0020544364,0.000708833,0.0040296866,0.0024879556,0.06743093,0.00047263104,0.00007692097,0.77065563,0.0015317006,0.13542554,0.014962393,0.00016338911],"about_ca_topic_score_codex":0.003328291,"about_ca_topic_score_gemma":0.0028797158,"teacher_disagreement_score":0.93973696,"about_ca_system_score_codex":0.0017744562,"about_ca_system_score_gemma":0.001997916,"threshold_uncertainty_score":0.31870514},"labels":[],"label_agreement":null},{"id":"W3201651551","doi":"10.1177/09622802241293776","title":"A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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":"Université de Montréal; McGill University","funders":"Institut de Valorisation des Données","keywords":"Bayesian probability; Hierarchical database model; Bayesian hierarchical modeling; Scaling; Econometrics; Statistics; Multilevel model; Bayesian inference; Computer science; Mathematics; Data mining","score_opus":0.2650383660648522,"score_gpt":0.5778734444830911,"score_spread":0.3128350784182389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201651551","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":[0.015306529,0.0007580071,0.97615814,0.0014664155,0.00011412878,0.00017749026,0.0018752369,0.00052955415,0.0036144743],"genre_scores_gemma":[0.5618547,0.0024149446,0.402324,0.0009396923,0.0005173459,0.0016007145,0.0040709367,0.000302446,0.025975162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970438,0.0015575383,0.00012636752,0.0007092569,0.0003162732,0.00024682196],"domain_scores_gemma":[0.9937937,0.0044572772,0.00060915475,0.00038026416,0.00053337007,0.00022620567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060536247,0.0012317243,0.002406565,0.0022790744,0.0011922778,0.0024154936,0.005651236,0.0025575624,0.008648461],"category_scores_gemma":[0.0133626405,0.0011939723,0.0024165555,0.003776635,0.0017172652,0.0030583283,0.0017454487,0.0029533105,0.0015817168],"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":[0.00020048475,0.00018256048,0.012238454,0.00034709126,0.00048134467,0.00066991826,0.0010288924,0.49853256,0.0011282129,0.39753368,0.010800417,0.07685641],"study_design_scores_gemma":[0.00008687066,0.00006800315,0.0026762516,0.000061958744,0.00015193544,0.00022721912,0.00007818603,0.81934357,0.000112412876,0.17082345,0.0063057314,0.00006442162],"about_ca_topic_score_codex":0.048942216,"about_ca_topic_score_gemma":0.050030477,"teacher_disagreement_score":0.048942216,"about_ca_system_score_codex":0.0029144748,"about_ca_system_score_gemma":0.0027248168,"threshold_uncertainty_score":0.097314656},"labels":[],"label_agreement":null},{"id":"W3204069683","doi":"10.1002/sim.9203","title":"A comparison of methods for analyzing a binary composite endpoint with partially observed components in randomized controlled trials","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"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":"Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research; NHS Blood and Transplant","keywords":"Composite number; Binary number; Randomized controlled trial; Statistics; Computer science; Mathematics; Medicine; Algorithm; Internal medicine; Arithmetic","score_opus":0.28725368993790196,"score_gpt":0.5240229339826206,"score_spread":0.2367692440447186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204069683","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":[0.0070669837,0.014576739,0.97159326,0.0015256156,0.0004922834,0.0026294717,0.00031939018,0.00034113752,0.0014550905],"genre_scores_gemma":[0.09147165,0.008932898,0.8822473,0.0010286081,0.00037152637,0.014575187,0.00038956688,0.00025986088,0.0007234378],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5145625,0.4505728,0.012065898,0.005682296,0.016486026,0.00063044985],"domain_scores_gemma":[0.25049475,0.7111535,0.014208901,0.016432429,0.006830898,0.00087954564],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.32434765,0.0026294908,0.005622129,0.005677556,0.00085878646,0.0038311963,0.0043518962,0.0047714645,0.0052605],"category_scores_gemma":[0.5592189,0.0016309081,0.00943195,0.0052847643,0.0035805968,0.0057060397,0.003347064,0.0062540723,0.0007417557],"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":[0.016499847,0.00081234693,0.0072904713,0.019096486,0.024636189,0.00016484488,0.0014106084,0.10071779,0.0009876041,0.349067,0.007279706,0.4720371],"study_design_scores_gemma":[0.012638826,0.007099178,0.007179766,0.00810032,0.0077618337,0.000521887,0.00025243158,0.47592255,0.0017533457,0.46243227,0.015783671,0.0005539509],"about_ca_topic_score_codex":0.0018604349,"about_ca_topic_score_gemma":0.0017094112,"teacher_disagreement_score":0.32434765,"about_ca_system_score_codex":0.004944125,"about_ca_system_score_gemma":0.006662661,"threshold_uncertainty_score":0.8331996},"labels":[],"label_agreement":null},{"id":"W3204702238","doi":"10.1007/s10985-021-09536-2","title":"Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction","year":2021,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","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":"Queen's University","funders":"Ministry of Science and Technology, Taiwan","keywords":"Statistics; Mathematics; Covariate; Censoring (clinical trials); Likelihood function; Nonparametric statistics; Proportional hazards model; Population; Survival function; Sample size determination; Expectation–maximization algorithm; Econometrics; Poisson distribution; Confidence interval; Survival analysis; Maximum likelihood; Demography","score_opus":0.08077903343702811,"score_gpt":0.3893215050757799,"score_spread":0.3085424716387518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204702238","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":[0.010216422,0.0006326782,0.9880015,0.00039817442,0.000024502277,0.00003710217,0.0002542131,0.00013486628,0.0003004816],"genre_scores_gemma":[0.3854985,0.0028461514,0.5982156,0.0005531868,0.00054788485,0.0008453679,0.0031581183,0.0004069716,0.00792825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99119437,0.0059079053,0.00045581974,0.0012938222,0.0008234025,0.00032450794],"domain_scores_gemma":[0.81061524,0.17164123,0.0067815264,0.007886177,0.0023761415,0.0006997412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03539755,0.0012964185,0.0027874385,0.0026681991,0.00094484544,0.0028199472,0.005694378,0.0028472077,0.0034209122],"category_scores_gemma":[0.16471106,0.0014090436,0.0019858673,0.00315226,0.0031413946,0.005814246,0.0024674947,0.0040500597,0.00069911627],"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":[0.00041784614,0.00018436948,0.012101223,0.0008676989,0.0006324318,0.0007638773,0.0006963903,0.35979122,0.0017652,0.5147214,0.004531412,0.103526995],"study_design_scores_gemma":[0.000034927903,0.000045493398,0.0017953909,0.000112042384,0.00007284948,0.0001968867,0.000057400666,0.68176955,0.00061285566,0.31364745,0.001607949,0.000047152826],"about_ca_topic_score_codex":0.0036727532,"about_ca_topic_score_gemma":0.002883253,"teacher_disagreement_score":0.03539755,"about_ca_system_score_codex":0.0019314095,"about_ca_system_score_gemma":0.0019913209,"threshold_uncertainty_score":0.18720233},"labels":[],"label_agreement":null},{"id":"W3205328601","doi":"10.1007/s40840-021-01189-6","title":"A State-Space Model for Bivariate Time-Series Counts with Excessive Zeros: An Application to Workplace Injury Data","year":2021,"lang":"en","type":"article","venue":"Bulletin of the Malaysian Mathematical Sciences Society","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Mount Saint Vincent University; University of New Brunswick","funders":"","keywords":"Bivariate analysis; Mathematics; Statistics; Poisson distribution; Random effects model; Series (stratigraphy); Correlation; Poisson regression; State space; Time series; Econometrics; Medicine; Meta-analysis","score_opus":0.0535391243787652,"score_gpt":0.3516300444459207,"score_spread":0.29809092006715554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205328601","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":[0.026060812,0.00022440843,0.9718023,0.0005866443,0.0000519489,0.000058849593,0.00028536332,0.00033033348,0.0005994163],"genre_scores_gemma":[0.74538445,0.0011194262,0.23998262,0.00030826393,0.00026367034,0.00070478383,0.0015626547,0.00023463629,0.010439484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997505,0.00131241,0.00014920579,0.0005573359,0.00027797796,0.00019798572],"domain_scores_gemma":[0.9592295,0.035977244,0.0015469267,0.0011317309,0.0016652081,0.00044948873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011398838,0.0010653244,0.002921428,0.0015272516,0.0012730925,0.0035030358,0.0033441382,0.0034042927,0.005251294],"category_scores_gemma":[0.03797627,0.001336063,0.0023257989,0.0026213052,0.0023708302,0.00411378,0.0028049375,0.004676626,0.0010021806],"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":[0.0003240151,0.00025704186,0.0063161296,0.00022130791,0.0002529345,0.00045574384,0.00070843723,0.7377899,0.0010804017,0.21504982,0.0024268606,0.035117414],"study_design_scores_gemma":[0.000016850538,0.000023304938,0.00041344424,0.000013538097,0.000029301666,0.00003765413,0.000028153081,0.97979456,0.000073338844,0.019279217,0.00026605648,0.00002455047],"about_ca_topic_score_codex":0.01578822,"about_ca_topic_score_gemma":0.016335998,"teacher_disagreement_score":0.01578822,"about_ca_system_score_codex":0.0017029162,"about_ca_system_score_gemma":0.0025171954,"threshold_uncertainty_score":0.06028354},"labels":[],"label_agreement":null},{"id":"W3207678906","doi":"10.5539/ijsp.v10n6p5","title":"Integration of Nonprobability and Probability Samples via Survey Weights","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sample (material); Statistics; Population; Sample size determination; Mathematics; Survey sampling; Variance (accounting); Sampling (signal processing); Nonprobability sampling; Covariate; Econometrics; Computer science; Demography; Chemistry","score_opus":0.08649304405829086,"score_gpt":0.3700747258689662,"score_spread":0.28358168181067533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207678906","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":[0.004400183,0.000093213326,0.99331325,0.00017954802,0.00007139461,0.0002252563,0.0000426708,0.00017526421,0.0014992101],"genre_scores_gemma":[0.14003395,0.0004404753,0.85105425,0.00027897963,0.00021227013,0.00145536,0.00032831356,0.00023760932,0.0059586787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9658604,0.022476267,0.0011187796,0.003090579,0.00689951,0.00055440544],"domain_scores_gemma":[0.9549603,0.029355971,0.0028322153,0.008080985,0.0042517073,0.0005187769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023920745,0.0011635593,0.0015143617,0.0027962215,0.00081653806,0.0026182288,0.003149699,0.001145084,0.0075195828],"category_scores_gemma":[0.12793575,0.0011060701,0.001236955,0.0031858315,0.0017810134,0.006313341,0.0038383845,0.0029761528,0.0009679524],"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":[0.00015799841,0.00029588336,0.008088069,0.00032634946,0.00020744647,0.00019288067,0.00072339596,0.03473901,0.0013196397,0.6251764,0.0025002414,0.32627258],"study_design_scores_gemma":[0.00007116224,0.00027532445,0.004986043,0.0001248191,0.00010817965,0.00016582731,0.00017901654,0.5509933,0.0026859655,0.4238054,0.01653855,0.00006628732],"about_ca_topic_score_codex":0.0034981137,"about_ca_topic_score_gemma":0.0031172896,"teacher_disagreement_score":0.023920745,"about_ca_system_score_codex":0.0015785557,"about_ca_system_score_gemma":0.0018088948,"threshold_uncertainty_score":0.1265065},"labels":[],"label_agreement":null},{"id":"W3208391743","doi":"10.5281/zenodo.3267531","title":"CamDavidsonPilon/lifelines: v0.22.0","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","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":"Bell (Canada)","funders":"","keywords":"Computer science","score_opus":0.07594689455677812,"score_gpt":0.3332292661522704,"score_spread":0.2572823715954923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208391743","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073499663,0.0005284247,0.09028673,0.00076484354,0.00069159956,0.00025662797,0.085548244,0.7885864,0.032602184],"genre_scores_gemma":[0.010319595,0.00096431724,0.05724516,0.0027665712,0.00048938714,0.0016384686,0.1183669,0.73301715,0.07519251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997903,0.00040252393,0.0002073708,0.0004905434,0.0007095338,0.00028703953],"domain_scores_gemma":[0.9897063,0.0044267504,0.0006441974,0.0020938206,0.0026444504,0.00048446792],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005158221,0.0038191192,0.0019244812,0.0024684737,0.0009043309,0.0047534015,0.0066823857,0.0034268734,0.52071065],"category_scores_gemma":[0.025613463,0.0042271833,0.0038721194,0.0018356038,0.00090353505,0.004490277,0.0041123577,0.0044382843,0.46389547],"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":[0.00019864469,0.000040936822,0.0010507858,0.0008190383,0.000060146547,0.00012327108,0.00014579564,0.0017970388,0.0007020571,0.004685237,0.9557775,0.034599576],"study_design_scores_gemma":[0.00024099319,0.000057405283,0.0011839129,0.00044938023,0.000051731142,0.00025186667,0.000032561922,0.008253572,0.003464867,0.014248122,0.9716275,0.00013819213],"about_ca_topic_score_codex":0.008671689,"about_ca_topic_score_gemma":0.0070456546,"teacher_disagreement_score":0.52071065,"about_ca_system_score_codex":0.0022865562,"about_ca_system_score_gemma":0.0033721623,"threshold_uncertainty_score":0.6836481},"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":"W3209217553","doi":"10.48550/arxiv.2111.02863","title":"Nonparametric Simulation Extrapolation for Measurement Error Models","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian 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":"","keywords":"Extrapolation; Nonparametric statistics; Replicate; Observational error; Computer science; Normality; Errors-in-variables models; Algorithm; Statistics; Mathematics; Machine learning","score_opus":0.46783536824969535,"score_gpt":0.31937653927664467,"score_spread":0.14845882897305068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209217553","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":[0.0021096165,0.00022491174,0.99604225,0.000208386,0.000036105157,0.00005503773,0.0000599555,0.00016377633,0.0010999608],"genre_scores_gemma":[0.26971233,0.0016251545,0.7208339,0.0006062326,0.00034853007,0.0015452342,0.0007506118,0.0003650817,0.004212869],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9781991,0.01813415,0.0004812623,0.0010288974,0.0018953001,0.00026135956],"domain_scores_gemma":[0.89603364,0.08725179,0.00398714,0.008836591,0.0033284,0.0005625089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030295942,0.0013259813,0.002064344,0.0022841003,0.00091335,0.0016192055,0.0028488745,0.0023165639,0.0047215973],"category_scores_gemma":[0.1254474,0.00085525005,0.0020083857,0.0021174406,0.003439591,0.0031073727,0.0046076253,0.0043649925,0.0011305285],"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":[0.00024166422,0.00008100449,0.0035452931,0.00046713217,0.00022171358,0.00048386463,0.00043096882,0.28332418,0.0010045974,0.6339444,0.0031542503,0.0731009],"study_design_scores_gemma":[0.000030097108,0.00006095795,0.00047291926,0.0001331505,0.000024841402,0.00014749581,0.000031818665,0.58233434,0.0004498491,0.41240832,0.0038795783,0.000026666898],"about_ca_topic_score_codex":0.0019378997,"about_ca_topic_score_gemma":0.001333104,"teacher_disagreement_score":0.030295942,"about_ca_system_score_codex":0.0015009207,"about_ca_system_score_gemma":0.0021634654,"threshold_uncertainty_score":0.16022217},"labels":[],"label_agreement":null},{"id":"W3209485542","doi":"10.1080/00031305.2021.2000495","title":"Comparative Probability Metrics: Using Posterior Probabilities to Account for Practical Equivalence in A/B tests","year":2021,"lang":"en","type":"article","venue":"The American Statistician","topic":"Statistical Methods and Bayesian 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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Equivalence (formal languages); Posterior probability; Bayesian probability; Discrete mathematics","score_opus":0.2844714646661516,"score_gpt":0.5027176178365009,"score_spread":0.21824615317034934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209485542","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":[0.0044636815,0.00069260236,0.9901799,0.0006672005,0.00014442549,0.00023580018,0.00016913287,0.00020150485,0.0032456652],"genre_scores_gemma":[0.19533046,0.00076746725,0.7988912,0.00080197223,0.0005011876,0.002010027,0.00040149337,0.0003474957,0.00094878173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.852631,0.106941275,0.007131678,0.01111703,0.02101407,0.0011649998],"domain_scores_gemma":[0.4380431,0.4938458,0.023756046,0.027471272,0.014866778,0.002016984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12328608,0.0030094706,0.0028884164,0.011318844,0.0022449968,0.007562865,0.0047956407,0.0053503183,0.006259874],"category_scores_gemma":[0.5218622,0.0013253714,0.0024824217,0.008108482,0.012492264,0.016456077,0.007323822,0.008775358,0.0009365876],"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":[0.00030942785,0.00014422348,0.013400739,0.00069305685,0.00055951125,0.00036219796,0.0016618869,0.053887855,0.0013608985,0.7331965,0.0051025203,0.1893212],"study_design_scores_gemma":[0.00008235128,0.00035682667,0.0045793033,0.00037828335,0.0001522109,0.00044228803,0.00027595682,0.11707869,0.0014418222,0.8645194,0.010557282,0.00013563984],"about_ca_topic_score_codex":0.0026145084,"about_ca_topic_score_gemma":0.0017344177,"teacher_disagreement_score":0.12328608,"about_ca_system_score_codex":0.003477973,"about_ca_system_score_gemma":0.004043447,"threshold_uncertainty_score":0.6520069},"labels":[],"label_agreement":null},{"id":"W3210093870","doi":"10.1214/23-sts888","title":"Living on the Edge: An Unified Approach to Antithetic Sampling","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agence Nationale de la Recherche; European Commission","keywords":"Mathematics; Estimator; Markov chain Monte Carlo; Sampling (signal processing); Monte Carlo method; Applied mathematics; Markov chain; Mathematical optimization; Computer science; Statistics","score_opus":0.17420648527956095,"score_gpt":0.4392437903483808,"score_spread":0.26503730506881984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210093870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006529419,0.00020123066,0.98910797,0.00030549202,0.000045878358,0.00003139727,0.000045794164,0.00004883395,0.0036839521],"genre_scores_gemma":[0.41880623,0.0009414339,0.56923234,0.0008289479,0.00037049747,0.00041023258,0.00025622867,0.00023532,0.008918751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9925311,0.0039911647,0.00030920946,0.0011804331,0.0016271407,0.0003609647],"domain_scores_gemma":[0.9788801,0.012423403,0.0016500934,0.0044314545,0.0020093347,0.000605643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012472045,0.0007010231,0.0014880536,0.0022431605,0.0018082217,0.0036169926,0.0038384174,0.0025627925,0.0059186895],"category_scores_gemma":[0.04242055,0.0007622027,0.001415289,0.002709607,0.0047614053,0.0058687152,0.004394708,0.0037737852,0.00091403304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001883448,0.0000129353075,0.0004611962,0.000026513344,0.000012156998,0.000051936062,0.00015787252,0.010947645,0.00022406389,0.9770628,0.00038702326,0.010636926],"study_design_scores_gemma":[0.000014288716,0.00005105964,0.00030951318,0.000038979466,0.000016534987,0.000101312755,0.000041501695,0.16609238,0.00034259478,0.8289104,0.004055518,0.000025936568],"about_ca_topic_score_codex":0.001866729,"about_ca_topic_score_gemma":0.001750991,"teacher_disagreement_score":0.012472045,"about_ca_system_score_codex":0.0016449686,"about_ca_system_score_gemma":0.001400733,"threshold_uncertainty_score":0.065959275},"labels":[],"label_agreement":null},{"id":"W3210129212","doi":"10.1002/sim.9246","title":"Weighted generalized estimating equations and unified estimation for longitudinal data with nonmonotone missing data patterns","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Missing data; Estimator; Covariate; Generalized estimating equation; Estimating equations; Computer science; Statistics; Mathematics","score_opus":0.22131465097394634,"score_gpt":0.4628701982297937,"score_spread":0.24155554725584735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210129212","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":[0.0008741748,0.0006995396,0.99780566,0.00019508155,0.000048873146,0.00005374329,0.0001092495,0.00007170233,0.0001419652],"genre_scores_gemma":[0.046663456,0.0036927017,0.94459677,0.0004220287,0.00031620762,0.0015490813,0.0011601098,0.00011169868,0.0014879899],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9514809,0.040320836,0.0019639065,0.0030816938,0.002554948,0.00059764896],"domain_scores_gemma":[0.95122015,0.03682136,0.004589047,0.0045862645,0.0024878245,0.0002952893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044447694,0.0024575666,0.003971436,0.0041849297,0.00082566927,0.0021575256,0.0057034083,0.0027758584,0.0033966787],"category_scores_gemma":[0.102370806,0.0023055223,0.0059348573,0.006721614,0.001984996,0.0048003537,0.0040424294,0.005153259,0.0010920417],"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":[0.00010011088,0.000098207325,0.0072408784,0.00096977275,0.0024630704,0.0004417055,0.00058656425,0.11823199,0.00054687023,0.6715618,0.005769731,0.1919893],"study_design_scores_gemma":[0.000086982946,0.00015243796,0.0020781416,0.00029773198,0.00042470996,0.00024534424,0.00012434744,0.38496497,0.0004695035,0.6003488,0.010687654,0.00011935749],"about_ca_topic_score_codex":0.0062127663,"about_ca_topic_score_gemma":0.006768411,"teacher_disagreement_score":0.044447694,"about_ca_system_score_codex":0.0016593483,"about_ca_system_score_gemma":0.003422883,"threshold_uncertainty_score":0.23506469},"labels":[],"label_agreement":null},{"id":"W3213506774","doi":"10.1111/rssa.12696","title":"Combining Non-Probability and Probability Survey Samples Through Mass Imputation","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":57,"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":"Statistics; Probability mass function; Imputation (statistics); Estimator; Mathematics; Probability sampling; Probability distribution; Conditional probability; Population; Survey sampling; Sample (material); Econometrics; Missing data; Demography","score_opus":0.06967204401665635,"score_gpt":0.34869692127216884,"score_spread":0.2790248772555125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213506774","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":[0.04535054,0.00030827045,0.9514921,0.00043711855,0.00008355651,0.00044997488,0.00029536724,0.00016608926,0.0014170342],"genre_scores_gemma":[0.5796529,0.00037355948,0.41495594,0.00036498898,0.00020667969,0.001178136,0.001147983,0.00006430248,0.00205562],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9581997,0.033902504,0.0011005814,0.002724573,0.0034679798,0.00060459494],"domain_scores_gemma":[0.8832322,0.08914158,0.0069546844,0.015125609,0.0049717207,0.00057417125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056348093,0.001005142,0.0026540428,0.0028188187,0.001002278,0.0031821476,0.003492424,0.001821933,0.0029622668],"category_scores_gemma":[0.13952711,0.0009468141,0.0017593607,0.00488465,0.001605352,0.002913206,0.0041491883,0.0023230717,0.0007078956],"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":[0.0012649008,0.00083705195,0.10541054,0.0008121034,0.0025682352,0.0015487953,0.0026428988,0.23768015,0.0013995756,0.27444234,0.0067788674,0.36461455],"study_design_scores_gemma":[0.00019210698,0.0005172647,0.01828525,0.00024030214,0.0005025086,0.00030785595,0.00043532185,0.6481192,0.0016927824,0.32276624,0.006863767,0.00007741384],"about_ca_topic_score_codex":0.003223506,"about_ca_topic_score_gemma":0.0026457226,"teacher_disagreement_score":0.056348093,"about_ca_system_score_codex":0.001118801,"about_ca_system_score_gemma":0.0012511535,"threshold_uncertainty_score":0.2980008},"labels":[],"label_agreement":null},{"id":"W3214942615","doi":"","title":"An Alternative Perspective on the Robust Poisson Model for Estimating Risk or Prevalence Ratios.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian 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":"Université Laval","funders":"","keywords":"Poisson distribution; Poisson regression; Econometrics; Outcome (game theory); Negative binomial distribution; Quasi-likelihood; Binomial distribution; Counterintuitive; Logistic regression; Poisson binomial distribution; Statistics; Mathematics; Generalized linear model; Perspective (graphical); Zero-inflated model; Binomial regression; Beta-binomial distribution; Mathematical economics; Population; Medicine","score_opus":0.26889604864840183,"score_gpt":0.3248959454407377,"score_spread":0.05599989679233586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214942615","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":[0.00073311455,0.002341822,0.98382324,0.00661513,0.0006089046,0.000042044678,0.0003559106,0.00013004658,0.005349795],"genre_scores_gemma":[0.10976727,0.007337909,0.8531118,0.0105245365,0.004376413,0.0007365986,0.0009872128,0.0004944931,0.012663769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9742009,0.016542612,0.0009689031,0.0030625176,0.004776223,0.00044887848],"domain_scores_gemma":[0.9667293,0.022983076,0.0029056412,0.0043883123,0.0024213875,0.00057235546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024386028,0.0016632606,0.0019695163,0.004654054,0.00095742533,0.0050021396,0.005577301,0.004824504,0.010926789],"category_scores_gemma":[0.07113801,0.0011127547,0.0043348367,0.004274846,0.004006929,0.0058986833,0.0035839467,0.007707498,0.0029667742],"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":[0.000029102424,0.00003714526,0.00061595096,0.00021367002,0.00013070107,0.00011853055,0.0002236016,0.0038706327,0.00031211702,0.96472913,0.005646101,0.024073325],"study_design_scores_gemma":[0.00002760285,0.00008686712,0.00041839498,0.00016887364,0.00007638185,0.0005137811,0.00008646173,0.027026534,0.0003525205,0.9351258,0.036067087,0.000049696366],"about_ca_topic_score_codex":0.0027808219,"about_ca_topic_score_gemma":0.0018402052,"teacher_disagreement_score":0.024386028,"about_ca_system_score_codex":0.0021498133,"about_ca_system_score_gemma":0.0030864635,"threshold_uncertainty_score":0.12896723},"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":"W372913089","doi":"10.22237/jmasm/1304223000","title":"Weighting Large Datasets with Complex Sampling Designs: Choosing the Appropriate Variance Estimation Method","year":2011,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Bootstrapping (finance); Weighting; Statistics; Inference; Variance (accounting); Econometrics; Logit; Cluster sampling; Sampling design; Mathematics; Sampling (signal processing); Standard error; Statistical inference; Computer science; Data mining; Artificial intelligence","score_opus":0.23891974736748553,"score_gpt":0.4584332681939149,"score_spread":0.21951352082642936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W372913089","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":[0.003798069,0.00035796303,0.9942562,0.00035591808,0.00007997479,0.00060001784,0.00005366274,0.00009703834,0.00040114878],"genre_scores_gemma":[0.050348785,0.0005165604,0.94565034,0.00022907651,0.00008295992,0.0026741766,0.00015030256,0.000078580015,0.00026928636],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7796429,0.19198062,0.0070014475,0.0052579837,0.01494081,0.001176395],"domain_scores_gemma":[0.6441607,0.30664304,0.010898538,0.022521088,0.014923844,0.0008528746],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19125217,0.0016614802,0.0034024476,0.0042036846,0.001498797,0.0041866507,0.0038480689,0.0036083846,0.0017567012],"category_scores_gemma":[0.44477454,0.0014023628,0.0024004492,0.005550652,0.0029252253,0.004075987,0.004779428,0.0037059712,0.00049639994],"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":[0.00073096756,0.0003498974,0.023752814,0.0026005118,0.002172986,0.00042916753,0.0029361357,0.08690598,0.0029141838,0.33957714,0.010363683,0.52726656],"study_design_scores_gemma":[0.0006433139,0.0006805125,0.008504173,0.0015706163,0.00063294003,0.00048472575,0.001000509,0.33767265,0.004346046,0.6205378,0.02359025,0.00033648175],"about_ca_topic_score_codex":0.0030866782,"about_ca_topic_score_gemma":0.0030586438,"teacher_disagreement_score":0.8087478,"about_ca_system_score_codex":0.002431786,"about_ca_system_score_gemma":0.0040352577,"threshold_uncertainty_score":0.99733007},"labels":[],"label_agreement":null},{"id":"W4200043579","doi":"10.1177/09622802211032705","title":"Repeated measures discriminant analysis using multivariate generalized estimation equations","year":2021,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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 Manitoba; University of Calgary","funders":"","keywords":"Multivariate statistics; Generalized estimating equation; Statistics; Mathematics; Estimator; Multivariate analysis; Linear discriminant analysis; Covariance; Repeated measures design; Multivariate normal distribution","score_opus":0.47383637507703746,"score_gpt":0.6449874666862396,"score_spread":0.17115109160920217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200043579","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":[0.015102512,0.0002939084,0.9830421,0.00010884043,0.000061467224,0.0003222877,0.0002402478,0.00049899565,0.0003296852],"genre_scores_gemma":[0.16968967,0.00045420654,0.82627606,0.00008985938,0.000076367316,0.0017018968,0.00047948223,0.0001384046,0.0010941018],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98374593,0.011622574,0.0006661656,0.0021091201,0.0015578484,0.00029830923],"domain_scores_gemma":[0.97618335,0.01735143,0.0020579782,0.002342222,0.0019294062,0.00013563162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02093452,0.0020123003,0.0026188989,0.002697501,0.0005581851,0.0014185247,0.0019874715,0.00097131374,0.003805096],"category_scores_gemma":[0.058883246,0.0005488686,0.003939346,0.002889673,0.0010167747,0.0012643713,0.0014550831,0.0026225285,0.00092899095],"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":[0.0011992374,0.00071850745,0.04034477,0.0013528863,0.004643423,0.00040551787,0.0015027439,0.1457853,0.009925094,0.086033456,0.005734639,0.70235443],"study_design_scores_gemma":[0.00038262812,0.0016038975,0.028296344,0.00020042708,0.00080143235,0.0003553319,0.00027219893,0.8610715,0.0061821993,0.09049814,0.010057555,0.00027835992],"about_ca_topic_score_codex":0.0034045712,"about_ca_topic_score_gemma":0.002906793,"teacher_disagreement_score":0.02093452,"about_ca_system_score_codex":0.00096712826,"about_ca_system_score_gemma":0.0020170491,"threshold_uncertainty_score":0.11071366},"labels":[],"label_agreement":null},{"id":"W4200358049","doi":"10.5539/ijsp.v11n1p68","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 1","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Probability and statistics; Mathematics; Library science; Computer science","score_opus":0.06271183517881176,"score_gpt":0.3894542073640744,"score_spread":0.3267423721852626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200358049","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011405524,0.002842595,0.001457007,0.13504077,0.85682166,0.0005193927,0.0007952121,0.0004930884,0.0019161778],"genre_scores_gemma":[0.004260266,0.0069684195,0.0042321905,0.16241306,0.7745913,0.0033476606,0.0018163999,0.0016444403,0.040726207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9205261,0.016390309,0.016733076,0.0057355077,0.038122334,0.0024926374],"domain_scores_gemma":[0.12508081,0.045048624,0.012131997,0.0067791766,0.8024258,0.0085335905],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06201458,0.0032965466,0.009947462,0.013226643,0.00523192,0.011411252,0.0060478286,0.017524032,0.09314664],"category_scores_gemma":[0.57017326,0.0019027195,0.0060996343,0.0059195003,0.003971915,0.007396062,0.004431623,0.014274867,0.06405184],"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":[0.000034751476,0.000004173887,0.0000754309,0.00043806885,0.000011371486,0.000048719827,0.00003559245,0.000011204459,0.000032210897,0.00012543894,0.9958419,0.0033410995],"study_design_scores_gemma":[0.00030185308,0.000070335074,0.0014121102,0.0049862317,0.00015573521,0.0012682554,0.00044711167,0.0005277219,0.0003457569,0.0028968377,0.98736584,0.00022214795],"about_ca_topic_score_codex":0.0035797276,"about_ca_topic_score_gemma":0.005517523,"teacher_disagreement_score":0.9379854,"about_ca_system_score_codex":0.00569904,"about_ca_system_score_gemma":0.012121368,"threshold_uncertainty_score":0.32796836},"labels":[],"label_agreement":null},{"id":"W4206016270","doi":"10.1007/s10985-021-09543-3","title":"Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes","year":2022,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Actua; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs; Natural Science Foundation of Shanghai","keywords":"Computer science; Accelerated failure time model; Markov chain Monte Carlo; Bayesian inference; Bayesian probability; Event (particle physics); Inference; Markov chain; Observational error; Statistics; Data mining; Artificial intelligence; Machine learning; Mathematics; Covariate","score_opus":0.07777117424996129,"score_gpt":0.3640172824412617,"score_spread":0.2862461081913004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206016270","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":[0.03215677,0.0005883455,0.96482044,0.0009081965,0.000072506344,0.000106510466,0.0004268367,0.00017964806,0.00074072933],"genre_scores_gemma":[0.77589726,0.002836976,0.20392369,0.0006453292,0.0007872139,0.001110697,0.0027531455,0.00032045812,0.011725232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9820802,0.010385557,0.0008665657,0.0032200995,0.002210322,0.0012372165],"domain_scores_gemma":[0.74084073,0.22503197,0.014278996,0.010852216,0.0072555738,0.0017404277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06298692,0.002399246,0.0043984586,0.0033080522,0.0010828081,0.0036413458,0.0070349406,0.0033335069,0.0051196227],"category_scores_gemma":[0.1767888,0.0019992343,0.0032123225,0.002871198,0.004381182,0.007178363,0.0039630644,0.005834241,0.0006286309],"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":[0.00043932346,0.00020607423,0.0112058045,0.00043210792,0.0006792834,0.0005775632,0.00059659476,0.47825092,0.0007548993,0.4709313,0.0030515934,0.03287453],"study_design_scores_gemma":[0.000057133777,0.000073148294,0.0017602631,0.00006102671,0.0001330029,0.00013754574,0.00005774808,0.7603102,0.00032969308,0.23625602,0.00077296066,0.000051259984],"about_ca_topic_score_codex":0.009306499,"about_ca_topic_score_gemma":0.0054400414,"teacher_disagreement_score":0.06298692,"about_ca_system_score_codex":0.0024346947,"about_ca_system_score_gemma":0.004524661,"threshold_uncertainty_score":0.3331107},"labels":[],"label_agreement":null},{"id":"W4210320674","doi":"10.1002/cjs.11688","title":"Statistical data integration using multilevel models to predict employee compensation","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Survey data collection; Variable (mathematics); Econometrics; Estimation; Multilevel model; Variables; Statistics; Computer science; Wage; Hierarchical database model; Data mining; Mathematics; Engineering; Economics","score_opus":0.2992427249109633,"score_gpt":0.3907442439379452,"score_spread":0.09150151902698189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210320674","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":[0.07706849,0.00046013974,0.9190648,0.0011853619,0.000046587924,0.00016869174,0.0005811498,0.00041500796,0.0010097749],"genre_scores_gemma":[0.68974507,0.0002598794,0.3068611,0.00017211578,0.000076870914,0.00042671335,0.0013918655,0.0000898395,0.0009765131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9777328,0.017685618,0.0006432354,0.0018167052,0.001682042,0.00043967683],"domain_scores_gemma":[0.93151814,0.056182466,0.0041831774,0.004167888,0.003279977,0.00066826545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028006233,0.00062584155,0.0018728925,0.0032851729,0.0009106407,0.0031149194,0.002384583,0.001546298,0.0022967686],"category_scores_gemma":[0.080972955,0.0010294238,0.0026238032,0.0042334544,0.0011326002,0.002576223,0.0039018367,0.0031656255,0.0003562582],"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":[0.00031546684,0.0003169478,0.09651056,0.0001742681,0.0012992672,0.00024198157,0.0009838194,0.67757744,0.0005613953,0.11167401,0.002572051,0.107772805],"study_design_scores_gemma":[0.00001447705,0.00003849235,0.0038607933,0.000025651338,0.00004757783,0.000013727762,0.000058729092,0.96500576,0.0001116494,0.03018355,0.0006252479,0.000014259906],"about_ca_topic_score_codex":0.024677143,"about_ca_topic_score_gemma":0.026341556,"teacher_disagreement_score":0.028006233,"about_ca_system_score_codex":0.0021702854,"about_ca_system_score_gemma":0.0020855404,"threshold_uncertainty_score":0.14811295},"labels":[],"label_agreement":null},{"id":"W4210672984","doi":"10.4236/ojs.2022.121001","title":"Quasi-Binomial Regression Model for the Analysis of Data with Extra-Binomial Variation","year":2022,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Binomial regression; Negative binomial distribution; Mathematics; Statistics; Count data; Quasi-likelihood; Binomial distribution; Beta-binomial distribution; Continuity correction; Binomial test; Binary data; Binomial proportion confidence interval; Regression analysis; Econometrics; Binary number; Poisson distribution","score_opus":0.20479152681040733,"score_gpt":0.43820705365536794,"score_spread":0.2334155268449606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210672984","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":[0.00076525274,0.00022630084,0.99769104,0.00017355771,0.00006637534,0.00017039731,0.00015341323,0.00012470684,0.0006290422],"genre_scores_gemma":[0.042966176,0.0010030512,0.9455495,0.0004724195,0.00023284846,0.004184166,0.00076176284,0.00024014547,0.00458991],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95116335,0.04018616,0.001087678,0.0028109655,0.00424226,0.00050955766],"domain_scores_gemma":[0.8903984,0.095430434,0.0048429095,0.0045913965,0.0042255553,0.0005112544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051302616,0.0013641553,0.0023442707,0.0018815177,0.000894253,0.002186863,0.00519579,0.0019230308,0.019295787],"category_scores_gemma":[0.09831228,0.00095509517,0.0026299912,0.0031413573,0.002742655,0.0034571455,0.0022629092,0.004638855,0.0041989847],"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":[0.00022246769,0.00012384259,0.0037344515,0.0011371346,0.00040478655,0.00046208018,0.00092119625,0.059469916,0.0014301942,0.83258575,0.010038936,0.08946914],"study_design_scores_gemma":[0.00010149116,0.00028409308,0.0020859628,0.00036233044,0.000112777576,0.00045788445,0.00014439433,0.5216029,0.00048591496,0.45041487,0.023853377,0.00009394726],"about_ca_topic_score_codex":0.0035948493,"about_ca_topic_score_gemma":0.0031007992,"teacher_disagreement_score":0.051302616,"about_ca_system_score_codex":0.0021037732,"about_ca_system_score_gemma":0.0036006216,"threshold_uncertainty_score":0.27131736},"labels":[],"label_agreement":null},{"id":"W4211119714","doi":"10.1186/s12874-016-0256-6","title":"Erratum to: A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design","year":2016,"lang":"en","type":"erratum","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Logistic regression; Statistics; Regression analysis; Linear regression; BETA (programming language); Mathematics; Monte Carlo method; Econometrics; Regression diagnostic; Bayesian multivariate linear regression; Computer science","score_opus":0.5399558078900126,"score_gpt":0.5516702108883331,"score_spread":0.011714402998320494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211119714","genre_codex":"editorial","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":[0.008834689,0.0045610904,0.048049394,0.14098431,0.76148826,0.0006618129,0.013310519,0.0019259237,0.020184044],"genre_scores_gemma":[0.15098254,0.010512606,0.23231335,0.1443064,0.07347047,0.0038735785,0.017293375,0.0072564655,0.35999125],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928255,0.002873012,0.0013038667,0.0005870454,0.0022422464,0.00016829617],"domain_scores_gemma":[0.8719243,0.09477785,0.0036008814,0.003746935,0.02483998,0.0011102252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01132793,0.0013071956,0.0015926878,0.0020996819,0.002135537,0.0020380197,0.0020251165,0.003727394,0.051895767],"category_scores_gemma":[0.23681268,0.0009162072,0.0015852262,0.0025589557,0.0015591995,0.0016567111,0.0009732929,0.0041627917,0.011696939],"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":[0.0005466961,0.00010549099,0.0010938781,0.00056382257,0.00008254994,0.00043827645,0.00014844925,0.0021052363,0.000121543235,0.008386124,0.9579114,0.028496528],"study_design_scores_gemma":[0.0017699507,0.0008310288,0.008188022,0.0036033923,0.0009590604,0.0024151162,0.0008035104,0.019361435,0.0023842026,0.039176393,0.9201113,0.00039653067],"about_ca_topic_score_codex":0.01458939,"about_ca_topic_score_gemma":0.022683414,"teacher_disagreement_score":0.051895767,"about_ca_system_score_codex":0.0031401375,"about_ca_system_score_gemma":0.0051982943,"threshold_uncertainty_score":0.17360866},"labels":[],"label_agreement":null},{"id":"W4214478519","doi":"10.1002/0470011815.b2a15034","title":"Estimating Functions","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","topic":"Statistical Methods and Bayesian 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; University of Guelph","funders":"","keywords":"Overdispersion; Inference; Unification; Biostatistics; Econometrics; Computer science; Statistical inference; Parametric statistics; Statistics; Sampling (signal processing); Mathematics; Artificial intelligence; Count data; Programming language; Poisson distribution; Medicine","score_opus":0.02684269489857899,"score_gpt":0.34248628781356666,"score_spread":0.31564359291498767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214478519","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":[0.0028538422,0.0010798416,0.9856457,0.0009936693,0.000106448344,0.00020429693,0.0014660341,0.00032879956,0.007321424],"genre_scores_gemma":[0.21914862,0.0068593468,0.715541,0.000976893,0.0008878187,0.0027633915,0.010308821,0.00071124174,0.04280292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9797729,0.015439021,0.0006801282,0.0020340928,0.0014427922,0.00063102524],"domain_scores_gemma":[0.9467085,0.04281559,0.0018270056,0.0052098,0.0031439823,0.00029514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026606213,0.0027582373,0.0035058367,0.0044381074,0.0009669859,0.0049472773,0.0037802032,0.003488508,0.026649328],"category_scores_gemma":[0.09995795,0.0010713597,0.00244941,0.004509435,0.0020509826,0.0054675993,0.0026579078,0.0042221644,0.010419508],"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":[0.00010118422,0.000121792866,0.006202948,0.0006091537,0.00041906227,0.00020375219,0.0003314815,0.08630202,0.0002955811,0.67122656,0.022326628,0.21185993],"study_design_scores_gemma":[0.000051605202,0.000083092564,0.00222645,0.0005760266,0.00020944366,0.0002777627,0.00022155629,0.2508015,0.0006881495,0.70555747,0.03923983,0.00006708127],"about_ca_topic_score_codex":0.00540418,"about_ca_topic_score_gemma":0.00277615,"teacher_disagreement_score":0.026649328,"about_ca_system_score_codex":0.0023993556,"about_ca_system_score_gemma":0.0029166888,"threshold_uncertainty_score":0.1407088},"labels":[],"label_agreement":null},{"id":"W4214659234","doi":"10.5539/ijsp.v11n2p77","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 2","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Library science; Computer science","score_opus":0.05872665396825481,"score_gpt":0.38280421746733684,"score_spread":0.324077563499082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214659234","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000109406654,0.0025165644,0.0015216739,0.11785473,0.87431324,0.00049719674,0.00069545524,0.0005163024,0.0019754833],"genre_scores_gemma":[0.004026954,0.0058002328,0.0039381576,0.15517,0.78061986,0.0031236762,0.0015734814,0.0016545983,0.044093106],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92331177,0.015120267,0.015464476,0.0053413636,0.038354762,0.0024073783],"domain_scores_gemma":[0.13493174,0.039584436,0.010891206,0.0067209154,0.79904926,0.00882244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05806612,0.0036068063,0.010043968,0.012818389,0.0050259903,0.0119597,0.0059789387,0.018308869,0.09443361],"category_scores_gemma":[0.5373298,0.0019982697,0.0062624733,0.0056929365,0.0039683855,0.007512035,0.004395741,0.014108997,0.06530114],"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":[0.000035667566,0.0000041177946,0.00006897478,0.00038161906,0.0000116771635,0.00005317088,0.00002995578,0.000010756512,0.000035160534,0.000112409456,0.9962291,0.003027313],"study_design_scores_gemma":[0.00035411483,0.00007572031,0.0014426166,0.0045384658,0.00016136543,0.0014817012,0.0004245524,0.00063957414,0.00039729392,0.0028541384,0.9873946,0.00023589109],"about_ca_topic_score_codex":0.0034632524,"about_ca_topic_score_gemma":0.0050844927,"teacher_disagreement_score":0.09443361,"about_ca_system_score_codex":0.005222677,"about_ca_system_score_gemma":0.011120786,"threshold_uncertainty_score":0.3159119},"labels":[],"label_agreement":null},{"id":"W4214879208","doi":"10.22215/etd/2013-10011","title":"Order Restricted Testing of Random Effects in Generalized Linear Mixed Models","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"Centers for Disease Control and Prevention","keywords":"Generalized linear mixed model; Wald test; Random effects model; Mixed model; Mathematics; Statistics; Generalized linear model; Unobservable; Likelihood-ratio test; Hierarchical generalized linear model; Test statistic; Generalized estimating equation; Statistic; Applied mathematics; Score test; Generalized linear array model; Quasi-likelihood; Inference; Statistical hypothesis testing; Econometrics; Count data; Computer science","score_opus":0.07445747248076638,"score_gpt":0.370038885517264,"score_spread":0.2955814130364976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214879208","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":[0.009434643,0.00040054152,0.9874535,0.00045658398,0.00008754544,0.00022767214,0.00016564115,0.00022945012,0.0015443731],"genre_scores_gemma":[0.23099203,0.0012747534,0.7612791,0.00059875,0.0003539778,0.003020057,0.0006355173,0.00026484331,0.0015809631],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.79365814,0.17802829,0.004917977,0.010599494,0.011424322,0.0013717398],"domain_scores_gemma":[0.4861281,0.4799186,0.011190558,0.015872667,0.00605809,0.0008320152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09805597,0.0020161048,0.004142001,0.004262946,0.0017305104,0.004662599,0.0044638515,0.003185568,0.007146469],"category_scores_gemma":[0.42863455,0.0015962359,0.005063149,0.004816273,0.008062483,0.006417425,0.0046351156,0.0066427235,0.0011264929],"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":[0.00051634014,0.0002462709,0.0140128,0.0012757616,0.0016038303,0.0010127426,0.002817631,0.048129942,0.0012621239,0.7321041,0.002651462,0.19436695],"study_design_scores_gemma":[0.0001718166,0.0005266108,0.0041803448,0.00040373803,0.0003249102,0.00036176798,0.00046391744,0.1842922,0.0014489058,0.80322206,0.004482373,0.00012138042],"about_ca_topic_score_codex":0.0027942278,"about_ca_topic_score_gemma":0.0020255637,"teacher_disagreement_score":0.09805597,"about_ca_system_score_codex":0.0023232375,"about_ca_system_score_gemma":0.005109323,"threshold_uncertainty_score":0.5185758},"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":"W4220980829","doi":"10.1002/sim.9299","title":"Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"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":"Health Technology Assessment Programme; Medical Research Council; Medical Research Council Canada; University of Exeter; University of Bristol; Department of Health and Social Care; Elizabeth Blackwell Institute for Health Research, University of Bristol; University Hospitals Bristol NHS Foundation Trust; UK Research and Innovation; National Institute for Health and Care Research; Cancer Research UK; Wellcome Trust","keywords":"Estimator; Missing data; Statistics; Dropout (neural networks); Randomized controlled trial; Mathematics; Econometrics; Sensitivity (control systems); Imputation (statistics); Medicine; Computer science; Internal medicine","score_opus":0.20896445576742714,"score_gpt":0.5020260976696712,"score_spread":0.29306164190224404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220980829","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":[0.0151809845,0.020912103,0.9445459,0.009851051,0.0012802253,0.0026184001,0.00062190025,0.0008203754,0.0041690003],"genre_scores_gemma":[0.55331796,0.00610721,0.41337976,0.012709215,0.0015557407,0.010720538,0.0005708263,0.00040866484,0.0012300614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.29523873,0.6481547,0.027460275,0.012823747,0.01500236,0.0013202413],"domain_scores_gemma":[0.09392046,0.83851624,0.03589512,0.02560438,0.0055774446,0.00048634253],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.56198114,0.0024872725,0.00773687,0.0072497115,0.0015523747,0.006956242,0.0063810186,0.007943279,0.0040023723],"category_scores_gemma":[0.8416746,0.00253669,0.0099516995,0.0064010397,0.0063839485,0.0072929775,0.008388259,0.009553875,0.0005923807],"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":[0.0057388037,0.00027628458,0.043484244,0.026628071,0.040924106,0.0024279947,0.00759145,0.12453506,0.0013031926,0.32061446,0.012606359,0.4138699],"study_design_scores_gemma":[0.0019134772,0.0016999142,0.0127894655,0.01155319,0.012306906,0.0020658313,0.0006319156,0.18663791,0.003539562,0.7475809,0.018617837,0.00066300936],"about_ca_topic_score_codex":0.0023373899,"about_ca_topic_score_gemma":0.0015628084,"teacher_disagreement_score":0.43801886,"about_ca_system_score_codex":0.005291652,"about_ca_system_score_gemma":0.0048993463,"threshold_uncertainty_score":0.5401553},"labels":[],"label_agreement":null},{"id":"W4221102477","doi":"10.3329/jsr.v55i2.58810","title":"Approximate Likelihood Inference in Generalized Linear Models with Censored Covariates","year":2022,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Censoring (clinical trials); Estimator; Inference; Monte Carlo method; Statistics; Mathematics; Generalized linear model; Expectation–maximization algorithm; Statistical inference; Econometrics; Computer science; Maximum likelihood; Artificial intelligence","score_opus":0.17718753866211254,"score_gpt":0.4736309661492325,"score_spread":0.29644342748711994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221102477","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":[0.002631299,0.0007372791,0.9957424,0.00027941196,0.000023854753,0.00002760244,0.00007055215,0.00013186636,0.00035567506],"genre_scores_gemma":[0.22764571,0.004337842,0.7599376,0.00054542423,0.00045122983,0.0010299982,0.0010107729,0.00031191233,0.0047295564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9878758,0.009602235,0.00033644593,0.000995456,0.00092044537,0.00026951917],"domain_scores_gemma":[0.93166304,0.06338332,0.0021645487,0.0014883282,0.0009950169,0.0003058755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019180361,0.0017909656,0.0036583846,0.0023128123,0.00088109775,0.0024072737,0.003631027,0.0025790317,0.0030122432],"category_scores_gemma":[0.09204075,0.0018397394,0.002056933,0.0042384746,0.003323096,0.0035217179,0.0031296397,0.0037709433,0.00086901506],"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":[0.00014645814,0.000079499376,0.0019864633,0.00045387214,0.0002925115,0.00029042165,0.00030529013,0.6162507,0.0003361889,0.32441974,0.002164409,0.05327438],"study_design_scores_gemma":[0.000047235506,0.000026504402,0.00023803602,0.000042000192,0.00002710433,0.00004480112,0.00002352791,0.7548171,0.00012700594,0.243478,0.0011071278,0.0000214453],"about_ca_topic_score_codex":0.010120414,"about_ca_topic_score_gemma":0.0075464267,"teacher_disagreement_score":0.019180361,"about_ca_system_score_codex":0.0023679098,"about_ca_system_score_gemma":0.0028671436,"threshold_uncertainty_score":0.101436615},"labels":[],"label_agreement":null},{"id":"W4224213250","doi":"10.1080/00031305.2022.2066725","title":"Bias Analysis for Misclassification Errors in both the Response Variable and Covariate","year":2022,"lang":"en","type":"article","venue":"The American Statistician","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":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Variable (mathematics); Econometrics; Inference; Computer science; Observational error; Variables; Mathematics; Artificial intelligence","score_opus":0.13364623097300488,"score_gpt":0.3955855856048989,"score_spread":0.26193935463189405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224213250","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":[0.017850816,0.0020317524,0.9746285,0.0021296216,0.0002783117,0.00024615653,0.00022465983,0.00021665491,0.0023935463],"genre_scores_gemma":[0.63250256,0.0022810614,0.35543957,0.0026721745,0.00074579724,0.0013026958,0.00063786795,0.0003750998,0.0040432583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.935142,0.04962135,0.0024900956,0.0050932853,0.006591773,0.0010614832],"domain_scores_gemma":[0.7026946,0.25409746,0.011618146,0.022366427,0.008524376,0.0006990033],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11961042,0.0010728928,0.0020294297,0.0034920124,0.0011563534,0.0024036325,0.0026757952,0.0034982187,0.0058316914],"category_scores_gemma":[0.33863488,0.00049486494,0.0047111954,0.0030002196,0.003321596,0.003450961,0.0029947434,0.0037651488,0.0007216811],"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":[0.0012456774,0.00020909522,0.066504136,0.0016148713,0.0041864254,0.00060386595,0.0013222699,0.053113826,0.0037428658,0.5889057,0.009756658,0.2687947],"study_design_scores_gemma":[0.00030704527,0.00049055705,0.02992079,0.0012003763,0.0024240452,0.0012449091,0.00034115862,0.29622525,0.01018329,0.6390311,0.018415056,0.00021635124],"about_ca_topic_score_codex":0.0035055317,"about_ca_topic_score_gemma":0.0017510325,"teacher_disagreement_score":0.8803896,"about_ca_system_score_codex":0.0026100609,"about_ca_system_score_gemma":0.0033740606,"threshold_uncertainty_score":0.63256794},"labels":[],"label_agreement":null},{"id":"W4229736847","doi":"10.1002/9781118445112.stat06788","title":"Overdispersion","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","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":"Simon Fraser University","funders":"","keywords":"Overdispersion; Quasi-likelihood; Covariate; Statistics; Econometrics; Poisson distribution; Mathematics; Negative binomial distribution; Count data","score_opus":0.07023591113378734,"score_gpt":0.38929016433728314,"score_spread":0.3190542532034958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229736847","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":[0.1377783,0.02297633,0.7379243,0.008564512,0.0049152924,0.0027765338,0.011690815,0.0032743798,0.07009963],"genre_scores_gemma":[0.785863,0.008201701,0.1594365,0.013410831,0.0014900524,0.0028893466,0.005236583,0.0020819004,0.02139004],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8598139,0.06705327,0.0145924445,0.02078103,0.036010306,0.0017489798],"domain_scores_gemma":[0.61511993,0.27518907,0.038678903,0.053864714,0.015598619,0.0015487393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07952749,0.0010259149,0.002160034,0.005505163,0.0014271132,0.0035857058,0.0030445955,0.0010897886,0.011526275],"category_scores_gemma":[0.22006513,0.0005955946,0.0016615418,0.006471959,0.0044078613,0.0031073315,0.0041785934,0.0025631117,0.0023527883],"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":[0.0010784045,0.00035257608,0.112438135,0.005958762,0.0024976723,0.0028823076,0.012252257,0.006724459,0.00777175,0.20576324,0.07253999,0.5697404],"study_design_scores_gemma":[0.00012160398,0.00062435505,0.106181525,0.005058403,0.0014969967,0.0093419785,0.004777724,0.020381713,0.0241979,0.50537336,0.32192183,0.0005225876],"about_ca_topic_score_codex":0.0012560197,"about_ca_topic_score_gemma":0.0021883326,"teacher_disagreement_score":0.07952749,"about_ca_system_score_codex":0.0022710303,"about_ca_system_score_gemma":0.0020959915,"threshold_uncertainty_score":0.4205866},"labels":[],"label_agreement":null},{"id":"W4230814105","doi":"10.1093/biostatistics/5.3.361","title":"Analysis of longitudinal marginal structural models","year":2004,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"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 British Columbia","funders":"","keywords":"Marginal structural model; Econometrics; Marginal model; Computer science; Statistics; Mathematics; Regression analysis; Causal inference","score_opus":0.08612267330736914,"score_gpt":0.3841181678001392,"score_spread":0.2979954944927701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230814105","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":[0.045334183,0.0006548362,0.95142514,0.0011536482,0.000049330192,0.00005343147,0.00036015152,0.00031284685,0.0006564078],"genre_scores_gemma":[0.72601265,0.0019542095,0.2630853,0.00035980297,0.0002981441,0.00062299846,0.0014637051,0.00029651084,0.0059066913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9920587,0.0062187356,0.00017242985,0.0008057264,0.00046016823,0.00028427574],"domain_scores_gemma":[0.9005547,0.09060265,0.002782887,0.0037295774,0.0014781078,0.0008521166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021848205,0.00097478705,0.0016872999,0.0017628645,0.0008436494,0.0020682171,0.002789063,0.0014821372,0.008254713],"category_scores_gemma":[0.09654679,0.0011186848,0.0023480768,0.0016034426,0.0017296516,0.002988005,0.002298241,0.0028330819,0.00067324284],"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":[0.000705726,0.00031794034,0.02429232,0.0003930962,0.0013288562,0.00037814328,0.00089347246,0.2029542,0.0010464966,0.6282952,0.0052208686,0.13417365],"study_design_scores_gemma":[0.00006444907,0.00007722664,0.0027273728,0.00004711738,0.00015235867,0.000089507696,0.00008864926,0.5903964,0.00028649892,0.40479216,0.00124773,0.00003052106],"about_ca_topic_score_codex":0.005591903,"about_ca_topic_score_gemma":0.0060310094,"teacher_disagreement_score":0.021848205,"about_ca_system_score_codex":0.0014578882,"about_ca_system_score_gemma":0.0035071094,"threshold_uncertainty_score":0.11554569},"labels":[],"label_agreement":null},{"id":"W4231445950","doi":"10.1007/978-1-4939-7131-2_101260","title":"Statistical Models","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.15851295320292647,"score_gpt":0.3875023320164054,"score_spread":0.22898937881347892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231445950","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":[0.0008359505,0.010889056,0.66918546,0.008837598,0.0020305282,0.000074787196,0.0016898128,0.0016056636,0.30485108],"genre_scores_gemma":[0.062027484,0.025105882,0.21498138,0.005242397,0.0054772077,0.00058728445,0.0045941956,0.0024174144,0.67956674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99881124,0.0003957063,0.00004607666,0.00021549746,0.00049061753,0.00004087534],"domain_scores_gemma":[0.9984666,0.00092652586,0.00005370985,0.00029828778,0.00021355737,0.000041405397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001419011,0.001489811,0.0010225689,0.0014012922,0.0005389816,0.0027961961,0.0013048098,0.0015088243,0.047623735],"category_scores_gemma":[0.005566392,0.00062500767,0.0008630876,0.0013673089,0.0016533856,0.0027289547,0.0013622001,0.0033780476,0.029832175],"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":[0.000005294509,0.000017303777,0.0000793161,0.0001185293,0.000020528903,0.000028030317,0.000076326956,0.0033565115,0.00016819987,0.7943655,0.115021504,0.0867429],"study_design_scores_gemma":[0.0000026363857,0.0000035443238,0.00006527778,0.00006248185,0.000007880437,0.000051212155,0.000014755317,0.005137755,0.00012701945,0.79193634,0.20258261,0.000008477437],"about_ca_topic_score_codex":0.0014601342,"about_ca_topic_score_gemma":0.0017966947,"teacher_disagreement_score":0.047623735,"about_ca_system_score_codex":0.0012664347,"about_ca_system_score_gemma":0.0014283239,"threshold_uncertainty_score":0.15931726},"labels":[],"label_agreement":null},{"id":"W4231658903","doi":"10.4018/978-1-60566-663-1.ch013","title":"Sensitivity Analysis","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Statistical Methods and Bayesian 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 Saskatchewan","funders":"","keywords":"Sensitivity (control systems); Robustness (evolution); Computer science; Bayesian probability; Algorithm; Data mining; Artificial intelligence; Engineering","score_opus":0.04849609429912965,"score_gpt":0.3413144913183145,"score_spread":0.29281839701918483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231658903","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":[0.032825857,0.013979215,0.6829096,0.0050124326,0.0027562133,0.008917941,0.0169675,0.0021725453,0.23445876],"genre_scores_gemma":[0.6367772,0.01147826,0.25896117,0.0051601925,0.0007888531,0.013690639,0.011210131,0.0019938645,0.059939608],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9529417,0.029076293,0.0020082965,0.004311533,0.010306212,0.0013558588],"domain_scores_gemma":[0.88514614,0.08994695,0.003388854,0.008032076,0.012987764,0.0004982563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038734294,0.002382813,0.0025950738,0.0048427847,0.001285881,0.005147908,0.0027377543,0.0027129527,0.06573898],"category_scores_gemma":[0.1391603,0.0006788795,0.004916061,0.0047134943,0.0016780581,0.0038381128,0.0031201001,0.0034061654,0.008454896],"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":[0.0014146793,0.00044167766,0.0077750552,0.0071946913,0.0029618372,0.0005511281,0.00086572336,0.28036582,0.003147959,0.29721954,0.0807203,0.31734148],"study_design_scores_gemma":[0.0002771356,0.0016976548,0.007328692,0.0046922886,0.0021357343,0.0009615903,0.0018364991,0.20235826,0.011582802,0.39558718,0.37106842,0.00047382733],"about_ca_topic_score_codex":0.003415998,"about_ca_topic_score_gemma":0.0023566016,"teacher_disagreement_score":0.06573898,"about_ca_system_score_codex":0.004989622,"about_ca_system_score_gemma":0.0040437947,"threshold_uncertainty_score":0.21991879},"labels":[],"label_agreement":null},{"id":"W4231724352","doi":"10.32920/ryerson.14643855.v1","title":"Exploration Of Theoretical And Application Issues In Using Fully Bayesian Methods For Road Safety Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian 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":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Univariate; Multivariate statistics; Bayesian probability; Statistics; Poisson regression; Ranking (information retrieval); Poisson distribution; Multivariate analysis; Univariate analysis; Mathematics; Computer science; Econometrics; Medicine; Artificial intelligence","score_opus":0.08759181864643606,"score_gpt":0.47753398370413297,"score_spread":0.3899421650576969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231724352","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":[0.006664239,0.00068096694,0.98860115,0.0013257953,0.000029985515,0.00006776261,0.000050555816,0.000088221466,0.0024913177],"genre_scores_gemma":[0.2516777,0.0014863467,0.7434283,0.00095465116,0.000183599,0.00045816464,0.0001605693,0.00015064912,0.0015000013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9602341,0.03350849,0.0006618162,0.0013849771,0.003855386,0.00035511804],"domain_scores_gemma":[0.75539917,0.22460303,0.0034142882,0.007070786,0.008815262,0.00069756835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07930581,0.001331552,0.0015970165,0.0024022348,0.0011970117,0.0033243138,0.00363641,0.0024311335,0.0034529457],"category_scores_gemma":[0.19872572,0.0012690483,0.0013463855,0.0019513431,0.0043350426,0.0056425342,0.0032459048,0.0031217234,0.0004953556],"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":[0.00012842806,0.00010914255,0.007658502,0.00055630616,0.00026595787,0.00021835172,0.00086129276,0.23672907,0.00083682616,0.60945326,0.0020074158,0.14117545],"study_design_scores_gemma":[0.00003247178,0.00009673171,0.0012253361,0.00027030107,0.000036991994,0.000116890216,0.0001560274,0.6255801,0.00051435304,0.36874548,0.00317313,0.000052166673],"about_ca_topic_score_codex":0.009698487,"about_ca_topic_score_gemma":0.008564135,"teacher_disagreement_score":0.07930581,"about_ca_system_score_codex":0.0022751314,"about_ca_system_score_gemma":0.0038912965,"threshold_uncertainty_score":0.41941422},"labels":[],"label_agreement":null},{"id":"W4231749629","doi":"10.22215/etd/2005-06288","title":"Contributions to imputation for missing survey data","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University; Statistics Canada","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Mathematics","score_opus":0.18920197496658694,"score_gpt":0.5199037617420478,"score_spread":0.3307017867754608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231749629","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":[0.00067284214,0.006424874,0.9774316,0.005462513,0.0031309365,0.0001585738,0.0010462898,0.00069955306,0.004972747],"genre_scores_gemma":[0.029955037,0.01765638,0.9127546,0.0035147124,0.0133032845,0.00080187724,0.0057244822,0.0013080272,0.014981668],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9418458,0.041043628,0.0024876501,0.005433714,0.008356617,0.000832663],"domain_scores_gemma":[0.81574816,0.1181867,0.003125597,0.046231493,0.014945814,0.0017621896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.058039214,0.0026532225,0.0037286035,0.0065127145,0.0021155986,0.0054903547,0.008528555,0.0041592643,0.019871809],"category_scores_gemma":[0.2453342,0.002229449,0.007582546,0.016781611,0.0024626378,0.0060243467,0.008401263,0.010105391,0.011348091],"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":[0.00030087755,0.00030910055,0.0064962413,0.0012887497,0.001886524,0.00047227374,0.0007443072,0.035461333,0.00036685463,0.26356044,0.12643434,0.562679],"study_design_scores_gemma":[0.00014190047,0.000058299123,0.0021557703,0.0011345667,0.00048101507,0.0005804114,0.00015870456,0.1284956,0.0007842518,0.6188128,0.24701284,0.00018387276],"about_ca_topic_score_codex":0.007564977,"about_ca_topic_score_gemma":0.00533759,"teacher_disagreement_score":0.058039214,"about_ca_system_score_codex":0.0021654582,"about_ca_system_score_gemma":0.006027777,"threshold_uncertainty_score":0.30694437},"labels":[],"label_agreement":null},{"id":"W4233857119","doi":"10.1002/0470013192.bsa168","title":"Design Effects","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Statistical Methods and Bayesian 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":"Western University; Cancer Care Ontario","funders":"","keywords":"Relevance (law); Sample size determination; Context (archaeology); Sampling (signal processing); Sampling design; Sample (material); Cluster sampling; Computer science; Cluster (spacecraft); Management science; Statistics; Geography; Sociology; Mathematics; Engineering; Political science; Archaeology; Demography; Physics","score_opus":0.052055908850338374,"score_gpt":0.3962595389911793,"score_spread":0.34420363014084093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233857119","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":[0.042674433,0.015071159,0.40710458,0.01313736,0.021740882,0.109623685,0.019131497,0.004201336,0.36731514],"genre_scores_gemma":[0.39559442,0.0035112067,0.24853288,0.020319333,0.0025028288,0.23332755,0.004404334,0.0016633143,0.09014416],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.8981156,0.061875924,0.010153676,0.008885278,0.017422555,0.0035469327],"domain_scores_gemma":[0.87740445,0.08063212,0.006977907,0.020626102,0.010990615,0.0033687085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.070112616,0.0023794477,0.0026604861,0.0021028626,0.0014878106,0.0036777467,0.0030020701,0.0046194214,0.14371222],"category_scores_gemma":[0.18168016,0.0011733527,0.0036611054,0.0012903055,0.002568877,0.0035938348,0.005707663,0.0045139217,0.0186062],"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":[0.025097612,0.0022470127,0.00796093,0.013781411,0.0020733406,0.00022384498,0.001442498,0.0025719034,0.0024169474,0.31824803,0.06630678,0.5576297],"study_design_scores_gemma":[0.018086834,0.014328669,0.017131748,0.009341577,0.004237575,0.00070462195,0.00050832884,0.0033336007,0.009938236,0.2740628,0.64801216,0.00031396028],"about_ca_topic_score_codex":0.000544672,"about_ca_topic_score_gemma":0.00074856455,"teacher_disagreement_score":0.14371222,"about_ca_system_score_codex":0.0031305235,"about_ca_system_score_gemma":0.0050635044,"threshold_uncertainty_score":0.48076528},"labels":[],"label_agreement":null},{"id":"W4234121373","doi":"10.1111/j.1751-5823.2011.00169.x","title":"Discussion","year":2012,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia","funders":"","keywords":"Library science; Citation; History; Computer science","score_opus":0.10926021726014894,"score_gpt":0.4779763054084461,"score_spread":0.36871608814829715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234121373","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":[0.011081948,0.026562288,0.050716102,0.5294228,0.028344315,0.00049658946,0.007867846,0.0006800105,0.3448281],"genre_scores_gemma":[0.26528388,0.018733814,0.036223795,0.4553366,0.039135154,0.0013716338,0.006897262,0.0014347305,0.17558315],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9867545,0.005464863,0.00069876964,0.002294415,0.0038511804,0.0009363055],"domain_scores_gemma":[0.9343042,0.0413578,0.001904846,0.0045016855,0.01567629,0.0022551916],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.024685327,0.0006271062,0.00082661555,0.0016920086,0.0015287043,0.0043895096,0.00236529,0.0033155172,0.1536296],"category_scores_gemma":[0.11147766,0.00023677223,0.0014963505,0.0013400504,0.0019191405,0.003971074,0.0021265016,0.004915352,0.022492142],"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":[0.0005417151,0.00008565732,0.0069778147,0.0012205091,0.00019745149,0.00031080787,0.0007417906,0.00068719895,0.00057694403,0.31564933,0.4756699,0.1973409],"study_design_scores_gemma":[0.000089083034,0.00004940475,0.0074271555,0.0014960238,0.00010431571,0.00062652794,0.0011495387,0.0006321299,0.0009476678,0.11989708,0.86755526,0.000025800553],"about_ca_topic_score_codex":0.004197193,"about_ca_topic_score_gemma":0.004136774,"teacher_disagreement_score":0.8463704,"about_ca_system_score_codex":0.0032727974,"about_ca_system_score_gemma":0.004537889,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4235103581","doi":"10.1007/978-1-4939-2137-9_3","title":"Regression Models For Univariate Longitudinal Stationary Categorical Data","year":2014,"lang":"en","type":"book-chapter","venue":"Springer series in statistics","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Multinomial distribution; Categorical variable; Covariate; Multinomial logistic regression; Univariate; Econometrics; Statistics; Longitudinal data; Count data; Mathematics; Computer science; Poisson distribution; Multivariate statistics; Data mining","score_opus":0.17976143627421487,"score_gpt":0.39586189682686085,"score_spread":0.21610046055264598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235103581","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":[0.0008209903,0.011196374,0.97697234,0.0021506932,0.00048200696,0.000014985854,0.0006465156,0.0006179743,0.007098093],"genre_scores_gemma":[0.08822653,0.07836233,0.6958462,0.0030598529,0.00562707,0.0008444115,0.00542471,0.0019170153,0.120691955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99874276,0.00063323614,0.00006594408,0.00022295066,0.00029459648,0.000040560033],"domain_scores_gemma":[0.99394727,0.004969468,0.0002728928,0.0003882508,0.00037376795,0.000048354337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039126673,0.001761194,0.0017272029,0.0011463959,0.00027276896,0.0015252162,0.002562094,0.001873883,0.012989338],"category_scores_gemma":[0.0120397275,0.0011099417,0.0016231006,0.0027573197,0.0010010627,0.0027463026,0.0009748028,0.0041769114,0.009505505],"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":[0.000023901825,0.00005056592,0.0006435219,0.00050367217,0.00015721242,0.00011067006,0.00016915919,0.04469484,0.0005943754,0.7042015,0.07111487,0.17773573],"study_design_scores_gemma":[0.00001011759,0.000021142445,0.00044182627,0.00014946093,0.00005734591,0.00013646443,0.000022059536,0.12618573,0.00018794843,0.8246394,0.04811357,0.000034928675],"about_ca_topic_score_codex":0.0024293743,"about_ca_topic_score_gemma":0.0031272527,"teacher_disagreement_score":0.012989338,"about_ca_system_score_codex":0.0009327026,"about_ca_system_score_gemma":0.001115724,"threshold_uncertainty_score":0.043453693},"labels":[],"label_agreement":null},{"id":"W4235297061","doi":"10.22215/etd/2015-11013","title":"Methods for Analyzing Longitudinal Binary Data with Missing Responses","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Generalized estimating equation; Bivariate analysis; Estimator; Missing data; Monotone polygon; Binary number; Estimating equations; Drop out; Mathematics; Drop (telecommunication); Maximum likelihood; Statistics; Applied mathematics; Computer science","score_opus":0.3239453403479938,"score_gpt":0.5574172324264538,"score_spread":0.23347189207845997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235297061","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":[0.0013481898,0.0012179747,0.99552035,0.00060809124,0.00012566516,0.00014347114,0.00032077424,0.00016832784,0.00054715615],"genre_scores_gemma":[0.044641867,0.0036522984,0.9426444,0.0008116285,0.00056602736,0.0027722889,0.001492468,0.00032209526,0.003096906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9539909,0.03775778,0.0014401211,0.0028383192,0.0035947342,0.00037820154],"domain_scores_gemma":[0.74900985,0.2176051,0.009594755,0.016673364,0.006388591,0.0007282961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07679367,0.0017833436,0.001984248,0.004471376,0.0012417631,0.002403515,0.0045043584,0.0023509245,0.010195331],"category_scores_gemma":[0.24343148,0.0013966464,0.0029155088,0.005276329,0.0025169775,0.004096895,0.0029762988,0.0064966413,0.002650744],"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":[0.0003098791,0.00025230585,0.013703057,0.0018610434,0.001624971,0.00018237894,0.0010094554,0.031113015,0.0007924825,0.63558644,0.013813107,0.2997519],"study_design_scores_gemma":[0.0002769341,0.00016496671,0.0042913137,0.00078994024,0.0003491606,0.00031157568,0.00022830543,0.14252257,0.0012070972,0.82571954,0.023999436,0.00013912379],"about_ca_topic_score_codex":0.0028670032,"about_ca_topic_score_gemma":0.002762656,"teacher_disagreement_score":0.07679367,"about_ca_system_score_codex":0.0014298884,"about_ca_system_score_gemma":0.0027421883,"threshold_uncertainty_score":0.4061286},"labels":[],"label_agreement":null},{"id":"W4237148472","doi":"10.1002/9781118445112.stat05817","title":"Ancillary Statistics","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Sufficient statistic; Statistics; Likelihood function; Mathematics; Likelihood principle; Marginal likelihood; Statistic; Estimator; Conditional probability distribution; M-estimator; Restricted maximum likelihood; Maximum likelihood; Quasi-maximum likelihood","score_opus":0.07911271300905846,"score_gpt":0.38672619553211796,"score_spread":0.3076134825230595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237148472","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":[0.0034515576,0.0010296593,0.9724511,0.0024821903,0.00071228814,0.00015598549,0.0028795903,0.0008094818,0.016028235],"genre_scores_gemma":[0.3228795,0.0040852493,0.62439054,0.004214423,0.0039642937,0.0018291583,0.009364566,0.0016125663,0.027659675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98231584,0.008702532,0.0011439262,0.003093885,0.004109493,0.00063422613],"domain_scores_gemma":[0.94258356,0.03478096,0.0046677818,0.011534024,0.005660316,0.00077351154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021739408,0.001985799,0.0036722107,0.0052715857,0.0016168423,0.0071426164,0.0049083624,0.0035500592,0.034341577],"category_scores_gemma":[0.1043432,0.0011321768,0.0021548101,0.0055210874,0.0050412267,0.010346788,0.004121899,0.0064954963,0.009294334],"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":[0.00008527201,0.000054608965,0.00149225,0.0003159164,0.00010421583,0.00025096658,0.00012215723,0.011534014,0.00050752226,0.9221442,0.015990853,0.047397997],"study_design_scores_gemma":[0.000026731157,0.000041618845,0.00052632,0.00022901152,0.000039912236,0.00024516732,0.000041019193,0.056940746,0.00076270994,0.9169295,0.024165567,0.000051608717],"about_ca_topic_score_codex":0.0021151137,"about_ca_topic_score_gemma":0.0015868676,"teacher_disagreement_score":0.034341577,"about_ca_system_score_codex":0.0024415303,"about_ca_system_score_gemma":0.004292443,"threshold_uncertainty_score":0.11497033},"labels":[],"label_agreement":null},{"id":"W4237880394","doi":"10.1002/047147326x.ch3","title":"Model Analysis","year":2003,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Weibull distribution; Computer science; Context (archaeology); Set (abstract data type); Mathematics; Statistics; Geography","score_opus":0.05404078750584129,"score_gpt":0.3392595899787009,"score_spread":0.28521880247285963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237880394","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":[0.0031029636,0.0010111767,0.959484,0.0013781411,0.00016861882,0.00027945972,0.0018609155,0.0007676452,0.03194704],"genre_scores_gemma":[0.2989957,0.0052750087,0.6374942,0.0012237728,0.0004898245,0.0024402456,0.008955579,0.0012035883,0.043921985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965869,0.0014537005,0.00018317942,0.00057812966,0.0010132176,0.00018476759],"domain_scores_gemma":[0.99473214,0.0030479713,0.00036993952,0.00070382206,0.0010531843,0.0000928229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043038055,0.0014668313,0.0012035348,0.002098956,0.0009328253,0.003301815,0.002921417,0.0014451755,0.023256555],"category_scores_gemma":[0.018225417,0.0004432191,0.002227299,0.0018394521,0.00085627957,0.0028630935,0.0021629115,0.002224229,0.0069282963],"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":[0.00004516318,0.00007176253,0.002062454,0.00041455228,0.00024435716,0.00020737096,0.00027942305,0.21507338,0.00078258815,0.6631035,0.0274422,0.0902733],"study_design_scores_gemma":[0.000017166229,0.000045882207,0.00049032917,0.00016896304,0.00007316114,0.00020330875,0.000150092,0.47649843,0.00065512495,0.4639261,0.057732143,0.00003935532],"about_ca_topic_score_codex":0.0054784743,"about_ca_topic_score_gemma":0.0033292514,"teacher_disagreement_score":0.023256555,"about_ca_system_score_codex":0.0020922488,"about_ca_system_score_gemma":0.002523777,"threshold_uncertainty_score":0.07780099},"labels":[],"label_agreement":null},{"id":"W4239340622","doi":"10.1007/978-1-0716-1138-8_7","title":"Longitudinal Studies 2: Modeling Data Using Multivariate Analysis","year":2021,"lang":"en","type":"book-chapter","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Calgary","funders":"","keywords":"Statistics; Multivariate statistics; Econometrics; Confidence interval; Component (thermodynamics); Point estimation; Statistical model; Longitudinal data; Mathematics; Computer science; Data mining","score_opus":0.4345708543469886,"score_gpt":0.5626842146774412,"score_spread":0.12811336033045262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239340622","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":[0.0010941912,0.015519377,0.9684767,0.004596759,0.0010651256,0.00006736219,0.00063955487,0.0007454392,0.0077954684],"genre_scores_gemma":[0.04131643,0.033678535,0.8736586,0.0047860756,0.004246853,0.0011916438,0.0013260472,0.0016748442,0.03812096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969463,0.0020667894,0.00011831115,0.00032944194,0.00046845878,0.00007063617],"domain_scores_gemma":[0.981205,0.016678112,0.00055106543,0.00095849996,0.00043873175,0.00016864105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009984767,0.0015262477,0.0014393685,0.0019187424,0.00038821818,0.0020955577,0.0016418096,0.001757936,0.009048703],"category_scores_gemma":[0.019429017,0.0013980976,0.0015054088,0.0024395962,0.0015681611,0.0033394804,0.0015892431,0.0031420204,0.0021515694],"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":[0.00006249361,0.00008156177,0.0024449243,0.0009843083,0.00035164237,0.00023657533,0.0006284033,0.014778527,0.0008024468,0.5290741,0.117911525,0.33264357],"study_design_scores_gemma":[0.000016746755,0.000040037372,0.0019557024,0.00027687426,0.00010917019,0.00030445482,0.00007174941,0.02876563,0.00036969606,0.88434786,0.08369401,0.000048102615],"about_ca_topic_score_codex":0.0018159421,"about_ca_topic_score_gemma":0.0024733548,"teacher_disagreement_score":0.009984767,"about_ca_system_score_codex":0.00073324685,"about_ca_system_score_gemma":0.0018810012,"threshold_uncertainty_score":0.052805126},"labels":[],"label_agreement":null},{"id":"W4241614324","doi":"10.1007/978-1-4614-6170-8_110048","title":"Statistical Modeling","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.12807680251064235,"score_gpt":0.3788862833092108,"score_spread":0.25080948079856846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241614324","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":[0.0005903369,0.014347448,0.6190193,0.0076692705,0.0018599608,0.00008129338,0.0008532178,0.0015099789,0.3540693],"genre_scores_gemma":[0.039532535,0.027416587,0.19729592,0.00435145,0.0045984234,0.00045710677,0.0021591475,0.0018513567,0.7223375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99882513,0.00039591908,0.000045812736,0.00018649653,0.0005087683,0.000037780606],"domain_scores_gemma":[0.9987269,0.00074596406,0.00004038189,0.00025229825,0.00019783108,0.000036533067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013969411,0.0013218669,0.000997889,0.0015223055,0.00059268565,0.0024907328,0.0012552154,0.0013042153,0.040314175],"category_scores_gemma":[0.004395246,0.0005552259,0.00071150967,0.0015056933,0.0016532055,0.0024466864,0.0013065335,0.0029367243,0.025983067],"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":[0.0000040747695,0.000017242648,0.00007699136,0.00012086813,0.00001829443,0.000029763713,0.00010549215,0.0026931546,0.00018613982,0.70870715,0.1477624,0.1402784],"study_design_scores_gemma":[0.0000021570308,0.000004305252,0.000086730885,0.000093090996,0.0000073590663,0.000066837056,0.000022993434,0.0045487275,0.0001689747,0.66908246,0.32590675,0.000009627726],"about_ca_topic_score_codex":0.001664368,"about_ca_topic_score_gemma":0.0024809795,"teacher_disagreement_score":0.040314175,"about_ca_system_score_codex":0.0013720648,"about_ca_system_score_gemma":0.0015148332,"threshold_uncertainty_score":0.13486433},"labels":[],"label_agreement":null},{"id":"W4242743071","doi":"10.1002/(issn)1708-945x","title":"Canadian Journal of Statistics","year":2018,"lang":"en","type":"paratext","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Statistics; Library science; Geography; Data science; Computer science; Mathematics","score_opus":0.05925370840004699,"score_gpt":0.3410722214921932,"score_spread":0.2818185130921462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242743071","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":[0.001953854,0.065372355,0.103421375,0.07011591,0.028829087,0.002678956,0.09490711,0.0062680524,0.62645334],"genre_scores_gemma":[0.06675885,0.1627006,0.16887449,0.021937728,0.014639547,0.007883746,0.1326537,0.005353705,0.4191977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97160214,0.0047261915,0.0038634054,0.002478107,0.016272614,0.0010575016],"domain_scores_gemma":[0.900168,0.021883048,0.004038466,0.010394187,0.060659043,0.0028571992],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01458793,0.0024431276,0.003859937,0.010954787,0.003444336,0.012096499,0.0035800985,0.003523184,0.20334966],"category_scores_gemma":[0.12516414,0.0010986076,0.0014416017,0.016378455,0.0032047294,0.0028549333,0.0031807837,0.005639913,0.10707632],"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":[0.00004969135,0.000037727048,0.0008759263,0.0014876921,0.00007778563,0.00016443036,0.00018337551,0.0006731362,0.00017087228,0.04279011,0.7904926,0.1629966],"study_design_scores_gemma":[0.000028978926,0.000023923067,0.0012749342,0.00094871956,0.000032269247,0.00016676808,0.00015066356,0.0011553963,0.000089419074,0.028302481,0.96777904,0.000047360583],"about_ca_topic_score_codex":0.17415059,"about_ca_topic_score_gemma":0.12817644,"teacher_disagreement_score":0.79665035,"about_ca_system_score_codex":0.016897462,"about_ca_system_score_gemma":0.07424907,"threshold_uncertainty_score":0.6802724},"labels":[],"label_agreement":null},{"id":"W4243364735","doi":"10.1007/978-1-4939-7131-2_100702","title":"Multiple Imputation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Imputation (statistics); Computer science; Statistics; Mathematics; Missing data","score_opus":0.09122506567680257,"score_gpt":0.3692636206937893,"score_spread":0.27803855501698677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243364735","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":[0.0005096428,0.0024624737,0.92030543,0.0024968856,0.0011401058,0.00012888468,0.0025461444,0.003062696,0.067347705],"genre_scores_gemma":[0.018619196,0.004897102,0.7280702,0.0029454562,0.001723567,0.0006404936,0.010840073,0.0040613115,0.22820266],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947572,0.0023611062,0.00027194378,0.0011236575,0.0012990648,0.00018702922],"domain_scores_gemma":[0.9900025,0.0051380727,0.00034518147,0.0029058782,0.0014318355,0.0001766143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007912062,0.0016706429,0.0022496693,0.002216051,0.0013151354,0.003799414,0.004207591,0.0025666542,0.13856934],"category_scores_gemma":[0.025342239,0.0012197056,0.002527928,0.0037566859,0.0010454775,0.0033147694,0.003130303,0.005390613,0.076693065],"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":[0.00006937682,0.0000890354,0.0006110144,0.0004918721,0.0001788942,0.00011891255,0.00016657337,0.00507011,0.00043248927,0.18222408,0.2612405,0.5493071],"study_design_scores_gemma":[0.00003704696,0.00004023366,0.00068586285,0.00053875794,0.00012489084,0.00064423843,0.00008668461,0.025501214,0.0015791425,0.46174625,0.5089428,0.000072866525],"about_ca_topic_score_codex":0.0012070506,"about_ca_topic_score_gemma":0.002442751,"teacher_disagreement_score":0.13856934,"about_ca_system_score_codex":0.00083131704,"about_ca_system_score_gemma":0.0025657278,"threshold_uncertainty_score":0.46356064},"labels":[],"label_agreement":null},{"id":"W4243837185","doi":"10.1007/978-1-4614-6170-8_100159","title":"Statistical Models","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.12980472148990296,"score_gpt":0.3710110668094698,"score_spread":0.24120634531956686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243837185","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":[0.00079748494,0.010610876,0.6680047,0.007735252,0.0016629074,0.000072507195,0.0012803128,0.0013030197,0.30853286],"genre_scores_gemma":[0.05957669,0.024586914,0.21241519,0.004723818,0.004360975,0.00054004317,0.0033520774,0.0018267421,0.6886175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99894184,0.00036482315,0.000040832525,0.00018309713,0.00043382653,0.00003563912],"domain_scores_gemma":[0.9987558,0.00076453056,0.00004397797,0.00023470791,0.0001688039,0.00003223913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012789352,0.0014098596,0.0009868366,0.0013572494,0.00051698956,0.0024654954,0.0012582383,0.001395477,0.04014406],"category_scores_gemma":[0.004524998,0.0006155156,0.0007946395,0.0012870523,0.0016265985,0.0025481428,0.0012443418,0.0031530573,0.022963827],"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":[0.0000043830137,0.000015778645,0.000073108844,0.00010103693,0.000019504514,0.000028669861,0.00007597406,0.003542478,0.00015964435,0.8089427,0.100593485,0.086443245],"study_design_scores_gemma":[0.0000023668924,0.000003631304,0.000064867396,0.000057654546,0.000007731481,0.00005602568,0.000015687883,0.00558539,0.00013158197,0.800497,0.19356966,0.0000084061485],"about_ca_topic_score_codex":0.0016316639,"about_ca_topic_score_gemma":0.0020811309,"teacher_disagreement_score":0.04014406,"about_ca_system_score_codex":0.0012597662,"about_ca_system_score_gemma":0.0013587802,"threshold_uncertainty_score":0.13429523},"labels":[],"label_agreement":null},{"id":"W4244039756","doi":"10.1002/9781118445112.stat07621","title":"Longitudinal Studies","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian 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":"Simon Fraser University; Western University","funders":"","keywords":"Longitudinal study; Longitudinal data; Psychology; Computer science; Statistics; Mathematics; Data mining","score_opus":0.18478224167487015,"score_gpt":0.4497118643305009,"score_spread":0.2649296226556308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244039756","genre_codex":"other","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":[0.0821715,0.1824556,0.10251284,0.035929453,0.013728163,0.0060269353,0.23487385,0.0013433964,0.34095818],"genre_scores_gemma":[0.56627315,0.106781654,0.0754355,0.027454268,0.009681745,0.013659029,0.09916074,0.00076631823,0.10078758],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9803152,0.011861134,0.0014799217,0.0030012676,0.0027318227,0.0006106569],"domain_scores_gemma":[0.933659,0.026237393,0.011937016,0.011650113,0.013977423,0.0025390505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015752554,0.00068716746,0.0012107614,0.004275857,0.0013861266,0.002188896,0.0013567726,0.0013248741,0.056360606],"category_scores_gemma":[0.0808915,0.00040597914,0.0011036667,0.0070937346,0.0010536008,0.0028838124,0.0023018245,0.0019797252,0.00876589],"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":[0.00087547424,0.0004110795,0.15049537,0.006940213,0.0031492466,0.0007407545,0.004141976,0.00072895264,0.0006664746,0.15938728,0.360454,0.31200925],"study_design_scores_gemma":[0.00016770785,0.00060897035,0.087654,0.0080070365,0.0012377418,0.0017387412,0.0020309594,0.00050033,0.00055331807,0.048364073,0.8490225,0.0001145477],"about_ca_topic_score_codex":0.0054896465,"about_ca_topic_score_gemma":0.004647679,"teacher_disagreement_score":0.056360606,"about_ca_system_score_codex":0.001598572,"about_ca_system_score_gemma":0.0039052854,"threshold_uncertainty_score":0.18854499},"labels":[],"label_agreement":null},{"id":"W4244362551","doi":"10.1007/978-94-007-0753-5_103252","title":"Proportionate Sample","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Sample (material); Physics; Thermodynamics","score_opus":0.09753626631697077,"score_gpt":0.3630897924724866,"score_spread":0.26555352615551586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244362551","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":[0.0009049145,0.0013718667,0.91950125,0.0014340895,0.00052723027,0.00010800491,0.0004090551,0.0006727499,0.0750708],"genre_scores_gemma":[0.09354194,0.004175524,0.61953795,0.00430359,0.0020334304,0.0014073934,0.0020163173,0.0026509676,0.27033287],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99524,0.0022709519,0.00016096667,0.0009160398,0.0012921941,0.00011984362],"domain_scores_gemma":[0.9929442,0.004497426,0.00018660823,0.0015272767,0.0007447172,0.000099703175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005461981,0.0010348744,0.0015357205,0.00125274,0.00093353883,0.002717533,0.0018091317,0.0014298611,0.06486136],"category_scores_gemma":[0.03169207,0.0010169486,0.0010134252,0.0014438487,0.0023554873,0.0039871014,0.0026916233,0.003448286,0.021259462],"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":[0.00007973204,0.000034665263,0.0003246594,0.00019255385,0.000044891378,0.0000368805,0.0001442833,0.0027794007,0.00040585332,0.7590026,0.050175488,0.18677895],"study_design_scores_gemma":[0.000026750902,0.000027045047,0.00028167266,0.0000829952,0.00003010322,0.00023677721,0.000036539957,0.014973184,0.00089703826,0.87503475,0.1083537,0.000019437268],"about_ca_topic_score_codex":0.00066609454,"about_ca_topic_score_gemma":0.00091336307,"teacher_disagreement_score":0.06486136,"about_ca_system_score_codex":0.0010414167,"about_ca_system_score_gemma":0.0012572196,"threshold_uncertainty_score":0.21698284},"labels":[],"label_agreement":null},{"id":"W4244801524","doi":"10.5539/ijsp.v3n3p157","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 3, No. 3","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Mathematical economics","score_opus":0.05330897860332165,"score_gpt":0.3808538526587293,"score_spread":0.32754487405540766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244801524","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000106493266,0.0014759015,0.0017802999,0.19637223,0.79731345,0.00033842828,0.00065969455,0.00052771333,0.0014259047],"genre_scores_gemma":[0.0056811925,0.00432814,0.0066282614,0.28762388,0.645967,0.0026571136,0.0014022993,0.0020759217,0.043636233],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.944144,0.011655146,0.011058943,0.0039116717,0.027219934,0.002010365],"domain_scores_gemma":[0.15851635,0.05631197,0.009661276,0.00880184,0.7585075,0.008201045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.058716096,0.0024880215,0.005805462,0.0093055675,0.0048660734,0.008891397,0.0053712614,0.018381549,0.07334579],"category_scores_gemma":[0.558806,0.0016039677,0.005221389,0.0051174834,0.003798183,0.0066005625,0.003841739,0.016988335,0.04513848],"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":[0.000024547138,0.0000036977985,0.00007033755,0.00024738236,0.000009162645,0.000043796743,0.000040482042,0.00001245617,0.000037855236,0.00020446615,0.9967424,0.0025633832],"study_design_scores_gemma":[0.00019319281,0.00003448351,0.0011956269,0.0024651366,0.000093285635,0.0007962963,0.00035530105,0.0005441765,0.00032202163,0.0032461013,0.99061245,0.00014186795],"about_ca_topic_score_codex":0.0064521697,"about_ca_topic_score_gemma":0.00845618,"teacher_disagreement_score":0.07334579,"about_ca_system_score_codex":0.0056689265,"about_ca_system_score_gemma":0.014070213,"threshold_uncertainty_score":0.31052417},"labels":[],"label_agreement":null},{"id":"W4245005141","doi":"10.22215/etd/2020-14333","title":"Analyzing Incomplete Longitudinal Binary Data Using Approximate Likelihood Methods","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Missing data; Estimator; Bivariate analysis; Likelihood function; Statistics; Longitudinal data; Maximum likelihood; Mathematics; Binary number; Binary data; Computer science; Estimating equations; Econometrics; Data mining","score_opus":0.2755549857148068,"score_gpt":0.4915966771271042,"score_spread":0.2160416914122974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245005141","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":[0.0013196145,0.0005930721,0.9973156,0.00025339134,0.000031748266,0.000036131907,0.0001245209,0.00008122319,0.00024474447],"genre_scores_gemma":[0.09808431,0.0036206911,0.89102364,0.00046473418,0.00052495237,0.0011993194,0.0017762914,0.00024909424,0.0030569884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9792286,0.014991235,0.0008034962,0.0018113084,0.0027746314,0.00039077108],"domain_scores_gemma":[0.9012911,0.08654991,0.005087394,0.0040549063,0.002495762,0.0005209528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02981481,0.0014145627,0.0032492867,0.0031548385,0.0009021049,0.0030384492,0.004800005,0.0020902217,0.0044192383],"category_scores_gemma":[0.11926327,0.001567511,0.0023453305,0.0046763816,0.0021444357,0.005117156,0.0034635928,0.0046796096,0.0012166534],"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":[0.00036115295,0.00022821542,0.0070054135,0.0012434606,0.0013936487,0.00052504917,0.00087010645,0.32002547,0.00063594,0.44306505,0.0063249934,0.21832152],"study_design_scores_gemma":[0.000054899803,0.000048440586,0.0007222615,0.00013104742,0.00007923392,0.000099888624,0.00006234914,0.55211115,0.00024629023,0.44271642,0.0036956044,0.000032382854],"about_ca_topic_score_codex":0.0046968916,"about_ca_topic_score_gemma":0.0039243684,"teacher_disagreement_score":0.02981481,"about_ca_system_score_codex":0.0017216417,"about_ca_system_score_gemma":0.0027639421,"threshold_uncertainty_score":0.15767765},"labels":[],"label_agreement":null},{"id":"W4246282759","doi":"10.32920/ryerson.14643855","title":"Exploration Of Theoretical And Application Issues In Using Fully Bayesian Methods For Road Safety Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian 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":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Univariate; Multivariate statistics; Bayesian probability; Statistics; Ranking (information retrieval); Poisson regression; Poisson distribution; Multivariate analysis; Univariate analysis; Mathematics; Identification (biology); Computer science; Econometrics; Medicine; Artificial intelligence","score_opus":0.08759181864643606,"score_gpt":0.47753398370413297,"score_spread":0.3899421650576969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246282759","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":[0.006664239,0.00068096694,0.98860115,0.0013257953,0.000029985515,0.00006776261,0.000050555816,0.000088221466,0.0024913177],"genre_scores_gemma":[0.2516777,0.0014863467,0.7434283,0.00095465116,0.000183599,0.00045816464,0.0001605693,0.00015064912,0.0015000013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9602341,0.03350849,0.0006618162,0.0013849771,0.003855386,0.00035511804],"domain_scores_gemma":[0.75539917,0.22460303,0.0034142882,0.007070786,0.008815262,0.00069756835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07930581,0.001331552,0.0015970165,0.0024022348,0.0011970117,0.0033243138,0.00363641,0.0024311335,0.0034529457],"category_scores_gemma":[0.19872572,0.0012690483,0.0013463855,0.0019513431,0.0043350426,0.0056425342,0.0032459048,0.0031217234,0.0004953556],"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":[0.00012842806,0.00010914255,0.007658502,0.00055630616,0.00026595787,0.00021835172,0.00086129276,0.23672907,0.00083682616,0.60945326,0.0020074158,0.14117545],"study_design_scores_gemma":[0.00003247178,0.00009673171,0.0012253361,0.00027030107,0.000036991994,0.000116890216,0.0001560274,0.6255801,0.00051435304,0.36874548,0.00317313,0.000052166673],"about_ca_topic_score_codex":0.009698487,"about_ca_topic_score_gemma":0.008564135,"teacher_disagreement_score":0.07930581,"about_ca_system_score_codex":0.0022751314,"about_ca_system_score_gemma":0.0038912965,"threshold_uncertainty_score":0.41941422},"labels":[],"label_agreement":null},{"id":"W4246939024","doi":"10.1007/978-1-4939-7131-2_101259","title":"Statistical Modeling","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.15642088282245298,"score_gpt":0.3953606447380463,"score_spread":0.2389397619155933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246939024","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":[0.00062092405,0.01514369,0.6042905,0.009050885,0.0023658667,0.000083493214,0.00114566,0.0018528453,0.3654461],"genre_scores_gemma":[0.040876552,0.027877985,0.18805797,0.0048309923,0.0058825137,0.00048062185,0.0029103493,0.002355801,0.72672725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871635,0.00041950154,0.00005039352,0.0002160144,0.0005553958,0.000042426655],"domain_scores_gemma":[0.9984439,0.0008981547,0.00004870594,0.00031775353,0.00024524482,0.000046325084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001514017,0.0013979845,0.0010175863,0.0015480355,0.0006158973,0.0028304388,0.0012873,0.0014065143,0.048577655],"category_scores_gemma":[0.0053140903,0.0005596808,0.000770029,0.0015846225,0.0016791782,0.002634201,0.0014137513,0.003134751,0.034055896],"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":[0.0000047329872,0.000018322351,0.00007924935,0.00013654656,0.000018364984,0.000028498547,0.00010247729,0.0024928784,0.00018853867,0.6943318,0.16870803,0.13389064],"study_design_scores_gemma":[0.0000022898955,0.0000040201107,0.000083362014,0.00009530742,0.0000070831147,0.000059405258,0.00002096653,0.0039916043,0.00015457885,0.65653753,0.33903456,0.000009296871],"about_ca_topic_score_codex":0.0014642153,"about_ca_topic_score_gemma":0.0020834673,"teacher_disagreement_score":0.048577655,"about_ca_system_score_codex":0.0013778447,"about_ca_system_score_gemma":0.0015780283,"threshold_uncertainty_score":0.16250849},"labels":[],"label_agreement":null},{"id":"W4247585342","doi":"10.1002/9781118445112.stat06788.pub2","title":"Overdispersion","year":2016,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Overdispersion; Quasi-likelihood; Covariate; Statistics; Econometrics; Poisson distribution; Mathematics; Negative binomial distribution; Count data","score_opus":0.07573905880162594,"score_gpt":0.40998483606810554,"score_spread":0.3342457772664796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247585342","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":[0.13655758,0.023851896,0.74270177,0.0083920285,0.005145991,0.0028269826,0.011509004,0.003215248,0.06579952],"genre_scores_gemma":[0.78834885,0.008368895,0.15784414,0.013361117,0.0015564094,0.0029423975,0.005206956,0.002061362,0.020309927],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85749847,0.06779369,0.015230369,0.021546787,0.03617322,0.0017574909],"domain_scores_gemma":[0.6128195,0.27770284,0.03904081,0.053226653,0.015659036,0.0015511813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08164184,0.0010351371,0.002188014,0.005437666,0.001428858,0.0036206995,0.0030554265,0.0011023496,0.011256793],"category_scores_gemma":[0.22283512,0.00060123566,0.0017063705,0.00633419,0.0044292547,0.0030891648,0.004204499,0.0025913233,0.0022957548],"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":[0.001123323,0.00035406978,0.11346512,0.00622435,0.0026996147,0.0029319944,0.012897122,0.006837125,0.007908575,0.20346099,0.07143558,0.57066214],"study_design_scores_gemma":[0.00012797712,0.0006534497,0.10791182,0.0052860766,0.001614531,0.009178722,0.0048607932,0.020814022,0.024323327,0.505505,0.31917912,0.0005451439],"about_ca_topic_score_codex":0.0012803118,"about_ca_topic_score_gemma":0.0022317201,"teacher_disagreement_score":0.08164184,"about_ca_system_score_codex":0.0022620761,"about_ca_system_score_gemma":0.002130055,"threshold_uncertainty_score":0.43176848},"labels":[],"label_agreement":null},{"id":"W4247820985","doi":"10.22215/etd/2012-06973","title":"Analysis of incomplete binary longitudinal data with an application to the national population health survey","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian 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":"Carleton University; Library and Archives Canada","funders":"","keywords":"Population; Binary number; Library science; Political science; Humanities; Computer science; Demography; Sociology; Mathematics; Art; Arithmetic","score_opus":0.23859331699838954,"score_gpt":0.48724492981175577,"score_spread":0.24865161281336623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247820985","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":[0.00988165,0.001508323,0.9832849,0.0015306298,0.00013450679,0.00038416247,0.0016421981,0.0009387316,0.00069484534],"genre_scores_gemma":[0.07039722,0.0022660666,0.91858125,0.00020678069,0.00030149115,0.0023082634,0.0039569316,0.0003312098,0.0016508376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9707207,0.024441153,0.0010626386,0.0013590092,0.0021751218,0.00024135747],"domain_scores_gemma":[0.8843411,0.10479906,0.0039469223,0.0042330623,0.0020860247,0.0005938662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035802342,0.0012452684,0.002806944,0.0045381775,0.0012702607,0.0019535804,0.0020680134,0.0011461605,0.0075445026],"category_scores_gemma":[0.16181092,0.0013756839,0.0037846789,0.0066915136,0.0012149469,0.0017934273,0.003185995,0.0041012494,0.0012715328],"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":[0.00071522273,0.000594296,0.0418723,0.0018080947,0.0038703976,0.0006111003,0.0015945433,0.112070955,0.00086599373,0.1555943,0.027609993,0.6527928],"study_design_scores_gemma":[0.0003551043,0.00035207113,0.027220871,0.0005567091,0.0008854428,0.00042436147,0.00033378613,0.69130915,0.00061348744,0.2558248,0.022010539,0.000113663446],"about_ca_topic_score_codex":0.011804513,"about_ca_topic_score_gemma":0.0156188,"teacher_disagreement_score":0.035802342,"about_ca_system_score_codex":0.0013475162,"about_ca_system_score_gemma":0.0049293865,"threshold_uncertainty_score":0.18934321},"labels":[],"label_agreement":null},{"id":"W4248009289","doi":"10.1002/0470011815.b2a04051","title":"Sensitivity Analysis","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","topic":"Statistical Methods and Bayesian 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":"McMaster University","funders":"","keywords":"Sensitivity (control systems); Monte Carlo method; Interpretation (philosophy); Computer science; Outcome (game theory); Statistics; Econometrics; Mathematics; Mathematical economics; Engineering","score_opus":0.02202452421983665,"score_gpt":0.33820444793262033,"score_spread":0.3161799237127837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248009289","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":[0.021416025,0.033289008,0.6289963,0.008682457,0.010568747,0.12391809,0.08554169,0.0026670445,0.08492075],"genre_scores_gemma":[0.47511977,0.011511535,0.22675574,0.009554986,0.0018870211,0.22740316,0.017596778,0.0018867452,0.028284192],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.66980696,0.27363598,0.01845651,0.009722574,0.025432011,0.0029460206],"domain_scores_gemma":[0.5168818,0.4127167,0.01391731,0.022993816,0.03259882,0.00089153595],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19407852,0.0042994595,0.0060281996,0.00803949,0.001076612,0.004661942,0.0036831393,0.003589473,0.071642436],"category_scores_gemma":[0.52872497,0.0011333396,0.021693327,0.0065413225,0.0017413824,0.003963648,0.0035746475,0.006737362,0.005654667],"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":[0.015862921,0.0009637055,0.00782788,0.08039821,0.06911758,0.0010688648,0.001323006,0.17987803,0.0017905358,0.16423555,0.16382407,0.31370974],"study_design_scores_gemma":[0.0040560346,0.0063719084,0.0106017,0.036358252,0.04219717,0.0010670474,0.0011147063,0.112493105,0.00958928,0.38316175,0.39213946,0.000849573],"about_ca_topic_score_codex":0.001992922,"about_ca_topic_score_gemma":0.0014514468,"teacher_disagreement_score":0.19407852,"about_ca_system_score_codex":0.0056476467,"about_ca_system_score_gemma":0.0057163206,"threshold_uncertainty_score":0.9938447},"labels":[],"label_agreement":null},{"id":"W4248340588","doi":"10.1002/9781118445112.stat05261.pub2","title":"Sensitivity Analysis: Introduction","year":2016,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Statistical Methods and Bayesian Inference","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":"Sensitivity (control systems); Monte Carlo method; Interpretation (philosophy); Computer science; Econometrics; Data mining; Statistics; Mathematics; Engineering","score_opus":0.05729887102148126,"score_gpt":0.391421062240089,"score_spread":0.33412219121860776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248340588","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":[0.0054178284,0.047507983,0.78010863,0.022477439,0.009353636,0.020385185,0.020994944,0.001808477,0.09194586],"genre_scores_gemma":[0.24674162,0.03091319,0.5773189,0.021790212,0.0064550852,0.075877465,0.0071975845,0.0024926902,0.031213192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.74094373,0.22083741,0.011376699,0.0074900305,0.017746923,0.0016051613],"domain_scores_gemma":[0.63504815,0.30860668,0.009864435,0.019075904,0.026655734,0.00074918376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.122526884,0.003751803,0.004773607,0.006044373,0.0011968149,0.006326832,0.003565171,0.004179165,0.052728765],"category_scores_gemma":[0.36816627,0.0011062999,0.010166169,0.0060640196,0.0028774792,0.005050889,0.0039400267,0.006411611,0.005650156],"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":[0.0017046666,0.00025903518,0.0022412944,0.058164276,0.00788569,0.00045525824,0.001133149,0.06753024,0.0015819402,0.42289525,0.17031787,0.26583126],"study_design_scores_gemma":[0.0004133047,0.00077920384,0.0017441466,0.024486689,0.0025129437,0.00045573647,0.00040295505,0.03293383,0.003292289,0.57916594,0.35348856,0.00032438055],"about_ca_topic_score_codex":0.0020050386,"about_ca_topic_score_gemma":0.0013645185,"teacher_disagreement_score":0.122526884,"about_ca_system_score_codex":0.005060889,"about_ca_system_score_gemma":0.0061625694,"threshold_uncertainty_score":0.64799184},"labels":[],"label_agreement":null},{"id":"W4248381369","doi":"10.1002/0470011815.b2a15002","title":"Ancillary Statistics","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Statistics; Likelihood function; Sufficient statistic; Mathematics; Likelihood principle; Marginal likelihood; Statistic; Estimator; Conditional probability distribution; M-estimator; Restricted maximum likelihood; Maximum likelihood; Quasi-maximum likelihood","score_opus":0.02386953428331505,"score_gpt":0.3352108935469497,"score_spread":0.31134135926363465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248381369","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":[0.0034595379,0.0012878387,0.97008127,0.0024058386,0.00078935694,0.0001819367,0.0027551055,0.0008657964,0.018173289],"genre_scores_gemma":[0.30428496,0.004746585,0.6381348,0.0040339697,0.003909979,0.0019838398,0.009416586,0.0015803815,0.031908955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98216647,0.00844424,0.0011386729,0.0029752327,0.0046390845,0.0006364195],"domain_scores_gemma":[0.9408408,0.035595827,0.0046524913,0.0117417425,0.0063783396,0.0007907975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020762421,0.0021266646,0.0036805645,0.0052820677,0.0015724128,0.007448531,0.0049762973,0.003589802,0.03744629],"category_scores_gemma":[0.10709501,0.0010653528,0.0021055199,0.005615319,0.0044356957,0.010137098,0.0040540393,0.0061277715,0.010762716],"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":[0.00010361537,0.00006658265,0.0017354685,0.000368253,0.00012052583,0.00035611217,0.00014185629,0.0124007575,0.0005696667,0.89982444,0.01962138,0.06469122],"study_design_scores_gemma":[0.000028792352,0.000046106416,0.0005744243,0.0002523542,0.00004633236,0.00032880783,0.000052515516,0.055708665,0.0008821646,0.9104702,0.031552386,0.000057232915],"about_ca_topic_score_codex":0.0019932531,"about_ca_topic_score_gemma":0.001508392,"teacher_disagreement_score":0.03744629,"about_ca_system_score_codex":0.0023582242,"about_ca_system_score_gemma":0.0041774744,"threshold_uncertainty_score":0.1252703},"labels":[],"label_agreement":null},{"id":"W4248511272","doi":"10.1007/978-1-0716-1138-8_8","title":"Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions","year":2021,"lang":"en","type":"book-chapter","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland; University of Calgary","funders":"","keywords":"Longitudinal data; Outcome (game theory); Generalized linear model; Linear regression; Regression analysis; Statistics; Linear model; Generalized linear mixed model; Mixed model; Econometrics; Computer science; Regression; Mathematics; Data mining","score_opus":0.47802350884855266,"score_gpt":0.5590137511763804,"score_spread":0.08099024232782776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248511272","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":[0.0018726146,0.020877974,0.96134424,0.0048265457,0.0009936285,0.00007562014,0.00105705,0.00086522615,0.008087088],"genre_scores_gemma":[0.059815086,0.047748666,0.8212684,0.0050286376,0.004383616,0.0012788605,0.0027729028,0.0019876815,0.055716068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943763,0.003798028,0.00027631572,0.00075008854,0.0007076448,0.00009159285],"domain_scores_gemma":[0.9727102,0.02274194,0.0008116836,0.0024311605,0.0011163168,0.00018866871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02016273,0.0014898657,0.0015908047,0.0015544078,0.0003832474,0.0023926003,0.002364634,0.002050565,0.010867813],"category_scores_gemma":[0.040791668,0.001510761,0.0023892291,0.0026070075,0.0013908201,0.0039329464,0.0016335858,0.0034015633,0.003061533],"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":[0.0000914056,0.00012289228,0.0028191595,0.00108307,0.0006645383,0.00020400842,0.00048102345,0.02135758,0.00051194674,0.5123964,0.10669372,0.35357422],"study_design_scores_gemma":[0.00003010889,0.00006345485,0.0020200114,0.00044559716,0.00021854178,0.00024950708,0.00008159552,0.048051268,0.00033159903,0.86836135,0.08009668,0.0000503393],"about_ca_topic_score_codex":0.0032457754,"about_ca_topic_score_gemma":0.0033909387,"teacher_disagreement_score":0.02016273,"about_ca_system_score_codex":0.0008947277,"about_ca_system_score_gemma":0.0020867852,"threshold_uncertainty_score":0.106631994},"labels":[],"label_agreement":null},{"id":"W4250741417","doi":"10.1046/j.1360-0443.95.11s3.6.x","title":"Multivariate modeling of missing data within and across assessment waves","year":2000,"lang":"en","type":"article","venue":"Addiction","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":73,"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":"Missing data; Multivariate statistics; Imputation (statistics); Latent variable; Multivariate analysis; Computer science; Latent variable model; Data mining; Statistics; Context (archaeology); Longitudinal data; Econometrics; Mathematics; Geography","score_opus":0.1426379706913055,"score_gpt":0.44618665540916297,"score_spread":0.30354868471785745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250741417","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":[0.012067706,0.0014757016,0.98144114,0.0018592809,0.00018194238,0.00021702779,0.0012410997,0.00022519998,0.0012909235],"genre_scores_gemma":[0.38347235,0.008724276,0.59127784,0.001196899,0.0007015275,0.003178766,0.004590774,0.00028547787,0.0065721],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9677259,0.025545018,0.0013920367,0.0023896273,0.0023161382,0.00063131534],"domain_scores_gemma":[0.8829791,0.09253831,0.010682915,0.009887312,0.0031900913,0.00072231813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047936555,0.0014863859,0.0037279124,0.002865827,0.0015371594,0.0031517986,0.0057866704,0.002525911,0.0070718834],"category_scores_gemma":[0.13246301,0.0011113881,0.0032193046,0.0077115865,0.002256563,0.0047216397,0.003821498,0.0051576803,0.0011635161],"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":[0.0005741862,0.00034687208,0.06469554,0.0021083897,0.0023456186,0.0010012706,0.0033505757,0.10928916,0.00090879656,0.4993832,0.013836936,0.30215955],"study_design_scores_gemma":[0.000091406386,0.00034529553,0.019109368,0.00077612774,0.00059797516,0.00073236314,0.0007059456,0.29343423,0.00092011737,0.66833866,0.014777287,0.00017123095],"about_ca_topic_score_codex":0.0039224657,"about_ca_topic_score_gemma":0.0051656207,"teacher_disagreement_score":0.047936555,"about_ca_system_score_codex":0.0016139906,"about_ca_system_score_gemma":0.0034862775,"threshold_uncertainty_score":0.25351578},"labels":[],"label_agreement":null},{"id":"W4251373018","doi":"10.1007/978-94-007-0753-5_103405","title":"Random Effect Modeling","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Computer science","score_opus":0.06775674344892112,"score_gpt":0.35269935889960735,"score_spread":0.2849426154506862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251373018","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":[0.00032421446,0.012379313,0.90312713,0.003358155,0.0011586569,0.00007379167,0.0007744922,0.0011082594,0.07769598],"genre_scores_gemma":[0.032447748,0.03126421,0.5863234,0.0052731843,0.0033433894,0.00085871236,0.0029267822,0.0026559057,0.33490673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977982,0.0012103694,0.00006808299,0.00032492628,0.00054785394,0.000050601735],"domain_scores_gemma":[0.9968635,0.0024915428,0.00007398436,0.00029857637,0.00022725371,0.000045203265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034123883,0.001890367,0.0016839891,0.0013426578,0.0004609654,0.0019514285,0.0019602757,0.0017896097,0.038233485],"category_scores_gemma":[0.008686196,0.00094243995,0.0013096818,0.0013388201,0.0013536176,0.0019215324,0.0012488301,0.0034101673,0.01781711],"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":[0.000017073598,0.000040334136,0.00016639127,0.0003879949,0.0001319389,0.00006323335,0.00012561357,0.007970297,0.00029319752,0.5831489,0.15733244,0.25032255],"study_design_scores_gemma":[0.000009046378,0.000011972405,0.00016444038,0.0002292607,0.00005208776,0.00012822456,0.000020936497,0.010193204,0.00026946535,0.7832309,0.2056668,0.000023713303],"about_ca_topic_score_codex":0.0019643118,"about_ca_topic_score_gemma":0.0037585192,"teacher_disagreement_score":0.038233485,"about_ca_system_score_codex":0.0010398588,"about_ca_system_score_gemma":0.0015881947,"threshold_uncertainty_score":0.12790376},"labels":[],"label_agreement":null},{"id":"W4252671350","doi":"10.1007/978-1-4939-7131-2_101258","title":"Statistical Inference","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Statistical inference; Inference; Computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.11948148983748225,"score_gpt":0.40803036724661507,"score_spread":0.2885488774091328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252671350","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":[0.00041202593,0.009203481,0.6891144,0.008930329,0.0025159009,0.000109910514,0.0013777713,0.0019050614,0.2864311],"genre_scores_gemma":[0.035209365,0.02132859,0.32552612,0.007609868,0.008921311,0.00082326686,0.00428049,0.004041532,0.59225947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99715096,0.001086837,0.00011228494,0.00045793428,0.0011173903,0.00007457269],"domain_scores_gemma":[0.9953216,0.0030889525,0.00012202937,0.0007861429,0.00060153013,0.00007980885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033797303,0.001422628,0.0013426692,0.0020202382,0.0007820793,0.003152407,0.0016406976,0.0017756579,0.084833995],"category_scores_gemma":[0.013672762,0.00075173244,0.00092015095,0.0016218927,0.0026623455,0.0028431248,0.0017835024,0.0045358795,0.054353643],"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":[0.000009378051,0.000025461257,0.000104712206,0.00024784636,0.000032529344,0.00003291056,0.000112445705,0.001699061,0.00019903903,0.60343266,0.2220862,0.17201777],"study_design_scores_gemma":[0.0000047365024,0.0000044912913,0.000105801126,0.00013641974,0.000010944195,0.00006520388,0.000020997819,0.002577921,0.00018529741,0.7319922,0.26488495,0.000011066371],"about_ca_topic_score_codex":0.0011895304,"about_ca_topic_score_gemma":0.0019178914,"teacher_disagreement_score":0.084833995,"about_ca_system_score_codex":0.0014455856,"about_ca_system_score_gemma":0.002170464,"threshold_uncertainty_score":0.28379798},"labels":[],"label_agreement":null},{"id":"W4253423488","doi":"10.1002/9780471462422.eoct967","title":"Overdispersion","year":2008,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Clinical Trials","topic":"Statistical Methods and Bayesian Inference","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":"Overdispersion; Quasi-likelihood; Covariate; Econometrics; Statistics; Poisson distribution; Mathematics; Negative binomial distribution; Count data","score_opus":0.30815139468200614,"score_gpt":0.5324664972897644,"score_spread":0.22431510260775822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253423488","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":[0.071009375,0.034748957,0.81391156,0.011518871,0.006263899,0.004817499,0.008833493,0.0022270207,0.04666924],"genre_scores_gemma":[0.73187786,0.012445575,0.20366506,0.019781051,0.0028030307,0.006414326,0.0036324195,0.0014491435,0.017931426],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.7485189,0.15063603,0.029815057,0.024020655,0.04524574,0.0017636393],"domain_scores_gemma":[0.44695473,0.42532107,0.046320587,0.06642744,0.013637026,0.0013391669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15089719,0.0012124002,0.0027525641,0.004357373,0.00093790423,0.003026646,0.002785964,0.0014227418,0.00989143],"category_scores_gemma":[0.36523762,0.00057417894,0.002328899,0.0058902116,0.004120126,0.0030926454,0.0036112505,0.0028459516,0.0016494949],"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":[0.0021766739,0.00035516452,0.06537336,0.008530834,0.004972673,0.003083586,0.006970506,0.007824101,0.0031728798,0.19169417,0.06223297,0.64361304],"study_design_scores_gemma":[0.00033784172,0.0012779996,0.077204995,0.007783865,0.0032119132,0.010485235,0.0025644433,0.029234808,0.014368597,0.6033267,0.24967875,0.00052488677],"about_ca_topic_score_codex":0.00076724484,"about_ca_topic_score_gemma":0.0012445729,"teacher_disagreement_score":0.15089719,"about_ca_system_score_codex":0.0025610595,"about_ca_system_score_gemma":0.002418911,"threshold_uncertainty_score":0.79803014},"labels":[],"label_agreement":null},{"id":"W4280606004","doi":"10.3390/math10101630","title":"Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Statistical Methods and Bayesian Inference","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":"Toronto Metropolitan University","funders":"","keywords":"Categorical variable; Generalized linear model; Variable (mathematics); Econometrics; Variables; Inference; Statistical model; Computer science; Linear model; Statistical inference; Benchmark (surveying); Statistics; Mathematics; Machine learning; Artificial intelligence","score_opus":0.15234496238944384,"score_gpt":0.35669884661460366,"score_spread":0.20435388422515982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280606004","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":[0.007817367,0.00035319035,0.99100715,0.00024102848,0.000031651594,0.000062238054,0.000082897015,0.0001413663,0.00026309266],"genre_scores_gemma":[0.38373142,0.001599091,0.61041987,0.00037402834,0.00041800798,0.0010890033,0.0008619199,0.0002499751,0.0012567086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.971314,0.023207355,0.00069958006,0.0021661825,0.0021038556,0.0005089415],"domain_scores_gemma":[0.8460433,0.14183442,0.004912305,0.0045479797,0.0021481393,0.0005138884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032597054,0.0025251363,0.0021695856,0.003542303,0.00093872496,0.0025276886,0.0028381287,0.0021478299,0.0024022963],"category_scores_gemma":[0.11944911,0.0010518258,0.0028924004,0.0037431244,0.0034898873,0.0036887832,0.0028016479,0.005891555,0.0005701393],"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":[0.00029624422,0.00027755566,0.019314498,0.00075272017,0.0009341472,0.0002642207,0.00090139563,0.545609,0.001667481,0.271868,0.0024010676,0.15571372],"study_design_scores_gemma":[0.000021397243,0.000130899,0.0019895984,0.00009700805,0.000083390834,0.00005280935,0.00007221832,0.8209823,0.000426309,0.17492135,0.0011795149,0.000043263462],"about_ca_topic_score_codex":0.004031107,"about_ca_topic_score_gemma":0.0037373533,"teacher_disagreement_score":0.032597054,"about_ca_system_score_codex":0.0016551435,"about_ca_system_score_gemma":0.0017711628,"threshold_uncertainty_score":0.17239171},"labels":[],"label_agreement":null},{"id":"W4281712587","doi":"10.1002/cjs.11704","title":"Likelihood identifiability and parameter estimation with nonignorable missing data","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Identifiability; Inverse probability weighting; Estimator; Covariate; Mathematics; Weighting; Estimation theory; Estimating equations; Statistics; Applied mathematics; Econometrics","score_opus":0.09716382581356199,"score_gpt":0.3315870536896224,"score_spread":0.2344232278760604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281712587","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":[0.0076889168,0.00017703921,0.9912844,0.0003383646,0.000019910682,0.000034920216,0.000058142694,0.00006413471,0.0003341396],"genre_scores_gemma":[0.48957267,0.0009320605,0.5053947,0.0005133157,0.00030388814,0.0006764346,0.00072878256,0.00013276421,0.0017454164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9816067,0.012964048,0.0009245208,0.0020139837,0.0018478049,0.00064290664],"domain_scores_gemma":[0.7825383,0.18480742,0.011771501,0.014132446,0.0058298893,0.00092048565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035476033,0.0010893388,0.0030487245,0.0023451573,0.0007684265,0.0016168045,0.0033180541,0.0028542439,0.0032441807],"category_scores_gemma":[0.23385362,0.0011357681,0.0018362402,0.0025706762,0.0041087507,0.005663771,0.0036040756,0.0041948296,0.00053760706],"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":[0.00029954733,0.00018941407,0.011999115,0.0008176444,0.0003906802,0.0008484795,0.0010063684,0.23734005,0.0024090973,0.6397164,0.003374738,0.101608545],"study_design_scores_gemma":[0.000076951605,0.00006308978,0.0016093656,0.00014382458,0.000052728516,0.00020894047,0.00013720802,0.554507,0.0016690572,0.44017965,0.0012945362,0.000057725123],"about_ca_topic_score_codex":0.001978676,"about_ca_topic_score_gemma":0.001074272,"teacher_disagreement_score":0.035476033,"about_ca_system_score_codex":0.0010191032,"about_ca_system_score_gemma":0.0032782338,"threshold_uncertainty_score":0.18761742},"labels":[],"label_agreement":null},{"id":"W4283027542","doi":"10.1007/s10260-022-00650-5","title":"Maximum likelihood estimation of missing data probability for nonmonotone missing at random data","year":2022,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Statistical Methods and Bayesian Inference","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":"University of Regina","funders":"","keywords":"Missing data; Estimator; Covariate; Curse of dimensionality; Computer science; Parametric statistics; Semiparametric model; Expectation–maximization algorithm; Statistics; Mathematics; Maximum likelihood","score_opus":0.20789293719568186,"score_gpt":0.4920344555373569,"score_spread":0.28414151834167506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283027542","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":[0.002738489,0.0003814063,0.9962042,0.00018169831,0.000032754688,0.000035693523,0.00017453088,0.00012360464,0.00012761513],"genre_scores_gemma":[0.20374295,0.0016994464,0.7871434,0.0004367519,0.0004613118,0.0007365765,0.0027244706,0.0002741149,0.002780947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98262537,0.0115655465,0.0010265887,0.0027850156,0.0014883477,0.0005091075],"domain_scores_gemma":[0.8656315,0.11711674,0.0044903182,0.00897922,0.002913836,0.00086848787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032129914,0.0015704595,0.005690721,0.0029957385,0.0014643101,0.0033174497,0.007270118,0.0036949518,0.0039622965],"category_scores_gemma":[0.13336828,0.0026037437,0.0035854334,0.0043485607,0.0034216973,0.006320079,0.004096961,0.0058383304,0.0012746869],"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":[0.001381743,0.00042184163,0.009538892,0.002979224,0.0017988014,0.0015404831,0.0009562231,0.4959234,0.0031227183,0.27041337,0.0069648265,0.20495844],"study_design_scores_gemma":[0.00012664468,0.00008530713,0.0010947085,0.0002230221,0.00013681623,0.00040357825,0.00008325402,0.76010484,0.00097422785,0.2351266,0.0015642609,0.000076680226],"about_ca_topic_score_codex":0.0026112774,"about_ca_topic_score_gemma":0.002821707,"teacher_disagreement_score":0.032129914,"about_ca_system_score_codex":0.0013703039,"about_ca_system_score_gemma":0.004167269,"threshold_uncertainty_score":0.16992128},"labels":[],"label_agreement":null},{"id":"W4283169482","doi":"10.1002/cjs.11705","title":"Reducing bias due to misclassified exposures using instrumental variables","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Simons Foundation","keywords":"Identifiability; Instrumental variable; Inference; Markov chain Monte Carlo; Causal inference; Statistics; Computer science; Econometrics; Variable (mathematics); Bayesian probability; Mathematics; Data mining; Artificial intelligence","score_opus":0.1406598783673715,"score_gpt":0.3340043728408001,"score_spread":0.19334449447342858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283169482","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":[0.011678667,0.00032427517,0.986717,0.00037965193,0.00004870717,0.000055238215,0.00007997347,0.00017088802,0.0005455941],"genre_scores_gemma":[0.42787334,0.0007506434,0.5687287,0.00045609655,0.00014240055,0.0003117673,0.0002597677,0.00015222302,0.0013249606],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97410226,0.020815516,0.00086161395,0.001645948,0.002205414,0.00036925756],"domain_scores_gemma":[0.87561154,0.10550977,0.006279371,0.009569038,0.0026806192,0.0003496685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04436203,0.0008908304,0.0014533548,0.002821106,0.00080140214,0.0023851309,0.002574375,0.0015690364,0.0017684998],"category_scores_gemma":[0.15768594,0.00070999074,0.0014744111,0.002438407,0.0020850592,0.0017886637,0.0033118292,0.0023899921,0.00029000235],"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":[0.00035301215,0.00019352642,0.059779663,0.00068596576,0.0020517542,0.00037789874,0.00092055235,0.30787808,0.0025808495,0.28549278,0.004344976,0.33534098],"study_design_scores_gemma":[0.00008417759,0.00010214194,0.007397914,0.00030316896,0.00030764143,0.00013733275,0.00013025638,0.73500246,0.0031156626,0.24941812,0.0039200718,0.000081138984],"about_ca_topic_score_codex":0.0052386974,"about_ca_topic_score_gemma":0.004296937,"teacher_disagreement_score":0.04436203,"about_ca_system_score_codex":0.0011191336,"about_ca_system_score_gemma":0.002897427,"threshold_uncertainty_score":0.23461163},"labels":[],"label_agreement":null},{"id":"W4283527500","doi":"10.25011/cim.v45i2.38100","title":"Estimating Disease Prevalence in Administrative Data","year":2022,"lang":"en","type":"review","venue":"Clinical and investigative medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Identification (biology); Estimation; Disease; Population; Observational error; Medicine; Prevalence; Econometrics; Mathematics; Environmental health; Internal medicine","score_opus":0.8085487540728347,"score_gpt":0.5951514981483315,"score_spread":0.2133972559245032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283527500","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":[0.0008012777,0.9460815,0.04621496,0.0028640355,0.0005166281,0.00012928242,0.000576215,0.00012903754,0.0026869725],"genre_scores_gemma":[0.0144609595,0.9506717,0.03158112,0.0009478403,0.0007547111,0.0002881457,0.0007353976,0.000039713268,0.00052040216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98134536,0.010752538,0.0016833006,0.0013630587,0.004642926,0.00021282413],"domain_scores_gemma":[0.95978594,0.031895313,0.003288795,0.0012236065,0.0036508117,0.0001556502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020994842,0.0011679211,0.0035829318,0.011469246,0.0002907615,0.0022883236,0.0025090207,0.0012935122,0.0020977138],"category_scores_gemma":[0.06587967,0.000796799,0.0025234192,0.0100479,0.001742331,0.002666674,0.0017939985,0.0026811413,0.0012841247],"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":[0.000046111167,0.00004488837,0.0038291942,0.029957179,0.0014952399,0.00006392367,0.00016656212,0.0039073853,0.00019800673,0.029674295,0.014796553,0.9158207],"study_design_scores_gemma":[0.00017788658,0.00033348583,0.026186673,0.12872267,0.0030775191,0.0019473467,0.00052653207,0.015896263,0.0019494558,0.14580664,0.6750791,0.00029641786],"about_ca_topic_score_codex":0.005816424,"about_ca_topic_score_gemma":0.0032078656,"teacher_disagreement_score":0.020994842,"about_ca_system_score_codex":0.0027067403,"about_ca_system_score_gemma":0.0036582383,"threshold_uncertainty_score":0.111032665},"labels":[],"label_agreement":null},{"id":"W4283716116","doi":"10.5539/ijsp.v11n4p63","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 4","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Mathematical economics","score_opus":0.059989785445832534,"score_gpt":0.3833602682881854,"score_spread":0.32337048284235287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283716116","genre_codex":"editorial","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":[0.00012630591,0.0023686704,0.0015929034,0.12646477,0.86583406,0.0005094625,0.0006895269,0.0005043794,0.0019098924],"genre_scores_gemma":[0.004670411,0.0061143446,0.0045626406,0.16016689,0.77011573,0.0033827387,0.0016047342,0.0015973968,0.047785122],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9304794,0.01382229,0.014671588,0.0045424635,0.034226708,0.0022574954],"domain_scores_gemma":[0.13830581,0.03892302,0.0111911185,0.0064283474,0.7966406,0.008511144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05532402,0.0033426017,0.009654338,0.012251934,0.004930304,0.010863788,0.006273073,0.018235654,0.09209779],"category_scores_gemma":[0.5368561,0.001899549,0.0064061033,0.005402558,0.004124315,0.0070236516,0.004302081,0.013729472,0.06104205],"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":[0.000037892845,0.0000042622664,0.00008377674,0.00040364047,0.00001299538,0.000060242364,0.000031384385,0.000012567572,0.000036609334,0.00012779691,0.9958144,0.0033744792],"study_design_scores_gemma":[0.0003568554,0.00007349064,0.0014610956,0.0043985187,0.00018123226,0.0014195397,0.00044229766,0.0006656841,0.00041331374,0.0033267308,0.9870226,0.00023864734],"about_ca_topic_score_codex":0.0038016739,"about_ca_topic_score_gemma":0.005366194,"teacher_disagreement_score":0.09209779,"about_ca_system_score_codex":0.005577484,"about_ca_system_score_gemma":0.0116573945,"threshold_uncertainty_score":0.30809778},"labels":[],"label_agreement":null},{"id":"W4283789493","doi":"10.1002/cjs.11708","title":"Pseudo empirical likelihood inference for nonprobability survey samples","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Carleton University; University of Waterloo; Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Nonprobability sampling; Estimator; Inference; Survey sampling; Point estimation; Statistical inference; Statistics; Sampling (signal processing); Computer science; Survey data collection; Range (aeronautics); Econometrics; Survey research; Mathematics; Artificial intelligence; Engineering; Psychology","score_opus":0.16475279778977409,"score_gpt":0.3922188911423007,"score_spread":0.2274660933525266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283789493","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":[0.0042458065,0.0002071149,0.99439114,0.00026847713,0.000027452596,0.00006765809,0.0000652682,0.000053696804,0.00067330874],"genre_scores_gemma":[0.40260145,0.0012431244,0.58991957,0.00071191095,0.00025477752,0.0011752823,0.00072065863,0.00013385215,0.003239352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96029794,0.033045422,0.0008788826,0.0019723668,0.0035030306,0.0003024705],"domain_scores_gemma":[0.7705116,0.20877884,0.0054348814,0.009608541,0.0050181937,0.0006479502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03794508,0.0008109709,0.0015701783,0.0023234333,0.0006783282,0.0024650218,0.003396199,0.0015239097,0.0049649724],"category_scores_gemma":[0.24028336,0.000861693,0.0012853808,0.0024590539,0.0045168563,0.004818644,0.0028042386,0.0033015877,0.0005975053],"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":[0.00010555578,0.00006761364,0.003976581,0.0003002829,0.0001552388,0.00017280888,0.00032001533,0.05677347,0.00025961705,0.87900615,0.0015752183,0.057287488],"study_design_scores_gemma":[0.00006364433,0.000049165705,0.0016579594,0.00010944671,0.000028559212,0.00010608222,0.00008259108,0.41368175,0.0003960993,0.58103,0.00276969,0.000025040194],"about_ca_topic_score_codex":0.0037925062,"about_ca_topic_score_gemma":0.0028573803,"teacher_disagreement_score":0.03794508,"about_ca_system_score_codex":0.001678872,"about_ca_system_score_gemma":0.0023312878,"threshold_uncertainty_score":0.20067513},"labels":[],"label_agreement":null},{"id":"W4283804698","doi":"10.1002/sim.9512","title":"Unified estimation for Cox regression model with nonmonotone missing at random covariates","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Regina","funders":"","keywords":"Covariate; Estimator; Missing data; Computer science; Proportional hazards model; Imputation (statistics); Statistics; Mathematics; Econometrics","score_opus":0.06925157385471965,"score_gpt":0.40727885511625317,"score_spread":0.33802728126153353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283804698","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":[0.0025698452,0.00037364292,0.9963856,0.00012340274,0.000028470085,0.000052118037,0.00010464199,0.00009217924,0.0002701401],"genre_scores_gemma":[0.23014486,0.001793195,0.76186484,0.00045436458,0.00034078854,0.0011718032,0.0016270225,0.00012659022,0.0024764999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98000526,0.01461467,0.00068887195,0.0020236822,0.0021308826,0.00053654826],"domain_scores_gemma":[0.9724204,0.018522652,0.0028315561,0.0038952786,0.0019803771,0.00034974408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025119184,0.0010414892,0.0025604018,0.0022825315,0.00068239454,0.00196885,0.003919985,0.0014053796,0.0033047367],"category_scores_gemma":[0.059831966,0.0010393942,0.0027809183,0.0037448248,0.0010596855,0.0030733559,0.0029178206,0.0026856968,0.0006227695],"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":[0.00020999463,0.00015365497,0.018211264,0.00070195145,0.0012992258,0.00036712288,0.0005653497,0.11709067,0.0012694919,0.59103173,0.008299593,0.2607999],"study_design_scores_gemma":[0.000119629854,0.00030448558,0.0055781277,0.00016848213,0.00049892766,0.00033978253,0.00014577188,0.74346006,0.001142462,0.23742333,0.010717652,0.00010126079],"about_ca_topic_score_codex":0.004428439,"about_ca_topic_score_gemma":0.0046846163,"teacher_disagreement_score":0.025119184,"about_ca_system_score_codex":0.0013123439,"about_ca_system_score_gemma":0.0038371196,"threshold_uncertainty_score":0.13284457},"labels":[],"label_agreement":null},{"id":"W4283808675","doi":"10.1111/sjos.12605","title":"General purpose multiply robust data integration procedures for handling nonprobability samples","year":2022,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Nonparametric statistics; Econometrics; Curse of dimensionality; Statistics; Mathematics; Parametric statistics; Variance (accounting); Percentile","score_opus":0.17367602145811703,"score_gpt":0.3918521265369958,"score_spread":0.21817610507887877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283808675","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":[0.00094483787,0.00008288186,0.9986085,0.000033746655,0.000010609626,0.000054727985,0.000018211416,0.000157646,0.0000888223],"genre_scores_gemma":[0.035668578,0.00017544412,0.9625454,0.00008155533,0.00007886406,0.0005048111,0.00016627263,0.00013816939,0.00064101175],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9850691,0.009989939,0.0007037409,0.0015253184,0.00243182,0.00028009704],"domain_scores_gemma":[0.9636488,0.02563367,0.0031181255,0.004167391,0.0031227637,0.00030929648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0244569,0.0015735661,0.0024752307,0.0034371351,0.0008708644,0.0016712309,0.0033055567,0.002104333,0.005555375],"category_scores_gemma":[0.07387583,0.0012391171,0.0025770047,0.0035646816,0.0018167885,0.0025290367,0.004505216,0.0035762459,0.0013052765],"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":[0.00039340334,0.00027743485,0.0056938203,0.0005945868,0.0007838035,0.0005621657,0.0005184658,0.15512298,0.00885971,0.23443502,0.0037851115,0.5889734],"study_design_scores_gemma":[0.0000663395,0.00014927427,0.0014038521,0.00008100861,0.000103230486,0.00020561538,0.000035138484,0.9160486,0.0032211435,0.074096665,0.0045322543,0.000056941364],"about_ca_topic_score_codex":0.002092,"about_ca_topic_score_gemma":0.0018720691,"teacher_disagreement_score":0.0244569,"about_ca_system_score_codex":0.00091710285,"about_ca_system_score_gemma":0.0019825962,"threshold_uncertainty_score":0.12934202},"labels":[],"label_agreement":null},{"id":"W4285727872","doi":"10.1186/s12874-022-01671-0","title":"The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation","year":2022,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian 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":"Sunnybrook Hospital; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Imputation (statistics); Missing data; Logistic regression; Statistics; Sample size determination; Standard error; Confidence interval; Odds ratio; Standard deviation; Regression analysis; Regression; Mathematics; Econometrics; Computer science","score_opus":0.6698292803895878,"score_gpt":0.6058880685071995,"score_spread":0.06394121188238833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285727872","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","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":[0.15988968,0.009952899,0.81043863,0.009389939,0.0011779104,0.00091488153,0.001283691,0.0012791957,0.0056731896],"genre_scores_gemma":[0.7413529,0.0016315938,0.2508091,0.002894072,0.00030838617,0.0013132009,0.0007342694,0.0004434108,0.00051306136],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.3884761,0.55563307,0.021925826,0.014502731,0.01750671,0.0019556163],"domain_scores_gemma":[0.080098726,0.8691796,0.023744352,0.018551657,0.0077419193,0.00068373035],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.38852847,0.0018197907,0.0029137614,0.0028247624,0.0023387244,0.005103214,0.0042545386,0.004778779,0.0031516398],"category_scores_gemma":[0.75840646,0.0019460734,0.004839166,0.0050600944,0.0040075346,0.0057807565,0.004320573,0.007028648,0.00063507835],"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":[0.0048552635,0.00050020986,0.4295463,0.006948073,0.012528361,0.003549772,0.009052018,0.18014587,0.0025059283,0.03237899,0.016760502,0.30122867],"study_design_scores_gemma":[0.00096979475,0.003035173,0.15077697,0.012003935,0.006983947,0.0070301397,0.0030634624,0.6581132,0.012381442,0.12143966,0.023183051,0.0010191805],"about_ca_topic_score_codex":0.0063801934,"about_ca_topic_score_gemma":0.0061074765,"teacher_disagreement_score":0.38852847,"about_ca_system_score_codex":0.003253286,"about_ca_system_score_gemma":0.0041114986,"threshold_uncertainty_score":0.7540533},"labels":[],"label_agreement":null},{"id":"W4285730034","doi":"10.1037/met0000508","title":"Correcting bias in extreme groups design using a missing data approach.","year":2022,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Missing data; PsycINFO; Statistics; Data collection; Statistical power; Econometrics; Computer science; Psychology; Data mining; Mathematics; MEDLINE; Political science","score_opus":0.8796631252223159,"score_gpt":0.5764435918938456,"score_spread":0.3032195333284703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285730034","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":[0.0015982513,0.00032567902,0.9932092,0.0006233011,0.00028356723,0.0012986388,0.00072640576,0.0009734279,0.0009615135],"genre_scores_gemma":[0.018068792,0.00018805107,0.97407997,0.00039880179,0.00011093606,0.0057725064,0.00044804058,0.00025718837,0.0006757608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.68013895,0.2881325,0.008308144,0.010825424,0.011540971,0.0010540361],"domain_scores_gemma":[0.51918316,0.41816938,0.014951053,0.037949592,0.008776949,0.00096991495],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.21943875,0.0026667123,0.003240548,0.00488132,0.0026403163,0.00417852,0.007266161,0.0045255865,0.032599907],"category_scores_gemma":[0.48033747,0.0022285136,0.0057503884,0.0058014346,0.0051625855,0.0046146573,0.006716487,0.0072443527,0.0045477734],"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":[0.0014287801,0.00038396334,0.010948137,0.0042973426,0.005046571,0.00046894385,0.0035450524,0.015828697,0.0015271571,0.16923356,0.03663296,0.7506588],"study_design_scores_gemma":[0.0018288398,0.0022499566,0.014820216,0.003581533,0.0031212005,0.000946323,0.0010489316,0.14681943,0.009314166,0.73994,0.075854756,0.0004747376],"about_ca_topic_score_codex":0.0021858697,"about_ca_topic_score_gemma":0.0029837282,"teacher_disagreement_score":0.78056127,"about_ca_system_score_codex":0.0019635884,"about_ca_system_score_gemma":0.0055469414,"threshold_uncertainty_score":0.96257097},"labels":[],"label_agreement":null},{"id":"W4293085540","doi":"10.37394/23206.2022.21.18","title":"Unbiased Estimation of the Standard Deviation for Non-Normal Populations","year":2022,"lang":"en","type":"article","venue":"WSEAS TRANSACTIONS ON MATHEMATICS","topic":"Statistical Methods and Bayesian 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 Victoria","funders":"","keywords":"Kurtosis; Standard deviation; Estimator; Statistics; Bias of an estimator; Mathematics; Population; Standard error; Normal distribution; Sample (material); Econometrics; Minimum-variance unbiased estimator; Physics; Medicine","score_opus":0.08141296177005829,"score_gpt":0.3764047344893145,"score_spread":0.2949917727192562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293085540","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":[0.0041787396,0.001028037,0.9923907,0.0001835329,0.0001041686,0.000043907985,0.00010599009,0.00016605011,0.0017988817],"genre_scores_gemma":[0.24702074,0.004463933,0.7389962,0.0008008492,0.00049388903,0.0008963228,0.0010537931,0.00044928404,0.005824976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9878956,0.0058755325,0.00064757775,0.0020767872,0.003076126,0.00042825323],"domain_scores_gemma":[0.9636624,0.026291348,0.0019130773,0.0038967403,0.0039826524,0.00025371567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015621782,0.0008252811,0.0018599207,0.0030421952,0.0007830055,0.0024176214,0.0023219085,0.0017693723,0.003191838],"category_scores_gemma":[0.10433657,0.0006248915,0.0011376614,0.0023808097,0.0024775194,0.00424025,0.0025628558,0.0027958478,0.0015528772],"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":[0.000119364624,0.000064490836,0.010086717,0.0008595007,0.00047897035,0.00040019277,0.0007359997,0.13734184,0.004918948,0.64098734,0.0077788653,0.19622777],"study_design_scores_gemma":[0.000035408288,0.000074011754,0.005078317,0.00047998692,0.000119075565,0.0007397527,0.00018320513,0.36117858,0.005337353,0.6077936,0.01885923,0.000121405945],"about_ca_topic_score_codex":0.001800758,"about_ca_topic_score_gemma":0.0016794405,"teacher_disagreement_score":0.015621782,"about_ca_system_score_codex":0.001482522,"about_ca_system_score_gemma":0.0017324301,"threshold_uncertainty_score":0.082616866},"labels":[],"label_agreement":null},{"id":"W4293104237","doi":"10.3390/stats5020019","title":"A Bootstrap Variance Estimation Method for Multistage Sampling and Two-Phase Sampling When Poisson Sampling Is Used at the Second Phase","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Statistics Canada","funders":"","keywords":"Poisson sampling; Sampling (signal processing); Sampling design; Stratified sampling; Statistics; Weighting; Variance (accounting); Multistage sampling; Cluster sampling; Poisson distribution; Slice sampling; Computer science; Simple random sample; Systematic sampling; Mathematics; Importance sampling; Monte Carlo method","score_opus":0.23498961174964875,"score_gpt":0.5044269009477197,"score_spread":0.269437289198071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293104237","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":[0.0009533966,0.00012418353,0.99727964,0.00008149764,0.0000678741,0.00022075787,0.0000690173,0.00021383591,0.000989678],"genre_scores_gemma":[0.04897519,0.00029158517,0.94532967,0.00029202932,0.00016351222,0.0017986344,0.00040755692,0.00021308346,0.0025287317],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9789677,0.01559674,0.0006102007,0.0014792299,0.0029635862,0.00038257334],"domain_scores_gemma":[0.9803736,0.0121007105,0.0012781159,0.003425088,0.002503673,0.00031878875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014272384,0.001051071,0.0016109087,0.0027569328,0.0010661392,0.0011783494,0.0025478655,0.0021697897,0.008094558],"category_scores_gemma":[0.052372824,0.00072107714,0.0021561545,0.00356023,0.0014652307,0.0018867349,0.0024599289,0.0030204596,0.0023847758],"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":[0.00030471667,0.0003787804,0.0059801536,0.0008962919,0.00048388727,0.00044739948,0.0007852453,0.025758078,0.0048114248,0.324243,0.022487074,0.61342394],"study_design_scores_gemma":[0.0004673229,0.0006480632,0.010243289,0.0007317243,0.00039750774,0.0016988667,0.00030822598,0.4397462,0.00955938,0.3771281,0.15872926,0.00034208375],"about_ca_topic_score_codex":0.0019043211,"about_ca_topic_score_gemma":0.0022459656,"teacher_disagreement_score":0.014272384,"about_ca_system_score_codex":0.0010759035,"about_ca_system_score_gemma":0.0027743569,"threshold_uncertainty_score":0.07548046},"labels":[],"label_agreement":null},{"id":"W4293225463","doi":"10.1080/10705511.2021.1962325","title":"R-squared Measures for Multilevel Mixture Models with Random Effects","year":2022,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multilevel model; Random effects model; Statistics; Econometrics; Mean squared error; Variance (accounting); Mathematics; Regression; Interpretation (philosophy); Regression analysis; Computer science; Meta-analysis","score_opus":0.10316251204698246,"score_gpt":0.36780228202806053,"score_spread":0.2646397699810781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293225463","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":[0.0015039128,0.00067571964,0.99141484,0.00042433405,0.00013159298,0.0001883597,0.0015577342,0.0013870362,0.0027163972],"genre_scores_gemma":[0.04493664,0.00070088974,0.9424981,0.00060317304,0.00028720355,0.0033732567,0.002713946,0.0025583077,0.002328476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9568897,0.029005447,0.0028680894,0.004494639,0.0061642453,0.00057796633],"domain_scores_gemma":[0.8471603,0.11604713,0.011695974,0.017520413,0.0067679095,0.00080836006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038697664,0.002667609,0.0017768254,0.0046398635,0.0011934061,0.003455431,0.0040342514,0.00250396,0.031006446],"category_scores_gemma":[0.21333563,0.0012587928,0.0035972446,0.0061577614,0.0027494205,0.005491891,0.0037506195,0.0058229347,0.011047924],"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":[0.00017350937,0.00018024388,0.010755926,0.0017882339,0.00090613344,0.00031482117,0.0012407412,0.028619548,0.001660414,0.68773705,0.056411825,0.21021152],"study_design_scores_gemma":[0.00007727775,0.00023862999,0.005948959,0.00065673713,0.0002643046,0.0005506525,0.0003042151,0.09485561,0.0018810846,0.7857905,0.109254524,0.00017762215],"about_ca_topic_score_codex":0.0020474663,"about_ca_topic_score_gemma":0.0022618899,"teacher_disagreement_score":0.038697664,"about_ca_system_score_codex":0.0015802265,"about_ca_system_score_gemma":0.0027976395,"threshold_uncertainty_score":0.20465523},"labels":[],"label_agreement":null},{"id":"W4293241499","doi":"10.5539/ijsp.v11n3p51","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 3","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.059585650312992434,"score_gpt":0.3829228432457368,"score_spread":0.3233371929327443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293241499","genre_codex":"editorial","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":[0.00012791183,0.002537479,0.0018364572,0.12912399,0.8622786,0.00057450344,0.0007390656,0.0005972728,0.0021846427],"genre_scores_gemma":[0.0049646096,0.0064932085,0.00480839,0.17658871,0.7555284,0.00342717,0.0016343667,0.0018764001,0.044678748],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.927672,0.013839318,0.014882189,0.0048166886,0.03644922,0.0023405768],"domain_scores_gemma":[0.13491276,0.038032055,0.011165005,0.006431899,0.80072737,0.008730884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057135478,0.003235159,0.009681858,0.011552311,0.0049916273,0.011430133,0.005919279,0.018321749,0.090190135],"category_scores_gemma":[0.52557176,0.0018291518,0.0066807964,0.005608433,0.0041733626,0.0066359057,0.00410289,0.014376071,0.06359709],"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":[0.0000365453,0.0000040933733,0.00007804171,0.00037672181,0.000012086454,0.000057269295,0.0000319267,0.0000117258605,0.000036002984,0.00012723124,0.99588495,0.0033433603],"study_design_scores_gemma":[0.00035267998,0.000070003305,0.0014675966,0.004629708,0.00017438357,0.0015286875,0.0004308398,0.0006881968,0.00042445058,0.003459783,0.9865349,0.00023883421],"about_ca_topic_score_codex":0.0038229926,"about_ca_topic_score_gemma":0.005520522,"teacher_disagreement_score":0.090190135,"about_ca_system_score_codex":0.005505926,"about_ca_system_score_gemma":0.011949084,"threshold_uncertainty_score":0.3021649},"labels":[],"label_agreement":null},{"id":"W4294176560","doi":"10.4038/sljastats.v23i1.8058","title":"ptsuite: Fast Tail Index Estimation for Power Law Distributions in R","year":2022,"lang":"en","type":"article","venue":"Sri Lankan Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"R package; Pareto distribution; Code (set theory); Computer science; Index (typography); Pareto principle; Heuristic; Estimation; Power law; Power (physics); Algorithm; Data mining; Statistics; Mathematics; Set (abstract data type); Computational science; Programming language; Engineering; Artificial intelligence","score_opus":0.029790634628057377,"score_gpt":0.3462156382777814,"score_spread":0.31642500364972403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294176560","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022308133,0.00041693173,0.8396723,0.00035404792,0.00017901989,0.00017417336,0.015531693,0.13972484,0.0017161694],"genre_scores_gemma":[0.051861063,0.00073156145,0.8040427,0.0008783758,0.0002167094,0.0018153911,0.028375397,0.10629722,0.005781537],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946515,0.0024327463,0.00050848007,0.0008201086,0.001359155,0.00022803344],"domain_scores_gemma":[0.95984876,0.03127562,0.0016906237,0.0042247623,0.0026058306,0.0003544189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010429745,0.0026669835,0.0023764726,0.0034458747,0.0007281066,0.0034749862,0.0030136614,0.0014736174,0.06003771],"category_scores_gemma":[0.10409205,0.002240122,0.003272175,0.002807318,0.0011456765,0.0040387106,0.0033001897,0.0038751408,0.04315565],"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":[0.0006858425,0.000117956726,0.013239299,0.0031823555,0.0015940629,0.00077917916,0.0009945564,0.060774453,0.005076912,0.038178254,0.57383674,0.30154037],"study_design_scores_gemma":[0.0007483679,0.00018951375,0.0085853925,0.00067881856,0.00039878025,0.0015516084,0.0001910896,0.5454365,0.011884153,0.16395497,0.26590392,0.00047686166],"about_ca_topic_score_codex":0.0039685904,"about_ca_topic_score_gemma":0.006169635,"teacher_disagreement_score":0.06003771,"about_ca_system_score_codex":0.0008785759,"about_ca_system_score_gemma":0.0025343723,"threshold_uncertainty_score":0.20084614},"labels":[],"label_agreement":null},{"id":"W4295886911","doi":"10.5539/ijsp.v11n5p44","title":"Parsimonious Bivariate T-distribution Type Symmetry Models for Square Contingency Tables","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contingency table; Bivariate analysis; Mathematics; Symmetry (geometry); Type (biology); Square (algebra); Distribution (mathematics); Applied mathematics; Statistics; Mathematical analysis; Geometry","score_opus":0.06470542405507194,"score_gpt":0.3625121066585108,"score_spread":0.29780668260343884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295886911","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":[0.03220335,0.00025682844,0.96295255,0.00053079665,0.00006368515,0.00018342925,0.0006339583,0.00017872512,0.0029966156],"genre_scores_gemma":[0.71356076,0.00073629466,0.27589378,0.0003881577,0.00015583159,0.0008818683,0.0017746512,0.00011796319,0.006490677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99495935,0.0027113315,0.00028670035,0.0009028153,0.00072271697,0.00041709878],"domain_scores_gemma":[0.98453635,0.009407303,0.0020073478,0.00225704,0.0013582362,0.00043376308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008599959,0.001081386,0.001259492,0.001536749,0.000902098,0.0022755468,0.0024326015,0.0013298311,0.0098725995],"category_scores_gemma":[0.030003376,0.00069339725,0.0026042168,0.0032012323,0.0015857319,0.0046231374,0.0014573268,0.0027830813,0.0016338106],"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":[0.00038869603,0.00016831607,0.012331178,0.00026093452,0.0002815384,0.0008943739,0.0011700734,0.10689748,0.0011200121,0.80667734,0.0077522257,0.062057834],"study_design_scores_gemma":[0.00009423853,0.00011041394,0.0020095718,0.000048177935,0.00007063984,0.00047553316,0.00022362552,0.39238265,0.00033286618,0.60133475,0.002854847,0.00006258571],"about_ca_topic_score_codex":0.0029456823,"about_ca_topic_score_gemma":0.0027405613,"teacher_disagreement_score":0.0098725995,"about_ca_system_score_codex":0.0011480242,"about_ca_system_score_gemma":0.0014800254,"threshold_uncertainty_score":0.045481443},"labels":[],"label_agreement":null},{"id":"W4296588736","doi":"10.5539/ijsp.v11n5p53","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 5","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.0595824685933461,"score_gpt":0.38303938866866416,"score_spread":0.32345692007531807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296588736","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011780052,0.0025004696,0.001599896,0.12099607,0.8710929,0.00050127925,0.0006656837,0.0005081017,0.0020178244],"genre_scores_gemma":[0.0048849485,0.0059134094,0.004182129,0.1578438,0.7780782,0.0027555143,0.0015819483,0.0014347581,0.04332528],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92210865,0.014797707,0.016439883,0.0052116252,0.039050564,0.002391504],"domain_scores_gemma":[0.14007492,0.037625127,0.011747633,0.00618408,0.79547894,0.008889229],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05644165,0.0031890313,0.0091264695,0.012276403,0.0047578723,0.011606855,0.005912307,0.018745825,0.096399784],"category_scores_gemma":[0.5319564,0.0017832759,0.006448399,0.0052916924,0.003784918,0.0067613143,0.0041391007,0.013349344,0.06382508],"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":[0.0000374627,0.0000043143637,0.000084067906,0.0004019242,0.000013230033,0.000058821486,0.000032378623,0.000011939795,0.000033559932,0.0001325837,0.9955959,0.0035939096],"study_design_scores_gemma":[0.0003253622,0.00007478893,0.0015826679,0.0049905577,0.00017827442,0.0014013373,0.00043685883,0.0006576717,0.0003851365,0.002869807,0.9868667,0.00023079161],"about_ca_topic_score_codex":0.0035762326,"about_ca_topic_score_gemma":0.005193599,"teacher_disagreement_score":0.94355834,"about_ca_system_score_codex":0.0054639727,"about_ca_system_score_gemma":0.010959128,"threshold_uncertainty_score":0.32248938},"labels":[],"label_agreement":null},{"id":"W4297271369","doi":"10.1002/sim.9549","title":"Substantive model compatible multilevel multiple imputation: A joint modeling approach","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"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":"Medical Research Council; Medical Research Council Canada","keywords":"Imputation (statistics); Covariate; Computer science; Missing data; Statistics; Multilevel model; Data mining; Econometrics; Mathematics; Machine learning","score_opus":0.19222843533564607,"score_gpt":0.40369911782895407,"score_spread":0.211470682493308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297271369","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":[0.0008944612,0.00008711181,0.9979936,0.00030316782,0.000015471862,0.000046094734,0.000088695335,0.00016627775,0.000404987],"genre_scores_gemma":[0.05945329,0.00019687567,0.93870103,0.00023699344,0.000082606304,0.00033196408,0.00037054758,0.00014751311,0.00047926232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9730576,0.02228673,0.0007082682,0.0012393426,0.0024031943,0.0003048306],"domain_scores_gemma":[0.95212364,0.03402144,0.0028249968,0.006873973,0.0034794975,0.0006765082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026746912,0.0006822515,0.0014364758,0.002033247,0.00096595194,0.002606472,0.0045401813,0.0016103219,0.00499276],"category_scores_gemma":[0.073917836,0.0007935,0.0025373418,0.0035889666,0.001143918,0.0017870705,0.0040338645,0.0037934426,0.0012754527],"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":[0.00021080059,0.00019181106,0.012555591,0.0008246595,0.0013212566,0.0004718941,0.0012866739,0.10728419,0.0013604899,0.5324106,0.016845703,0.32523632],"study_design_scores_gemma":[0.0000732151,0.00009494709,0.0018956569,0.00020606391,0.00018084465,0.00029834133,0.00011679928,0.5823267,0.00078945345,0.40067866,0.013290715,0.000048665694],"about_ca_topic_score_codex":0.0020128072,"about_ca_topic_score_gemma":0.0031886827,"teacher_disagreement_score":0.026746912,"about_ca_system_score_codex":0.0011965054,"about_ca_system_score_gemma":0.0043570506,"threshold_uncertainty_score":0.14145291},"labels":[],"label_agreement":null},{"id":"W4306802871","doi":"10.48550/arxiv.2210.08892","title":"Modified Wilcoxon-Mann-Whitney tests of stochastic dominance","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistics; Wilcoxon signed-rank test; Mathematics; Null hypothesis; Mann–Whitney U test; Univariate; Stochastic dominance; Dominance (genetics); Resampling; Econometrics; Population; Inference; Statistical hypothesis testing; Applied mathematics; Computer science; Artificial intelligence; Multivariate statistics; Biology; Demography","score_opus":0.15688792214797045,"score_gpt":0.27948445856725856,"score_spread":0.12259653641928811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306802871","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":[0.06147205,0.000389057,0.92931557,0.00047172076,0.00019746087,0.0004782422,0.0011011361,0.00035221153,0.006222526],"genre_scores_gemma":[0.63582397,0.00029151991,0.35615075,0.00037440786,0.00025068637,0.0022973572,0.0012761367,0.00018324314,0.0033519818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9604411,0.026597451,0.0021404275,0.0031468815,0.0068052094,0.00086883415],"domain_scores_gemma":[0.82918334,0.1398095,0.007541808,0.017236,0.0050840736,0.0011453346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028676847,0.00083162327,0.0017822805,0.0024259456,0.0010226911,0.0022477668,0.0032887938,0.0015042509,0.011188408],"category_scores_gemma":[0.21631111,0.00045883676,0.001447994,0.0037776001,0.0035310911,0.0036490888,0.0027914324,0.0027205714,0.001127748],"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":[0.0011995689,0.00043833168,0.022233672,0.0005459472,0.0010582937,0.0009085246,0.0011493922,0.042819638,0.0043094708,0.60701376,0.0073881834,0.31093523],"study_design_scores_gemma":[0.00036032603,0.0008986498,0.022984799,0.00017043938,0.00017851745,0.00072251586,0.0004743462,0.2806545,0.0053352066,0.67421764,0.013843591,0.00015940984],"about_ca_topic_score_codex":0.0013487437,"about_ca_topic_score_gemma":0.0012665524,"teacher_disagreement_score":0.028676847,"about_ca_system_score_codex":0.0012669071,"about_ca_system_score_gemma":0.002396903,"threshold_uncertainty_score":0.15165949},"labels":[],"label_agreement":null},{"id":"W4307761721","doi":"10.1177/09622802221129040","title":"Revisiting Gaussian Markov random fields and Bayesian disease mapping","year":2022,"lang":"en","type":"review","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Covariance; Bayesian probability; Gaussian; Bayesian inference; Variable-order Bayesian network; Computer science; Random field; Markov chain; Conditional independence; Mathematics; Econometrics; Artificial intelligence; Statistics; Machine learning; Physics","score_opus":0.3179237316478053,"score_gpt":0.6023718682123013,"score_spread":0.28444813656449597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307761721","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":[0.00065092137,0.9265384,0.059708714,0.007234747,0.00073162414,0.000019977157,0.00009543302,0.00005323991,0.0049670343],"genre_scores_gemma":[0.017909085,0.95417535,0.021563979,0.0021573429,0.0019554743,0.00006455262,0.00014377885,0.000035224813,0.001995063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99866724,0.0007241741,0.00007764761,0.00016025912,0.00032945472,0.000041260246],"domain_scores_gemma":[0.99269956,0.006185053,0.00023907507,0.00017841786,0.00062067446,0.00007729624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056239846,0.0008057419,0.001337131,0.0030239956,0.00030248592,0.0017641169,0.0017238247,0.0019861772,0.0017542586],"category_scores_gemma":[0.010879751,0.0005586162,0.00078806723,0.0038583588,0.002651817,0.0032926945,0.0010737957,0.0032527263,0.0008992562],"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":[0.00002357321,0.000034894143,0.00040890448,0.002875348,0.00011391973,0.000117968826,0.00024920102,0.010307108,0.00019501128,0.6113132,0.015362152,0.35899875],"study_design_scores_gemma":[0.000015912294,0.00004776209,0.00086579635,0.0030702099,0.000086829605,0.0005795827,0.00010540564,0.010850182,0.00026376417,0.6067491,0.37729782,0.0000676356],"about_ca_topic_score_codex":0.007205632,"about_ca_topic_score_gemma":0.0049145822,"teacher_disagreement_score":0.007205632,"about_ca_system_score_codex":0.0021403702,"about_ca_system_score_gemma":0.0032764114,"threshold_uncertainty_score":0.029742837},"labels":[],"label_agreement":null},{"id":"W4307835686","doi":"10.1177/09622802221134172","title":"Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations","year":2022,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","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":"Centre for Global Health Research; St. Michael's Hospital; University of Waterloo; University of Toronto","funders":"","keywords":"Covariate; Laplace's method; Markov chain Monte Carlo; Bayesian probability; Inference; Parametric statistics; Proportional hazards model; Computer science; Bayesian inference; Mathematics; Econometrics; Applied mathematics; Statistics; Algorithm; Artificial intelligence","score_opus":0.23490634914787176,"score_gpt":0.5408256184953694,"score_spread":0.3059192693474976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307835686","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":[0.0013704464,0.00009595639,0.9981238,0.00012263322,0.000008399165,0.000019217468,0.00004197401,0.0000601469,0.00015741147],"genre_scores_gemma":[0.153574,0.0010074821,0.84190774,0.00024494567,0.00018184076,0.0005826562,0.0004452903,0.00014102162,0.0019149213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962437,0.0023368897,0.00014823233,0.00044201696,0.00067097787,0.00015822888],"domain_scores_gemma":[0.98651516,0.011258955,0.00079161016,0.0007340703,0.0005250343,0.00017512389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010240088,0.0007567176,0.0014810382,0.0009924273,0.000619221,0.0016222262,0.0026986764,0.001026171,0.0029733838],"category_scores_gemma":[0.033794563,0.000956874,0.0014445818,0.001461023,0.0014365017,0.0018736469,0.0021578602,0.002459351,0.00049199216],"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":[0.00012613813,0.000071172886,0.003061749,0.00026913956,0.00020121303,0.00025134246,0.0002351036,0.64395213,0.0010883451,0.24520354,0.0026967232,0.102843404],"study_design_scores_gemma":[0.000040878494,0.0000177888,0.00027868748,0.0000205226,0.0000281582,0.000051638013,0.0000144446585,0.9011579,0.00021576355,0.09664145,0.0015170608,0.000015648158],"about_ca_topic_score_codex":0.008809822,"about_ca_topic_score_gemma":0.008283811,"teacher_disagreement_score":0.010240088,"about_ca_system_score_codex":0.0012557763,"about_ca_system_score_gemma":0.0037729966,"threshold_uncertainty_score":0.05415541},"labels":[],"label_agreement":null},{"id":"W4307958056","doi":"10.1016/j.csda.2022.107646","title":"A unified framework of multiply robust estimation approaches for handling incomplete data","year":2022,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 Ottawa","funders":"National Institute on Minority Health and Health Disparities; National Institute of General Medical Sciences","keywords":"Imputation (statistics); Missing data; Quantile; Weighting; Computer science; Inverse probability weighting; Data mining; Population; Mathematics; Statistics; Mathematical optimization; Algorithm; Estimator","score_opus":0.38403047526707323,"score_gpt":0.4281909063231017,"score_spread":0.04416043105602846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307958056","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":[0.00011788634,0.00008926449,0.99946624,0.00005086787,0.000014098541,0.0000071827785,0.000014905945,0.000035882364,0.00020368991],"genre_scores_gemma":[0.01774908,0.0006614051,0.9789083,0.00016404792,0.0003413888,0.00020246592,0.00016833941,0.00019546904,0.0016093517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9900927,0.00567729,0.00057606783,0.0012906438,0.0020848408,0.0002784123],"domain_scores_gemma":[0.9827843,0.010227543,0.0013005807,0.0030005786,0.0023770747,0.00030995847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012807117,0.002319034,0.0030791308,0.0030012764,0.0011573194,0.004233338,0.0057780026,0.0025270635,0.0050113634],"category_scores_gemma":[0.03837289,0.0014999934,0.0033267427,0.0041223275,0.00260086,0.004625231,0.0061420756,0.005112992,0.0019712388],"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":[0.000054492528,0.0000373607,0.00043129077,0.0002646167,0.000319973,0.00017613385,0.00023102511,0.09717631,0.0019796314,0.77369714,0.0040459046,0.12158615],"study_design_scores_gemma":[0.000019813533,0.000062191684,0.00022350765,0.00007710705,0.00011118122,0.00019490148,0.000039568327,0.46086594,0.0011891279,0.52430326,0.012849353,0.00006403235],"about_ca_topic_score_codex":0.0028701676,"about_ca_topic_score_gemma":0.0031099338,"teacher_disagreement_score":0.012807117,"about_ca_system_score_codex":0.0012113404,"about_ca_system_score_gemma":0.0030634094,"threshold_uncertainty_score":0.06773132},"labels":[],"label_agreement":null},{"id":"W4307961609","doi":"10.5539/ijsp.v11n6p74","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 6","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Mathematical statistics; Mathematical economics","score_opus":0.05958086732892203,"score_gpt":0.3830227658838649,"score_spread":0.3234418985549429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307961609","genre_codex":"editorial","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":[0.00011252249,0.0024070002,0.0015147203,0.123034544,0.8693838,0.00044915752,0.000701147,0.00047675404,0.0019202179],"genre_scores_gemma":[0.004126216,0.0062988875,0.004296685,0.15037377,0.7856457,0.0027221304,0.0016317089,0.0014681739,0.04343678],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9277059,0.01371019,0.015235039,0.004647072,0.036545463,0.002156314],"domain_scores_gemma":[0.13721772,0.03855651,0.011524137,0.006371475,0.797286,0.0090441555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054981302,0.0031704681,0.009178286,0.0114389835,0.004604001,0.010583977,0.005894052,0.017653268,0.09215488],"category_scores_gemma":[0.5373125,0.001767314,0.0062332875,0.005158118,0.0037089493,0.00690221,0.0041387943,0.014507253,0.066398844],"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":[0.00003621605,0.000004310989,0.000077741395,0.00039679924,0.000011437825,0.00005624561,0.000030332189,0.00001146842,0.000035035013,0.00012348978,0.99579394,0.0034229173],"study_design_scores_gemma":[0.00030030054,0.00007184909,0.0014321321,0.004552654,0.00015025248,0.0014705182,0.00040201176,0.00061403326,0.00038986767,0.0028918553,0.98750615,0.00021831399],"about_ca_topic_score_codex":0.0032230695,"about_ca_topic_score_gemma":0.004820826,"teacher_disagreement_score":0.09215488,"about_ca_system_score_codex":0.004777734,"about_ca_system_score_gemma":0.010127634,"threshold_uncertainty_score":0.3082888},"labels":[],"label_agreement":null},{"id":"W4308119864","doi":"10.3390/e24111579","title":"Model Checking with Right Censored Data Using Relative Belief Ratio","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"American University of Sharjah","keywords":"Computer science; Model checking; Process (computing); Distribution (mathematics); Statistics; Algorithm; Mathematics","score_opus":0.13947953718827513,"score_gpt":0.3803655616630772,"score_spread":0.2408860244748021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308119864","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":[0.008385723,0.0001494311,0.9907313,0.00014533752,0.00001420353,0.000028221026,0.000031997344,0.00021744917,0.0002963409],"genre_scores_gemma":[0.5086497,0.00036917996,0.48959464,0.00018038311,0.00009949706,0.00025042094,0.00024408103,0.00015335005,0.00045876513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97324955,0.017922906,0.0010600213,0.0030130069,0.004203996,0.0005504975],"domain_scores_gemma":[0.80761033,0.17070177,0.008606708,0.008481366,0.0037596782,0.0008402026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031280886,0.001826815,0.0034954487,0.0049776314,0.0011955007,0.0040920805,0.004025253,0.0019421801,0.0018970132],"category_scores_gemma":[0.14912349,0.0010401718,0.0030045589,0.0026393987,0.004542507,0.0077296216,0.0038157098,0.005046154,0.0003413717],"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":[0.0006852282,0.00021862895,0.0067756292,0.00042045527,0.00085066917,0.0006266498,0.0007412674,0.56518996,0.0035686605,0.3308815,0.0010272113,0.08901407],"study_design_scores_gemma":[0.00003799162,0.00008883921,0.00033336427,0.00003959061,0.000038945418,0.00010771104,0.0000371085,0.863869,0.002267904,0.13279477,0.00034500784,0.000039813734],"about_ca_topic_score_codex":0.0022959318,"about_ca_topic_score_gemma":0.0014485361,"teacher_disagreement_score":0.031280886,"about_ca_system_score_codex":0.0020201239,"about_ca_system_score_gemma":0.0020263551,"threshold_uncertainty_score":0.16543114},"labels":[],"label_agreement":null},{"id":"W4308372578","doi":"10.1111/ejn.15858","title":"Beyond ANOVA and MANOVA for repeated measures: Advantages of generalized estimated equations and generalized linear mixed models and its use in neuroscience research","year":2022,"lang":"en","type":"article","venue":"European Journal of Neuroscience","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":107,"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":"Generalized estimating equation; Repeated measures design; Multivariate analysis of variance; Generalized linear mixed model; Gee; Missing data; Mixed model; Statistics; Generalized linear model; Variance (accounting); Analysis of variance; Marginal model; Multivariate statistics; Linear model; Mathematics; Regression analysis","score_opus":0.43958833666082453,"score_gpt":0.4584034702255692,"score_spread":0.01881513356474468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308372578","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":[0.0013561709,0.0031484037,0.98967326,0.0009462797,0.0005833149,0.00016942334,0.000578035,0.0018652641,0.0016798509],"genre_scores_gemma":[0.030393315,0.0051793735,0.9541657,0.0012117898,0.0011667018,0.0024675298,0.0011091512,0.0024062756,0.0019001171],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9600376,0.03005082,0.0017500379,0.0037535292,0.0040547224,0.0003534009],"domain_scores_gemma":[0.87665075,0.09458229,0.006621927,0.015202628,0.0061393743,0.0008030257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04940933,0.003007963,0.003339396,0.003018981,0.00095327833,0.003997741,0.0037165778,0.002740762,0.009730405],"category_scores_gemma":[0.15408637,0.0013346411,0.0043803314,0.0061869444,0.003609083,0.005120026,0.003158303,0.0074735386,0.00393027],"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":[0.00059104967,0.0002769164,0.00718706,0.00614237,0.003345792,0.0006671512,0.0029879175,0.016281756,0.0056565553,0.25915745,0.055244923,0.64246106],"study_design_scores_gemma":[0.00023880543,0.00083573785,0.013270728,0.0018531366,0.0010658992,0.0013709865,0.0004905921,0.085142724,0.0057657417,0.67656446,0.21285388,0.0005472113],"about_ca_topic_score_codex":0.0030651647,"about_ca_topic_score_gemma":0.0034885048,"teacher_disagreement_score":0.04940933,"about_ca_system_score_codex":0.0013326743,"about_ca_system_score_gemma":0.0046889987,"threshold_uncertainty_score":0.26130462},"labels":[],"label_agreement":null},{"id":"W4308545243","doi":"10.1007/s42519-022-00304-5","title":"Frequentist Conditional Variance for Nonlinear Mixed-Effects Models","year":2022,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute; Memorial University of Newfoundland","keywords":"Mathematics; Statistics; Frequentist inference; Estimator; Mean squared error; Conditional expectation; Conditional variance; Marginal likelihood; Laplace's method; Econometrics; Applied mathematics; Bayesian probability; Bayesian inference","score_opus":0.0670750308345224,"score_gpt":0.4132439847468493,"score_spread":0.34616895391232694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308545243","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":[0.0034458744,0.00019965807,0.9956697,0.00014189385,0.00001710267,0.000026549882,0.00006131791,0.000096063945,0.00034173287],"genre_scores_gemma":[0.4292235,0.0013216109,0.5629616,0.000395996,0.00026733833,0.0007273256,0.0007185717,0.00025922418,0.0041249287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9891571,0.0073020835,0.0003878701,0.0014471789,0.0013888294,0.00031694525],"domain_scores_gemma":[0.90202415,0.0892059,0.002885351,0.0031537206,0.002417608,0.00031336024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023640454,0.0011041941,0.0019388775,0.0021502974,0.00064636715,0.0019339584,0.0031638383,0.0017474788,0.0025787177],"category_scores_gemma":[0.08855537,0.0011770211,0.0023765913,0.0016794262,0.0027357724,0.0025964268,0.0022428615,0.003195428,0.00044786965],"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":[0.00009527471,0.00005016465,0.0037714,0.00028966647,0.0003529378,0.0003024587,0.00031097664,0.5555662,0.00060192536,0.40343627,0.0012470203,0.033975687],"study_design_scores_gemma":[0.0000087284425,0.000018531338,0.00048311902,0.000042894408,0.00003764132,0.000070101334,0.000016592368,0.8494326,0.00022523984,0.14879064,0.0008555257,0.000018412638],"about_ca_topic_score_codex":0.0077452036,"about_ca_topic_score_gemma":0.007280009,"teacher_disagreement_score":0.023640454,"about_ca_system_score_codex":0.002399793,"about_ca_system_score_gemma":0.0016711259,"threshold_uncertainty_score":0.12502414},"labels":[],"label_agreement":null},{"id":"W4310525280","doi":"10.3390/stats5040075","title":"A Bayesian One-Sample Test for Proportion","year":2022,"lang":"en","type":"article","venue":"Stats","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":"Canada Research Chairs; McMaster University; University of Toronto","funders":"","keywords":"Mathematics; Statistics; A priori and a posteriori; Divergence (linguistics); Bayesian probability; Null hypothesis; Bernoulli's principle; Sample size determination; Kullback–Leibler divergence; Null (SQL); Binomial distribution; Measure (data warehouse); Binomial (polynomial); Negative binomial distribution; Sample (material); Computer science; Poisson distribution; Physics; Data mining","score_opus":0.11426534449300238,"score_gpt":0.3971139948043215,"score_spread":0.2828486503113191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310525280","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":[0.015502257,0.0008687855,0.97758156,0.00072982215,0.00023908766,0.00023497903,0.0002884836,0.00029553077,0.0042595267],"genre_scores_gemma":[0.40513608,0.0008295754,0.5870424,0.001126428,0.00058606535,0.0013184522,0.0008478595,0.00023281918,0.0028802203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9659473,0.02104671,0.0011476141,0.004589003,0.006683083,0.0005863754],"domain_scores_gemma":[0.9172116,0.07127765,0.0030735598,0.004039248,0.0035669121,0.0008310547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024882259,0.00091055175,0.0036918093,0.0040198276,0.0011241812,0.0030613919,0.0031626294,0.0026982594,0.0072487886],"category_scores_gemma":[0.14552517,0.0005648704,0.0018933916,0.0023945246,0.004436755,0.005310611,0.002955197,0.0037517352,0.0012290974],"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":[0.0011191784,0.00038400292,0.017134666,0.0011839861,0.0012854314,0.0007006845,0.0007523771,0.037103567,0.0049198754,0.56986016,0.005486694,0.36006945],"study_design_scores_gemma":[0.00046731863,0.001197299,0.010445473,0.0005114369,0.0005104726,0.0020972611,0.00037967783,0.28890568,0.0051229997,0.67141235,0.018650217,0.0002997527],"about_ca_topic_score_codex":0.0008758127,"about_ca_topic_score_gemma":0.00055412727,"teacher_disagreement_score":0.024882259,"about_ca_system_score_codex":0.0012015204,"about_ca_system_score_gemma":0.0027794326,"threshold_uncertainty_score":0.1315915},"labels":[],"label_agreement":null},{"id":"W4311050699","doi":"10.3390/math10234542","title":"Explicit Gaussian Variational Approximation for the Poisson Lognormal Mixed Model","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Statistical Methods and Bayesian Inference","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":"York University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Gaussian; Poisson distribution; Applied mathematics; Estimator; Mathematics; Log-normal distribution; Inference; Count data; Laplace's method; Mathematical optimization; Statistical physics; Statistics; Computer science; Artificial intelligence; Physics","score_opus":0.10569409965732635,"score_gpt":0.3549548469018378,"score_spread":0.24926074724451147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311050699","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":[0.0038727403,0.00068937574,0.992465,0.00032696337,0.000043044707,0.000020351405,0.00010375293,0.00005509546,0.0024236862],"genre_scores_gemma":[0.4341478,0.004446119,0.5339131,0.00079830695,0.00040683124,0.00057099265,0.0009997773,0.00040141164,0.024315646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985948,0.00074569817,0.00004911897,0.00017492635,0.00029682257,0.00013866466],"domain_scores_gemma":[0.99726796,0.0018737696,0.00022599378,0.00012454487,0.00038944278,0.00011826302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041416488,0.0011328624,0.0015791669,0.0014739921,0.0006871023,0.0015196088,0.0028944802,0.0020312674,0.003428524],"category_scores_gemma":[0.010333857,0.00082030526,0.00186402,0.001597749,0.0017991635,0.0020045387,0.0018807172,0.0024673457,0.00058804324],"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":[0.000028666811,0.000025702522,0.0012748487,0.00012867915,0.000061233186,0.00020612328,0.00018259892,0.40502498,0.0008283495,0.5779612,0.0019193647,0.012358296],"study_design_scores_gemma":[0.00000435155,0.0000042012757,0.000096304415,0.000011910876,0.000008517509,0.0000311823,0.000014794924,0.94557273,0.00006788571,0.053257413,0.00092202483,0.000008642297],"about_ca_topic_score_codex":0.018064464,"about_ca_topic_score_gemma":0.01457283,"teacher_disagreement_score":0.018064464,"about_ca_system_score_codex":0.001909625,"about_ca_system_score_gemma":0.0022697332,"threshold_uncertainty_score":0.035918653},"labels":[],"label_agreement":null},{"id":"W4311193885","doi":"10.1155/2022/2833537","title":"Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance","year":2022,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"Farm Service Agency; Deutscher Akademischer Austauschdienst; International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete; Carnegie Corporation of New York","keywords":"Overdispersion; Statistics; Mathematics; Generalized linear model; Negative binomial distribution; Categorical variable; Random forest; Poisson distribution; Regression analysis; Linear regression; Covariate; Regression; Quasi-likelihood; Count data; Poisson regression; Sample size determination; Mean squared error; Artificial intelligence; Computer science; Population","score_opus":0.1191788818123905,"score_gpt":0.38589183826721757,"score_spread":0.26671295645482707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311193885","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":[0.32430315,0.004524139,0.66713315,0.0006607547,0.00017640999,0.00029187594,0.00053910766,0.0012589137,0.0011125497],"genre_scores_gemma":[0.84381264,0.0009365221,0.15281533,0.00019866231,0.00012649837,0.00038982122,0.0009045791,0.00037939328,0.00043652952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95142394,0.036215145,0.0023302082,0.004781113,0.004290534,0.00095905416],"domain_scores_gemma":[0.6522933,0.31501418,0.012344889,0.013223869,0.00630104,0.00082283706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.103034474,0.0021762322,0.0022234693,0.002990657,0.0011103611,0.0026358801,0.002048635,0.0020095587,0.00079977984],"category_scores_gemma":[0.18121648,0.0007961531,0.0030491855,0.0033866514,0.0019408717,0.0038669254,0.001911128,0.0027266834,0.00041021942],"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":[0.0015196536,0.00022855679,0.138121,0.0009068933,0.0021915433,0.000859633,0.0011651699,0.69213057,0.004362746,0.008629202,0.0018866295,0.14799842],"study_design_scores_gemma":[0.00004483855,0.00040544686,0.01827404,0.00020533902,0.0003463479,0.0004141057,0.00016656413,0.9657349,0.003719683,0.008946726,0.001631069,0.0001110183],"about_ca_topic_score_codex":0.004406922,"about_ca_topic_score_gemma":0.002629931,"teacher_disagreement_score":0.103034474,"about_ca_system_score_codex":0.0010213042,"about_ca_system_score_gemma":0.0014275163,"threshold_uncertainty_score":0.5449049},"labels":[],"label_agreement":null},{"id":"W4311277378","doi":"10.1002/sim.9621","title":"An exact regression‐based approach for the estimation of natural direct and indirect effects with a binary outcome and a continuous mediator","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outcome (game theory); Binary number; Regression; Estimation; Econometrics; Statistics; Regression analysis; Computer science; Natural (archaeology); Mathematics; Mathematical economics; Economics; Biology","score_opus":0.035421275230070264,"score_gpt":0.38196971108714584,"score_spread":0.34654843585707557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311277378","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":[0.0007346316,0.0001133994,0.99870515,0.000054401633,0.000013900672,0.00004128877,0.00004060363,0.00006261799,0.00023400877],"genre_scores_gemma":[0.07089354,0.000558095,0.92578703,0.00028372795,0.000079154044,0.0007546179,0.00022700832,0.00008935647,0.0013274646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9716186,0.022051306,0.0010342641,0.0024672756,0.0024910271,0.00033755833],"domain_scores_gemma":[0.9566014,0.034794323,0.0023886722,0.004862504,0.0011679779,0.00018512234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03455978,0.0012520726,0.0022120327,0.0022012198,0.0004888197,0.0017079471,0.003815175,0.0016264885,0.006470163],"category_scores_gemma":[0.09445259,0.0010051879,0.0026814435,0.0024510685,0.0020461883,0.0029522004,0.0033934533,0.0038143126,0.0009677892],"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":[0.00018468249,0.00015215359,0.0073191766,0.00084324833,0.0010497927,0.0004304452,0.0007652183,0.0927529,0.001711436,0.62386405,0.001953853,0.26897302],"study_design_scores_gemma":[0.0001258825,0.00037038256,0.0040706703,0.00027522896,0.00035776556,0.0007079869,0.00014430944,0.4243093,0.0018116747,0.5567995,0.010888942,0.0001383757],"about_ca_topic_score_codex":0.0024176168,"about_ca_topic_score_gemma":0.0020244352,"teacher_disagreement_score":0.03455978,"about_ca_system_score_codex":0.0009431428,"about_ca_system_score_gemma":0.0024653818,"threshold_uncertainty_score":0.18277174},"labels":[],"label_agreement":null},{"id":"W4311936803","doi":"10.1111/anzs.12377","title":"Small area estimation under a semi‐parametric covariate measured with error","year":2022,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Manitoba","funders":"","keywords":"Covariate; Mathematics; Statistics; Frequentist inference; Small area estimation; Spline (mechanical); Regression analysis; Mean squared error; Parametric statistics; Linear regression; Errors-in-variables models; Bayesian probability; Bayesian inference","score_opus":0.16238653347251436,"score_gpt":0.35708714435543065,"score_spread":0.1947006108829163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311936803","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":[0.020909924,0.00010050474,0.978496,0.00011363086,0.0000133147205,0.000018763822,0.000052640156,0.000075323616,0.00022000079],"genre_scores_gemma":[0.67488444,0.000383925,0.3207439,0.00014786616,0.00012379093,0.00033031197,0.00048489458,0.000088091634,0.0028126806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9915971,0.0053201835,0.00031857632,0.0014942832,0.0009719526,0.00029799316],"domain_scores_gemma":[0.94879717,0.040491164,0.00402318,0.0037046517,0.002541633,0.000442304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014997543,0.0006421504,0.0018100052,0.0011213293,0.0005023918,0.0012979308,0.0026238381,0.0016717337,0.0019568382],"category_scores_gemma":[0.055755675,0.00065725856,0.0015802229,0.0012854518,0.0018248336,0.0021612048,0.00196101,0.0019951712,0.00031925837],"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":[0.00032774525,0.00015184001,0.016182901,0.000252482,0.00031141777,0.0003363702,0.00045326125,0.78441626,0.0021884895,0.119663656,0.0010934259,0.074622154],"study_design_scores_gemma":[0.000017129061,0.00008261461,0.0022895616,0.000025948335,0.000023876246,0.00006568539,0.00002617805,0.95932126,0.00044789657,0.03688282,0.0007957967,0.000021241967],"about_ca_topic_score_codex":0.004242105,"about_ca_topic_score_gemma":0.0030169052,"teacher_disagreement_score":0.014997543,"about_ca_system_score_codex":0.00080627337,"about_ca_system_score_gemma":0.0011755147,"threshold_uncertainty_score":0.07931554},"labels":[],"label_agreement":null},{"id":"W4311979890","doi":"10.1002/cjs.11749","title":"Confidence sequences with composite likelihoods","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Padova","keywords":"Frequentist inference; Confidence interval; Inference; Confidence distribution; Mathematics; Parametric statistics; Confidence and prediction bands; Coverage probability; Sequence (biology); Statistics; CDF-based nonparametric confidence interval; Statistical inference; Robust confidence intervals; Sample size determination; Algorithm; Computer science; Bayesian probability; Bayesian inference; Artificial intelligence","score_opus":0.04467842461794406,"score_gpt":0.3092267258621449,"score_spread":0.2645483012442008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311979890","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":[0.02299736,0.0002745311,0.97354865,0.00024716792,0.00006091886,0.00007282187,0.000098139935,0.0001971478,0.002503308],"genre_scores_gemma":[0.55070907,0.00031607505,0.4449135,0.00025081774,0.00018458223,0.0005282016,0.00034720593,0.00023543401,0.0025150748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.976598,0.014784652,0.00093953166,0.0023158821,0.004853383,0.00050849584],"domain_scores_gemma":[0.7316524,0.23166154,0.009470343,0.014427424,0.010877338,0.0019109967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034833178,0.00093590247,0.001322921,0.004628259,0.0009929063,0.0036413288,0.0038429268,0.0024912413,0.0070810337],"category_scores_gemma":[0.2470203,0.0010223584,0.0018037201,0.0030471976,0.005380141,0.007383572,0.0039029876,0.0048493105,0.00096394354],"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":[0.00032628755,0.000062646315,0.0044798767,0.00022395175,0.000081035025,0.00032412668,0.000563324,0.1681734,0.0011005455,0.77660066,0.0010143603,0.047049835],"study_design_scores_gemma":[0.000044582554,0.00012692373,0.000888812,0.000092705406,0.000018708079,0.0002285139,0.00007156167,0.5607981,0.0011006736,0.43482843,0.0017486466,0.000052422387],"about_ca_topic_score_codex":0.0013600638,"about_ca_topic_score_gemma":0.0005238905,"teacher_disagreement_score":0.034833178,"about_ca_system_score_codex":0.0016247126,"about_ca_system_score_gemma":0.0013283342,"threshold_uncertainty_score":0.18421763},"labels":[],"label_agreement":null},{"id":"W4312089372","doi":"10.5539/ijsp.v11n6p70","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 11, No. 6","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics","score_opus":0.05958086732892203,"score_gpt":0.3830227658838649,"score_spread":0.3234418985549429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312089372","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011252249,0.0024070002,0.0015147203,0.123034544,0.8693838,0.00044915752,0.000701147,0.00047675404,0.0019202179],"genre_scores_gemma":[0.004126216,0.0062988875,0.004296685,0.15037377,0.7856457,0.0027221304,0.0016317089,0.0014681739,0.04343678],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9277059,0.01371019,0.015235039,0.004647072,0.036545463,0.002156314],"domain_scores_gemma":[0.13721772,0.03855651,0.011524137,0.006371475,0.797286,0.0090441555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054981302,0.0031704681,0.009178286,0.0114389835,0.004604001,0.010583977,0.005894052,0.017653268,0.09215488],"category_scores_gemma":[0.5373125,0.001767314,0.0062332875,0.005158118,0.0037089493,0.00690221,0.0041387943,0.014507253,0.066398844],"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":[0.00003621605,0.000004310989,0.000077741395,0.00039679924,0.000011437825,0.00005624561,0.000030332189,0.00001146842,0.000035035013,0.00012348978,0.99579394,0.0034229173],"study_design_scores_gemma":[0.00030030054,0.00007184909,0.0014321321,0.004552654,0.00015025248,0.0014705182,0.00040201176,0.00061403326,0.00038986767,0.0028918553,0.98750615,0.00021831399],"about_ca_topic_score_codex":0.0032230695,"about_ca_topic_score_gemma":0.004820826,"teacher_disagreement_score":0.09215488,"about_ca_system_score_codex":0.004777734,"about_ca_system_score_gemma":0.010127634,"threshold_uncertainty_score":0.3082888},"labels":[],"label_agreement":null},{"id":"W4312834984","doi":"10.1214/22-ejs2069","title":"Semiparametric empirical likelihood inference with estimating equations under density ratio models","year":2022,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Empirical likelihood; Estimator; Quantile; Estimating equations; Statistics; Asymptotic distribution; Likelihood function; Inference; Applied mathematics; Likelihood-ratio test; Fisher information; Statistical inference; Confidence interval; Delta method; Generalized estimating equation; Ratio estimator; Estimation theory; Efficient estimator; Computer science","score_opus":0.07183809390455385,"score_gpt":0.3788179462929314,"score_spread":0.30697985238837755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312834984","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":[0.0035795546,0.000195535,0.99550974,0.00013772059,0.000009765493,0.000030693533,0.000055394976,0.000072396826,0.0004091862],"genre_scores_gemma":[0.28063577,0.0012507668,0.71327484,0.0003588714,0.00022364776,0.00075545174,0.00059355976,0.0001532292,0.0027538359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9788907,0.016858444,0.000657128,0.0014609809,0.0017792579,0.00035351692],"domain_scores_gemma":[0.88822806,0.09919209,0.004762237,0.0050628674,0.0024166608,0.00033810711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026401082,0.0014637873,0.002501586,0.0023264373,0.000444287,0.0029248595,0.0035106759,0.002490412,0.0028459541],"category_scores_gemma":[0.14173287,0.001142382,0.0019808814,0.0028233982,0.0027843162,0.0057876473,0.004277181,0.004116326,0.000821398],"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":[0.00012195121,0.00009643484,0.003849311,0.00036270623,0.0002867326,0.00035799475,0.0003695329,0.20105042,0.00093893026,0.72791463,0.0011291468,0.06352226],"study_design_scores_gemma":[0.00004930154,0.000053567786,0.00054720853,0.000049202903,0.0000485659,0.00014342129,0.000039342165,0.6763556,0.000493351,0.3209349,0.0012504985,0.00003506912],"about_ca_topic_score_codex":0.0022774464,"about_ca_topic_score_gemma":0.0011675017,"teacher_disagreement_score":0.026401082,"about_ca_system_score_codex":0.0012101261,"about_ca_system_score_gemma":0.0013508126,"threshold_uncertainty_score":0.13962394},"labels":[],"label_agreement":null},{"id":"W4313438126","doi":"10.5539/ijsp.v12n1p33","title":"Bayesian Predictive Inference Under Nine Methods for Incorporating Survey Weights","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Estimator; Mean squared error; Population; Selection (genetic algorithm); Computer science; Artificial intelligence; Medicine","score_opus":0.11026905574699314,"score_gpt":0.45461250411707693,"score_spread":0.3443434483700838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313438126","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":[0.011903067,0.0005217896,0.9860917,0.00019102833,0.00004888383,0.00014430552,0.00019153144,0.00017726437,0.00073054753],"genre_scores_gemma":[0.24491663,0.0014233289,0.7486457,0.0003046542,0.00023231549,0.00105361,0.001302965,0.00014928078,0.0019714716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96961236,0.021217432,0.0012869572,0.003337702,0.0038890284,0.0006564896],"domain_scores_gemma":[0.8971785,0.08393738,0.0055423356,0.0070922617,0.005660473,0.00058908993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050910804,0.0014295174,0.002182219,0.0037913152,0.001098707,0.0032088296,0.00407364,0.0022420431,0.0024358786],"category_scores_gemma":[0.15723425,0.0010951572,0.0026820528,0.004091591,0.0023907197,0.0046231416,0.0029865748,0.003826177,0.00043166053],"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":[0.00053881237,0.00024058568,0.01895729,0.0007608436,0.0010299496,0.0002041243,0.0006026068,0.41617683,0.0010503862,0.24035405,0.0031683228,0.31691626],"study_design_scores_gemma":[0.000095541305,0.00008415079,0.0029079434,0.00015325124,0.00017579876,0.00009766654,0.00006428529,0.88439775,0.0007996414,0.1088882,0.0022766145,0.00005922792],"about_ca_topic_score_codex":0.008885306,"about_ca_topic_score_gemma":0.0049275206,"teacher_disagreement_score":0.050910804,"about_ca_system_score_codex":0.0023886245,"about_ca_system_score_gemma":0.003147641,"threshold_uncertainty_score":0.26924527},"labels":[],"label_agreement":null},{"id":"W4313469948","doi":"10.5539/ijsp.v12n1p66","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 1","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Probability and statistics; Mathematics; Mathematical economics; Computer science","score_opus":0.06321294672193459,"score_gpt":0.38166046535768555,"score_spread":0.31844751863575094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313469948","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012151075,0.0030891958,0.0015186497,0.14226688,0.84917307,0.0005500728,0.00082396896,0.00053697074,0.0019196323],"genre_scores_gemma":[0.004300638,0.0072189686,0.004433555,0.16899514,0.7684029,0.0035958902,0.001788182,0.0017437952,0.039520986],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.91956335,0.016852574,0.017027311,0.005901154,0.038146798,0.002508763],"domain_scores_gemma":[0.12436242,0.045480866,0.0127403485,0.0069906986,0.8017285,0.008697161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06349764,0.003358466,0.010310821,0.013447428,0.0052759536,0.01159287,0.006140137,0.017988969,0.091985054],"category_scores_gemma":[0.5739095,0.0019711093,0.0061477656,0.0060105626,0.0040380987,0.007485493,0.0044116564,0.014509414,0.06400359],"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":[0.000035838435,0.00000434532,0.00007716785,0.00045565653,0.000011761986,0.000048147947,0.000037184247,0.000010828945,0.000032835265,0.00012056639,0.99578387,0.0033817454],"study_design_scores_gemma":[0.0003208376,0.00007097293,0.0014712022,0.005250418,0.00016609063,0.0012925676,0.0004628981,0.0005408342,0.0003582079,0.0029062182,0.98692864,0.00023112063],"about_ca_topic_score_codex":0.0035469667,"about_ca_topic_score_gemma":0.0055060796,"teacher_disagreement_score":0.091985054,"about_ca_system_score_codex":0.005642735,"about_ca_system_score_gemma":0.012168461,"threshold_uncertainty_score":0.33581167},"labels":[],"label_agreement":null},{"id":"W4313705718","doi":"10.1093/ije/dyac237","title":"Dealing with missing data using the Heckman selection model: methods primer for epidemiologists","year":2023,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","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 British Columbia","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Library science; Agency (philosophy); MEDLINE; Center (category theory); Medicine; Family medicine; Sociology; Political science; Computer science; Law; Social science","score_opus":0.6270906549660715,"score_gpt":0.6191390944000694,"score_spread":0.007951560566002103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313705718","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":[0.0002166903,0.008057482,0.9811633,0.0047353418,0.00075679563,0.0009354729,0.001069208,0.0013106276,0.001755091],"genre_scores_gemma":[0.0030491247,0.012583472,0.97249097,0.00202725,0.0011095115,0.0050570737,0.0010653884,0.00081947754,0.0017977771],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.96534,0.028644925,0.0014492953,0.0017537093,0.0025301191,0.0002819403],"domain_scores_gemma":[0.90190816,0.08529287,0.0035576418,0.0048231296,0.0036581678,0.00076010614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04560108,0.0028888776,0.0037614107,0.005892219,0.00095016084,0.0037644785,0.008685472,0.0055592568,0.015558833],"category_scores_gemma":[0.087198794,0.002689773,0.004410017,0.0073895855,0.0028347676,0.004194718,0.0037859431,0.0108954245,0.008545567],"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":[0.00014839978,0.0005291365,0.0036667862,0.0054737907,0.0016531354,0.0007344813,0.0015490825,0.020597886,0.00054213766,0.22156748,0.2441694,0.49936828],"study_design_scores_gemma":[0.00016589112,0.00019010625,0.0017435325,0.004675559,0.00036902013,0.00083293195,0.0004764921,0.05901478,0.00039541858,0.56579804,0.36611003,0.0002281769],"about_ca_topic_score_codex":0.003339877,"about_ca_topic_score_gemma":0.0035650202,"teacher_disagreement_score":0.04560108,"about_ca_system_score_codex":0.001826647,"about_ca_system_score_gemma":0.0063613546,"threshold_uncertainty_score":0.24116445},"labels":[],"label_agreement":null},{"id":"W4313822352","doi":"10.1007/978-3-319-69909-7_1821-2","title":"Missing Data","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.30744379255690596,"score_gpt":0.42735354437707584,"score_spread":0.11990975182016989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313822352","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":[0.00054748263,0.0059026494,0.7474276,0.0059876367,0.0019003181,0.00013359502,0.0029465787,0.0028907747,0.2322634],"genre_scores_gemma":[0.022331245,0.009640305,0.33100456,0.0043679588,0.0022920268,0.00056157925,0.0065533062,0.0029013627,0.6203476],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868506,0.00045722953,0.00005978601,0.00023244778,0.00052335334,0.00004212348],"domain_scores_gemma":[0.99513334,0.0032644612,0.0001056513,0.00093464187,0.0004792084,0.00008252958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028583452,0.0011672777,0.0012340554,0.0016277629,0.00083473424,0.0025355106,0.0021952102,0.0016977458,0.12617551],"category_scores_gemma":[0.012314234,0.0009264268,0.00080440973,0.001751682,0.0012848423,0.003816881,0.001951617,0.0034548892,0.0681827],"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":[0.000030894167,0.000035406792,0.00013368134,0.00036889664,0.00003180095,0.00008324943,0.00017155007,0.0023455608,0.0003253791,0.3283962,0.3549352,0.3131422],"study_design_scores_gemma":[0.000008378237,0.000015001416,0.0001505481,0.00021288672,0.000020320389,0.00026618305,0.000052765572,0.006869207,0.00063259475,0.4836074,0.5081419,0.000022853255],"about_ca_topic_score_codex":0.0009175731,"about_ca_topic_score_gemma":0.0015496608,"teacher_disagreement_score":0.12617551,"about_ca_system_score_codex":0.00081211654,"about_ca_system_score_gemma":0.0014113508,"threshold_uncertainty_score":0.4220991},"labels":[],"label_agreement":null},{"id":"W4315701213","doi":"10.22489/cinc.2022.419","title":"Segmentation Uncertainty Quantification in Cardiac Propagation Models","year":2022,"lang":"en","type":"article","venue":"Computing in cardiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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 General Medical Sciences; National Institutes of Health; Dalhousie University","keywords":"Segmentation; Pipeline transport; Torso; Computer science; Pipeline (software); Eikonal equation; Computation; Image segmentation; Algorithm; Artificial intelligence; Mathematics; Engineering; Mathematical analysis","score_opus":0.1016723115243171,"score_gpt":0.3783310634152719,"score_spread":0.2766587518909548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315701213","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":[0.113749094,0.00021188424,0.88386,0.0002491065,0.000020910747,0.000035389356,0.00011731347,0.00043183423,0.0013245108],"genre_scores_gemma":[0.93552727,0.00015906767,0.06323236,0.000050381244,0.000024078667,0.000047716017,0.00011466471,0.00015656883,0.000687866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924314,0.00027752988,0.00004258584,0.00011225633,0.00026075947,0.00006372009],"domain_scores_gemma":[0.99293303,0.005529337,0.0006242663,0.0003507187,0.00044064573,0.000121933816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020657473,0.0006641757,0.0004849323,0.00080473593,0.00043102895,0.0011018378,0.0008390377,0.001108492,0.00065401464],"category_scores_gemma":[0.012265451,0.00051993487,0.00062058435,0.0004922018,0.0009404728,0.0012018759,0.0010860767,0.0009737979,0.00011005274],"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":[0.000026405309,0.000006789533,0.0008093911,0.000017384371,0.000013109852,0.00003056679,0.000043661938,0.9892314,0.0013641032,0.003975631,0.000079370686,0.0044021867],"study_design_scores_gemma":[0.0000012734187,0.000008046634,0.00014946699,0.0000030522974,0.0000026888613,0.000010115028,0.0000033400959,0.9969144,0.0006458282,0.0021716126,0.00008602629,0.0000041442413],"about_ca_topic_score_codex":0.0060069314,"about_ca_topic_score_gemma":0.0031573002,"teacher_disagreement_score":0.0060069314,"about_ca_system_score_codex":0.0010001708,"about_ca_system_score_gemma":0.0009351261,"threshold_uncertainty_score":0.011943936},"labels":[],"label_agreement":null},{"id":"W4315705841","doi":"10.48550/arxiv.2301.03710","title":"A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Poisson distribution; Constant (computer programming); Gamma process; Econometrics; Generalization; Bayesian probability; Poisson process; Gamma distribution; Poisson regression; Statistics; Range (aeronautics); Computer science; Mathematics; Multicenter AIDS Cohort Study; Compound Poisson process; Engineering; Demography; Medicine; Population","score_opus":0.6448778137186387,"score_gpt":0.363454303008753,"score_spread":0.28142351070988575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315705841","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":[0.029560797,0.0015105148,0.96194965,0.0026136432,0.0001751587,0.0002559739,0.0011253897,0.00035761623,0.002451265],"genre_scores_gemma":[0.6687579,0.00492201,0.30194095,0.0010782565,0.0007887595,0.002242732,0.0031532152,0.0002210818,0.016895035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995271,0.0030444732,0.00020118948,0.00073166646,0.0004388,0.0003129142],"domain_scores_gemma":[0.9796651,0.016282842,0.001584546,0.0009070374,0.0010805475,0.00047995755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019440776,0.0011429258,0.0022867555,0.0026345954,0.0013129915,0.0022684247,0.0059876707,0.0042297714,0.005333771],"category_scores_gemma":[0.04146838,0.0011137547,0.0021827724,0.00374957,0.0018422437,0.0027753003,0.0019319786,0.0042289966,0.0012723404],"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":[0.000197038,0.000096742595,0.011350528,0.000272749,0.00014954279,0.00051279087,0.00071977405,0.5122462,0.0005011242,0.4262417,0.006714217,0.040997643],"study_design_scores_gemma":[0.0000615798,0.000063714164,0.0015289714,0.00009035827,0.000062123596,0.00012724998,0.00012067311,0.8183192,0.00014448659,0.17529085,0.0041386867,0.00005206068],"about_ca_topic_score_codex":0.032200824,"about_ca_topic_score_gemma":0.019508673,"teacher_disagreement_score":0.032200824,"about_ca_system_score_codex":0.0027770137,"about_ca_system_score_gemma":0.0031515905,"threshold_uncertainty_score":0.10281384},"labels":[],"label_agreement":null},{"id":"W4317181638","doi":"10.1289/isee.2022.o-op-091","title":"Integrating biological knowledge in Kernel-based analyses of environmental mixtures and health","year":2022,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"","keywords":"Interpretability; Prior probability; Computer science; Set (abstract data type); Flexibility (engineering); Bayesian probability; Index (typography); Parametric statistics; Data mining; Function (biology); Machine learning; Statistics; Artificial intelligence; Mathematics","score_opus":0.22722895023772277,"score_gpt":0.4444028201480957,"score_spread":0.2171738699103729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317181638","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":[0.007161101,0.00021157466,0.9919958,0.00014934826,0.000009297165,0.00001691933,0.00004361288,0.00009368918,0.000318712],"genre_scores_gemma":[0.36759838,0.00094459706,0.62805784,0.00022835784,0.000112551774,0.00024501202,0.00049514446,0.00013436234,0.0021838446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943796,0.0035491262,0.00021264468,0.00079999026,0.0008730529,0.00018566029],"domain_scores_gemma":[0.9691657,0.025131643,0.0018140764,0.0026121081,0.0010174367,0.00025898247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013131827,0.0007841999,0.0011072878,0.0028269512,0.0006141391,0.0019214592,0.0018421025,0.0013229714,0.0017778752],"category_scores_gemma":[0.057631064,0.0008110714,0.0015672551,0.0022722506,0.0026268787,0.0033443847,0.0031438998,0.002148937,0.0004130959],"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":[0.00023622921,0.0001610758,0.009357735,0.00031793237,0.0004057395,0.00014032992,0.00037849715,0.4449555,0.0039410247,0.31494406,0.0012279276,0.22393394],"study_design_scores_gemma":[0.000023771463,0.00005895541,0.0024843216,0.000034215373,0.000061827945,0.000060800758,0.00004409152,0.6951531,0.0014209352,0.29875928,0.0018581006,0.0000406034],"about_ca_topic_score_codex":0.004077774,"about_ca_topic_score_gemma":0.00414562,"teacher_disagreement_score":0.013131827,"about_ca_system_score_codex":0.0013385637,"about_ca_system_score_gemma":0.0015548555,"threshold_uncertainty_score":0.06944859},"labels":[],"label_agreement":null},{"id":"W4317213533","doi":"10.1139/cjfas-2022-0234","title":"Response to Comment on Courter et al. (2022)","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biology; Environmental science; Chemistry","score_opus":0.08523889819207747,"score_gpt":0.36127802994061614,"score_spread":0.27603913174853867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317213533","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":[0.00018888387,0.0008101683,0.00015691148,0.94538605,0.051057193,0.000033559616,0.0004164167,0.000086749176,0.0018639939],"genre_scores_gemma":[0.0008214605,0.00028593437,0.00011192705,0.97719544,0.016013954,0.00006589642,0.00004950201,0.00003520393,0.0054207277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99295217,0.0010813341,0.0009862903,0.0011333992,0.0029111858,0.00093561044],"domain_scores_gemma":[0.97235143,0.015756328,0.0017068315,0.00090918475,0.006797606,0.002478628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010531873,0.0012458747,0.0023582994,0.0013250276,0.005265103,0.005740397,0.0047489847,0.07353004,0.016699754],"category_scores_gemma":[0.06834389,0.0014426865,0.0022518998,0.0020948132,0.0044185463,0.004000871,0.002956743,0.06155797,0.021486906],"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":[0.0000309463,0.0000068507943,0.00010215488,0.00004311614,0.000007319849,0.00009849027,0.000078416575,0.000021631851,0.000060924292,0.0005787422,0.997883,0.0010883155],"study_design_scores_gemma":[0.0000744325,0.00003585261,0.0026836966,0.0004159392,0.000040173698,0.00025519077,0.00047999067,0.00024315501,0.00032471027,0.0033275278,0.9920243,0.00009511005],"about_ca_topic_score_codex":0.03872416,"about_ca_topic_score_gemma":0.040157557,"teacher_disagreement_score":0.07353004,"about_ca_system_score_codex":0.006339937,"about_ca_system_score_gemma":0.009278322,"threshold_uncertainty_score":0.07699752},"labels":[],"label_agreement":null},{"id":"W4317359118","doi":"10.1002/sim.9650","title":"Practical strategies for operationalizing optimal allocation in stratified cluster‐based outcome‐dependent sampling designs","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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; Grand Challenges Canada; Harvard T.H. Chan School of Public Health; Government of the United Kingdom; Bill and Melinda Gates Foundation; National Heart, Lung, and Blood Institute; United States Agency for International Development","keywords":"Computer science; Statistics; Sampling design; Covariate; Sampling (signal processing); Sample size determination; Cluster sampling; Inverse probability weighting; Outcome (game theory); Weighting; Missing data; Stratified sampling; Adaptive sampling; Mathematical optimization; Mathematics; Estimator; Filter (signal processing); Population; Monte Carlo method","score_opus":0.3702937272011516,"score_gpt":0.5299317334396962,"score_spread":0.15963800623854463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317359118","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":[0.0027615197,0.000093991686,0.99495584,0.00014937649,0.000023846938,0.0011908123,0.00003912313,0.00008757805,0.00069790264],"genre_scores_gemma":[0.048139922,0.0001346409,0.9474599,0.00012579314,0.000021911124,0.0037623853,0.00008793267,0.000026271695,0.00024112778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8984617,0.09213491,0.0023237853,0.0024193046,0.0039932537,0.00066702184],"domain_scores_gemma":[0.90719575,0.07480709,0.0050140964,0.0077823405,0.004573615,0.00062710023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08724955,0.0016286146,0.0019803443,0.0027073273,0.0009191726,0.002143954,0.0031163797,0.0016655204,0.005266905],"category_scores_gemma":[0.21259494,0.0014547318,0.0015607621,0.003198729,0.0028248269,0.0021251512,0.004388712,0.0024176282,0.00083432475],"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":[0.0006967949,0.0004394559,0.0068957573,0.00089365954,0.00050517695,0.000114441,0.0017863093,0.18295872,0.0015905289,0.45619145,0.0034307998,0.34449697],"study_design_scores_gemma":[0.0007038446,0.00106299,0.0028727897,0.0004996368,0.00020248843,0.000087891385,0.00041292337,0.54873514,0.00231377,0.43234986,0.010664051,0.000094590825],"about_ca_topic_score_codex":0.0031088784,"about_ca_topic_score_gemma":0.003573467,"teacher_disagreement_score":0.08724955,"about_ca_system_score_codex":0.00247778,"about_ca_system_score_gemma":0.0066289906,"threshold_uncertainty_score":0.46142524},"labels":[],"label_agreement":null},{"id":"W4317815178","doi":"10.1109/wsc57314.2022.10015497","title":"Likelihood Ratio Density Estimation for Simulation Models","year":2022,"lang":"en","type":"article","venue":"2022 Winter Simulation Conference (WSC)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université de Montréal; University of Waterloo","funders":"","keywords":"Estimator; Applied mathematics; Cover (algebra); Maximum likelihood; Random variable; Mathematics; Probability density function; Density estimation; Estimation; Density ratio; Statistics; Computer science; Algorithm; Mathematical optimization","score_opus":0.12042008204578183,"score_gpt":0.3935577508051745,"score_spread":0.2731376687593927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317815178","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":[0.0004928522,0.00036051834,0.9979692,0.00023726378,0.000023342134,0.00002325947,0.000027761827,0.00012914494,0.00073666294],"genre_scores_gemma":[0.123160005,0.0024175693,0.868494,0.0003972212,0.00042906008,0.00083195977,0.00043145142,0.00045461988,0.0033841429],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9772096,0.018828874,0.00044702014,0.0011660544,0.0020759562,0.0002724284],"domain_scores_gemma":[0.9296596,0.062038742,0.0021447765,0.0034844282,0.0022801978,0.0003922347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020574031,0.0018488157,0.002549172,0.0029943248,0.0008510756,0.0033722394,0.0040642796,0.0035121846,0.0054668575],"category_scores_gemma":[0.15053035,0.001163812,0.0020077033,0.0032576742,0.0040101735,0.0057224436,0.0036230225,0.0047467495,0.0020508743],"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":[0.00004202748,0.000033176733,0.00083473494,0.00020802008,0.00013479046,0.00016535218,0.00017824252,0.20956983,0.00028120165,0.7520401,0.0026071207,0.03390546],"study_design_scores_gemma":[0.000023609231,0.00001818325,0.0001250665,0.000050205752,0.000022686463,0.000093451774,0.000023477724,0.4995759,0.00020972897,0.4954155,0.004417613,0.000024505318],"about_ca_topic_score_codex":0.0033424655,"about_ca_topic_score_gemma":0.0016000126,"teacher_disagreement_score":0.020574031,"about_ca_system_score_codex":0.0023717382,"about_ca_system_score_gemma":0.0019092171,"threshold_uncertainty_score":0.108807206},"labels":[],"label_agreement":null},{"id":"W4317892506","doi":"10.1093/jrsssa/qnac010","title":"Multivariate claim count regression model with varying dispersion and dependence parameters","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"","keywords":"Overdispersion; Poisson regression; Multivariate statistics; Econometrics; Statistics; Mathematics; Dispersion (optics); Count data; Bivariate analysis; Poisson distribution; Regression; Population","score_opus":0.046266872239897415,"score_gpt":0.3391948264382115,"score_spread":0.29292795419831413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317892506","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":[0.1731788,0.00046841623,0.8206929,0.0015922089,0.000042950098,0.0001409084,0.0012966231,0.0006814435,0.0019057989],"genre_scores_gemma":[0.90048224,0.00052568526,0.08796317,0.00023064982,0.00014086305,0.00041735946,0.0015636501,0.00017261732,0.008503681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99353445,0.0038511641,0.00028719928,0.0012414863,0.0006997109,0.00038592744],"domain_scores_gemma":[0.9624558,0.028439028,0.0045843143,0.0023118749,0.00179882,0.00041020912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017883487,0.0013586417,0.002462712,0.0024401275,0.00063393934,0.0022433535,0.004979996,0.003217946,0.0041418634],"category_scores_gemma":[0.037074514,0.0010813654,0.0018255858,0.00354163,0.0020697352,0.0031913815,0.0019805687,0.0041594976,0.0009046148],"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":[0.00044719432,0.00027613644,0.016798401,0.00016392693,0.00030042842,0.0006771909,0.0005331141,0.75129366,0.0024029643,0.19664398,0.0019896924,0.028473264],"study_design_scores_gemma":[0.00003351905,0.00003678031,0.002023964,0.000019177203,0.000037448513,0.000079660094,0.000028597013,0.9743988,0.00020488638,0.022669695,0.00043692323,0.00003058523],"about_ca_topic_score_codex":0.014296603,"about_ca_topic_score_gemma":0.0063529843,"teacher_disagreement_score":0.017883487,"about_ca_system_score_codex":0.0020499679,"about_ca_system_score_gemma":0.0010757145,"threshold_uncertainty_score":0.09457809},"labels":[],"label_agreement":null},{"id":"W4319460509","doi":"10.1136/bmj-2022-071018","title":"Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist","year":2023,"lang":"en","type":"article","venue":"BMJ","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":74,"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":"NIHR School for Primary Care Research; Medizinische Universität Wien; Vlaamse regering; Keele University; Fonds Wetenschappelijk Onderzoek; Universität Wien; Universiteit Maastricht; ZonMw; Department of Health and Social Care; National Institute for Health and Care Research; Universiteit Leiden; Cancer Research UK; Universiteit Utrecht; European Commission; Vanderbilt University; McMaster University; Albert-Ludwigs-Universität Freiburg; Brigham and Women's Hospital; Cleveland Clinic; KU Leuven; Brown University","keywords":"Computer science; Cluster analysis; Data mining; Predictive modelling; Checklist; Multivariable calculus; Cluster (spacecraft); Data science; Machine learning","score_opus":0.5857741832646703,"score_gpt":0.5009133730568724,"score_spread":0.0848608102077979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319460509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010767888,0.0021444536,0.6416564,0.015278234,0.00476017,0.10088647,0.19236293,0.015574299,0.01656919],"genre_scores_gemma":[0.04664892,0.0019231874,0.4968007,0.0047704256,0.0008454906,0.37367997,0.064575404,0.0036711902,0.007084683],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.6472846,0.20102143,0.12090833,0.006944356,0.021420956,0.0024203777],"domain_scores_gemma":[0.21740492,0.47319394,0.0713078,0.11230062,0.12223192,0.0035607622],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.30687362,0.002079659,0.0032308823,0.009511652,0.0024826957,0.006135675,0.005978386,0.0035466754,0.034510143],"category_scores_gemma":[0.66640055,0.0026528814,0.0072628194,0.0076029687,0.002883083,0.00410265,0.008141291,0.0059908866,0.010929387],"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":[0.0034952932,0.0003335502,0.014049317,0.039106138,0.0022476113,0.00082783186,0.006012921,0.0071419915,0.0017026764,0.054559287,0.66259634,0.20792702],"study_design_scores_gemma":[0.0029395218,0.000832082,0.023295177,0.030966444,0.0014248488,0.00088163826,0.0029142774,0.025031077,0.0064108265,0.09068301,0.8139093,0.00071185705],"about_ca_topic_score_codex":0.0049520326,"about_ca_topic_score_gemma":0.00536133,"teacher_disagreement_score":0.6931264,"about_ca_system_score_codex":0.004703497,"about_ca_system_score_gemma":0.029964495,"threshold_uncertainty_score":0.85474825},"labels":[],"label_agreement":null},{"id":"W4319841290","doi":"10.1002/cjs.11756","title":"Regression model selection via log‐likelihood ratio and constrained minimum criterion","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Bayesian information criterion; Likelihood-ratio test; Statistics; Deviance information criterion; Model selection; Mathematics; Frequentist inference; Information Criteria; Likelihood principle; Sample size determination; Score test; Regression analysis; Selection (genetic algorithm); Ratio test; Bayesian probability; Likelihood function; Maximum likelihood; Bayesian inference; Computer science; Artificial intelligence; Quasi-maximum likelihood","score_opus":0.05334615844499688,"score_gpt":0.33304388302719357,"score_spread":0.27969772458219666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319841290","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":[0.008045462,0.0002796447,0.99017906,0.0002485566,0.00002142462,0.000086003456,0.000070779875,0.00035626508,0.00071270304],"genre_scores_gemma":[0.32859358,0.00036386933,0.6680859,0.00025932168,0.00008031538,0.0005800498,0.00045459642,0.00038252067,0.001199848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9836902,0.013279664,0.00036615203,0.00075714506,0.0016435019,0.00026342287],"domain_scores_gemma":[0.9342611,0.058872808,0.002416378,0.0013223945,0.002673822,0.00045354725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016106576,0.0016282721,0.002615079,0.0040485314,0.0008474148,0.0019476764,0.0034090185,0.0017688745,0.004265368],"category_scores_gemma":[0.09269112,0.00081449933,0.0017144589,0.0026739505,0.0017005161,0.0020490356,0.002124953,0.002515691,0.0008747673],"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":[0.00034584643,0.00022258489,0.004326706,0.0004257693,0.00043921138,0.0004929825,0.00015698156,0.77288836,0.0015849315,0.09330279,0.004626536,0.12118733],"study_design_scores_gemma":[0.000032570722,0.000045219625,0.0003321032,0.000024833265,0.000015929785,0.000045082266,0.0000116366755,0.97516,0.00029620776,0.023602106,0.00041776092,0.000016598367],"about_ca_topic_score_codex":0.0059284186,"about_ca_topic_score_gemma":0.0036329236,"teacher_disagreement_score":0.016106576,"about_ca_system_score_codex":0.0015082245,"about_ca_system_score_gemma":0.0029560693,"threshold_uncertainty_score":0.08518076},"labels":[],"label_agreement":null},{"id":"W4320057135","doi":"10.1080/00031305.2022.2139293","title":"Bayes Factors and Posterior Estimation: Two Sides of the Very Same Coin","year":2022,"lang":"en","type":"article","venue":"The American Statistician","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"","keywords":"Bayes factor; Bayes' rule; Prior probability; Bayes' theorem; Bayesian probability; Posterior probability; Odds; Point estimation; Bayes estimator; Estimation; Statistics; Mathematics; Econometrics; Computer science; Economics; Logistic regression","score_opus":0.041926484876412824,"score_gpt":0.3594317233608289,"score_spread":0.3175052384844161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320057135","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":[0.00646995,0.06339111,0.73569477,0.15225029,0.009481621,0.00024623383,0.0003841857,0.00031327267,0.031768523],"genre_scores_gemma":[0.38060027,0.061916426,0.4594392,0.050267335,0.03643087,0.0016352104,0.00048707708,0.0010929424,0.008130681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8518019,0.108040705,0.007618685,0.008682997,0.022004154,0.0018515125],"domain_scores_gemma":[0.6145619,0.34011808,0.008987963,0.018996691,0.015369548,0.0019658674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12548709,0.0022028375,0.0049371417,0.008014689,0.0030428648,0.014449163,0.004861951,0.010025418,0.005209652],"category_scores_gemma":[0.31628445,0.0017191808,0.0025313979,0.0072846827,0.04528764,0.03106852,0.0061668,0.022094887,0.0017108618],"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":[0.00006396627,0.000018722192,0.00049366057,0.00025202223,0.00011262729,0.000057880432,0.0007459987,0.0007823761,0.000054672983,0.9656852,0.0039027752,0.027830034],"study_design_scores_gemma":[0.000025142826,0.000012185056,0.00020810301,0.0003382834,0.000026333028,0.00005129772,0.000111867186,0.0009559679,0.00006537613,0.9911084,0.0070654354,0.000031571562],"about_ca_topic_score_codex":0.0031043168,"about_ca_topic_score_gemma":0.0016182422,"teacher_disagreement_score":0.12548709,"about_ca_system_score_codex":0.0050108884,"about_ca_system_score_gemma":0.0043252404,"threshold_uncertainty_score":0.66364706},"labels":[],"label_agreement":null},{"id":"W4320880712","doi":"10.21203/rs.3.rs-2580049/v1","title":"Some results on maximum likelihood from incomplete data: finite sample properties and an improved M-estimator for resampling method","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"Leonard N. Stern School of Business, New York University; York University","keywords":"Mathematics; Estimator; Applied mathematics; Minimum-variance unbiased estimator; Statistics; Consistent estimator; Fisher information; Matrix (chemical analysis); Minimax estimator; Efficient estimator; Covariance matrix; Bias of an estimator","score_opus":0.549323775066077,"score_gpt":0.5324742046576015,"score_spread":0.016849570408475456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320880712","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":[0.0017554035,0.0006482138,0.9965404,0.000180875,0.000034364966,0.000019497107,0.000033273824,0.00005226337,0.0007357501],"genre_scores_gemma":[0.1614198,0.003052626,0.8309354,0.00048552293,0.0005325095,0.00041119632,0.000361413,0.00021508467,0.0025864888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9921172,0.005133317,0.0003573236,0.0007246774,0.0014680288,0.00019945901],"domain_scores_gemma":[0.9541047,0.037782874,0.002136569,0.0024185923,0.003219217,0.0003379768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021819478,0.0011248672,0.0019628904,0.003003703,0.00059074134,0.0016725805,0.0024726207,0.0019918394,0.0032649296],"category_scores_gemma":[0.076368086,0.0008161697,0.002520689,0.0019283704,0.0026913506,0.0042302897,0.0024769139,0.0032569172,0.0006566816],"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":[0.0001295627,0.000078876044,0.0023970234,0.0007946304,0.00026855682,0.0005882312,0.0005314888,0.28111768,0.0031246403,0.5966269,0.0028696605,0.11147278],"study_design_scores_gemma":[0.000017785755,0.000060769446,0.00076458027,0.00013955733,0.000044609118,0.00017818394,0.000048073958,0.81616235,0.0017412364,0.17726871,0.0035228413,0.00005126023],"about_ca_topic_score_codex":0.0019981957,"about_ca_topic_score_gemma":0.0013383834,"teacher_disagreement_score":0.021819478,"about_ca_system_score_codex":0.0013016843,"about_ca_system_score_gemma":0.0012977362,"threshold_uncertainty_score":0.11539376},"labels":[],"label_agreement":null},{"id":"W4321276814","doi":"10.48550/arxiv.2302.08076","title":"Augmented two-step estimating equations with nuisance functionals and complex survey data","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Empirical likelihood; Estimator; Nonparametric statistics; Quantile; Mathematics; Inference; Orthogonality; Nuisance parameter; Estimating equations; Econometrics; Statistical inference; Applied mathematics; Statistics; Mathematical optimization; Computer science; Artificial intelligence","score_opus":0.6126734453865608,"score_gpt":0.35426303275886484,"score_spread":0.25841041262769593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321276814","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":[0.007737288,0.0003136591,0.9910678,0.00022509499,0.000025276042,0.00009118282,0.0001691322,0.00008085341,0.0002896599],"genre_scores_gemma":[0.23475984,0.0013463349,0.75693774,0.00033163832,0.00014755949,0.0013390695,0.0010105618,0.00006161023,0.0040656473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97822535,0.01759908,0.00066332053,0.0019602762,0.0012219788,0.00033004695],"domain_scores_gemma":[0.9343258,0.053933606,0.004085135,0.005209687,0.002143348,0.00030233257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024422837,0.0012846782,0.002380342,0.0017877477,0.00058809004,0.002289175,0.0028766803,0.002175049,0.0029359064],"category_scores_gemma":[0.06290863,0.0013688476,0.002445789,0.0029152276,0.0017611737,0.0031607433,0.0029103223,0.0029885194,0.00060022884],"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":[0.00021429703,0.00018105167,0.02145081,0.00068457954,0.0011680181,0.0005378566,0.0007814051,0.30877343,0.0011103083,0.5095417,0.00257991,0.15297666],"study_design_scores_gemma":[0.000080253645,0.00015118431,0.004498056,0.00010655085,0.00020435697,0.00015149316,0.00008176373,0.7578905,0.0006147111,0.23100185,0.005139232,0.00008001414],"about_ca_topic_score_codex":0.00638642,"about_ca_topic_score_gemma":0.006476731,"teacher_disagreement_score":0.024422837,"about_ca_system_score_codex":0.0011751474,"about_ca_system_score_gemma":0.002197507,"threshold_uncertainty_score":0.12916183},"labels":[],"label_agreement":null},{"id":"W4321351204","doi":"10.1002/sim.9685","title":"Impute‐then‐exclude versus exclude‐then‐impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Institute for Clinical Evaluative Sciences; Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Missing data; Imputation (statistics); Statistics; Sample size determination; Variable (mathematics); Random variable; Computer science; Mathematics; Medicine; Econometrics","score_opus":0.09486507976885547,"score_gpt":0.4341099087394667,"score_spread":0.33924482897061126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321351204","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":[0.02355164,0.0014659115,0.9598555,0.010834203,0.0003800208,0.00060861715,0.0003377505,0.0005308304,0.002435588],"genre_scores_gemma":[0.22770236,0.0011330406,0.7617737,0.005136517,0.0005627738,0.0010850031,0.00046918372,0.0008238064,0.0013135187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6377845,0.33990732,0.005363295,0.006837366,0.008649953,0.001457675],"domain_scores_gemma":[0.27265966,0.6755845,0.00822499,0.033610437,0.0085193375,0.0014010995],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.38047433,0.0021302516,0.0046919635,0.0024656293,0.002169766,0.0064564385,0.0077979234,0.0048490968,0.0034505036],"category_scores_gemma":[0.63441414,0.0024472042,0.0055680606,0.0038851537,0.0065036165,0.007279988,0.0061604492,0.014458095,0.0008525943],"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":[0.0024644542,0.0005356178,0.07610411,0.0017073705,0.005961111,0.0014324836,0.010387472,0.12085008,0.00072883,0.29715756,0.023181556,0.45948946],"study_design_scores_gemma":[0.0008143933,0.0011420569,0.0116552785,0.0014691114,0.0007872205,0.0010699343,0.0021833165,0.47461182,0.0037761186,0.47572103,0.026305338,0.00046444059],"about_ca_topic_score_codex":0.008383493,"about_ca_topic_score_gemma":0.010068768,"teacher_disagreement_score":0.6195257,"about_ca_system_score_codex":0.0022787787,"about_ca_system_score_gemma":0.0067448583,"threshold_uncertainty_score":0.76398546},"labels":[],"label_agreement":null},{"id":"W4321490378","doi":"10.3758/s13428-023-02079-4","title":"Multilevel mediation analysis in R: A comparison of bootstrap and Bayesian approaches","year":2023,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Resampling; Bayesian probability; Statistics; Computer science; Mediation; Random effects model; Context (archaeology); Type I and type II errors; Econometrics; Estimator; Multilevel model; Mathematics; Meta-analysis","score_opus":0.7501815697635115,"score_gpt":0.663230774126511,"score_spread":0.08695079563700048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321490378","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":[0.010685062,0.0012479981,0.98420125,0.0007747342,0.00013643209,0.00033212476,0.00026739025,0.0012121567,0.0011428741],"genre_scores_gemma":[0.1138471,0.0011532822,0.88028055,0.00035479225,0.00013740746,0.0017410971,0.00026682412,0.0016484321,0.00057039637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7770864,0.20494181,0.0030059712,0.005395079,0.008555533,0.0010152146],"domain_scores_gemma":[0.3984038,0.56323963,0.007014685,0.022456117,0.0074980347,0.0013878199],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1423216,0.0017712218,0.0046330066,0.0033701165,0.0013834448,0.0039507207,0.0054821675,0.0027510945,0.011838737],"category_scores_gemma":[0.46538854,0.0016172942,0.004593319,0.005613791,0.0032639632,0.007589564,0.0047013676,0.006379652,0.001919558],"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":[0.007173172,0.001104653,0.015232164,0.004589541,0.012184932,0.00039831732,0.004926884,0.041128203,0.0024944146,0.29150727,0.017920388,0.60134006],"study_design_scores_gemma":[0.00326469,0.0024349962,0.022720326,0.0017624267,0.0064302757,0.0009835066,0.001793443,0.40399632,0.0036663162,0.5274677,0.024811659,0.0006683079],"about_ca_topic_score_codex":0.0054219267,"about_ca_topic_score_gemma":0.007753978,"teacher_disagreement_score":0.8576784,"about_ca_system_score_codex":0.0014347763,"about_ca_system_score_gemma":0.004968137,"threshold_uncertainty_score":0.75267756},"labels":[],"label_agreement":null},{"id":"W4321788752","doi":"10.1002/icd.2407","title":"Best practices for addressing missing data through multiple imputation","year":2023,"lang":"en","type":"article","venue":"Infant and Child Development","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"","keywords":"Missing data; Imputation (statistics); Attrition; Computer science; Data collection; Psychology; Statistics; Data science; Data mining; Machine learning; Mathematics","score_opus":0.3445098573815719,"score_gpt":0.47066308591485717,"score_spread":0.12615322853328526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321788752","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":[0.00024418675,0.0013101955,0.9918094,0.0035916138,0.0002739003,0.00040620996,0.00030694107,0.0009609498,0.0010965918],"genre_scores_gemma":[0.0061679706,0.001589156,0.9883221,0.0008673092,0.00022330345,0.0016189468,0.000356543,0.00045265272,0.00040207102],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.5035696,0.43518987,0.025143383,0.008659974,0.026110925,0.00132635],"domain_scores_gemma":[0.3064308,0.5410778,0.028794065,0.068552256,0.052776948,0.0023681913],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.34597135,0.003489085,0.0045263534,0.01243487,0.0035317405,0.011461079,0.014051619,0.006631431,0.01677707],"category_scores_gemma":[0.6837725,0.004468624,0.008445327,0.015549654,0.0068079606,0.010290973,0.010267647,0.012882944,0.009203772],"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":[0.00032167006,0.00022375947,0.0047350195,0.008893839,0.0029402704,0.0005530392,0.007493836,0.021009877,0.0008935007,0.22575358,0.07232526,0.6548563],"study_design_scores_gemma":[0.00033142397,0.00016525299,0.0017818939,0.016263267,0.0008403748,0.0007359641,0.0013756611,0.064026795,0.0038674662,0.759365,0.15078415,0.00046275987],"about_ca_topic_score_codex":0.0059158565,"about_ca_topic_score_gemma":0.007841033,"teacher_disagreement_score":0.34597135,"about_ca_system_score_codex":0.0031366795,"about_ca_system_score_gemma":0.01596253,"threshold_uncertainty_score":0.8065338},"labels":[],"label_agreement":null},{"id":"W4323848287","doi":"10.3390/jrfm16030186","title":"The Naive Estimator of a Poisson Regression Model with a Measurement Error","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Mathematics; Poisson distribution; Mean squared error; Statistics; Poisson regression; Efficient estimator; Bias of an estimator; Regression analysis; Applied mathematics; Errors-in-variables models; Minimum-variance unbiased estimator","score_opus":0.05993783842101228,"score_gpt":0.3354677520603373,"score_spread":0.275529913639325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323848287","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":[0.0067005814,0.00035649515,0.99104816,0.00043147578,0.00011092491,0.00006116474,0.00006111809,0.00010563815,0.0011244733],"genre_scores_gemma":[0.36147806,0.0013436349,0.6280018,0.0014154444,0.0006001519,0.0005984927,0.00047691676,0.000090632886,0.0059949923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98712754,0.0065699634,0.0006557706,0.0022777838,0.002886339,0.0004826251],"domain_scores_gemma":[0.97520065,0.014569305,0.002742609,0.0038894296,0.0033246612,0.00027323893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0147924805,0.0008605492,0.002070149,0.0015459652,0.0006447902,0.0025819845,0.0039849887,0.0024312888,0.003104637],"category_scores_gemma":[0.07317361,0.0008016935,0.001510671,0.0021243677,0.0019823282,0.0056056143,0.0025636605,0.0029330393,0.00076694734],"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":[0.000156195,0.00015110812,0.01263626,0.00054211396,0.0003806437,0.00047215546,0.00044697663,0.084436655,0.0019592487,0.7030681,0.0063396376,0.18941098],"study_design_scores_gemma":[0.00011067686,0.00018353623,0.0031686507,0.00015972693,0.00015763559,0.00077419024,0.00010054252,0.52105796,0.0012077134,0.4664559,0.006511112,0.00011234378],"about_ca_topic_score_codex":0.003385009,"about_ca_topic_score_gemma":0.0028117155,"teacher_disagreement_score":0.0147924805,"about_ca_system_score_codex":0.0011439894,"about_ca_system_score_gemma":0.0025795582,"threshold_uncertainty_score":0.078231096},"labels":[],"label_agreement":null},{"id":"W4324031213","doi":"10.1016/j.conctc.2023.101115","title":"Performance of methods for analyzing continuous data from stratified cluster randomized trials – A simulation study","year":2023,"lang":"en","type":"article","venue":"Contemporary Clinical Trials Communications","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hamilton Health Sciences; McMaster University; St. Joseph’s Healthcare Hamilton; Impact","funders":"","keywords":"CRTS; Statistics; Sample size determination; Mathematics; Mean squared error; Confidence interval; Regression analysis; Linear regression; Type I and type II errors; Generalized estimating equation; Regression; Cluster sampling; Computer science; Medicine; Population","score_opus":0.8728268367206937,"score_gpt":0.6820762488834597,"score_spread":0.19075058783723398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324031213","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":[0.07548163,0.011747169,0.89452225,0.0017653655,0.00059140427,0.010446963,0.0013707152,0.00081984064,0.0032546474],"genre_scores_gemma":[0.4383726,0.0028229295,0.5389792,0.0011183562,0.00017125453,0.016747674,0.0009729113,0.00016781728,0.0006473685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.68307304,0.297135,0.007462735,0.005162634,0.006349074,0.0008175242],"domain_scores_gemma":[0.23486577,0.7218206,0.017205385,0.015328639,0.009939012,0.00084057037],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.26024893,0.002515345,0.004176475,0.0030745687,0.000825057,0.002725863,0.0031232696,0.0033354433,0.0043965587],"category_scores_gemma":[0.48498073,0.0014438893,0.010181756,0.0029773745,0.0018130695,0.002596944,0.0020395245,0.003994687,0.00051127473],"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":[0.022860864,0.0010716058,0.039388545,0.012297739,0.030092226,0.00062816363,0.0014267919,0.67193735,0.00093719596,0.046478365,0.0060848994,0.16679634],"study_design_scores_gemma":[0.007899387,0.0050039394,0.0052262465,0.0026148013,0.0069992025,0.0005392025,0.00020048508,0.91562515,0.0013288392,0.04941509,0.004904712,0.00024299632],"about_ca_topic_score_codex":0.003994808,"about_ca_topic_score_gemma":0.002197044,"teacher_disagreement_score":0.7397511,"about_ca_system_score_codex":0.0029937183,"about_ca_system_score_gemma":0.0060831876,"threshold_uncertainty_score":0.9122448},"labels":[],"label_agreement":null},{"id":"W4327738676","doi":"10.1186/s12874-023-01871-2","title":"Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","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 Ottawa; Ottawa Public Health; Ottawa Hospital","funders":"Medical Research Council","keywords":"Statistics; Correlation; Estimator; Generalized least squares; Cluster randomised controlled trial; Econometrics; Cluster (spacecraft); Generalized estimating equation; Mathematics; Computer science; Medicine; Randomized controlled trial; Surgery","score_opus":0.802471853529521,"score_gpt":0.6469527604550042,"score_spread":0.15551909307451683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327738676","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":[0.06321032,0.008159186,0.9092254,0.0074130357,0.0007688049,0.007310521,0.00043415197,0.00053617236,0.0029423716],"genre_scores_gemma":[0.5437874,0.001649865,0.43926817,0.0027046534,0.00031670803,0.01110938,0.00022527196,0.00012144608,0.0008170353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.25760522,0.7120797,0.010869889,0.009500295,0.008244759,0.0017000869],"domain_scores_gemma":[0.0801775,0.8651375,0.024931965,0.022833137,0.006336335,0.0005835271],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5755979,0.002250152,0.0054236986,0.002024473,0.0022793578,0.0040663085,0.005129809,0.007704789,0.0040061413],"category_scores_gemma":[0.67172664,0.0018747501,0.012889052,0.0033044068,0.006923953,0.0050907196,0.0038757701,0.00790819,0.00045051673],"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":[0.0170316,0.0021773998,0.10447402,0.016086765,0.030569851,0.0042327866,0.0077952226,0.27292737,0.0016936248,0.25694844,0.013095134,0.27296785],"study_design_scores_gemma":[0.0062755244,0.009897121,0.019254662,0.0054929107,0.0100241145,0.0015873117,0.00091792725,0.68440264,0.002624385,0.24553815,0.013454992,0.0005303788],"about_ca_topic_score_codex":0.004947498,"about_ca_topic_score_gemma":0.005023754,"teacher_disagreement_score":0.42440212,"about_ca_system_score_codex":0.0059234328,"about_ca_system_score_gemma":0.011323178,"threshold_uncertainty_score":0.5233634},"labels":[],"label_agreement":null},{"id":"W4361270682","doi":"10.5539/ijsp.v12n2p49","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 2","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Library science; Computer science","score_opus":0.07510709649382391,"score_gpt":0.3973875629362367,"score_spread":0.3222804664424128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361270682","genre_codex":"editorial","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":[0.000117540854,0.0027027,0.0016004121,0.12182726,0.87000793,0.0005183268,0.0007182731,0.00055715576,0.0019502905],"genre_scores_gemma":[0.004166714,0.0059962296,0.004157216,0.15836895,0.777883,0.0032985131,0.0015672112,0.0017649473,0.04279721],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92267257,0.015426979,0.015715264,0.0055007585,0.03826224,0.0024220631],"domain_scores_gemma":[0.1351011,0.039607763,0.01145285,0.0069019115,0.79793596,0.009000493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0592899,0.0036874553,0.010457873,0.013032556,0.0050208448,0.012002255,0.006076186,0.018573048,0.09226159],"category_scores_gemma":[0.5378416,0.0020573048,0.0063140234,0.005782922,0.0040237107,0.0075195725,0.004381731,0.014240092,0.06467702],"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":[0.00003669009,0.000004291406,0.000071848655,0.0003943928,0.000012164162,0.00005377035,0.000031214917,0.000010534055,0.000036378464,0.00010896729,0.99615866,0.0030811294],"study_design_scores_gemma":[0.00036949816,0.00007664935,0.0015142714,0.0046688654,0.00017147769,0.0015146389,0.00043849935,0.0006662787,0.00041416887,0.0028604635,0.98706144,0.00024376987],"about_ca_topic_score_codex":0.0034395729,"about_ca_topic_score_gemma":0.0051203,"teacher_disagreement_score":0.09226159,"about_ca_system_score_codex":0.0051774406,"about_ca_system_score_gemma":0.011084641,"threshold_uncertainty_score":0.3135587},"labels":[],"label_agreement":null},{"id":"W4362648691","doi":"10.1214/23-ejs2124","title":"Improving estimation efficiency for two-phase, outcome-dependent sampling studies","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"","keywords":"Covariate; Statistics; Mathematics; Outcome (game theory); Sampling (signal processing); Selection (genetic algorithm); Sample size determination; Econometrics; Conditional probability distribution; Data mining; Computer science; Machine learning","score_opus":0.12330545097360143,"score_gpt":0.47874701230979766,"score_spread":0.3554415613361962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362648691","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":[0.0043134033,0.00035704215,0.99438703,0.000231832,0.000029010647,0.00015480588,0.00006155264,0.00014499239,0.00032037575],"genre_scores_gemma":[0.092675194,0.0005039363,0.90463334,0.00028460808,0.00008793512,0.0006708893,0.00033511242,0.00010649561,0.00070244586],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96981347,0.02667769,0.00087970117,0.0011812763,0.0012377354,0.0002102063],"domain_scores_gemma":[0.8705947,0.11694067,0.003189595,0.005654601,0.003132219,0.00048827796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052507393,0.0011367422,0.002014211,0.0018601727,0.0005380002,0.0014794293,0.0021985571,0.0012862466,0.0033341313],"category_scores_gemma":[0.16501956,0.00082379265,0.0014409277,0.0024452691,0.0010467806,0.0019294075,0.0028067334,0.0021153875,0.00070266577],"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":[0.0013841408,0.00040829723,0.03140582,0.0017660178,0.0012924535,0.0005990943,0.00090697245,0.18640989,0.0053023375,0.1542416,0.0073955185,0.6088879],"study_design_scores_gemma":[0.00037645956,0.00031911858,0.0037072757,0.00017960746,0.00022379747,0.00026789214,0.00013309768,0.89053196,0.0017805413,0.09485344,0.007572743,0.00005407657],"about_ca_topic_score_codex":0.002551541,"about_ca_topic_score_gemma":0.0030927262,"teacher_disagreement_score":0.052507393,"about_ca_system_score_codex":0.0008181253,"about_ca_system_score_gemma":0.0024675792,"threshold_uncertainty_score":0.27768892},"labels":[],"label_agreement":null},{"id":"W4364353609","doi":"10.1080/00273171.2023.2193600","title":"Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients","year":2023,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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 British Columbia; McGill University","funders":"","keywords":"Missing data; Computer science; Regression; Regression analysis; Statistics; Econometrics; Data mining; Mathematics; Machine learning","score_opus":0.6757811365891347,"score_gpt":0.5899222742098614,"score_spread":0.08585886237927332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364353609","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":[0.027318398,0.0041303593,0.9478847,0.016204951,0.00027358741,0.000112908325,0.0001574679,0.0002451973,0.003672348],"genre_scores_gemma":[0.5271415,0.0068187187,0.45628223,0.0037655355,0.0010011065,0.00060396007,0.0002764382,0.00049069565,0.003619842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92200065,0.06621127,0.001707629,0.0036207398,0.005515275,0.0009445869],"domain_scores_gemma":[0.45993876,0.48327422,0.017279247,0.031251784,0.0073088007,0.0009471153],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.12866175,0.0016464038,0.002658153,0.0027120123,0.0018010784,0.004129342,0.0037761685,0.0033480027,0.0042282175],"category_scores_gemma":[0.45177087,0.0016309019,0.0033757796,0.0042725205,0.0068190643,0.011276601,0.0047246725,0.008549559,0.0007390319],"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":[0.00053126714,0.00018903823,0.022297127,0.0011455098,0.00137375,0.0006432316,0.0018424069,0.11054035,0.001861475,0.63531595,0.0058171125,0.21844277],"study_design_scores_gemma":[0.00014148967,0.00043339955,0.007154409,0.00088396267,0.00033736168,0.0005296553,0.0005269572,0.23343109,0.0024135031,0.7452386,0.008765339,0.00014417054],"about_ca_topic_score_codex":0.0045910296,"about_ca_topic_score_gemma":0.0027956525,"teacher_disagreement_score":0.87133825,"about_ca_system_score_codex":0.0024060775,"about_ca_system_score_gemma":0.0039548757,"threshold_uncertainty_score":0.6804365},"labels":[],"label_agreement":null},{"id":"W4366411838","doi":"10.1002/cjs.11773","title":"Bayesian instrumental variable estimation in linear measurement error models","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Prior probability; Instrumental variable; Estimator; Bayes' theorem; Mathematics; Applied mathematics; Bayes estimator; Statistics; Mean squared error; Linear model; Bias of an estimator; Variance (accounting); Minimum-variance unbiased estimator; Bayesian probability","score_opus":0.11564107966967692,"score_gpt":0.33687731147730704,"score_spread":0.22123623180763013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366411838","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":[0.0038188028,0.00023980958,0.9949976,0.00024316963,0.000021183647,0.000022156677,0.00003136451,0.00006314578,0.00056285725],"genre_scores_gemma":[0.5063511,0.0013460543,0.48625863,0.0005986198,0.00027905536,0.00051058794,0.00038447144,0.00015986076,0.0041115945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9821454,0.013167679,0.0004580313,0.0016094657,0.0020663606,0.0005530716],"domain_scores_gemma":[0.9346474,0.057881888,0.0035215793,0.002012975,0.0016721686,0.0002639354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026409099,0.0010904076,0.0026335008,0.0021339525,0.00089825084,0.0025943988,0.0039103637,0.002283293,0.0022964801],"category_scores_gemma":[0.10050778,0.0010695022,0.0012356485,0.0026269986,0.0039779185,0.0033779284,0.00302078,0.0034069375,0.00045867672],"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":[0.00010094478,0.00006902532,0.0029786653,0.00028881215,0.00027583598,0.00019545977,0.00016889407,0.43013123,0.0005630377,0.52447444,0.0015702646,0.039183486],"study_design_scores_gemma":[0.000028226623,0.000013253394,0.00040442185,0.00005416409,0.000027031045,0.000032054548,0.000019578134,0.842841,0.00032030133,0.15551154,0.000724198,0.000024132647],"about_ca_topic_score_codex":0.006870201,"about_ca_topic_score_gemma":0.0036371478,"teacher_disagreement_score":0.026409099,"about_ca_system_score_codex":0.0020211807,"about_ca_system_score_gemma":0.0023320152,"threshold_uncertainty_score":0.13966638},"labels":[],"label_agreement":null},{"id":"W4376112662","doi":"10.1515/ijb-2022-0040","title":"Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables","year":2023,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","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":"Impact; McMaster University; Population Health Research Institute","funders":"","keywords":"Zero (linguistics); Statistics; Mathematics; Applied mathematics; Computer science; Algorithm","score_opus":0.14832876308927137,"score_gpt":0.4169003802549265,"score_spread":0.26857161716565514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376112662","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":[0.008755968,0.00046580203,0.98900616,0.00018094404,0.00013737798,0.00031109963,0.00018469665,0.00047359642,0.00048432767],"genre_scores_gemma":[0.10421833,0.0002553956,0.8932655,0.00015440409,0.00008225426,0.0009210148,0.00028194676,0.00016030071,0.00066079857],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9511493,0.035197943,0.0030649435,0.00495492,0.0049867174,0.0006462178],"domain_scores_gemma":[0.796267,0.17559443,0.0059393514,0.015655551,0.006155949,0.0003877095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05017083,0.0008314589,0.00157526,0.0021293,0.0008674378,0.0017009799,0.0025981066,0.0018702479,0.0062640733],"category_scores_gemma":[0.27344173,0.0006906817,0.0021262607,0.0024723657,0.0018655378,0.0021578048,0.0016398443,0.002545197,0.0008754185],"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":[0.0019368608,0.00025929147,0.06244106,0.003086158,0.00222347,0.0013859781,0.0021492054,0.078002214,0.0084822625,0.10212323,0.010402874,0.72750735],"study_design_scores_gemma":[0.00062418345,0.0020434184,0.028514078,0.0016731032,0.0019498377,0.004091975,0.00073401455,0.6657361,0.025269123,0.21355498,0.055471435,0.00033779594],"about_ca_topic_score_codex":0.0026813122,"about_ca_topic_score_gemma":0.002607135,"teacher_disagreement_score":0.05017083,"about_ca_system_score_codex":0.00092291593,"about_ca_system_score_gemma":0.0023853078,"threshold_uncertainty_score":0.26533186},"labels":[],"label_agreement":null},{"id":"W4376115852","doi":"10.1186/s12874-023-01909-5","title":"Multiple imputation methods for missing multilevel ordinal outcomes","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","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":"Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Veterans Affairs","keywords":"Missing data; Imputation (statistics); Ordinal data; Multilevel model; Ordinal regression; Statistics; Computer science; Data science; Psychology; Data mining; Econometrics; Mathematics","score_opus":0.8069585171997071,"score_gpt":0.7009638253751819,"score_spread":0.10599469182452526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376115852","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":[0.0019735852,0.002943751,0.99054736,0.0011279047,0.000268651,0.0003669799,0.0011828527,0.00063119864,0.0009576503],"genre_scores_gemma":[0.08989141,0.004549121,0.89491326,0.0010436092,0.0007240388,0.003869704,0.0031449732,0.00060656393,0.0012573523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9186892,0.068310544,0.003602829,0.003564557,0.0052142986,0.00061856996],"domain_scores_gemma":[0.8452634,0.11865722,0.013379475,0.013780082,0.008187617,0.0007322469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06602593,0.0017088368,0.003519351,0.0037339565,0.0013952266,0.002442347,0.006772028,0.0026052226,0.010254557],"category_scores_gemma":[0.18035512,0.0011404668,0.0057087946,0.0061134356,0.0012738385,0.0024106107,0.0030649593,0.004978182,0.002304894],"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":[0.0006037558,0.0002941671,0.040961005,0.008421742,0.00893088,0.0007557092,0.0019129383,0.096633315,0.0010621239,0.15006612,0.05361537,0.6367429],"study_design_scores_gemma":[0.0006038604,0.00055628957,0.016285146,0.0047116037,0.0023873216,0.0010266327,0.00042545152,0.43981108,0.0024349366,0.4500194,0.08133539,0.00040293543],"about_ca_topic_score_codex":0.0035931652,"about_ca_topic_score_gemma":0.0037128064,"teacher_disagreement_score":0.06602593,"about_ca_system_score_codex":0.0014795173,"about_ca_system_score_gemma":0.0048077353,"threshold_uncertainty_score":0.34918267},"labels":[],"label_agreement":null},{"id":"W4376121573","doi":"10.1002/sim.9765","title":"Incorporating biological knowledge in analyses of environmental mixtures and health","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"University of Waterloo","funders":"National Institute of Environmental Health Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Prior probability; Computer science; Bayesian probability; Prior information; Nonparametric statistics; Dirichlet distribution; Set (abstract data type); Index (typography); Data mining; Statistics; Econometrics; Machine learning; Mathematics; Artificial intelligence","score_opus":0.23700524302746534,"score_gpt":0.5039231798181436,"score_spread":0.26691793679067827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376121573","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":[0.00509062,0.00039822562,0.99315083,0.0003824439,0.000015277445,0.000024867462,0.00007677863,0.00007878557,0.0007820963],"genre_scores_gemma":[0.30314502,0.0018426604,0.6912934,0.0005084947,0.00018148808,0.00039382148,0.00040982934,0.00010935809,0.0021159134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9902508,0.006988132,0.00024421583,0.0010105856,0.0013333231,0.0001728407],"domain_scores_gemma":[0.9551125,0.039254688,0.002386176,0.0023521162,0.0006827872,0.00021180314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01703657,0.0011004001,0.00118674,0.003432459,0.00068744906,0.0020884313,0.0018099869,0.0018212844,0.0018152309],"category_scores_gemma":[0.061249856,0.0010233715,0.0015629141,0.0022910207,0.0045951046,0.0037750294,0.003728049,0.0028229,0.00038452147],"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":[0.000116154595,0.00011471063,0.006338337,0.00032726387,0.0002723093,0.00013850511,0.00031226844,0.3399983,0.002260907,0.5270074,0.0009128872,0.1222009],"study_design_scores_gemma":[0.000018098208,0.000053177464,0.0015427781,0.00005059018,0.000038313614,0.000050608713,0.000040204166,0.31703955,0.0010629875,0.67737454,0.002696709,0.000032378703],"about_ca_topic_score_codex":0.0031455099,"about_ca_topic_score_gemma":0.0032220325,"teacher_disagreement_score":0.01703657,"about_ca_system_score_codex":0.0016014712,"about_ca_system_score_gemma":0.0018192199,"threshold_uncertainty_score":0.0900991},"labels":[],"label_agreement":null},{"id":"W4376653716","doi":"10.48550/arxiv.2305.08284","title":"Model-based standardization using multiple imputation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Parametric statistics; Computer science; Outcome (game theory); Statistics; Econometrics; Nonparametric statistics; Mathematics; Data mining","score_opus":0.3324911372355002,"score_gpt":0.31115216005013396,"score_spread":0.02133897718536626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376653716","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":[0.0011111804,0.00007294099,0.99814594,0.00006695548,0.000021268863,0.00006262371,0.00008712469,0.00019046394,0.00024154288],"genre_scores_gemma":[0.096382424,0.00038168303,0.89970237,0.00021921106,0.00013167805,0.0009810707,0.0011053057,0.0003273751,0.00076882145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97349393,0.020284941,0.0010021306,0.002613915,0.0022310936,0.00037406068],"domain_scores_gemma":[0.95673525,0.026872735,0.0027459585,0.0110517,0.002359022,0.00023529587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03998667,0.0013255426,0.0029326268,0.0029802138,0.0009968807,0.0027072125,0.0034737545,0.0019038962,0.0031782521],"category_scores_gemma":[0.10157718,0.0013251303,0.003803166,0.005167714,0.0018591312,0.003717867,0.0048999297,0.0035501975,0.0009849318],"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":[0.00029895984,0.00024212031,0.0110213095,0.0007304486,0.0015963275,0.0003580634,0.0008275102,0.2593364,0.0019448206,0.40070528,0.008937451,0.31400135],"study_design_scores_gemma":[0.00010846842,0.00008821574,0.0016813036,0.000112487134,0.00016488494,0.00018940496,0.000059144986,0.5181488,0.0018020296,0.4703416,0.007234581,0.00006918548],"about_ca_topic_score_codex":0.0019866847,"about_ca_topic_score_gemma":0.001826895,"teacher_disagreement_score":0.03998667,"about_ca_system_score_codex":0.0011281117,"about_ca_system_score_gemma":0.00332018,"threshold_uncertainty_score":0.21147227},"labels":[],"label_agreement":null},{"id":"W4376867787","doi":"10.1093/ije/dyad062","title":"Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial","year":2023,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Ottawa Hospital; University of Ottawa","funders":"Canadian Institutes of Health Research; National Institute for Health and Care Research; National Institutes of Health; National Institute on Aging; Queen Mary University of London; National Institute on Handicapped Research","keywords":"Autocorrelation; Correlation; Statistics; Computer science; Cluster (spacecraft); Correlation coefficient; CRTS; Data mining; Econometrics; Mathematics","score_opus":0.2987964370348309,"score_gpt":0.5264819676920615,"score_spread":0.22768553065723057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376867787","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":[0.00020368048,0.0056525194,0.9848623,0.0021151027,0.00046223018,0.0007080077,0.0012472083,0.0024062013,0.0023428057],"genre_scores_gemma":[0.0025638635,0.0069762594,0.98166466,0.0011012232,0.0006493107,0.002970337,0.0007971782,0.0010981582,0.0021790855],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9794732,0.015596794,0.0014272542,0.0011301034,0.0021834124,0.000189113],"domain_scores_gemma":[0.867856,0.11939008,0.004017208,0.003146018,0.0048543825,0.000736296],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03753034,0.0031309756,0.0023700702,0.0044372436,0.0006269433,0.002807694,0.0033079674,0.003071596,0.042633187],"category_scores_gemma":[0.14238063,0.002493138,0.0036039317,0.0034355074,0.0013877314,0.004028286,0.0019856913,0.007415098,0.017462429],"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":[0.00020754419,0.0001906808,0.0015802862,0.0069492985,0.00059172494,0.0004339095,0.0008331335,0.026607836,0.0014416022,0.15336667,0.20078439,0.60701287],"study_design_scores_gemma":[0.0004832059,0.0003593048,0.0023076208,0.007019945,0.0003679267,0.0013306253,0.00022252189,0.106803,0.0024922802,0.3797404,0.49844688,0.00042628052],"about_ca_topic_score_codex":0.004468081,"about_ca_topic_score_gemma":0.006410563,"teacher_disagreement_score":0.96246964,"about_ca_system_score_codex":0.0021343264,"about_ca_system_score_gemma":0.0047094896,"threshold_uncertainty_score":0.1984818},"labels":[],"label_agreement":null},{"id":"W4377092643","doi":"10.1093/ije/dyad064","title":"Key considerations for designing, conducting and analysing a cluster randomized trial","year":2023,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Ottawa Hospital; University of Ottawa","funders":"Collaboration for Leadership in Applied Health Research and Care - Greater Manchester; Medical Research Council; National Institute for Health and Care Research; National Institute on Handicapped Research","keywords":"Randomization; Randomized controlled trial; Sample size determination; Cluster randomised controlled trial; Cluster (spacecraft); Cluster analysis; Identification (biology); Sample (material); Inference; Computer science; Statistics; Data mining; Medicine; Artificial intelligence; Mathematics; Surgery","score_opus":0.41401492118492744,"score_gpt":0.5205296786459321,"score_spread":0.1065147574610047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377092643","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":[0.002235856,0.028215682,0.682966,0.18631224,0.011594242,0.07282967,0.0013552405,0.0012986565,0.013192461],"genre_scores_gemma":[0.010792632,0.0046602185,0.91489816,0.012461131,0.002508363,0.053315576,0.00014563474,0.00026460347,0.00095368933],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.20741403,0.6689798,0.074536,0.010502489,0.035907555,0.0026601548],"domain_scores_gemma":[0.124830574,0.76131004,0.030292207,0.029004252,0.04623431,0.008328653],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5804737,0.004328823,0.010840576,0.0076511544,0.006038898,0.01940831,0.008813275,0.024778418,0.013053598],"category_scores_gemma":[0.7966321,0.0068412763,0.007536166,0.009374382,0.017754635,0.012999961,0.0062986077,0.02868915,0.01003729],"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":[0.0056309793,0.0005142213,0.0027352455,0.049804233,0.002598005,0.001515601,0.013672663,0.011806875,0.0021114128,0.2817515,0.11399943,0.51385987],"study_design_scores_gemma":[0.005825915,0.0034260072,0.0051843645,0.07836847,0.0024251193,0.0034475029,0.0032744403,0.019870834,0.003623366,0.51550406,0.35760793,0.0014420749],"about_ca_topic_score_codex":0.0049378756,"about_ca_topic_score_gemma":0.007450541,"teacher_disagreement_score":0.41952628,"about_ca_system_score_codex":0.013932108,"about_ca_system_score_gemma":0.05400712,"threshold_uncertainty_score":0.5173507},"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":"W4378506279","doi":"10.1002/sta4.622","title":"Asymptotic tail properties of Poisson mixture distributions","year":2023,"lang":"en","type":"article","venue":"Stat","topic":"Statistical Methods and Bayesian 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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Poisson distribution; Overdispersion; Zero-inflated model; Mixing (physics); Mathematics; Compound Poisson distribution; Statistical physics; Maxima; Count data; Mixture model; Distribution (mathematics); Statistics; Applied mathematics; Mathematical analysis; Poisson regression; Physics","score_opus":0.08475699466675096,"score_gpt":0.35551963751692245,"score_spread":0.2707626428501715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378506279","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":[0.09834427,0.0010910479,0.8876932,0.0010575035,0.00007654889,0.00009983647,0.00025376247,0.0007588747,0.010624989],"genre_scores_gemma":[0.9281289,0.0011737586,0.063840434,0.00034538668,0.0002703025,0.00038144036,0.0008244625,0.0004768264,0.0045584566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971214,0.0014109074,0.00012243623,0.0003533187,0.0007817233,0.0002101449],"domain_scores_gemma":[0.91666365,0.069782816,0.004080324,0.0040399157,0.004296176,0.0011371407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015184092,0.00069509965,0.0012357532,0.0030935204,0.00094123214,0.0025841014,0.002255922,0.0014527695,0.0052141966],"category_scores_gemma":[0.12563497,0.0007878938,0.0011323057,0.0015051293,0.0035431886,0.0049880836,0.0025331117,0.0033925683,0.0010216687],"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":[0.00022455792,0.0000904521,0.013511492,0.00026033234,0.00012797171,0.00037776848,0.000985883,0.14766824,0.002913745,0.8047357,0.0032706435,0.025833074],"study_design_scores_gemma":[0.000025475816,0.000045773195,0.003926341,0.00015129281,0.000031936226,0.0003204329,0.00017792001,0.64910334,0.0010170754,0.34359217,0.0015298681,0.00007827424],"about_ca_topic_score_codex":0.002115174,"about_ca_topic_score_gemma":0.0010539256,"teacher_disagreement_score":0.015184092,"about_ca_system_score_codex":0.0015871101,"about_ca_system_score_gemma":0.00084044004,"threshold_uncertainty_score":0.08030212},"labels":[],"label_agreement":null},{"id":"W4378619514","doi":"10.3390/e25060863","title":"Tweedie Compound Poisson Models with Covariate-Dependent Random Effects for Multilevel Semicontinuous Data","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Bayesian Inference","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Random effects model; Multilevel model; Poisson distribution; Statistics; Econometrics; Mathematics; Computer science; Medicine","score_opus":0.152980711919905,"score_gpt":0.3871937647924034,"score_spread":0.2342130528724984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378619514","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":[0.008371359,0.0001666067,0.9905515,0.00024088687,0.000030120036,0.00008067015,0.0002101696,0.00007954667,0.00026907952],"genre_scores_gemma":[0.29331076,0.0010813912,0.69610155,0.00051554415,0.00023709795,0.0013383602,0.0012554652,0.00015092407,0.006008967],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908099,0.0064396225,0.00042746338,0.0012536547,0.0007932297,0.00027604352],"domain_scores_gemma":[0.9363362,0.055163927,0.003384842,0.0034867076,0.0011854103,0.00044289953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024808327,0.0007436813,0.0019974369,0.0021656847,0.0007624919,0.0018409551,0.003979285,0.0020402942,0.005668035],"category_scores_gemma":[0.0634428,0.00093835517,0.002356653,0.002819025,0.0027241276,0.0046603982,0.002687919,0.00407565,0.00074757624],"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":[0.00020836329,0.00007310056,0.009649087,0.00028738234,0.0002312846,0.0006737452,0.0007934586,0.24133745,0.0007935441,0.68820065,0.0021267715,0.055625156],"study_design_scores_gemma":[0.00003014303,0.00008566862,0.0014187379,0.00005368623,0.000039882412,0.00015682018,0.00007743842,0.74173546,0.0003186805,0.25375393,0.0022916,0.000037973114],"about_ca_topic_score_codex":0.003601496,"about_ca_topic_score_gemma":0.004490275,"teacher_disagreement_score":0.024808327,"about_ca_system_score_codex":0.0014238158,"about_ca_system_score_gemma":0.0016228079,"threshold_uncertainty_score":0.13120055},"labels":[],"label_agreement":null},{"id":"W4378717245","doi":"10.5539/ijsp.v12n3p58","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 3","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Library science; Computer science","score_opus":0.07617938615786862,"score_gpt":0.39750574104609443,"score_spread":0.3213263548882258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378717245","genre_codex":"editorial","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":[0.0001362279,0.002740867,0.0019182799,0.1323309,0.8587127,0.0005963509,0.00076079386,0.0006402034,0.0021637692],"genre_scores_gemma":[0.00511869,0.0067641423,0.0050385124,0.17877811,0.7536951,0.003607902,0.0016287117,0.0019981577,0.043370754],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92672163,0.014196035,0.015219299,0.0049668867,0.03653615,0.0023600215],"domain_scores_gemma":[0.13467208,0.038072195,0.011669861,0.0065803584,0.8001865,0.008818951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05832338,0.0033274877,0.010082731,0.011847147,0.0049892906,0.011505215,0.0060388437,0.01855448,0.08890629],"category_scores_gemma":[0.5274916,0.0018909321,0.0067463825,0.005755074,0.0041998043,0.0066659846,0.0041008815,0.0144325225,0.0632861],"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":[0.00003757749,0.0000042265524,0.00008061896,0.00039075807,0.000012619612,0.0000576272,0.000033178505,0.0000115228795,0.000036844318,0.00012359952,0.99579155,0.0034199595],"study_design_scores_gemma":[0.00036700553,0.00007107247,0.0015190312,0.004799232,0.00018555425,0.0015619736,0.0004432077,0.00071329926,0.00044071674,0.0034351854,0.9862169,0.0002467832],"about_ca_topic_score_codex":0.0037835017,"about_ca_topic_score_gemma":0.0055056466,"teacher_disagreement_score":0.08890629,"about_ca_system_score_codex":0.0054824483,"about_ca_system_score_gemma":0.011887495,"threshold_uncertainty_score":0.30844724},"labels":[],"label_agreement":null},{"id":"W4380574599","doi":"10.1080/00949655.2023.2222864","title":"Comparing estimation approaches for generalized additive mixed models with binary outcomes","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","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":"McGill University","funders":"Fonds de Recherche du Québec - Santé","keywords":"Mathematics; Multicollinearity; Covariate; Statistics; Generalized linear mixed model; Prior probability; Bayesian probability; Generalized linear model; Additive model; Econometrics; Regression analysis","score_opus":0.24790268235005997,"score_gpt":0.41801480829093124,"score_spread":0.17011212594087127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380574599","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":[0.006808552,0.001553642,0.9899407,0.0005204552,0.00006450272,0.00016376647,0.000099524244,0.00022523239,0.0006236142],"genre_scores_gemma":[0.07302312,0.0021028474,0.92229736,0.00033675702,0.00010891034,0.00097223313,0.0004186187,0.00022725483,0.0005129393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9216134,0.070986256,0.001578947,0.0023426923,0.0031172081,0.000361494],"domain_scores_gemma":[0.68586177,0.300045,0.0043283575,0.004794922,0.004399157,0.0005707675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.088840745,0.0017258388,0.0021112172,0.0035575193,0.00082998694,0.00275386,0.0038503455,0.0025163598,0.0028387317],"category_scores_gemma":[0.23241282,0.0013912998,0.0031946932,0.0034045957,0.0016898336,0.0039036218,0.003932414,0.0037433037,0.00057397754],"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":[0.0009021922,0.00030594002,0.014464007,0.0020833474,0.0041558906,0.0002031904,0.0017025009,0.3565971,0.000867787,0.26277313,0.003925777,0.3520191],"study_design_scores_gemma":[0.00021817787,0.00027781795,0.002884338,0.0005076807,0.00039650305,0.00013841712,0.0003259687,0.77718395,0.0006403279,0.21186027,0.0054225707,0.00014387476],"about_ca_topic_score_codex":0.007865208,"about_ca_topic_score_gemma":0.008144734,"teacher_disagreement_score":0.088840745,"about_ca_system_score_codex":0.002499104,"about_ca_system_score_gemma":0.0033467412,"threshold_uncertainty_score":0.4698404},"labels":[],"label_agreement":null},{"id":"W4380980112","doi":"10.4236/ojs.2023.133015","title":"Empirical Bayesian Approach to Testing Homogeneity of Several Means of Inflated Poisson Distributions (IPD)","year":2023,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Mathematics; Prior probability; Conjugate prior; Poisson distribution; Statistics; Homogeneity (statistics); Bayesian linear regression; Applied mathematics; Bayesian probability; Gamma distribution; Likelihood function; Bayesian inference; Estimation theory","score_opus":0.18183485904472557,"score_gpt":0.4296123084514148,"score_spread":0.24777744940668922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380980112","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":[0.00698092,0.00018295087,0.99115574,0.00023826295,0.00003075896,0.00013044283,0.00015753071,0.00018272994,0.000940684],"genre_scores_gemma":[0.21372555,0.0004243994,0.78103656,0.0005202286,0.00019241632,0.0014350007,0.00078991265,0.00019636189,0.0016796533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9689201,0.021047508,0.0013060083,0.0046217446,0.003505727,0.0005989412],"domain_scores_gemma":[0.9275669,0.06024767,0.0048500104,0.0031667645,0.0036185803,0.0005501073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03207368,0.0010467562,0.0020259144,0.0034813096,0.0011796155,0.0030811587,0.0039282213,0.0025682442,0.005528085],"category_scores_gemma":[0.10877782,0.00095251924,0.0022335928,0.0025109893,0.0037200719,0.0036132245,0.0034996385,0.0040938533,0.00067867606],"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":[0.0005158739,0.00033623519,0.024388006,0.0010105056,0.0010849157,0.0007642557,0.0017479629,0.1708209,0.0058841403,0.5736644,0.0048917523,0.21489091],"study_design_scores_gemma":[0.0001090395,0.0002014119,0.0063952277,0.00024668584,0.00017007845,0.00064678944,0.00025988396,0.49184164,0.0030011986,0.48982227,0.007171635,0.00013412804],"about_ca_topic_score_codex":0.0034921549,"about_ca_topic_score_gemma":0.0021152636,"teacher_disagreement_score":0.03207368,"about_ca_system_score_codex":0.0022812441,"about_ca_system_score_gemma":0.0027543735,"threshold_uncertainty_score":0.16962385},"labels":[],"label_agreement":null},{"id":"W4381736288","doi":"10.20982/tqmp.19.2.p123","title":"Handling Planned and Unplanned Missing Data in a Longitudinal Study","year":2023,"lang":"en","type":"article","venue":"The Quantitative Methods for Psychology","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":true,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Missing data; Longitudinal data; Computer science; Statistics; Data mining; Mathematics","score_opus":0.6126049514197016,"score_gpt":0.6425059734410256,"score_spread":0.029901022021323942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381736288","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":[0.00836103,0.0017410013,0.97334045,0.008191975,0.00065481133,0.004574004,0.00081428915,0.0005317094,0.0017907001],"genre_scores_gemma":[0.041750506,0.0018705426,0.9348327,0.0029308912,0.0004408588,0.016660329,0.0006193754,0.00016188945,0.00073291746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.50751704,0.42209753,0.033863816,0.0085674785,0.02622696,0.0017272196],"domain_scores_gemma":[0.31838414,0.5369684,0.054466676,0.05437313,0.032663185,0.0031444603],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4467438,0.0015679686,0.0030391423,0.0056505506,0.0056152735,0.0044174935,0.0064257057,0.0053081126,0.0074075353],"category_scores_gemma":[0.6498892,0.002501303,0.0037262465,0.0064082462,0.004863575,0.006583763,0.0074193147,0.007783688,0.0013140704],"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":[0.0011559479,0.0005408127,0.075898856,0.012982484,0.0023863572,0.002678615,0.06608417,0.010244716,0.0026593392,0.15214431,0.059310276,0.61391413],"study_design_scores_gemma":[0.0011679322,0.0025431588,0.052360766,0.023819525,0.0016693759,0.0044689216,0.018755931,0.05423283,0.011084441,0.643434,0.18544948,0.0010136474],"about_ca_topic_score_codex":0.005346098,"about_ca_topic_score_gemma":0.012594018,"teacher_disagreement_score":0.5532562,"about_ca_system_score_codex":0.0028778063,"about_ca_system_score_gemma":0.020839946,"threshold_uncertainty_score":0.68226343},"labels":[],"label_agreement":null},{"id":"W4381802283","doi":"10.20982/tqmp.19.2.p100","title":"How to Generate Missing Data For Simulation Studies","year":2023,"lang":"en","type":"article","venue":"The Quantitative Methods for Psychology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"York University","funders":"","keywords":"Missing data; Computer science; Machine learning","score_opus":0.7830093474901293,"score_gpt":0.6946673256827742,"score_spread":0.08834202180735506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381802283","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":[0.0006065878,0.00018952806,0.994392,0.0015526256,0.00020685472,0.0005850203,0.0003296428,0.0009711784,0.0011665794],"genre_scores_gemma":[0.007352351,0.00029578168,0.9895483,0.00038454484,0.000089556,0.0013025973,0.00031375996,0.0004050565,0.00030807185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9162519,0.0707774,0.0051625744,0.0019197358,0.0053891717,0.0004991344],"domain_scores_gemma":[0.6551837,0.2943436,0.008781458,0.022719245,0.017119369,0.0018526852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08642594,0.0019129806,0.0021762797,0.0043068146,0.0015679626,0.0049192254,0.0046661454,0.0040137824,0.019234996],"category_scores_gemma":[0.38941565,0.0022707668,0.0037695658,0.003325374,0.0020955696,0.0067999787,0.0033688373,0.00833637,0.006417756],"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":[0.0005917752,0.00047949154,0.0055169696,0.0035641044,0.00084937655,0.0008462201,0.0024404132,0.16818172,0.001674595,0.37752458,0.058408126,0.37992257],"study_design_scores_gemma":[0.00055411056,0.00015979186,0.0006728746,0.0022598484,0.00017585253,0.00039802265,0.00045996098,0.24822897,0.0039282395,0.67975897,0.063184544,0.0002186968],"about_ca_topic_score_codex":0.0012310446,"about_ca_topic_score_gemma":0.0019661253,"teacher_disagreement_score":0.08642594,"about_ca_system_score_codex":0.0013893831,"about_ca_system_score_gemma":0.004634984,"threshold_uncertainty_score":0.45706952},"labels":[],"label_agreement":null},{"id":"W4381839132","doi":"10.1111/ppe.12993","title":"A flexible approach to modelling stillbirths using the foetuses at risk approach","year":2023,"lang":"en","type":"article","venue":"Paediatric and Perinatal Epidemiology","topic":"Statistical Methods and Bayesian 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":"BC Children's Hospital; Children's & Women's Health Centre of British Columbia; University of British Columbia","funders":"Sick Kids Foundation","keywords":"Medicine; Obstetrics; Risk analysis (engineering)","score_opus":0.23201991923872764,"score_gpt":0.40266600527676594,"score_spread":0.1706460860380383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381839132","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":[0.0058415793,0.00025272652,0.9909835,0.00034337534,0.00005924501,0.000070609814,0.0004889317,0.0003174729,0.0016425903],"genre_scores_gemma":[0.29122898,0.0010059944,0.69721866,0.00035256287,0.00017836917,0.000843248,0.0013180161,0.0004266912,0.007427379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998147,0.0010024927,0.00011660151,0.0002991151,0.00028219627,0.00015259061],"domain_scores_gemma":[0.99314755,0.0051911105,0.000599097,0.00043385182,0.0004479547,0.00018036166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006941119,0.0011077198,0.0008839841,0.0015964813,0.00065734726,0.00177775,0.004061086,0.0017452693,0.0073137092],"category_scores_gemma":[0.017078696,0.00094440265,0.0038886983,0.001221922,0.0009852566,0.0012379993,0.0029950722,0.0026338852,0.0011707603],"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":[0.0001496568,0.000069613016,0.010956313,0.00027831626,0.00046245186,0.0006179856,0.00072977855,0.81008995,0.0009289855,0.12049828,0.0031334343,0.052085176],"study_design_scores_gemma":[0.000036315287,0.00007757079,0.001248593,0.000099612844,0.0001270275,0.0002877883,0.000116473435,0.9120231,0.00037616483,0.071651265,0.013901306,0.00005478025],"about_ca_topic_score_codex":0.013780536,"about_ca_topic_score_gemma":0.010080732,"teacher_disagreement_score":0.013780536,"about_ca_system_score_codex":0.0012857084,"about_ca_system_score_gemma":0.0024998554,"threshold_uncertainty_score":0.036708593},"labels":[],"label_agreement":null},{"id":"W4382202359","doi":"10.1002/cjs.11777","title":"Nonparametric simulation extrapolation for measurement‐error models","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Extrapolation; Nonparametric statistics; Replicate; Observational error; Normality; Computer science; Errors-in-variables models; Extension (predicate logic); Algorithm; Econometrics; Statistics; Mathematics; Machine learning","score_opus":0.2878061622943208,"score_gpt":0.3956340640213991,"score_spread":0.10782790172707829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382202359","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":[0.0064202338,0.000255784,0.9910019,0.00020264342,0.00004154665,0.00010002088,0.00006882678,0.00023058009,0.0016783812],"genre_scores_gemma":[0.5153087,0.0011364756,0.47714752,0.0004479923,0.00018933645,0.0011892717,0.0006477675,0.0002530354,0.003679927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9866599,0.011225502,0.00029967318,0.00050205516,0.001098767,0.00021401607],"domain_scores_gemma":[0.88544744,0.099274255,0.0034406842,0.007361328,0.0038395103,0.0006367321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02764911,0.0012299898,0.002145885,0.0021519063,0.0007182091,0.0010965321,0.0025673686,0.0018441938,0.0037722525],"category_scores_gemma":[0.113224775,0.0007080454,0.0018040591,0.0017243623,0.0025163672,0.0020884392,0.0041458537,0.0036053415,0.00078759016],"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":[0.00033110334,0.00010626636,0.0038313433,0.00032053646,0.000143652,0.000527344,0.0003420461,0.6594914,0.00075345725,0.2736273,0.0020207628,0.058504764],"study_design_scores_gemma":[0.000015950895,0.00004813066,0.00032101062,0.000088621106,0.000012235015,0.00007482771,0.000020585885,0.86079544,0.0002417429,0.13706475,0.0013012212,0.000015529722],"about_ca_topic_score_codex":0.0025785484,"about_ca_topic_score_gemma":0.0014226427,"teacher_disagreement_score":0.02764911,"about_ca_system_score_codex":0.0013107425,"about_ca_system_score_gemma":0.0015800016,"threshold_uncertainty_score":0.1462242},"labels":[],"label_agreement":null},{"id":"W4382342847","doi":"10.3390/math11092130","title":"Modified BIC Criterion for Model Selection in Linear Mixed Models","year":2023,"lang":"en","type":"article","venue":"Mathematics","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":"York University; University of Guelph-Humber","funders":"","keywords":"Generalized linear mixed model; Mixed model; Random effects model; Linear model; Model selection; Variance (accounting); Selection (genetic algorithm); Mathematics; Variance components; Component (thermodynamics); Boundary (topology); Statistics; Computer science; Applied mathematics; Mathematical optimization; Artificial intelligence","score_opus":0.22391517162210564,"score_gpt":0.4203897517221925,"score_spread":0.19647458010008687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382342847","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":[0.0038375147,0.00091176917,0.9928572,0.00047365838,0.00009215201,0.00021353996,0.00024416513,0.0003514201,0.0010184963],"genre_scores_gemma":[0.13516724,0.0011663451,0.8556092,0.00090614514,0.0002724516,0.0022556973,0.0019384021,0.0008936838,0.0017907263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9452912,0.04483001,0.002158071,0.0025792394,0.0044017504,0.00073965825],"domain_scores_gemma":[0.9376401,0.050402872,0.0016089327,0.0026399747,0.006980023,0.0007282016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033440933,0.0027028092,0.0048607495,0.0048452835,0.0025881566,0.0033701244,0.0047917617,0.0032623657,0.005162478],"category_scores_gemma":[0.12419988,0.0014308828,0.0035208445,0.0048656506,0.0024426987,0.0026173152,0.0034319826,0.00511272,0.001729551],"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":[0.0008654857,0.00027601616,0.01085213,0.0026055968,0.002991856,0.0011483629,0.0011084214,0.4516065,0.0027953198,0.2000833,0.023660958,0.3020061],"study_design_scores_gemma":[0.00012840203,0.00013973044,0.0015633592,0.00024679478,0.00025683577,0.00025710414,0.00015359579,0.8682527,0.0010763066,0.12047793,0.007341202,0.00010599863],"about_ca_topic_score_codex":0.011580904,"about_ca_topic_score_gemma":0.012004596,"teacher_disagreement_score":0.033440933,"about_ca_system_score_codex":0.0023429561,"about_ca_system_score_gemma":0.0068331524,"threshold_uncertainty_score":0.17685467},"labels":[],"label_agreement":null},{"id":"W4382516807","doi":"10.31234/osf.io/p2n8a","title":"Comparing the Accuracy of Three Predictive Information Criteria for Bayesian Linear Multilevel Model Selection","year":2023,"lang":"en","type":"preprint","venue":"","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Overfitting; Deviance information criterion; Akaike information criterion; Bayesian information criterion; Model selection; Information Criteria; Leverage (statistics); Computer science; Multilevel model; Bayesian probability; Selection (genetic algorithm); Deviance (statistics); Data mining; Context (archaeology); Linear model; Machine learning; Artificial intelligence; Econometrics; Statistics; Bayesian inference; Mathematics","score_opus":0.26685337677697546,"score_gpt":0.44058360384202333,"score_spread":0.17373022706504787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382516807","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":[0.14888255,0.010204308,0.8222618,0.004367699,0.00033442845,0.0007995376,0.001815184,0.0018662198,0.009468331],"genre_scores_gemma":[0.6446968,0.0021702796,0.346257,0.0007744144,0.00019331042,0.000898872,0.003457668,0.00059956923,0.0009521218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9401161,0.042303074,0.0044518267,0.0035662348,0.008522454,0.0010403739],"domain_scores_gemma":[0.51367843,0.4413697,0.011008393,0.011913023,0.019446276,0.0025841491],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1141385,0.0023237418,0.0034177697,0.0097129885,0.0017751948,0.0060590147,0.004725027,0.0036309457,0.0028753062],"category_scores_gemma":[0.33813965,0.0010925032,0.0042413175,0.0063648913,0.0030427044,0.0060249576,0.004716939,0.0055664685,0.0006601234],"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":[0.003001346,0.0004319579,0.07002166,0.0029502234,0.005971494,0.00034878898,0.00216434,0.443429,0.00094751874,0.13196416,0.011120513,0.327649],"study_design_scores_gemma":[0.00035862072,0.0005998403,0.013611559,0.00094123516,0.0009657008,0.00016488547,0.00049956643,0.89412916,0.0015448699,0.0840165,0.0028725,0.00029547806],"about_ca_topic_score_codex":0.011289564,"about_ca_topic_score_gemma":0.011891978,"teacher_disagreement_score":0.8858615,"about_ca_system_score_codex":0.0044338997,"about_ca_system_score_gemma":0.0053677163,"threshold_uncertainty_score":0.6036293},"labels":[],"label_agreement":null},{"id":"W4383554114","doi":"10.3390/math11133007","title":"Assessing Multinomial Distributions with a Bayesian Approach","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Statistical Methods and Bayesian 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":"McMaster University; University of Toronto","funders":"","keywords":"Multinomial distribution; Bayesian probability; Divergence (linguistics); Prior probability; Kullback–Leibler divergence; Computer science; Categorical distribution; Bayesian hierarchical modeling; Bayesian inference; Artificial intelligence; Mathematics; Statistics; Machine learning","score_opus":0.11658166366228202,"score_gpt":0.3965240033443518,"score_spread":0.2799423396820698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383554114","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":[0.0033101714,0.00020389009,0.99496317,0.0001392088,0.00001562477,0.000052427262,0.000032546493,0.00008984644,0.001193214],"genre_scores_gemma":[0.17525896,0.0007158669,0.82138413,0.00027276913,0.00011820555,0.0005211682,0.00023738582,0.00009747081,0.0013940762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9727121,0.016099809,0.0010990408,0.0023506072,0.0072585274,0.00047987123],"domain_scores_gemma":[0.9462416,0.044499807,0.0024655503,0.0023092998,0.0037819871,0.00070164853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027721211,0.0015058038,0.0021057273,0.006503986,0.0016524184,0.0044690054,0.003000147,0.002534963,0.005555236],"category_scores_gemma":[0.10800111,0.0010074989,0.0016884009,0.0028222087,0.0031794794,0.007375147,0.004726177,0.0031982134,0.0010582075],"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":[0.00015086093,0.00019333203,0.0049915863,0.00034385236,0.00030040042,0.00029611186,0.0008011606,0.108586386,0.0026429843,0.68241066,0.0017193248,0.19756338],"study_design_scores_gemma":[0.000051221905,0.00008303652,0.0013727649,0.00015947658,0.00006780004,0.00023288891,0.00016710514,0.38765326,0.0011047383,0.60454226,0.0044684294,0.000097049626],"about_ca_topic_score_codex":0.0028771523,"about_ca_topic_score_gemma":0.002965674,"teacher_disagreement_score":0.027721211,"about_ca_system_score_codex":0.0019881395,"about_ca_system_score_gemma":0.002811705,"threshold_uncertainty_score":0.14660555},"labels":[],"label_agreement":null},{"id":"W4383682323","doi":"10.3329/jsr.v56i2.67468","title":"Approximate methods for analyzing semiparametric longitudinal models with nonignorable missing responses","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Qassim University","keywords":"Missing data; Statistical inference; Inference; Econometrics; Semiparametric regression; Longitudinal data; Computer science; Variance (accounting); Statistics; Monte Carlo method; Regression; Mathematics; Data mining; Artificial intelligence","score_opus":0.4642544285858737,"score_gpt":0.592966117563545,"score_spread":0.12871168897767132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383682323","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":[0.00061976025,0.00024644003,0.99878746,0.000075774165,0.000012620213,0.000017508604,0.000031405347,0.000057993162,0.0001510758],"genre_scores_gemma":[0.09946416,0.002223362,0.89340323,0.00029089247,0.00030157212,0.0013447435,0.00062718184,0.00018973321,0.0021550797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98828864,0.00925787,0.0003674135,0.0006599554,0.0012555467,0.00017052593],"domain_scores_gemma":[0.9330147,0.056742076,0.0036678524,0.0044639185,0.0017315351,0.0003799389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020780578,0.0013605127,0.001947276,0.002944058,0.000693427,0.0015507066,0.0039481926,0.00178051,0.0035582783],"category_scores_gemma":[0.08164223,0.0015153635,0.0022282826,0.002966193,0.0024789409,0.0033501496,0.0032578954,0.003653082,0.0008187193],"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":[0.00010669542,0.00010342883,0.003434265,0.00063192705,0.00059191324,0.0001615872,0.00036757597,0.35965395,0.0008139731,0.5252728,0.002645361,0.10621651],"study_design_scores_gemma":[0.000028886814,0.000057151843,0.00040215367,0.00008057987,0.000049129972,0.000077297824,0.00004069236,0.6948925,0.00027040293,0.30112225,0.00295051,0.000028434357],"about_ca_topic_score_codex":0.0026068303,"about_ca_topic_score_gemma":0.0028177705,"teacher_disagreement_score":0.020780578,"about_ca_system_score_codex":0.0014230467,"about_ca_system_score_gemma":0.0021815111,"threshold_uncertainty_score":0.10989952},"labels":[],"label_agreement":null},{"id":"W4384119986","doi":"10.1177/17407745231186094","title":"Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial","year":2023,"lang":"en","type":"article","venue":"Clinical Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Western University","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; Medical Research Council; National Institutes of Health; Patient-Centered Outcomes Research Institute","keywords":"Cluster randomised controlled trial; Cluster (spacecraft); Medicine; Clinical trial; Randomized controlled trial; Statistics; Internal medicine; Computer science; Mathematics","score_opus":0.6225687483452658,"score_gpt":0.6093820177838946,"score_spread":0.013186730561371207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384119986","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":[0.23716812,0.1525624,0.47047094,0.08342871,0.00446517,0.02200768,0.0022544903,0.00060849666,0.02703397],"genre_scores_gemma":[0.7158333,0.017482016,0.22998145,0.014934416,0.0016032864,0.017655449,0.0007198733,0.0002578773,0.0015324043],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.4991529,0.46467403,0.014100261,0.0050165155,0.015082191,0.0019740437],"domain_scores_gemma":[0.20311137,0.7388366,0.02215366,0.0213341,0.013009681,0.0015545896],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.29655743,0.0011963602,0.0046711774,0.0020933582,0.0020633817,0.0036432182,0.0037852814,0.008365037,0.00509258],"category_scores_gemma":[0.590344,0.0008184871,0.008177774,0.004317756,0.0055274456,0.0041762074,0.0036782208,0.007868677,0.00059508794],"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":[0.04698653,0.0020372712,0.048733495,0.053419117,0.018650174,0.008918407,0.012424548,0.07556388,0.0012846335,0.22569376,0.043747418,0.46254066],"study_design_scores_gemma":[0.05502088,0.026088925,0.039130155,0.06524403,0.029023368,0.014413388,0.0057647615,0.16196091,0.0053313626,0.43340826,0.16277336,0.0018406308],"about_ca_topic_score_codex":0.0055652284,"about_ca_topic_score_gemma":0.0054160287,"teacher_disagreement_score":0.7034426,"about_ca_system_score_codex":0.0062745716,"about_ca_system_score_gemma":0.010016705,"threshold_uncertainty_score":0.8674699},"labels":[],"label_agreement":null},{"id":"W4384406223","doi":"10.1007/s00180-023-01389-7","title":"A new approach to modeling the cure rate in the presence of interval censored data","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Covariate; Statistics; Expectation–maximization algorithm; Population; Interval (graph theory); Mathematics; Proportional hazards model; Mixture model; Computer science; Econometrics; Maximum likelihood; Medicine","score_opus":0.24100725266925674,"score_gpt":0.42960296907324713,"score_spread":0.1885957164039904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384406223","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":[0.0019721985,0.0002918783,0.9966239,0.0004096852,0.00006145943,0.00002011125,0.0000892419,0.000058905247,0.00047272467],"genre_scores_gemma":[0.3034155,0.00401642,0.67154515,0.0014324379,0.0018231268,0.0009132376,0.0011068226,0.000345091,0.015402224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99354434,0.00368911,0.00027596002,0.0011543083,0.0009885608,0.0003476817],"domain_scores_gemma":[0.961589,0.032432158,0.0021560052,0.0018711069,0.0013001879,0.0006516946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015072337,0.0015874631,0.00326058,0.003387046,0.00079747353,0.0036794231,0.0073806224,0.0039372956,0.004651735],"category_scores_gemma":[0.051618468,0.0016761601,0.0032682877,0.0031324702,0.0030988012,0.005625267,0.0030814854,0.006821641,0.0009586077],"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":[0.00011676268,0.00011373539,0.0024018222,0.00022028727,0.00021289337,0.00025383325,0.0003106959,0.47759578,0.00061670045,0.48484546,0.0030054178,0.030306507],"study_design_scores_gemma":[0.000020340014,0.000027068974,0.00018316772,0.00002946427,0.000040286293,0.00012859565,0.000014593984,0.87902117,0.00010201218,0.11901125,0.001396635,0.000025316504],"about_ca_topic_score_codex":0.00425153,"about_ca_topic_score_gemma":0.003108976,"teacher_disagreement_score":0.015072337,"about_ca_system_score_codex":0.0018065941,"about_ca_system_score_gemma":0.0023991787,"threshold_uncertainty_score":0.07971108},"labels":[],"label_agreement":null},{"id":"W4384451748","doi":"10.5001/omj.2024.41","title":"Modeling Zero-inflated Count Data Using Generalized Poisson and Ordinal Logistic Regression Models in Medical Research","year":2023,"lang":"en","type":"article","venue":"Oman Medical Journal","topic":"Statistical Methods and Bayesian Inference","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":"Impact; McMaster University; Population Health Research Institute","funders":"","keywords":"Statistics; Count data; Ordinal data; Negative binomial distribution; Ordinal regression; Poisson distribution; Generalized linear model; Poisson regression; Overdispersion; Logistic regression; Mathematics; Regression analysis; Sample size determination; Zero-inflated model; Ordered logit; Medicine; Population","score_opus":0.52468310964574,"score_gpt":0.5513644762956534,"score_spread":0.026681366649913385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384451748","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":[0.054447856,0.0048679593,0.9324461,0.0032397183,0.00038688473,0.00089073647,0.0008021672,0.00036017643,0.0025582935],"genre_scores_gemma":[0.5575101,0.0071433852,0.42487323,0.0012237144,0.00047557804,0.0036976526,0.0012577546,0.00014289239,0.0036757097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9486718,0.04366782,0.0013295062,0.0027964192,0.0027618085,0.00077262946],"domain_scores_gemma":[0.90848315,0.07723199,0.00760674,0.0029016405,0.003208936,0.0005676205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05005648,0.001398882,0.002211662,0.0033765642,0.00092570053,0.004295175,0.0049833455,0.0032122277,0.003249313],"category_scores_gemma":[0.121112324,0.00085842074,0.00275391,0.005759266,0.0022703963,0.0038228345,0.0028877696,0.004191146,0.00080652966],"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":[0.00074691564,0.00048362947,0.07182025,0.0032216837,0.001613124,0.0018470656,0.0033893324,0.5028359,0.0009775287,0.26787698,0.006880982,0.1383065],"study_design_scores_gemma":[0.00015187783,0.00051139825,0.008064006,0.0009005696,0.00043225632,0.00046206004,0.0007713089,0.7720865,0.0004518483,0.20897506,0.0070318645,0.00016130549],"about_ca_topic_score_codex":0.006588324,"about_ca_topic_score_gemma":0.005896145,"teacher_disagreement_score":0.05005648,"about_ca_system_score_codex":0.0022094713,"about_ca_system_score_gemma":0.0036661446,"threshold_uncertainty_score":0.26472712},"labels":[],"label_agreement":null},{"id":"W4385172642","doi":"10.1002/env.2820","title":"Modeling temporally misaligned data across space: The case of total pollen concentration in Toronto","year":2023,"lang":"en","type":"article","venue":"Environmetrics","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":true,"ca_institutions":"Ottawa Public Health; Health Canada; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Health Canada; Natural Sciences and Engineering Research Council of Canada; Consejo Mexiquense de Ciencia y Tecnología; Consejo Nacional de Ciencia y Tecnología","keywords":"Inference; Temporal scales; Covariate; Scale (ratio); Bayesian inference; Bayesian probability; Computer science; Multivariate statistics; Temporal database; Statistics; Econometrics; Mathematics; Data mining; Geography; Cartography; Artificial intelligence; Ecology","score_opus":0.1425164826881099,"score_gpt":0.42718299540144544,"score_spread":0.28466651271333554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385172642","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":[0.9741924,0.00043083634,0.02195936,0.0011407641,0.000019952586,0.00003177089,0.0010629852,0.00006986701,0.0010919549],"genre_scores_gemma":[0.99575645,0.00010131864,0.003102168,0.000032802658,0.000009955829,0.000011004198,0.0004773694,0.000009198754,0.0004997956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989993,0.00042560877,0.00005031798,0.0002829894,0.00010474633,0.00013693534],"domain_scores_gemma":[0.9931289,0.0048151347,0.00086632266,0.00041666353,0.0005645705,0.00020838968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036130834,0.0005457732,0.00052086666,0.00083674147,0.0011318662,0.0012017442,0.002113144,0.0014384566,0.0011140694],"category_scores_gemma":[0.015265371,0.00046075805,0.0006763799,0.0021157186,0.0017444636,0.0008022093,0.0008724424,0.0010412683,0.00008212394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023097674,0.000059018414,0.14437392,0.00007833649,0.0002551742,0.00092599494,0.00074438826,0.8294731,0.0010063023,0.012694694,0.0013845764,0.00877356],"study_design_scores_gemma":[0.000039794893,0.00002984138,0.08841494,0.000027307731,0.00006411424,0.000071560346,0.00040643685,0.904858,0.00039975822,0.0046790326,0.0009561784,0.000053035707],"about_ca_topic_score_codex":0.85209167,"about_ca_topic_score_gemma":0.7756085,"teacher_disagreement_score":0.14790833,"about_ca_system_score_codex":0.008336444,"about_ca_system_score_gemma":0.0029261447,"threshold_uncertainty_score":0.29755872},"labels":[],"label_agreement":null},{"id":"W4385235004","doi":"10.1002/sim.9855","title":"A time‐dependent Poisson‐Gamma model for recruitment forecasting in multicenter studies","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","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":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Poisson distribution; Gamma process; Constant (computer programming); Gamma distribution; Generalization; Econometrics; Poisson process; Bayesian probability; Computer science; Poisson regression; Statistics; Compound Poisson process; Count data; Range (aeronautics); Stochastic modelling; Mathematics; Demography; Population; Engineering","score_opus":0.4169691259152335,"score_gpt":0.5039540648960044,"score_spread":0.08698493898077087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385235004","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":[0.028013112,0.0015792417,0.9637822,0.0024751688,0.00017376587,0.0002771411,0.0008810781,0.0003278195,0.0024904336],"genre_scores_gemma":[0.6603038,0.005495653,0.31073663,0.0011200368,0.0007528735,0.002480774,0.0028035569,0.00020829069,0.016098443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99562377,0.0027817343,0.00019610873,0.00063726254,0.00045000532,0.0003111352],"domain_scores_gemma":[0.9816109,0.014540516,0.0014985873,0.000801666,0.0010970922,0.00045126711],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019577812,0.001106047,0.0022498125,0.0024162324,0.0012084852,0.0019826537,0.0054780375,0.0037384452,0.004520097],"category_scores_gemma":[0.041159924,0.0010582816,0.0020900527,0.0033671474,0.0016151798,0.0025179246,0.00166473,0.0040237755,0.0011529059],"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":[0.00021393326,0.00009217368,0.010924459,0.0002757033,0.00014480518,0.0004992646,0.00072702736,0.5559424,0.0006101076,0.37484124,0.0073724953,0.04835645],"study_design_scores_gemma":[0.00006009462,0.000072687246,0.0015661848,0.00009303768,0.000057266887,0.00012150716,0.0001086547,0.8589609,0.00015006994,0.13448574,0.0042714784,0.000052477983],"about_ca_topic_score_codex":0.03058073,"about_ca_topic_score_gemma":0.01839098,"teacher_disagreement_score":0.9804222,"about_ca_system_score_codex":0.002553809,"about_ca_system_score_gemma":0.003274277,"threshold_uncertainty_score":0.10353863},"labels":[],"label_agreement":null},{"id":"W4385422357","doi":"10.5539/ijsp.v12n4p81","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 4","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Mathematical economics; Computer science","score_opus":0.0766843636289286,"score_gpt":0.39794697505159804,"score_spread":0.32126261142266943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385422357","genre_codex":"editorial","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":[0.0001357342,0.0025500662,0.0016753753,0.13056566,0.86138487,0.00053306116,0.0007145585,0.000542671,0.0018980434],"genre_scores_gemma":[0.0048589166,0.0063644946,0.0048395693,0.16395076,0.76661134,0.0035977867,0.0016033576,0.0017084735,0.04646535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9295092,0.01420055,0.015010755,0.0046836594,0.03432136,0.0022744301],"domain_scores_gemma":[0.13814002,0.038990404,0.011735057,0.006597065,0.79591745,0.0086199725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056793842,0.0034125713,0.010035746,0.012439591,0.0049340622,0.010892659,0.0063700983,0.018449826,0.09000467],"category_scores_gemma":[0.5389325,0.0019546556,0.0064673917,0.0054996344,0.0041645686,0.007031171,0.0042760256,0.01380708,0.06026793],"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":[0.000039409533,0.0000044278595,0.00008699621,0.0004202998,0.000013649412,0.000060824143,0.000032838245,0.000012321411,0.000037961487,0.0001246641,0.99572396,0.0034425787],"study_design_scores_gemma":[0.00037458952,0.00007459047,0.0015223892,0.0045616836,0.00019391406,0.001452929,0.0004546918,0.0006909931,0.00043223862,0.0033368482,0.9866585,0.00024670304],"about_ca_topic_score_codex":0.0037662706,"about_ca_topic_score_gemma":0.005383255,"teacher_disagreement_score":0.09000467,"about_ca_system_score_codex":0.0055271806,"about_ca_system_score_gemma":0.011566111,"threshold_uncertainty_score":0.3010956},"labels":[],"label_agreement":null},{"id":"W4385897997","doi":"10.1177/09622802231194753","title":"Does it decay? Obtaining decaying correlation parameter values from previously analysed cluster randomised trials","year":2023,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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":"Ottawa Hospital; University of Ottawa","funders":"National Health and Medical Research Council; Medical Research Council","keywords":"Correlation; Intracluster medium; Cluster (spacecraft); Statistics; Confidence interval; Physics; Computer science; Mathematics; Galaxy cluster; Astrophysics","score_opus":0.36825796646254416,"score_gpt":0.6185888883650825,"score_spread":0.25033092190253836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385897997","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":[0.01613729,0.0046586706,0.9594702,0.0070062703,0.0009753936,0.0026522507,0.0016714482,0.002375318,0.0050531453],"genre_scores_gemma":[0.39453152,0.002363972,0.5737185,0.008925145,0.00051641354,0.014896284,0.0015413931,0.0016862464,0.0018205427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.52341145,0.38528875,0.03610192,0.027236948,0.025384635,0.0025763197],"domain_scores_gemma":[0.098465994,0.7672425,0.04041075,0.08130407,0.011873908,0.0007027675],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.45920348,0.0027812938,0.005252243,0.0030291567,0.0014318812,0.009149702,0.0063896766,0.0067248777,0.00932618],"category_scores_gemma":[0.8670749,0.0030411237,0.0077790823,0.0045190393,0.00717729,0.015014197,0.005894535,0.011852065,0.0028947655],"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":[0.00859785,0.0004058203,0.050497953,0.020044785,0.01585244,0.0013592441,0.013526327,0.076823525,0.0026484863,0.26724872,0.032235563,0.5107592],"study_design_scores_gemma":[0.0025654247,0.0023744998,0.017282464,0.011774884,0.0056431056,0.0015073684,0.0010600376,0.16052976,0.007628957,0.7377838,0.05099499,0.0008547587],"about_ca_topic_score_codex":0.0019972725,"about_ca_topic_score_gemma":0.0015650614,"teacher_disagreement_score":0.5407965,"about_ca_system_score_codex":0.00445825,"about_ca_system_score_gemma":0.005208177,"threshold_uncertainty_score":0.6668984},"labels":[],"label_agreement":null},{"id":"W4386050762","doi":"10.1002/cjs.11792","title":"Special issue in honour of Nancy Reid: Guest Editors' introduction","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Honour; Citation; Library science; Computer science; Law; Political science","score_opus":0.03309332583323243,"score_gpt":0.31645949662309175,"score_spread":0.2833661707898593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386050762","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029022347,0.003611075,0.00017671207,0.03374147,0.96121985,0.000015154497,0.000059191807,0.000051064595,0.0010964207],"genre_scores_gemma":[0.0004661133,0.0027549248,0.00019344194,0.020964138,0.95932937,0.000043335716,0.00006135439,0.00012361488,0.016063767],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99319506,0.0011333678,0.0007872907,0.001242661,0.0029482506,0.0006932649],"domain_scores_gemma":[0.960741,0.008747245,0.0029928843,0.0015519395,0.018676935,0.0072899684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00930646,0.003910373,0.0046806852,0.004374222,0.0026315914,0.009960898,0.0041352836,0.012637419,0.0437075],"category_scores_gemma":[0.044839304,0.0010771726,0.0023930045,0.00208849,0.0019484209,0.004329569,0.0039530313,0.014668133,0.037813112],"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":[0.000020428866,0.0000057715865,0.000021225651,0.000053357053,0.000005213091,0.000041371157,0.000005301976,0.000014782097,0.000031295644,0.00015862727,0.9965779,0.0030646664],"study_design_scores_gemma":[0.000033697197,0.000033049997,0.00035594407,0.00018629059,0.000025694804,0.00018985818,0.00003458725,0.0001200438,0.00009283009,0.0011633446,0.99774027,0.000024269953],"about_ca_topic_score_codex":0.0018859144,"about_ca_topic_score_gemma":0.0047008432,"teacher_disagreement_score":0.0437075,"about_ca_system_score_codex":0.00257022,"about_ca_system_score_gemma":0.004164284,"threshold_uncertainty_score":0.1462161},"labels":[],"label_agreement":null},{"id":"W4386168039","doi":"10.3390/e25091262","title":"Profile Likelihood for Hierarchical Models Using Data Doubling","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Bayesian Inference","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":"University of Alberta","funders":"","keywords":"Likelihood function; Mathematics; Frequentist inference; Statistical inference; Marginal likelihood; Estimation theory; Algorithm; Applied mathematics; Mixture model; Estimator; Statistical model; Bayesian inference; Computer science; Bayesian probability; Statistics","score_opus":0.3277383071030227,"score_gpt":0.45440691575707215,"score_spread":0.12666860865404944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386168039","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":[0.00338011,0.00009364763,0.9953133,0.00017378462,0.00000911508,0.000035342797,0.00010805216,0.00009504728,0.0007914986],"genre_scores_gemma":[0.25434178,0.0007741333,0.73673207,0.00047575813,0.00016854491,0.00082775624,0.0012618575,0.00044217453,0.0049759103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994799,0.003029836,0.00026227185,0.00068053254,0.000977568,0.0002507379],"domain_scores_gemma":[0.974133,0.020675046,0.0012879134,0.0024344267,0.0010324233,0.00043707804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015911581,0.0009037369,0.0014366406,0.0028395797,0.0010449856,0.002285047,0.0030697612,0.0016151989,0.004048362],"category_scores_gemma":[0.06254125,0.00087239547,0.0018163658,0.002145886,0.0034400388,0.005282376,0.0050919587,0.0039463853,0.0011130738],"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":[0.00005246949,0.00002718338,0.0012015766,0.00015425142,0.00003707309,0.00018533717,0.0002701747,0.12729695,0.0009192928,0.83931553,0.0016382603,0.02890188],"study_design_scores_gemma":[0.000009732356,0.000010622705,0.00025912575,0.000024043882,0.0000068004438,0.00008151989,0.00003205721,0.45137206,0.00037795724,0.5463423,0.0014645326,0.000019215744],"about_ca_topic_score_codex":0.0033467696,"about_ca_topic_score_gemma":0.0027676655,"teacher_disagreement_score":0.015911581,"about_ca_system_score_codex":0.002434901,"about_ca_system_score_gemma":0.0025076605,"threshold_uncertainty_score":0.08414948},"labels":[],"label_agreement":null},{"id":"W4386502320","doi":"10.1111/2041-210x.14200","title":"Describing posterior distributions of variance components: Problems and the use of null distributions to aid interpretation","year":2023,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Markov chain Monte Carlo; Statistics; Posterior probability; Variance (accounting); Econometrics; Mathematics; Contrast (vision); Null (SQL); Null hypothesis; Bayesian probability; Computer science; Data mining; Artificial intelligence","score_opus":0.1545340138768306,"score_gpt":0.39658386413019575,"score_spread":0.24204985025336515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386502320","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":[0.0037938373,0.00063801673,0.9917008,0.0016448307,0.00016648139,0.000117436844,0.00016549678,0.00032180198,0.0014511752],"genre_scores_gemma":[0.3279797,0.0015320468,0.66252446,0.002675637,0.0005748889,0.0021030977,0.0005429506,0.0008411031,0.0012260934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8723191,0.10365062,0.0066613303,0.006783,0.00992328,0.00066268607],"domain_scores_gemma":[0.31597635,0.62868905,0.014038289,0.027117178,0.013600061,0.0005790591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15296443,0.0018210972,0.0023158477,0.0058334284,0.0016456478,0.009275858,0.0049581076,0.0043414934,0.0059810583],"category_scores_gemma":[0.5890238,0.001445413,0.002363369,0.0049497047,0.010040205,0.010762411,0.004562058,0.007874397,0.0010504134],"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":[0.00022175259,0.0000833515,0.010312531,0.0014809067,0.0006838288,0.0008832885,0.0047515407,0.049402263,0.0011499629,0.736459,0.011936045,0.18263552],"study_design_scores_gemma":[0.000059586728,0.00008103246,0.0025062677,0.0012529535,0.00013435328,0.0006506582,0.00078070944,0.10150051,0.0015376379,0.8799281,0.011428837,0.00013925994],"about_ca_topic_score_codex":0.0025420962,"about_ca_topic_score_gemma":0.0011648168,"teacher_disagreement_score":0.15296443,"about_ca_system_score_codex":0.0031490505,"about_ca_system_score_gemma":0.0025205621,"threshold_uncertainty_score":0.8089629},"labels":[],"label_agreement":null},{"id":"W4386514870","doi":"10.1214/23-aoas1729","title":"A Bayesian growth mixture model for complex survey data: Clustering postdisaster PTSD trajectories","year":2023,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Statistical Methods and Bayesian 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 Institute of Environmental Health Sciences; National Institute on Aging; National Institutes of Health","keywords":"Computer science; Mixture model; Statistics; Cluster analysis; Bayesian probability; Artificial intelligence; Data mining; Mathematics","score_opus":0.41289666394850244,"score_gpt":0.45495789631561145,"score_spread":0.042061232367109014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386514870","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":[0.01862088,0.00023600759,0.97784346,0.00064807246,0.000050847735,0.00027967748,0.0010117195,0.0005415452,0.00076785986],"genre_scores_gemma":[0.31934008,0.0010405413,0.6631229,0.00069013954,0.00019625075,0.0027571337,0.005346641,0.0004494189,0.007056873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99261206,0.0048235306,0.00026431508,0.0014165925,0.00057977164,0.00030363174],"domain_scores_gemma":[0.9805936,0.014136499,0.001477176,0.0019596117,0.0014850677,0.00034796732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018355697,0.0012763895,0.0022050405,0.0028744559,0.0012121085,0.0024472403,0.0045264782,0.0025512655,0.0050101224],"category_scores_gemma":[0.04838102,0.0016072576,0.0027163557,0.0029941325,0.0023585483,0.003114187,0.0030469352,0.0037244891,0.0015058819],"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":[0.00037216634,0.00015755284,0.034080643,0.00032956296,0.00047594716,0.00024482352,0.0015566176,0.530484,0.0011196482,0.308846,0.009458609,0.112874396],"study_design_scores_gemma":[0.0000546188,0.00005247411,0.0034721796,0.000080457125,0.00007181019,0.00007344704,0.00011987619,0.90445036,0.0002268163,0.086711556,0.004624765,0.000061651735],"about_ca_topic_score_codex":0.030575043,"about_ca_topic_score_gemma":0.025814971,"teacher_disagreement_score":0.030575043,"about_ca_system_score_codex":0.0025838222,"about_ca_system_score_gemma":0.0026983928,"threshold_uncertainty_score":0.09707534},"labels":[],"label_agreement":null},{"id":"W4386688562","doi":"10.1186/s12874-023-02027-y","title":"Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","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":"Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"Canadian Institutes of Health Research; Cundill Centre for Child and Youth Depression; University of Toronto; Centre for Addiction and Mental Health","keywords":"Statistics; Sample size determination; Cluster (spacecraft); Mathematics; Correlation; Cluster randomised controlled trial; Rank correlation; Randomized controlled trial; Medicine; Computer science; Surgery","score_opus":0.8717879191602349,"score_gpt":0.6638658868105302,"score_spread":0.20792203234970474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386688562","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":[0.21250427,0.0023591141,0.7671859,0.001087799,0.00019232277,0.011810401,0.0005453816,0.00043343724,0.0038813883],"genre_scores_gemma":[0.68855304,0.00068204815,0.30016848,0.0004425098,0.000030070209,0.0091276225,0.00025880407,0.00006173137,0.00067571376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8747458,0.116061114,0.0023425967,0.0025968514,0.003266646,0.000987108],"domain_scores_gemma":[0.4980271,0.4664312,0.014929776,0.010708507,0.008230572,0.0016728925],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15439086,0.0018046132,0.0031549735,0.0022460523,0.0005534624,0.0018998962,0.0033130716,0.0026239392,0.004317204],"category_scores_gemma":[0.3132645,0.0013290485,0.0038979556,0.0021270278,0.0017766629,0.0030856365,0.0027172961,0.0029600791,0.00041978073],"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":[0.009453441,0.001021902,0.008500546,0.0018734458,0.0018912993,0.00023523274,0.00053375814,0.890665,0.0005001726,0.03399105,0.0011380092,0.050196245],"study_design_scores_gemma":[0.0030400006,0.002871329,0.0012234786,0.00037650875,0.00057436357,0.00009239547,0.00013888191,0.96940607,0.0005180976,0.020814562,0.00087918655,0.00006506663],"about_ca_topic_score_codex":0.0038438728,"about_ca_topic_score_gemma":0.0018255997,"teacher_disagreement_score":0.8456091,"about_ca_system_score_codex":0.0038073903,"about_ca_system_score_gemma":0.0059949597,"threshold_uncertainty_score":0.8165066},"labels":[],"label_agreement":null},{"id":"W4386742233","doi":"10.1080/02664763.2023.2258301","title":"The spike-and-slab lasso and scalable algorithm to accommodate multinomial outcomes in variable selection problems","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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 Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Categorical variable; Prior probability; Multinomial distribution; Lasso (programming language); Computer science; Bayesian probability; Model selection; Algorithm; Multinomial probit; Variable (mathematics); Generalization; Econometrics; Mathematical optimization; Mathematics; Multinomial logistic regression; Machine learning; Artificial intelligence","score_opus":0.030928941227488013,"score_gpt":0.3379198239562676,"score_spread":0.30699088272877956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386742233","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":[0.0011553605,0.00012002682,0.9975793,0.00022068608,0.000028422568,0.000036292022,0.00009245467,0.00032982347,0.00043774047],"genre_scores_gemma":[0.0580619,0.00039462623,0.9357138,0.00068003114,0.00024074134,0.00072950986,0.001076393,0.0005418556,0.0025610963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954829,0.0029208711,0.00020484718,0.0004625839,0.0006674104,0.00026141873],"domain_scores_gemma":[0.9941981,0.0040466078,0.00039216617,0.00057297427,0.0005406162,0.0002496488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007504314,0.0018811928,0.0022864148,0.00096524344,0.0009835595,0.0016139399,0.0026478826,0.0023067344,0.0061765537],"category_scores_gemma":[0.01894497,0.0010649043,0.00172349,0.002106549,0.0014177191,0.0021350433,0.0033984855,0.0050570397,0.0022474686],"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":[0.00032901735,0.00018009453,0.0021255491,0.0004539318,0.00030661546,0.0003844699,0.0003954375,0.57154375,0.0032492054,0.13531062,0.026082994,0.25963834],"study_design_scores_gemma":[0.000058268997,0.000039594186,0.00015888868,0.00004191027,0.000014903077,0.00006360177,0.000030650186,0.91906977,0.0004686846,0.0767653,0.0032683704,0.00002006995],"about_ca_topic_score_codex":0.0034274666,"about_ca_topic_score_gemma":0.0056955554,"teacher_disagreement_score":0.007504314,"about_ca_system_score_codex":0.0006935476,"about_ca_system_score_gemma":0.0037416238,"threshold_uncertainty_score":0.039687097},"labels":[],"label_agreement":null},{"id":"W4386849819","doi":"10.3390/stats6030059","title":"A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data","year":2023,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Ministry of Higher Education; UCSI University","keywords":"Negative binomial distribution; Count data; Poisson distribution; Dispersion (optics); Mathematics; Mixture model; Applied mathematics; Binomial distribution; Overdispersion; Mixture distribution; Index of dispersion; Compound Poisson distribution; Binomial (polynomial); Statistical physics; Flexibility (engineering); Exponential family; Likelihood function; Probability distribution; Statistics; Probability density function; Estimation theory; Poisson regression; Physics","score_opus":0.2629780494088379,"score_gpt":0.4333171336539085,"score_spread":0.1703390842450706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386849819","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":[0.0020306858,0.00018263035,0.997129,0.00008589658,0.000023425691,0.000040113362,0.000046758294,0.00012689341,0.000334637],"genre_scores_gemma":[0.16412215,0.001493773,0.82856405,0.0002940216,0.00022338245,0.0009834941,0.00078499125,0.00030728948,0.0032269512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992853,0.00388089,0.00033948748,0.0011707403,0.0015085447,0.0002474006],"domain_scores_gemma":[0.98065954,0.014469204,0.0013014077,0.0016635164,0.0016097772,0.00029652234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014391233,0.0014393752,0.0015800919,0.004054602,0.0015011996,0.0027744845,0.0040250197,0.0026208288,0.0027581865],"category_scores_gemma":[0.04932615,0.0011235704,0.00303112,0.0034321703,0.0030383088,0.004918238,0.0021405914,0.004021454,0.001443883],"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":[0.00016561308,0.0001022553,0.007508684,0.00032184008,0.00026006933,0.0005083026,0.000940856,0.28112745,0.0032337934,0.5636315,0.0035805241,0.13861924],"study_design_scores_gemma":[0.000022061477,0.000064349275,0.000981039,0.00011193436,0.00005435397,0.00044897446,0.00008313616,0.78769046,0.00088568067,0.20309548,0.006480025,0.000082477156],"about_ca_topic_score_codex":0.0036801833,"about_ca_topic_score_gemma":0.0025485342,"teacher_disagreement_score":0.014391233,"about_ca_system_score_codex":0.0015564678,"about_ca_system_score_gemma":0.0012641305,"threshold_uncertainty_score":0.07610899},"labels":[],"label_agreement":null},{"id":"W4387033650","doi":"10.1177/09622802231198795","title":"Logistic regression vs. predictive mean matching for imputing binary covariates","year":2023,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Missing data; Imputation (statistics); Logistic regression; Statistics; Binary data; Parametric statistics; Covariate; Mathematics; Regression analysis; Multivariate statistics; Matching (statistics); Computer science; Binary number","score_opus":0.3427619520314442,"score_gpt":0.6290504285419702,"score_spread":0.286288476510526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387033650","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":[0.013798451,0.0015308191,0.9762683,0.0007939578,0.00031888782,0.00057447614,0.0016070654,0.0026028182,0.0025051355],"genre_scores_gemma":[0.297784,0.0013356953,0.6867305,0.0017132441,0.00033330216,0.0029331837,0.0038056825,0.001468253,0.0038962534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9536055,0.03427377,0.0024027585,0.005426126,0.003383029,0.00090886373],"domain_scores_gemma":[0.9175589,0.060759876,0.0053648846,0.011517367,0.0042130346,0.0005859397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052358236,0.0015884436,0.0027958944,0.002120198,0.0010075297,0.0022487328,0.0032649701,0.0022200805,0.013320961],"category_scores_gemma":[0.15928936,0.0010199633,0.0040524495,0.0040993723,0.0013885915,0.0033522423,0.003486672,0.0044728387,0.003638292],"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":[0.0040610335,0.0004141762,0.04679423,0.0025844488,0.0040830895,0.0004128937,0.0010223531,0.14838007,0.0018539232,0.12549499,0.032586046,0.6323127],"study_design_scores_gemma":[0.001166286,0.0012248068,0.01117687,0.0011659318,0.0012756775,0.00081501185,0.00019436034,0.72955656,0.006355714,0.20476152,0.042042665,0.00026451336],"about_ca_topic_score_codex":0.0035655827,"about_ca_topic_score_gemma":0.0031202456,"teacher_disagreement_score":0.052358236,"about_ca_system_score_codex":0.0012624022,"about_ca_system_score_gemma":0.0035030856,"threshold_uncertainty_score":0.27690017},"labels":[],"label_agreement":null},{"id":"W4387343347","doi":"10.1007/s00180-023-01417-6","title":"Variational Bayesian analysis for two-part latent variable model","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Latent variable; Markov chain Monte Carlo; Computer science; Bayesian inference; Inference; Bayesian probability; Bayes' theorem; Algorithm; Representation (politics); Variable (mathematics); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.0923508194694689,"score_gpt":0.3853531841519168,"score_spread":0.29300236468244795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387343347","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":[0.0046825767,0.00058966724,0.9930265,0.000557785,0.000034120894,0.000024620462,0.00014894785,0.00006820955,0.00086767774],"genre_scores_gemma":[0.44227627,0.003867105,0.5338235,0.0009655029,0.0006331863,0.0007705836,0.0020559228,0.0005349499,0.015073003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99601257,0.0024976227,0.00014601099,0.0005874061,0.0005257927,0.00023062284],"domain_scores_gemma":[0.9758212,0.021100437,0.0008505846,0.000870631,0.00091793726,0.00043920323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009523336,0.0014180035,0.0031072043,0.0019490942,0.0011037835,0.0032592255,0.004777371,0.003326996,0.004680445],"category_scores_gemma":[0.031566016,0.0017735974,0.002533813,0.00250856,0.0036943147,0.0048116865,0.003291568,0.004979783,0.0006169939],"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":[0.000091389295,0.00006559426,0.0010359751,0.00025447085,0.00019111905,0.00013921816,0.00019484098,0.27529335,0.00065650896,0.70042294,0.0027074204,0.01894712],"study_design_scores_gemma":[0.000016629254,0.00000857648,0.00023053277,0.000021970785,0.000021556005,0.000030517585,0.00001740419,0.706161,0.00007833361,0.29271707,0.00067596766,0.000020482968],"about_ca_topic_score_codex":0.014824934,"about_ca_topic_score_gemma":0.012462745,"teacher_disagreement_score":0.014824934,"about_ca_system_score_codex":0.003327001,"about_ca_system_score_gemma":0.0039641317,"threshold_uncertainty_score":0.050364792},"labels":[],"label_agreement":null},{"id":"W4387573002","doi":"10.3390/sym15101905","title":"Model Selection in Generalized Linear Models","year":2023,"lang":"en","type":"article","venue":"Symmetry","topic":"Statistical Methods and Bayesian Inference","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":"University of Windsor","funders":"University of Windsor","keywords":"Generalized linear model; Negative binomial distribution; Model selection; Mathematics; Wald test; Selection (genetic algorithm); Poisson regression; Binomial regression; Statistics; Poisson distribution; Likelihood-ratio test; Regression analysis; Linear regression; Linear model; Count data; Binomial (polynomial); Stepwise regression; Statistical hypothesis testing; Computer science; Population; Artificial intelligence","score_opus":0.1444628970261127,"score_gpt":0.4047763052497834,"score_spread":0.2603134082236707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387573002","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":[0.0019040128,0.0015041397,0.99404967,0.0006140147,0.00012383291,0.00019940127,0.0002905591,0.0003971152,0.00091728265],"genre_scores_gemma":[0.13998769,0.0052755047,0.8450276,0.0007330612,0.0007306379,0.0029668645,0.00199997,0.0005109825,0.0027675997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9348341,0.057769783,0.001347,0.002573877,0.0030020701,0.00047314414],"domain_scores_gemma":[0.91786194,0.074549384,0.0025001112,0.002419569,0.0023588426,0.000310269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036811963,0.0029505081,0.0045432835,0.003856598,0.0011663352,0.0035142126,0.00361221,0.002121826,0.006596493],"category_scores_gemma":[0.10430497,0.0012877942,0.004175384,0.00552824,0.00211028,0.002512686,0.0031404393,0.0043255957,0.0021100342],"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":[0.000272272,0.00016349186,0.0051125763,0.0023623963,0.0025438527,0.0008510923,0.0007918879,0.37067842,0.0006123798,0.33716598,0.01575211,0.26369354],"study_design_scores_gemma":[0.00011812674,0.00015368611,0.0009082878,0.00034719217,0.0002547068,0.00016608337,0.00012928221,0.5963257,0.00038405025,0.39113083,0.009997699,0.0000843418],"about_ca_topic_score_codex":0.0050151977,"about_ca_topic_score_gemma":0.0038180924,"teacher_disagreement_score":0.036811963,"about_ca_system_score_codex":0.0018607359,"about_ca_system_score_gemma":0.003880473,"threshold_uncertainty_score":0.1946826},"labels":[],"label_agreement":null},{"id":"W4387705506","doi":"10.5539/jmr.v15n5p1","title":"On the Implications of Ignoring Competing Risk in Survival Analysis: The Case of the Product-Limit Estimator","year":2023,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Complement (music); Mathematics; Statistics; Limit (mathematics); Event (particle physics); Product (mathematics); Econometrics; Receiver operating characteristic; Kaplan–Meier estimator","score_opus":0.30655669030255894,"score_gpt":0.5105938968283222,"score_spread":0.2040372065257633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387705506","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":[0.04908853,0.0054408927,0.9317997,0.009299353,0.00038277006,0.0001547441,0.00015467094,0.00012998194,0.0035493358],"genre_scores_gemma":[0.6644381,0.0036555403,0.32339343,0.0045870123,0.0011906925,0.00058464217,0.00029112297,0.00022469589,0.0016346499],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8499444,0.12749183,0.003853986,0.0082970485,0.009509579,0.00090318243],"domain_scores_gemma":[0.267091,0.6974301,0.012019319,0.015718943,0.006943129,0.0007974728],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.24473314,0.0010332391,0.002084347,0.0022695225,0.0013338043,0.0039006227,0.002870154,0.0042270874,0.0015217505],"category_scores_gemma":[0.58283365,0.0007445322,0.0019716178,0.002869338,0.008568367,0.008805106,0.0040323245,0.006277575,0.00028279363],"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":[0.0008787465,0.00016026317,0.07738494,0.0010728065,0.0013866442,0.0023938061,0.0030237136,0.07277434,0.0010576402,0.6753323,0.004586912,0.15994781],"study_design_scores_gemma":[0.0001357358,0.000652922,0.0120838685,0.00078222,0.0004949836,0.0021265685,0.00059100223,0.29585883,0.0015946623,0.6794631,0.006036026,0.00018013053],"about_ca_topic_score_codex":0.0043350817,"about_ca_topic_score_gemma":0.0021656498,"teacher_disagreement_score":0.75526685,"about_ca_system_score_codex":0.001983033,"about_ca_system_score_gemma":0.0025800476,"threshold_uncertainty_score":0.9313785},"labels":[],"label_agreement":null},{"id":"W4387773848","doi":"10.5539/ijsp.v12n5p42","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 5","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics; Library science; Computer science","score_opus":0.07617318379386495,"score_gpt":0.397621932915429,"score_spread":0.32144874912156407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387773848","genre_codex":"editorial","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":[0.00012649775,0.0027043081,0.0016813064,0.12537189,0.8663416,0.00052849995,0.000692425,0.00054624694,0.002007317],"genre_scores_gemma":[0.005026398,0.006179993,0.0044303606,0.16165458,0.77459955,0.0029607764,0.0015722959,0.0015250266,0.04205099],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9208426,0.015273682,0.016896367,0.0053596366,0.039222058,0.0024056318],"domain_scores_gemma":[0.13937868,0.037806552,0.012308239,0.0063534332,0.79519296,0.008960247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.058061954,0.0032637538,0.009526381,0.012497381,0.0047450103,0.011627808,0.006024033,0.019063719,0.09419141],"category_scores_gemma":[0.53516555,0.0018433533,0.0065009613,0.0054091876,0.0038340276,0.0067694816,0.0041103764,0.013438581,0.06315085],"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":[0.00003889177,0.0000044769,0.00008659388,0.00042009703,0.0000138465,0.000058916274,0.000033665787,0.000011677327,0.000034567343,0.00012814417,0.9955051,0.0036639245],"study_design_scores_gemma":[0.000345917,0.00007612374,0.0016473114,0.005209874,0.00019167936,0.0014375823,0.00044823578,0.0006843634,0.00040136508,0.0028844594,0.9864326,0.00024046769],"about_ca_topic_score_codex":0.0035555116,"about_ca_topic_score_gemma":0.005230789,"teacher_disagreement_score":0.09419141,"about_ca_system_score_codex":0.0054143006,"about_ca_system_score_gemma":0.010890019,"threshold_uncertainty_score":0.31510162},"labels":[],"label_agreement":null},{"id":"W4387789839","doi":"10.1007/978-3-031-40055-1_1","title":"Likelihood Ratios in Forensics: What They Are and What They Are Not","year":2023,"lang":"en","type":"book-chapter","venue":"Contributions to statistics","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University","funders":"","keywords":"Likelihood principle; Statistics; Likelihood-ratio test; Likelihood function; Marginal likelihood; Likelihood ratios in diagnostic testing; Restricted maximum likelihood; Univariate; Frequentist inference; Population; Bayesian probability; Bayes factor; Mathematics; Econometrics; Maximum likelihood; Multivariate statistics; Bayesian inference; Confidence interval; Demography; Quasi-maximum likelihood; Sociology","score_opus":0.05067118156284713,"score_gpt":0.34908538473458034,"score_spread":0.2984142031717332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387789839","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":[0.0018013819,0.31066042,0.47476125,0.07106269,0.013029356,0.00006275507,0.00029201817,0.00065152603,0.12767851],"genre_scores_gemma":[0.103074454,0.29245234,0.40620667,0.02933866,0.05128013,0.00035296485,0.00039161637,0.0017466304,0.1151567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9927672,0.0040695183,0.00027805002,0.0005009661,0.0022579073,0.00012639872],"domain_scores_gemma":[0.96846616,0.027173433,0.00077898364,0.0014617097,0.001778775,0.00034097055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01062403,0.001726404,0.0016505286,0.0036599645,0.001377326,0.007890816,0.0023787175,0.004705603,0.009324683],"category_scores_gemma":[0.04243785,0.0012066523,0.0005692957,0.0035165828,0.015766844,0.018420171,0.002804624,0.008512695,0.0052920966],"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":[0.000009330059,0.00001600234,0.00013312988,0.00027054263,0.000017009175,0.00005785301,0.0002151639,0.0007167539,0.0001186348,0.8561782,0.041516177,0.10075117],"study_design_scores_gemma":[0.000002515078,0.0000050734025,0.00008487446,0.00017603494,0.000006631519,0.00019100124,0.00006493936,0.0009104676,0.0001182518,0.9351502,0.06327264,0.000017322805],"about_ca_topic_score_codex":0.0010196975,"about_ca_topic_score_gemma":0.0010559668,"teacher_disagreement_score":0.01062403,"about_ca_system_score_codex":0.0019762905,"about_ca_system_score_gemma":0.0018025375,"threshold_uncertainty_score":0.05618596},"labels":[],"label_agreement":null},{"id":"W4387838723","doi":"10.48550/arxiv.2310.12427","title":"Fast Power Curve Approximation for Posterior Analyses","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sample size determination; Posterior probability; Consistency (knowledge bases); Sampling (signal processing); Mathematics; Bayesian probability; Statistics; Bayes' theorem; Power (physics); Sample (material); Sampling distribution; Statistical power; Bayes factor; Statistical hypothesis testing; Computer science; Geometry","score_opus":0.36559982353323045,"score_gpt":0.3443575222995424,"score_spread":0.021242301233688032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387838723","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":[0.00066560635,0.00015884868,0.9981908,0.000097195916,0.000016184125,0.00003524385,0.000044456483,0.00020829967,0.0005833163],"genre_scores_gemma":[0.08356146,0.0009672253,0.9100669,0.00033084437,0.00019147912,0.0008264332,0.0005357655,0.0008268392,0.0026931916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98893946,0.005968585,0.00047917874,0.0012652568,0.002961012,0.00038635047],"domain_scores_gemma":[0.9071376,0.078933105,0.0023051766,0.006904752,0.004205706,0.00051353883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020558951,0.0021681655,0.0024381008,0.0040143635,0.001085204,0.0038115813,0.0037146062,0.0025933995,0.009875266],"category_scores_gemma":[0.1759389,0.0016648902,0.0022137328,0.003545917,0.0027591425,0.0066568065,0.004657313,0.007495553,0.0031674346],"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":[0.00030403855,0.0000942407,0.0029950675,0.0004817976,0.00025836597,0.00035300062,0.0005985332,0.32768565,0.0029811782,0.43950984,0.0062181163,0.21852016],"study_design_scores_gemma":[0.000042511594,0.000041356707,0.00034234184,0.00009327953,0.000034121775,0.00012371172,0.000047359845,0.6735212,0.0014530101,0.31882012,0.0054571605,0.000023884078],"about_ca_topic_score_codex":0.0038680404,"about_ca_topic_score_gemma":0.00226814,"teacher_disagreement_score":0.020558951,"about_ca_system_score_codex":0.0024065925,"about_ca_system_score_gemma":0.0028426852,"threshold_uncertainty_score":0.108727455},"labels":[],"label_agreement":null},{"id":"W4388290285","doi":"10.1093/oso/9780198526155.003.0033","title":"Direct Bayes for Interest Parameters","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto; University of Waterloo","funders":"","keywords":"Marginal likelihood; Bayes' theorem; Bayesian probability; Nuisance parameter; Bayes factor; Simple (philosophy); Econometrics; Prior probability; Conditional probability; Mathematics; Computer science; Conditional dependence; Statistics","score_opus":0.17911913215161765,"score_gpt":0.3792856877461502,"score_spread":0.20016655559453253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388290285","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":[0.0011800805,0.0044372766,0.9327656,0.0020945393,0.00020901593,0.0000634455,0.00024907856,0.00020093717,0.058800053],"genre_scores_gemma":[0.14485767,0.010678801,0.7588534,0.002094457,0.0011621177,0.0007675666,0.0008760771,0.00061687094,0.08009309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99664474,0.00143979,0.00013500916,0.00061066635,0.0010724252,0.00009745494],"domain_scores_gemma":[0.9959384,0.002875545,0.0001855402,0.00038540227,0.0005485727,0.00006645657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00520352,0.00109666,0.0011260643,0.0021582802,0.00095326646,0.004133075,0.0016994543,0.0019204583,0.017798504],"category_scores_gemma":[0.017163914,0.0010917318,0.0008874713,0.0014648136,0.0036958032,0.0056412127,0.0019748942,0.0036930353,0.0055689323],"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":[0.000006888439,0.000005656519,0.00013058884,0.00008153428,0.000011687575,0.000047266607,0.00013981294,0.0019728257,0.0001335976,0.95599645,0.005958717,0.035514954],"study_design_scores_gemma":[0.0000038782305,0.0000023639373,0.00007170496,0.000077424265,0.0000070223605,0.000073324096,0.000018248676,0.005993209,0.00013130705,0.9707218,0.02289197,0.000007698306],"about_ca_topic_score_codex":0.0025106024,"about_ca_topic_score_gemma":0.0032948204,"teacher_disagreement_score":0.017798504,"about_ca_system_score_codex":0.002946024,"about_ca_system_score_gemma":0.0018565755,"threshold_uncertainty_score":0.05954194},"labels":[],"label_agreement":null},{"id":"W4388421561","doi":"10.1080/02664763.2023.2277669","title":"A computationally efficient sequential regression imputation algorithm for multilevel data","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Computer science; Missing data; Computation; Singleton; Algorithm; Regression; Data mining; Statistics; Machine learning; Mathematics","score_opus":0.13883124269288116,"score_gpt":0.43675601875730574,"score_spread":0.2979247760644246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388421561","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":[0.0010237108,0.000044268843,0.9984011,0.00009244659,0.000020550753,0.00003665098,0.00005480425,0.00018090467,0.00014555796],"genre_scores_gemma":[0.019427165,0.000087617715,0.97928363,0.0000622303,0.000039433195,0.00030778968,0.00023251072,0.00005624024,0.00050341163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625695,0.002245747,0.00024435858,0.00045780677,0.00069169985,0.00010335413],"domain_scores_gemma":[0.99431014,0.0034054175,0.0005321825,0.00075236306,0.0008810354,0.00011881847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007301848,0.00063186104,0.0013082611,0.0012158226,0.0008229653,0.0008927814,0.0020024364,0.0010452584,0.004344176],"category_scores_gemma":[0.019762645,0.0006736593,0.001451923,0.0030384604,0.0004548582,0.0012938898,0.0020018017,0.00284786,0.0014470036],"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":[0.00029243587,0.00018700355,0.0048575,0.00033204863,0.0005263479,0.00016041534,0.00033596208,0.16275178,0.0039764033,0.07818075,0.012908765,0.7354906],"study_design_scores_gemma":[0.00012876606,0.00012985212,0.0009743953,0.000042928954,0.000074924,0.0002187904,0.000040046638,0.9355165,0.0014945231,0.05287055,0.008470575,0.00003809364],"about_ca_topic_score_codex":0.0036585426,"about_ca_topic_score_gemma":0.005705607,"teacher_disagreement_score":0.007301848,"about_ca_system_score_codex":0.0005862832,"about_ca_system_score_gemma":0.00264474,"threshold_uncertainty_score":0.03861636},"labels":[],"label_agreement":null},{"id":"W4388498740","doi":"10.1177/09622802231210917","title":"A support vector machine-based cure rate model for interval censored data","year":2023,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":25,"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":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Support vector machine; Interval (graph theory); Interval data; Computer science; Statistics; Data mining; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.5100163218704702,"score_gpt":0.6517688622124422,"score_spread":0.14175254034197204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388498740","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":[0.0207947,0.00045009665,0.9768237,0.00047412273,0.000058112586,0.00008731115,0.0003693695,0.00036677375,0.00057585695],"genre_scores_gemma":[0.7337969,0.001179929,0.2535946,0.00037127105,0.00025005624,0.0010533687,0.0021299038,0.00012815486,0.0074957595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964729,0.0017334109,0.00018964702,0.0007666947,0.0005536448,0.00028365053],"domain_scores_gemma":[0.99174047,0.0058261636,0.0008002141,0.00047113723,0.000986085,0.00017579895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007864899,0.0010404448,0.0020009484,0.0016096905,0.00045373317,0.0013954339,0.0031151758,0.0018636511,0.003062533],"category_scores_gemma":[0.019927647,0.0005718657,0.0015867271,0.0018758354,0.0008976597,0.0023087075,0.0009889007,0.003574566,0.00092748395],"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":[0.0004661215,0.00023401788,0.008917282,0.00025025895,0.00016746945,0.0002592695,0.000288014,0.7736384,0.001418138,0.079693936,0.0035399348,0.13112716],"study_design_scores_gemma":[0.000014029426,0.000060893297,0.00066645176,0.000014493112,0.000015236351,0.000050207967,0.000009401483,0.98819613,0.00014987316,0.010167925,0.0006392039,0.000016141534],"about_ca_topic_score_codex":0.0046751923,"about_ca_topic_score_gemma":0.0023139114,"teacher_disagreement_score":0.007864899,"about_ca_system_score_codex":0.0010437582,"about_ca_system_score_gemma":0.0010557275,"threshold_uncertainty_score":0.04159403},"labels":[],"label_agreement":null},{"id":"W4389121274","doi":"10.1007/978-3-031-42413-7","title":"Bayesian Statistics, New Generations New Approaches","year":2023,"lang":"en","type":"book","venue":"Springer proceedings in mathematics & statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bayesian probability; Approximate Bayesian computation; Bayesian statistics; Computer science; Variable-order Bayesian network; Parametric statistics; Computation; Econometrics; Bayesian inference; Statistics; Machine learning; Artificial intelligence; Mathematics; Algorithm; Inference","score_opus":0.12453847370973882,"score_gpt":0.341471544811064,"score_spread":0.2169330711013252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389121274","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":[0.0015770455,0.25455776,0.6231201,0.029758625,0.009406549,0.000045002813,0.00058146653,0.0005836343,0.08036978],"genre_scores_gemma":[0.077668205,0.2499575,0.48132217,0.019158544,0.03365816,0.00035966854,0.0011504987,0.002041749,0.13468343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995082,0.0021409052,0.00024353931,0.00057944417,0.0018482204,0.00010581051],"domain_scores_gemma":[0.9857195,0.010920767,0.00032809746,0.0011851898,0.0015548406,0.00029162897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0083962865,0.0014763142,0.0022326082,0.0040756348,0.00073527603,0.005431632,0.0018056923,0.0026845746,0.011095272],"category_scores_gemma":[0.027178874,0.0014946195,0.0011424599,0.004396417,0.006875164,0.008588945,0.0025091807,0.007614704,0.005321549],"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":[0.000024287709,0.000028175737,0.00020748372,0.0002936178,0.00006528989,0.00003849184,0.00018457432,0.0022191226,0.00017875913,0.8115067,0.05691162,0.12834194],"study_design_scores_gemma":[0.000008136832,0.00000802003,0.00014851421,0.00012476166,0.00001640414,0.00007647722,0.000039887596,0.0040066214,0.000054697983,0.8821771,0.11331953,0.000019887473],"about_ca_topic_score_codex":0.002872341,"about_ca_topic_score_gemma":0.003934895,"teacher_disagreement_score":0.011095272,"about_ca_system_score_codex":0.002560453,"about_ca_system_score_gemma":0.0021420748,"threshold_uncertainty_score":0.044404328},"labels":[],"label_agreement":null},{"id":"W4389159922","doi":"10.1080/19466315.2023.2290642","title":"Joint Analysis of Longitudinal Ordinal Categorical Item Response Data and Survival Times with Cure Fraction","year":2023,"lang":"en","type":"article","venue":"Statistics in Biopharmaceutical Research","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Ordinal data; Statistics; Fraction (chemistry); Longitudinal data; Mathematics; Econometrics; Ordinal regression; Medicine; Computer science; Data mining","score_opus":0.48395811929375726,"score_gpt":0.5783926631729541,"score_spread":0.09443454387919686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389159922","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":[0.104175545,0.0004954995,0.8931199,0.000425045,0.000054172408,0.0002574105,0.00058626285,0.00029458155,0.00059155957],"genre_scores_gemma":[0.8332775,0.000708409,0.15929389,0.0001954451,0.0001339659,0.0012994735,0.0013932423,0.00011597521,0.003582132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9815943,0.014032007,0.0005511427,0.0019256831,0.001299053,0.0005977043],"domain_scores_gemma":[0.8918685,0.08885763,0.008235979,0.008159341,0.0021121528,0.0007664297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04587409,0.0012560466,0.0025451863,0.0023704907,0.00045092596,0.0015827457,0.0020372684,0.0017359159,0.003933475],"category_scores_gemma":[0.09805386,0.00072245585,0.0029692138,0.002704243,0.0024911347,0.0027683883,0.0019348937,0.002795666,0.0007328972],"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":[0.003895608,0.0010445544,0.1268741,0.0011069232,0.0019680746,0.0009428073,0.0022478485,0.43617472,0.0047558146,0.2014578,0.0029399956,0.21659179],"study_design_scores_gemma":[0.00014420348,0.001071382,0.023015538,0.00009308843,0.0003573324,0.00029910388,0.00022865043,0.8663237,0.0013689913,0.10493196,0.0020524678,0.00011358237],"about_ca_topic_score_codex":0.0023513886,"about_ca_topic_score_gemma":0.0014898728,"teacher_disagreement_score":0.04587409,"about_ca_system_score_codex":0.0011099259,"about_ca_system_score_gemma":0.0018117694,"threshold_uncertainty_score":0.24260825},"labels":[],"label_agreement":null},{"id":"W4389287837","doi":"10.20982/tqmp.19.3.p244","title":"The Bayesian Approach is Intuitive Conditionally to Prior Exposition to These Examples","year":2023,"lang":"en","type":"article","venue":"The Quantitative Methods for Psychology","topic":"Statistical Methods and Bayesian 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 Ottawa","funders":"","keywords":"Exposition (narrative); Bayesian probability; Computer science; Conditional independence; Artificial intelligence; Data science; Art","score_opus":0.30026350921348927,"score_gpt":0.574325324778592,"score_spread":0.27406181556510273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389287837","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":[0.0027712833,0.002192946,0.76853126,0.019359866,0.0012045008,0.0001142087,0.0004459004,0.0004929087,0.20488717],"genre_scores_gemma":[0.21923642,0.0059046033,0.6754282,0.0100806495,0.0022189636,0.0011312128,0.00076602475,0.00080916367,0.084424734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963834,0.0021622148,0.0001653689,0.00038174808,0.0007695269,0.00013768025],"domain_scores_gemma":[0.99239975,0.0055511803,0.00053822383,0.0005326783,0.0008239707,0.00015415065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032940079,0.0010894814,0.00055187446,0.002140472,0.0018939251,0.003040021,0.0017302757,0.0034018818,0.028712109],"category_scores_gemma":[0.014908409,0.0005240978,0.0008966856,0.0015992601,0.0071885213,0.0063301697,0.0018755229,0.004442964,0.0077386205],"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":[0.000011218334,0.000013663752,0.000105852414,0.000090436035,0.000005949297,0.0001371619,0.00080869434,0.00064614863,0.00018335134,0.975576,0.011393401,0.011028235],"study_design_scores_gemma":[0.000008390789,0.000016357626,0.00021420348,0.00016012207,0.0000072553235,0.00054927525,0.00027194805,0.0030492544,0.00031061296,0.7979888,0.19739874,0.000025102547],"about_ca_topic_score_codex":0.0017928684,"about_ca_topic_score_gemma":0.0025238094,"teacher_disagreement_score":0.028712109,"about_ca_system_score_codex":0.0014147749,"about_ca_system_score_gemma":0.0010489973,"threshold_uncertainty_score":0.09605163},"labels":[],"label_agreement":null},{"id":"W4389513713","doi":"10.1002/cjs.11801","title":"Modelling occurrence and quantity of longitudinal semicontinuous data simultaneously with nonparametric unobserved heterogeneity","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Nonparametric statistics; Econometrics; Longitudinal data; Statistics; Mathematics; Poisson distribution; Mixed model; Population; Random effects model; Sequence (biology); Correlation; Computer science; Biology; Data mining; Demography; Medicine","score_opus":0.19282182694998762,"score_gpt":0.3585829582406329,"score_spread":0.1657611312906453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389513713","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":[0.23410612,0.0006914403,0.7620093,0.0010756843,0.00006617868,0.00018342219,0.0007069475,0.00016302506,0.0009979603],"genre_scores_gemma":[0.9138393,0.00039466462,0.08217257,0.00021698471,0.00011709243,0.00048311966,0.0005360428,0.000044380125,0.0021957543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9887678,0.0069771945,0.0006377741,0.002128567,0.00092249404,0.00056622276],"domain_scores_gemma":[0.85687,0.12027719,0.01390908,0.006066795,0.0021011159,0.0007757917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033938885,0.0007649784,0.0016763575,0.0020819362,0.0006958224,0.002148104,0.0029519743,0.002721631,0.0017562447],"category_scores_gemma":[0.08301876,0.001005039,0.001734957,0.0026712334,0.0034888429,0.0027721953,0.0024356805,0.0024522638,0.00014605965],"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":[0.00087942265,0.0004439902,0.2162012,0.00074737053,0.0010888161,0.003405261,0.0030201154,0.34589493,0.004881432,0.3407585,0.001945156,0.080733865],"study_design_scores_gemma":[0.00008058181,0.0003044512,0.029745096,0.00014524853,0.0002356825,0.00058002496,0.00038348942,0.7671944,0.0011295544,0.19832975,0.00176117,0.0001104471],"about_ca_topic_score_codex":0.00782772,"about_ca_topic_score_gemma":0.006693371,"teacher_disagreement_score":0.033938885,"about_ca_system_score_codex":0.0016269882,"about_ca_system_score_gemma":0.001776893,"threshold_uncertainty_score":0.17948812},"labels":[],"label_agreement":null},{"id":"W4389721957","doi":"10.1162/imag_a_00058","title":"Spatial-extent inference for testing variance components in reliability and heritability studies","year":2023,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Statistical Methods and Bayesian 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":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute of Mental Health; Canadian Institutes of Health Research; McDonnell Center for Systems Neuroscience; National Institutes of Health; Connaught Fund; Natural Sciences and Engineering Research Council of Canada; Centre for Addiction and Mental Health Foundation; University of Toronto; National Alliance for Research on Schizophrenia and Depression","keywords":"Heritability; Variance components; Inference; Reliability (semiconductor); Variance (accounting); Statistics; Reliability engineering; Computer science; Mathematics; Artificial intelligence; Engineering; Evolutionary biology; Biology","score_opus":0.24726699858321868,"score_gpt":0.440883427656237,"score_spread":0.19361642907301832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389721957","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":[0.014827427,0.0003475349,0.98343277,0.00015887327,0.00004604054,0.00007172311,0.0002059576,0.0003547504,0.0005549603],"genre_scores_gemma":[0.4975824,0.0003271911,0.49913326,0.0002534099,0.00013246798,0.0006000916,0.00091514125,0.00054076227,0.0005152881],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9677871,0.024289533,0.0011156047,0.0045862137,0.0017891224,0.0004323724],"domain_scores_gemma":[0.8331994,0.14140032,0.005653807,0.015027552,0.003965638,0.00075334427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044458143,0.0010950002,0.001953417,0.0037416841,0.0014403324,0.0019551534,0.0026949777,0.0016196519,0.004097144],"category_scores_gemma":[0.18650505,0.000996687,0.0026969227,0.0030468395,0.003931361,0.002695802,0.0033243771,0.003260608,0.00055438187],"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":[0.00091519987,0.000251235,0.08610727,0.0015608928,0.0046893437,0.00096241105,0.0018266132,0.2454081,0.010531704,0.31422317,0.009708819,0.32381526],"study_design_scores_gemma":[0.00015886138,0.00025516486,0.023254601,0.00026870196,0.0005033763,0.0003852285,0.00019200079,0.4699068,0.0047124545,0.49558055,0.004676812,0.00010544068],"about_ca_topic_score_codex":0.0045064436,"about_ca_topic_score_gemma":0.0036406955,"teacher_disagreement_score":0.044458143,"about_ca_system_score_codex":0.0008161382,"about_ca_system_score_gemma":0.0021176585,"threshold_uncertainty_score":0.23511988},"labels":[],"label_agreement":null},{"id":"W4390051293","doi":"10.1177/1536867x231212433","title":"Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust","year":2023,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Statistical Methods and Bayesian 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":"Carleton University; Queen's University","funders":"Social Sciences and Humanities Research Council of Canada; Danmarks Grundforskningsfond; National Research Foundation; York University","keywords":"Leverage (statistics); Jackknife resampling; Estimator; Inference; Computer science; Regression; Econometrics; Variance (accounting); Data mining; Linear regression; Cluster (spacecraft); Statistics; Mathematics; Artificial intelligence; Machine learning; Accounting","score_opus":0.1913882021901753,"score_gpt":0.4374128927053959,"score_spread":0.2460246905152206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390051293","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":[0.0032299256,0.00016630458,0.9930824,0.0002516775,0.000034749075,0.000049870487,0.0003911607,0.002000372,0.00079354347],"genre_scores_gemma":[0.10749363,0.0003373534,0.88426244,0.0003574847,0.00015949011,0.0006836197,0.0020135555,0.003063761,0.0016286998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9734019,0.01839898,0.0012075511,0.0025826176,0.0038375948,0.0005713475],"domain_scores_gemma":[0.86366117,0.10689088,0.0070549417,0.015044829,0.0060800808,0.0012680396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032165617,0.002206262,0.0029449498,0.004575482,0.0022318806,0.0046950243,0.004973677,0.0021378899,0.0071237283],"category_scores_gemma":[0.23140186,0.0018916334,0.0025797298,0.007040086,0.003161355,0.005372744,0.0056242887,0.005483665,0.0028951163],"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":[0.0005182887,0.00015910588,0.027628021,0.0007919494,0.0010164758,0.0011471241,0.0019605749,0.29142845,0.0019866417,0.33925346,0.046299897,0.2878101],"study_design_scores_gemma":[0.0000719423,0.00004783099,0.0022308035,0.00014765163,0.000077856675,0.00020203383,0.00019771542,0.6997583,0.0025281848,0.28332412,0.011315798,0.00009777483],"about_ca_topic_score_codex":0.013178994,"about_ca_topic_score_gemma":0.021316148,"teacher_disagreement_score":0.032165617,"about_ca_system_score_codex":0.0018637637,"about_ca_system_score_gemma":0.004544599,"threshold_uncertainty_score":0.17011005},"labels":[],"label_agreement":null},{"id":"W4390478197","doi":"10.1080/00207721.2023.2294747","title":"A comparison of three algorithms in the filtering of a Markov-modulated non-homogeneous Poisson process","year":2024,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Statistical Methods and Bayesian Inference","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Smoothing; Overfitting; Computer science; Filter (signal processing); Expectation–maximization algorithm; Process (computing); Mathematical optimization; Mathematics; Maximum likelihood; Statistics; Artificial intelligence","score_opus":0.08693069305638408,"score_gpt":0.4564044910656355,"score_spread":0.36947379800925145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390478197","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":[0.019146193,0.0006747527,0.97751576,0.0002907932,0.00012561296,0.000081122555,0.00007078983,0.0009402097,0.0011548405],"genre_scores_gemma":[0.19813702,0.00090475887,0.79806,0.0002723227,0.00008765357,0.00022607586,0.0006129067,0.00020088916,0.0014983722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969098,0.0011659251,0.00034115088,0.0005678644,0.0007575945,0.00025767623],"domain_scores_gemma":[0.9842318,0.011301821,0.00074499776,0.0010655792,0.0022877862,0.00036796345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079623675,0.0013211884,0.0015632757,0.002183989,0.00095009926,0.0024453553,0.0027402642,0.0036505507,0.0015744728],"category_scores_gemma":[0.030592605,0.0005966713,0.0017173479,0.0019826547,0.0009267617,0.0026420045,0.0014178642,0.0020298045,0.0006297271],"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":[0.0010431906,0.00026948535,0.0070419805,0.00039988913,0.00040049254,0.0001484854,0.00037621715,0.53070796,0.0031729932,0.033471383,0.0029476094,0.42002034],"study_design_scores_gemma":[0.000057092202,0.00008963902,0.0013267722,0.000031166557,0.000044998516,0.00008642801,0.000060349248,0.9869703,0.0020031447,0.007881727,0.0014017805,0.00004665933],"about_ca_topic_score_codex":0.014506129,"about_ca_topic_score_gemma":0.009284806,"teacher_disagreement_score":0.014506129,"about_ca_system_score_codex":0.0015969161,"about_ca_system_score_gemma":0.003205462,"threshold_uncertainty_score":0.04210949},"labels":[],"label_agreement":null},{"id":"W4390608160","doi":"10.1002/cjs.11800","title":"Bayesian Model Selection via Composite Likelihood for High‐dimensional Data Integration","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Marginal likelihood; Model selection; Selection (genetic algorithm); Bayesian information criterion; Bayesian probability; Gaussian; Quasi-maximum likelihood; Infinity; Mathematics; Generalized linear model; Maximum likelihood; Statistics; Computer science; Machine learning; Likelihood function","score_opus":0.06534678948256736,"score_gpt":0.3426255660040473,"score_spread":0.2772787765214799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390608160","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":[0.004959889,0.00010896589,0.99440515,0.00019070473,0.000011445415,0.000040604427,0.00003661569,0.00009548463,0.00015111921],"genre_scores_gemma":[0.30439427,0.0006138406,0.6894462,0.00048627693,0.00031205863,0.00096956786,0.0010459844,0.00030673682,0.002424981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9805133,0.013975283,0.00066063716,0.0020563367,0.0022866474,0.0005078207],"domain_scores_gemma":[0.8836004,0.10296023,0.0045764265,0.0038299665,0.003579713,0.0014531951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037070174,0.002000065,0.0042320034,0.0033482555,0.001500102,0.0036646188,0.00565171,0.0029969306,0.0024696516],"category_scores_gemma":[0.0998841,0.002506306,0.0035350698,0.004394877,0.004644028,0.004393106,0.0072546205,0.0059090853,0.0006533113],"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":[0.00041442266,0.00021574541,0.005485202,0.00029665537,0.00061987515,0.0005245465,0.00042967973,0.8021679,0.0013889293,0.13086396,0.0016151677,0.055977933],"study_design_scores_gemma":[0.000025111834,0.0000286421,0.00027833975,0.00001538635,0.000022846538,0.00002852815,0.000011627812,0.9609402,0.00020805473,0.038179975,0.00024287045,0.000018567585],"about_ca_topic_score_codex":0.0053631817,"about_ca_topic_score_gemma":0.004039929,"teacher_disagreement_score":0.037070174,"about_ca_system_score_codex":0.0022215056,"about_ca_system_score_gemma":0.0034934015,"threshold_uncertainty_score":0.19604814},"labels":[],"label_agreement":null},{"id":"W4390972838","doi":"10.1080/00949655.2024.2304082","title":"Time series regression models for zero-inflated proportions","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian 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":"Vancouver Island University; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Covariate; Series (stratigraphy); Transformation (genetics); Statistics; Applied mathematics; Regression analysis; Generalized linear model; Data transformation; Econometrics; Computer science; Data mining","score_opus":0.07012677551197766,"score_gpt":0.41919182235229635,"score_spread":0.3490650468403187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390972838","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":[0.0060978155,0.0002434196,0.9915547,0.00038831163,0.000048302478,0.000052002488,0.00020920362,0.00018372787,0.0012224702],"genre_scores_gemma":[0.40097326,0.0022154052,0.56995815,0.00061932206,0.0003307688,0.0016819008,0.0016755793,0.00039615127,0.022149427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98959106,0.0068795364,0.0003904327,0.0015570847,0.0012456749,0.0003361727],"domain_scores_gemma":[0.9454084,0.04450823,0.0042099766,0.0035650847,0.0020323736,0.0002759208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021818709,0.0012645104,0.0017994454,0.002014042,0.0006706908,0.0025397325,0.0045327176,0.002614823,0.008234424],"category_scores_gemma":[0.08103008,0.00077282573,0.002304896,0.0040531135,0.00252131,0.00458344,0.0021569035,0.004463957,0.0023596617],"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":[0.000094333496,0.000061693725,0.004400432,0.00018065271,0.00014375254,0.00026778478,0.0005822786,0.1805605,0.00072554563,0.7630972,0.0019704986,0.04791525],"study_design_scores_gemma":[0.000026384496,0.0000642228,0.0011375961,0.000057472345,0.00003613387,0.00014566326,0.00007649993,0.5645154,0.0004007895,0.42805567,0.005443848,0.00004026214],"about_ca_topic_score_codex":0.0033901506,"about_ca_topic_score_gemma":0.002332903,"teacher_disagreement_score":0.021818709,"about_ca_system_score_codex":0.0016066448,"about_ca_system_score_gemma":0.0009994559,"threshold_uncertainty_score":0.115389764},"labels":[],"label_agreement":null},{"id":"W4390976339","doi":"10.6000/1929-6029.2024.13.01","title":"A Double Truncated Binomial Model to Assess Psychiatric Health through Brief Psychiatric Rating Scale: When is Intervention Useful?","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kurtosis; Brief Psychiatric Rating Scale; Mathematics; Statistics; Confidence interval; Skewness; Rating scale; Negative binomial distribution; Psychology; Psychiatry; Poisson distribution; Psychosis","score_opus":0.26935983563864385,"score_gpt":0.5687714749139448,"score_spread":0.2994116392753009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390976339","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":[0.087464936,0.0026281176,0.8927258,0.006082672,0.0008204969,0.0013360099,0.0018986157,0.00030967605,0.0067335786],"genre_scores_gemma":[0.63906395,0.003539425,0.3379739,0.00196875,0.00067752984,0.004835399,0.0021692517,0.00009309042,0.009678677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9878163,0.0094539365,0.00044335952,0.00091662235,0.0008590881,0.000510805],"domain_scores_gemma":[0.97057414,0.02545544,0.0014456973,0.0009767399,0.0010177161,0.00053027406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020836074,0.0008837707,0.0025464564,0.0012378602,0.00045846606,0.0018369218,0.0028927317,0.0025032584,0.00791996],"category_scores_gemma":[0.04267951,0.00043256552,0.0014096582,0.00140596,0.0012722022,0.0026291907,0.0017010269,0.0027131166,0.0010209968],"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":[0.007893378,0.0012840328,0.06594004,0.002499832,0.0011124006,0.0029004589,0.0027666504,0.1568099,0.0032047124,0.38031584,0.017498963,0.35777384],"study_design_scores_gemma":[0.0009125948,0.0026549806,0.00950601,0.00075900817,0.0004423187,0.0009778955,0.0008934086,0.77164906,0.00075960264,0.19912274,0.012112975,0.00020922787],"about_ca_topic_score_codex":0.0037344778,"about_ca_topic_score_gemma":0.0032188548,"teacher_disagreement_score":0.020836074,"about_ca_system_score_codex":0.0015338382,"about_ca_system_score_gemma":0.0017988089,"threshold_uncertainty_score":0.110193014},"labels":[],"label_agreement":null},{"id":"W4391138204","doi":"10.1002/sim.10011","title":"Multiple imputation strategies for missing event times in a multi‐state model analysis","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian 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":"Medical Research Council; University of Bristol; Medical Research Council Canada; Wellcome Trust; NHS Blood and Transplant","keywords":"Imputation (statistics); Computer science; Missing data; Data mining; Statistics; Machine learning; Mathematics","score_opus":0.09443405681533493,"score_gpt":0.4594438165445142,"score_spread":0.36500975972917926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391138204","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":[0.002100541,0.00032394417,0.9965313,0.0003725482,0.000050773524,0.00007276988,0.00016583205,0.00017769697,0.00020468756],"genre_scores_gemma":[0.16035762,0.0009862211,0.83279073,0.0007014548,0.00024747895,0.0013844162,0.001344197,0.00030640746,0.0018814482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97458863,0.019923609,0.0012423916,0.0025068775,0.0012677477,0.0004707261],"domain_scores_gemma":[0.8963637,0.08968614,0.0046282173,0.0059055905,0.0026606882,0.00075568847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05412706,0.0018216694,0.004566985,0.0030045973,0.0017174517,0.0030102232,0.0064452374,0.003270973,0.0057962034],"category_scores_gemma":[0.12266248,0.001965941,0.0046268427,0.0040264996,0.0017922915,0.004494563,0.0038954497,0.006341776,0.0010964564],"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":[0.00049153826,0.00022531414,0.012698936,0.001000164,0.002357799,0.00086977927,0.0011003673,0.4910536,0.000630282,0.32908818,0.008690097,0.1517939],"study_design_scores_gemma":[0.00007752144,0.00008468102,0.00065330206,0.00015281534,0.00016352936,0.00012854431,0.00007588702,0.71561944,0.0003510245,0.27948007,0.0031508247,0.00006228387],"about_ca_topic_score_codex":0.005405979,"about_ca_topic_score_gemma":0.0061284653,"teacher_disagreement_score":0.05412706,"about_ca_system_score_codex":0.0015555745,"about_ca_system_score_gemma":0.0039484906,"threshold_uncertainty_score":0.28625464},"labels":[],"label_agreement":null},{"id":"W4391716692","doi":"10.1186/s12874-024-02157-x","title":"Model-based standardization using multiple imputation","year":2024,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","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":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Public Health Ontario; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; Parametric statistics; Computer science; Statistics; Outcome (game theory); Econometrics; Imputation (statistics); Nonparametric statistics; Standardization; Missing data; Data mining; Mathematics; Machine learning","score_opus":0.7575667403630337,"score_gpt":0.646695353916137,"score_spread":0.11087138644689676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391716692","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":[0.0018978375,0.00018761563,0.9966978,0.00012083481,0.000029179959,0.000090422414,0.0001644369,0.00035629378,0.00045559308],"genre_scores_gemma":[0.1561756,0.00067592063,0.8389982,0.00024672342,0.00015467308,0.00095214794,0.0016286178,0.0004401298,0.00072797236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96748996,0.023833767,0.0014785212,0.0030043223,0.0037035896,0.0004898464],"domain_scores_gemma":[0.9290455,0.04598196,0.0057076183,0.013809975,0.0050480072,0.00040690016],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04170361,0.0016051786,0.0030082988,0.003066577,0.00087842945,0.0027302022,0.004259877,0.0017742772,0.003297551],"category_scores_gemma":[0.09748232,0.0011670025,0.00397441,0.0051289843,0.0016523123,0.002739257,0.0037575336,0.003299103,0.001119133],"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":[0.0003945308,0.00026258646,0.02084658,0.0013318552,0.0023689463,0.0005129018,0.0007666169,0.45832402,0.0018686039,0.14871928,0.013798475,0.35080564],"study_design_scores_gemma":[0.00015465962,0.00012050258,0.003283331,0.00026949332,0.00029993447,0.0003239757,0.000060890477,0.7866465,0.0025344698,0.19561043,0.010599965,0.0000958479],"about_ca_topic_score_codex":0.0029898512,"about_ca_topic_score_gemma":0.0021743092,"teacher_disagreement_score":0.9582964,"about_ca_system_score_codex":0.0014518539,"about_ca_system_score_gemma":0.0042248657,"threshold_uncertainty_score":0.22055238},"labels":[],"label_agreement":null},{"id":"W4391729377","doi":"10.1007/978-3-031-17299-1_1821","title":"Missing Data","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.4149743218253579,"score_gpt":0.4483436850533523,"score_spread":0.03336936322799444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391729377","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":[0.0005292848,0.0055830227,0.75422484,0.0056985156,0.0018264393,0.00012813139,0.0028866625,0.0028038018,0.22631927],"genre_scores_gemma":[0.022076003,0.0095686205,0.34226078,0.0040896605,0.0022960582,0.000547628,0.006599748,0.002865213,0.6096963],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986721,0.00045254477,0.00006187344,0.00023642475,0.0005353656,0.000041590996],"domain_scores_gemma":[0.9949705,0.0033422406,0.00011272125,0.0009742331,0.0005152319,0.00008515319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028930907,0.0011660191,0.0012292366,0.0016612184,0.0008229786,0.0025700245,0.0021720375,0.0016594108,0.12711394],"category_scores_gemma":[0.012662999,0.0009272917,0.0008173559,0.0017964877,0.0012810089,0.0037451412,0.002005179,0.0034634366,0.07015559],"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":[0.000030880077,0.00003493433,0.00013611092,0.0003653258,0.000032230833,0.00008294074,0.00016691258,0.002435653,0.00032548234,0.3250207,0.34765005,0.32371867],"study_design_scores_gemma":[0.000008459001,0.0000148564495,0.00015112144,0.00022031236,0.000020759697,0.00026967018,0.000051630963,0.007205109,0.0006370328,0.49112678,0.5002709,0.000023271188],"about_ca_topic_score_codex":0.00092883967,"about_ca_topic_score_gemma":0.0015915397,"teacher_disagreement_score":0.12711394,"about_ca_system_score_codex":0.0008144959,"about_ca_system_score_gemma":0.0014454308,"threshold_uncertainty_score":0.4252385},"labels":[],"label_agreement":null},{"id":"W4391785744","doi":"10.1080/00273171.2024.2307034","title":"Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections","year":2024,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayes' theorem; Statistics; Cluster (spacecraft); Population; Cluster sampling; Mathematics; Econometrics; Confidence interval; Monte Carlo method; Sample size determination; Computer science; Bayesian probability; Demography","score_opus":0.5093859889745762,"score_gpt":0.5862934565112842,"score_spread":0.07690746753670796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391785744","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":[0.002224983,0.00016844353,0.99612087,0.00019977255,0.00007370432,0.00019278818,0.000046454068,0.00040498085,0.00056803547],"genre_scores_gemma":[0.07377206,0.0002031875,0.9223892,0.00037879017,0.00012811564,0.0012590028,0.00018601965,0.00031845295,0.0013651313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9472215,0.03867868,0.0017146704,0.006612885,0.0050884914,0.0006837078],"domain_scores_gemma":[0.762152,0.19556296,0.006886677,0.021446789,0.013190203,0.0007612544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08901306,0.0017587092,0.0039973916,0.0036799368,0.0025652763,0.0031292527,0.0070538656,0.0035071932,0.006574625],"category_scores_gemma":[0.32018316,0.0016258407,0.0030320424,0.0036048838,0.0041441713,0.004315745,0.00427345,0.0064109336,0.0012984074],"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":[0.0004462571,0.00027654748,0.019038353,0.00077946833,0.0016015144,0.00075204804,0.0029872668,0.25657004,0.000994063,0.37641323,0.0110504655,0.3290908],"study_design_scores_gemma":[0.00014466599,0.00013236348,0.003189388,0.00045186246,0.0003397873,0.00027129325,0.0002847864,0.7200138,0.0016600327,0.26202056,0.01137022,0.00012123107],"about_ca_topic_score_codex":0.018636407,"about_ca_topic_score_gemma":0.015998844,"teacher_disagreement_score":0.08901306,"about_ca_system_score_codex":0.0030466602,"about_ca_system_score_gemma":0.0058873114,"threshold_uncertainty_score":0.4707517},"labels":[],"label_agreement":null},{"id":"W4391841493","doi":"10.1002/sim.10012","title":"Statistical plasmode simulations–Potentials, challenges and recommendations","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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 Waterloo","funders":"","keywords":"Computer science; Variety (cybernetics); Key (lock); Statistical model; Set (abstract data type); Parametric statistics; Data science; Data set; Data mining; Machine learning; Theoretical computer science; Artificial intelligence; Statistics; Mathematics","score_opus":0.11468469702013184,"score_gpt":0.44976894827582375,"score_spread":0.3350842512556919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391841493","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":[0.006107831,0.08544374,0.60965776,0.26918727,0.0071158432,0.00031475312,0.0016781181,0.0058345846,0.014660095],"genre_scores_gemma":[0.0933444,0.15972848,0.70414585,0.021325767,0.007152439,0.0012891002,0.0028264339,0.0019755499,0.008211926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9904355,0.0066406815,0.00049106777,0.0006853323,0.0015201512,0.00022739064],"domain_scores_gemma":[0.88196886,0.09098778,0.0016250687,0.00743971,0.015057154,0.0029214479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027157973,0.001336861,0.0016012074,0.002471907,0.000925959,0.004600569,0.0055283,0.0044841063,0.011729208],"category_scores_gemma":[0.14496382,0.00076129136,0.0013419585,0.0022176302,0.0028153437,0.011050316,0.0036978335,0.0071792705,0.0059877755],"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":[0.00019502924,0.0002702937,0.003903078,0.0025601976,0.00018466414,0.00041193137,0.0003785297,0.07856375,0.0005856191,0.39915374,0.1487325,0.36506072],"study_design_scores_gemma":[0.000091124966,0.00006909341,0.0005144507,0.0027889372,0.00004694699,0.00022225085,0.00036532918,0.11835811,0.000782723,0.70743734,0.16916943,0.00015434543],"about_ca_topic_score_codex":0.0056408574,"about_ca_topic_score_gemma":0.0053022015,"teacher_disagreement_score":0.027157973,"about_ca_system_score_codex":0.001633886,"about_ca_system_score_gemma":0.0048953374,"threshold_uncertainty_score":0.14362681},"labels":[],"label_agreement":null},{"id":"W4391875809","doi":"10.1080/10705511.2023.2300079","title":"Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators","year":2024,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Latent variable; Pooling; Missing data; Latent variable model; Continuous variable; Computer science; Econometrics; Variable (mathematics); Latent class model; Statistics; Data mining; Data science; Artificial intelligence; Mathematics; Machine learning","score_opus":0.2921339497355178,"score_gpt":0.4098606816698565,"score_spread":0.11772673193433869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391875809","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":[0.0074820807,0.00086196314,0.9877879,0.002325681,0.00018670163,0.00031593954,0.00021941817,0.0002475266,0.0005728226],"genre_scores_gemma":[0.21882243,0.0011253286,0.77123886,0.002297614,0.00067164405,0.0037775764,0.0009671375,0.0002863598,0.00081307656],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8032315,0.16573341,0.008981796,0.011873521,0.008686141,0.0014936825],"domain_scores_gemma":[0.59042746,0.3389827,0.018726587,0.039912976,0.010120499,0.0018297652],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21755055,0.0026852626,0.004848236,0.003835574,0.0041930513,0.0064451736,0.008141919,0.0051706145,0.0044810227],"category_scores_gemma":[0.45309675,0.0025659916,0.0047442676,0.00915567,0.006177083,0.010762949,0.009577412,0.009286262,0.0008581529],"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":[0.001468101,0.0006576984,0.039754435,0.0051042307,0.0057732672,0.0022361383,0.017138088,0.061688304,0.0031682868,0.38256285,0.01639019,0.4640584],"study_design_scores_gemma":[0.00042935583,0.00051101664,0.0075074662,0.0014056974,0.0009759586,0.0009620638,0.0021383138,0.14690438,0.0036011995,0.8197262,0.015498552,0.00033988786],"about_ca_topic_score_codex":0.004816424,"about_ca_topic_score_gemma":0.0053749713,"teacher_disagreement_score":0.21755055,"about_ca_system_score_codex":0.0024690903,"about_ca_system_score_gemma":0.008326005,"threshold_uncertainty_score":0.9648995},"labels":[],"label_agreement":null},{"id":"W4391999235","doi":"10.1002/cjs.11804","title":"Censored autoregressive regression models with Student‐<i>t</i> innovations","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Censoring (clinical trials); Missing data; Robustness (evolution); Autoregressive model; Computer science; Statistics; Expectation–maximization algorithm; Robust regression; Censored regression model; Asymptotic distribution; Linear regression; Regression analysis; Econometrics; Least absolute deviations; Mathematics; Regression; Maximum likelihood; Estimator","score_opus":0.05392270525402309,"score_gpt":0.3536866070369255,"score_spread":0.2997639017829024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391999235","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":[0.014989084,0.0003115325,0.98293555,0.00020618431,0.000048668204,0.00005618336,0.00024765823,0.00034514396,0.0008600002],"genre_scores_gemma":[0.53789306,0.0012104232,0.4463006,0.00025887796,0.00018897734,0.0006357782,0.0019343732,0.00029508822,0.01128295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99638426,0.0021496941,0.00015449055,0.00060125766,0.0004921039,0.00021814789],"domain_scores_gemma":[0.9894637,0.007832732,0.0011491547,0.0006310173,0.0008129691,0.000110533474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009176825,0.0009462432,0.0015807706,0.0012238241,0.0005197,0.0018861917,0.00319666,0.0017459232,0.0041207364],"category_scores_gemma":[0.021621853,0.0006267444,0.0021059597,0.0022275564,0.0013344593,0.0014069772,0.001455733,0.0026490819,0.0014527058],"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":[0.00016690056,0.00011392761,0.0063920426,0.00026311414,0.00024050579,0.0002676722,0.00024214902,0.78431505,0.0007735508,0.13674666,0.002939563,0.06753887],"study_design_scores_gemma":[0.000015922988,0.00003602421,0.0008297398,0.000042776217,0.000028600927,0.000030489493,0.000039701717,0.96323276,0.00037619978,0.033408415,0.0019317721,0.000027556047],"about_ca_topic_score_codex":0.01463348,"about_ca_topic_score_gemma":0.011478413,"teacher_disagreement_score":0.01463348,"about_ca_system_score_codex":0.0011744804,"about_ca_system_score_gemma":0.0017419087,"threshold_uncertainty_score":0.048532248},"labels":[],"label_agreement":null},{"id":"W4392068153","doi":"10.51644/9780889207905-012","title":"Statistical Methods","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.11368180454747395,"score_gpt":0.4464216213050125,"score_spread":0.33273981675753855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392068153","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":[0.00026355663,0.0039569647,0.86303866,0.0030319549,0.0015432208,0.0002652086,0.001599196,0.0025565058,0.12374473],"genre_scores_gemma":[0.014861568,0.008074795,0.6357297,0.0051293606,0.0030872,0.0020277016,0.004326223,0.0033482898,0.32341516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949261,0.0021559775,0.00025247608,0.00067349174,0.0018646814,0.00012727213],"domain_scores_gemma":[0.99109566,0.005185645,0.0002449955,0.0018505588,0.001466004,0.00015705782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005936526,0.0014794278,0.0015081614,0.0026733724,0.0007611154,0.00279092,0.002024916,0.0016060307,0.092446975],"category_scores_gemma":[0.018049696,0.00089771784,0.0010960867,0.0018938917,0.0017796322,0.0018714453,0.0017446346,0.0036730426,0.06617369],"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":[0.000019527144,0.000043415177,0.00021768168,0.0004560352,0.000064674365,0.000063831096,0.00015391651,0.0015991671,0.0004230337,0.36281252,0.3004812,0.33366495],"study_design_scores_gemma":[0.000015645528,0.000019156289,0.000282213,0.00023814473,0.000030942214,0.00024540426,0.00004147157,0.0038358152,0.00052489486,0.44827354,0.5464703,0.000022468537],"about_ca_topic_score_codex":0.0011518855,"about_ca_topic_score_gemma":0.0020231872,"teacher_disagreement_score":0.092446975,"about_ca_system_score_codex":0.0011373293,"about_ca_system_score_gemma":0.0028443104,"threshold_uncertainty_score":0.30926597},"labels":[],"label_agreement":null},{"id":"W4392297813","doi":"10.5539/ijsp.v13n1p22","title":"Comparisons of the Satterthwaite Approaches for Fixed Effects in Linear Mixed Models","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics","score_opus":0.11737985143433571,"score_gpt":0.37130214449397253,"score_spread":0.2539222930596368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392297813","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":[0.01371573,0.0016939393,0.98135746,0.00039639964,0.00019700252,0.00071644585,0.00018305096,0.00026021723,0.0014796729],"genre_scores_gemma":[0.09170496,0.0015351287,0.9014558,0.0002991505,0.00011774029,0.0030164102,0.0003684263,0.00023216974,0.0012702672],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.76250994,0.21017018,0.0059094676,0.0074425978,0.012944655,0.0010232182],"domain_scores_gemma":[0.39880317,0.5766469,0.0056284047,0.010194203,0.007980233,0.00074710336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15178037,0.0028352502,0.0044296435,0.006708186,0.0016201894,0.0036395888,0.005944546,0.0041556437,0.006313081],"category_scores_gemma":[0.4793596,0.0018840868,0.006963229,0.0063328934,0.00448785,0.0086815525,0.0054943585,0.006395504,0.0010394308],"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":[0.0038779543,0.0004725622,0.015384369,0.0024068116,0.007260186,0.00030105823,0.003132665,0.16058265,0.0010358243,0.2564963,0.0033565029,0.54569304],"study_design_scores_gemma":[0.0013005403,0.0044628293,0.014219984,0.001016412,0.002768639,0.0005846089,0.0012386761,0.66093457,0.0024149835,0.2998908,0.010603545,0.00056439603],"about_ca_topic_score_codex":0.006276094,"about_ca_topic_score_gemma":0.011318561,"teacher_disagreement_score":0.15178037,"about_ca_system_score_codex":0.0047169,"about_ca_system_score_gemma":0.005090582,"threshold_uncertainty_score":0.8027009},"labels":[],"label_agreement":null},{"id":"W4392742682","doi":"10.11124/jbies-23-00078","title":"Meta-analysis on studies with heterogeneous and partially observed covariates","year":2024,"lang":"en","type":"article","venue":"JBI Evidence Synthesis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Bill and Melinda Gates Foundation","keywords":"Covariate; Meta-analysis; Statistics; Econometrics; Medicine; Mathematics; Internal medicine","score_opus":0.2710077837538931,"score_gpt":0.421237829102026,"score_spread":0.1502300453481329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392742682","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":[0.011827999,0.6516012,0.31814462,0.0041371547,0.0021231931,0.0028890506,0.0057352027,0.0009323234,0.002609268],"genre_scores_gemma":[0.46932018,0.21092428,0.28967032,0.006122872,0.0016522848,0.0143169835,0.00545103,0.0005486994,0.0019934238],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86673015,0.11138208,0.010768586,0.0064530475,0.0041453196,0.00052082044],"domain_scores_gemma":[0.83257335,0.1484428,0.0077119567,0.009152255,0.0018309205,0.00028876527],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.107011005,0.0035683517,0.013880092,0.010712767,0.00074069656,0.004635985,0.0033728308,0.0028366551,0.0037930321],"category_scores_gemma":[0.2327016,0.001746737,0.035122473,0.009394613,0.001267026,0.0027187376,0.0026791312,0.0036877196,0.00046148134],"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":[0.0012651348,0.000044435874,0.0055447333,0.06972041,0.85626024,0.00031958421,0.00015102225,0.0109173525,0.0005911136,0.0069368877,0.00283393,0.045415096],"study_design_scores_gemma":[0.0019036846,0.00055496366,0.0052332166,0.020137414,0.8861164,0.0003311659,0.00010632664,0.011071886,0.0011566622,0.057182007,0.016083954,0.00012234831],"about_ca_topic_score_codex":0.0031283293,"about_ca_topic_score_gemma":0.0040604384,"teacher_disagreement_score":0.892989,"about_ca_system_score_codex":0.0022939448,"about_ca_system_score_gemma":0.0037830614,"threshold_uncertainty_score":0.565935},"labels":[],"label_agreement":null},{"id":"W4392771202","doi":"10.5539/ijsp.v13n1p67","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 1","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Probability and statistics; Mathematics; Mathematical economics","score_opus":0.06044676153257104,"score_gpt":0.3959962724048539,"score_spread":0.33554951087228285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392771202","genre_codex":"editorial","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":[0.00012295542,0.002858937,0.0016091531,0.13670193,0.8548562,0.0005606595,0.000820163,0.0005526191,0.0019174134],"genre_scores_gemma":[0.004405745,0.006970083,0.004575927,0.16539697,0.7710976,0.0035661154,0.0018468854,0.0017951095,0.04034569],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.91864645,0.016446736,0.017264616,0.005858929,0.03925071,0.0025326035],"domain_scores_gemma":[0.12092105,0.04406711,0.012354822,0.006748422,0.80741864,0.008490017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.063423604,0.0033589355,0.0103230225,0.013394702,0.0052523846,0.0115276985,0.006155451,0.01779908,0.0924701],"category_scores_gemma":[0.5712856,0.001966011,0.0062221917,0.005991114,0.004100046,0.0073489635,0.004417426,0.014392526,0.06443189],"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":[0.00003483443,0.0000043495866,0.00007828708,0.00043006922,0.000012008862,0.000049054852,0.000035350713,0.000011411159,0.000033155062,0.00012200237,0.99588555,0.003303938],"study_design_scores_gemma":[0.00032426647,0.00007058359,0.0014808311,0.0051011825,0.0001667566,0.0013059685,0.00045690738,0.00058671006,0.00038147633,0.0030216575,0.9868674,0.00023634349],"about_ca_topic_score_codex":0.0035997168,"about_ca_topic_score_gemma":0.00557142,"teacher_disagreement_score":0.0924701,"about_ca_system_score_codex":0.005706524,"about_ca_system_score_gemma":0.012411855,"threshold_uncertainty_score":0.33542007},"labels":[],"label_agreement":null},{"id":"W4392871975","doi":"10.1214/24-ejs2226","title":"A functional nonlinear mixed effects modeling framework for longitudinal functional responses","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute on Aging; National Institutes of Health; Alberta Machine Intelligence Institute","keywords":"Mathematics; Nonlinear system; Mixed model; Applied mathematics; Functional response; Generalized linear mixed model; Econometrics; Statistics; Ecology","score_opus":0.06961412931664702,"score_gpt":0.3757605998570907,"score_spread":0.3061464705404437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392871975","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":[0.00080435077,0.00011878592,0.99837095,0.00017057653,0.000021465139,0.000031901873,0.00015423303,0.00006304547,0.00026466645],"genre_scores_gemma":[0.115506805,0.0010532815,0.8756575,0.00047929448,0.00026198654,0.0016538646,0.0008828855,0.00018433829,0.0043200273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99538714,0.003281696,0.00014744236,0.0006788529,0.0003694289,0.0001354647],"domain_scores_gemma":[0.99378234,0.0046676574,0.00046225102,0.00050248276,0.000476329,0.00010896247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01335966,0.0016103627,0.0012665241,0.0017426743,0.00059118326,0.0014936976,0.0035298145,0.0018321284,0.004079705],"category_scores_gemma":[0.01979847,0.00073408586,0.0024735513,0.0014643715,0.0013944115,0.0018863569,0.0018015219,0.0025950058,0.0007731852],"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":[0.00012190779,0.00010785239,0.0044072694,0.00035972983,0.0006199767,0.00030688176,0.0004015293,0.30035326,0.0028126286,0.6088625,0.003535034,0.07811144],"study_design_scores_gemma":[0.000031411644,0.00012102723,0.0009994413,0.000063851476,0.00013665536,0.00013675746,0.000051946146,0.7894795,0.0005666339,0.20003568,0.008330159,0.000047019657],"about_ca_topic_score_codex":0.0069655864,"about_ca_topic_score_gemma":0.0074202237,"teacher_disagreement_score":0.01335966,"about_ca_system_score_codex":0.0013098919,"about_ca_system_score_gemma":0.0023692811,"threshold_uncertainty_score":0.0706535},"labels":[],"label_agreement":null},{"id":"W4392957163","doi":"10.1177/1536867x241233671","title":"A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling","year":2024,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multinomial distribution; Computer science; Bayesian probability; Estimation; Statistics; Data mining; Econometrics; Data science; Machine learning; Artificial intelligence; Mathematics; Engineering","score_opus":0.32862457902786585,"score_gpt":0.49844971980345915,"score_spread":0.1698251407755933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392957163","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":[0.00038199913,0.00006258026,0.9983584,0.00014783455,0.000026481726,0.000032675813,0.00009355267,0.000631999,0.00026441185],"genre_scores_gemma":[0.010813324,0.00014574692,0.9864818,0.00022298312,0.000077150245,0.00044230898,0.0002955534,0.00040665735,0.0011144967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98992085,0.0066739847,0.0005486863,0.00081492355,0.001868743,0.00017273877],"domain_scores_gemma":[0.97145635,0.022834774,0.0011848259,0.0018460439,0.0022785068,0.00039949644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016821846,0.0014075923,0.001351355,0.0030930599,0.0013505445,0.0016889748,0.0029409889,0.0019578747,0.013925001],"category_scores_gemma":[0.08519187,0.0011056162,0.0014870217,0.0027493578,0.0011693481,0.003161188,0.0036704976,0.0038332446,0.0048265345],"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":[0.0002721402,0.00020990775,0.007996083,0.00038439018,0.00032296512,0.00031213067,0.0010006105,0.05753862,0.0022975183,0.2661633,0.03928047,0.62422186],"study_design_scores_gemma":[0.0001398524,0.00008637257,0.0020186838,0.00022440997,0.00008678327,0.00063356204,0.00011605998,0.6008941,0.002817431,0.34278366,0.050057683,0.00014138695],"about_ca_topic_score_codex":0.0059287897,"about_ca_topic_score_gemma":0.0064044944,"teacher_disagreement_score":0.016821846,"about_ca_system_score_codex":0.00095698755,"about_ca_system_score_gemma":0.0029504169,"threshold_uncertainty_score":0.08896351},"labels":[],"label_agreement":null},{"id":"W4392962422","doi":"10.1007/s40300-024-00270-x","title":"Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources: tools and real data applications for official statistics”","year":2024,"lang":"en","type":"article","venue":"METRON","topic":"Statistical Methods and Bayesian Inference","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":"Statistics Canada","funders":"","keywords":"Data science; Computer science; Official statistics; Statistics; Data mining; Mathematics","score_opus":0.4188720226164224,"score_gpt":0.5374797152344185,"score_spread":0.11860769261799609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392962422","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005861203,0.008636911,0.0040787547,0.06826025,0.91093874,0.000042713807,0.0005152699,0.0002880133,0.0071807904],"genre_scores_gemma":[0.00086949515,0.007487429,0.002027309,0.046556573,0.8608094,0.00013166593,0.00090244383,0.00094193965,0.080273755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956801,0.001080911,0.00044516288,0.0005077655,0.0020909524,0.00019512992],"domain_scores_gemma":[0.97218657,0.012079018,0.001630204,0.0012685234,0.010834162,0.002001512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056263795,0.0022503852,0.0033507915,0.0046930434,0.0020209528,0.0068596574,0.0023338434,0.0064497124,0.07045253],"category_scores_gemma":[0.027254533,0.0008264368,0.0019375328,0.003158579,0.0015189709,0.0051431456,0.002242455,0.010591036,0.072660364],"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":[0.0000059899853,0.0000040789837,0.000010452788,0.000042650645,0.0000043585424,0.000005865983,0.0000025238205,0.000011294928,0.000024427172,0.00057222747,0.99628145,0.0030346925],"study_design_scores_gemma":[0.000013527996,0.000018278783,0.00032563473,0.0002223344,0.000018759903,0.000054741748,0.000018590088,0.00026675905,0.00010503656,0.0056496165,0.9932892,0.000017489856],"about_ca_topic_score_codex":0.0018451684,"about_ca_topic_score_gemma":0.0041209557,"teacher_disagreement_score":0.07045253,"about_ca_system_score_codex":0.0020437702,"about_ca_system_score_gemma":0.0029727803,"threshold_uncertainty_score":0.23568726},"labels":[],"label_agreement":null},{"id":"W4392967216","doi":"10.1002/bimj.202200333","title":"Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial","year":2024,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"International Development Research Centre","keywords":"Bivariate analysis; Statistics; Random effects model; Mixed model; Causal inference; Pairwise comparison; Multivariate statistics; Piecewise; Crossover study; Randomized controlled trial; Mathematics; Econometrics; Medicine; Internal medicine; Meta-analysis","score_opus":0.2363451445879031,"score_gpt":0.4287995722546368,"score_spread":0.19245442766673368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392967216","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":[0.06803235,0.0014436414,0.925857,0.0007578819,0.00033256278,0.001719581,0.00059280015,0.00057000277,0.00069413905],"genre_scores_gemma":[0.5878273,0.0007122189,0.40107784,0.0006123017,0.00015622083,0.0066114487,0.00083334226,0.00015321387,0.0020161963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8383956,0.15184218,0.0020801933,0.0050276513,0.001792576,0.00086189137],"domain_scores_gemma":[0.8123899,0.16781582,0.009211862,0.0068569384,0.0026450378,0.0010805286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13487568,0.00213194,0.003826299,0.001767485,0.0006139559,0.0021181088,0.0043460554,0.002810176,0.008435147],"category_scores_gemma":[0.19708781,0.0017491749,0.006801599,0.0017256292,0.0015552352,0.0028746512,0.0025489938,0.005257634,0.0007144289],"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":[0.033988334,0.0014069881,0.042209562,0.0037010845,0.02132696,0.001069423,0.0015566129,0.5973912,0.0023360774,0.08329338,0.0037864977,0.20793387],"study_design_scores_gemma":[0.0018862525,0.00567836,0.0056765275,0.0002546117,0.0027219125,0.0001716195,0.00019698597,0.9385456,0.0009517592,0.041395877,0.002404777,0.00011566941],"about_ca_topic_score_codex":0.0020656893,"about_ca_topic_score_gemma":0.0016552182,"teacher_disagreement_score":0.13487568,"about_ca_system_score_codex":0.0014905476,"about_ca_system_score_gemma":0.0022728126,"threshold_uncertainty_score":0.71329933},"labels":[],"label_agreement":null},{"id":"W4393308845","doi":"10.1007/s00362-024-01534-4","title":"Bootstrapping generalized linear models to accommodate overdispersed count data","year":2024,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Statistical Methods and Bayesian 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":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bootstrapping (finance); Count data; Generalized linear model; Computer science; Econometrics; Statistics; Mathematics; Poisson distribution","score_opus":0.19405983639474508,"score_gpt":0.42972403172365853,"score_spread":0.23566419532891345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393308845","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":[0.0030705177,0.000118454824,0.9959785,0.00014011898,0.00006793796,0.000048791095,0.00006986609,0.00029665197,0.0002092393],"genre_scores_gemma":[0.09591315,0.00043815435,0.89755803,0.00046611944,0.00036598626,0.00094131095,0.00094956136,0.00079936534,0.0025682207],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.962276,0.030787982,0.0010718897,0.0027463632,0.0024969238,0.0006208823],"domain_scores_gemma":[0.8267797,0.1429239,0.00495297,0.020272711,0.0040522,0.0010184849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04588272,0.0018159361,0.0038199753,0.0035694744,0.002032533,0.0029330337,0.009255928,0.0033775223,0.0064775445],"category_scores_gemma":[0.21896224,0.0024742738,0.004179923,0.006678955,0.0032011028,0.004552858,0.0049870163,0.0072162724,0.002373805],"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":[0.00042361565,0.00027964287,0.004789864,0.0006006235,0.00105332,0.0009384796,0.0016924641,0.28046355,0.0015805905,0.44692162,0.0105450405,0.25071123],"study_design_scores_gemma":[0.00005649587,0.00005386603,0.00060095685,0.00007271196,0.000095308824,0.0001659982,0.0000844731,0.6391598,0.00033692128,0.35528487,0.0040437435,0.000044822613],"about_ca_topic_score_codex":0.0060864114,"about_ca_topic_score_gemma":0.0074770413,"teacher_disagreement_score":0.04588272,"about_ca_system_score_codex":0.0016206924,"about_ca_system_score_gemma":0.0036291853,"threshold_uncertainty_score":0.2426539},"labels":[],"label_agreement":null},{"id":"W4396508832","doi":"10.22215/etd/2024-15900","title":"Comparing Machine Reading Substitution To Alternative Statistical Treatment Methods For Left-Censored Data: Considerations To Limit Bias In Maternal-Infant Research On Environmental Chemicals (MIREC) Study","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"Carleton University; Institut National de Santé Publique du Québec","funders":"Health Canada","keywords":"Reading (process); Substitution (logic); Limit (mathematics); Statistics; Econometrics; Computer science; Mathematics; Political science; Law","score_opus":0.4579658357686932,"score_gpt":0.5716490773257126,"score_spread":0.11368324155701937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396508832","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":[0.05454754,0.0049141506,0.9027674,0.022348082,0.0023915363,0.0033231308,0.0005939075,0.00074969686,0.008364431],"genre_scores_gemma":[0.16749458,0.0013820073,0.8115385,0.007659202,0.0009033989,0.006361527,0.00031756106,0.0005360785,0.0038071605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.39601123,0.5706455,0.011017027,0.0039328337,0.017377974,0.0010155275],"domain_scores_gemma":[0.14533703,0.7895211,0.016126635,0.030260626,0.017563682,0.0011910042],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.37859905,0.0014736097,0.0042938534,0.0020390567,0.0015432385,0.003625242,0.0059935194,0.005376073,0.009682233],"category_scores_gemma":[0.6473745,0.0013621016,0.0055165025,0.0032656435,0.0048683393,0.0067820065,0.004066886,0.008749994,0.0011166508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.027267965,0.0019979964,0.017390909,0.0046236357,0.00844264,0.00041946713,0.0045567765,0.021078294,0.0029538572,0.12829013,0.027184073,0.7557942],"study_design_scores_gemma":[0.019158099,0.018290795,0.06577095,0.00836621,0.011469211,0.0015283049,0.0038521853,0.28677756,0.021053605,0.48433217,0.0782582,0.0011427028],"about_ca_topic_score_codex":0.0028608236,"about_ca_topic_score_gemma":0.0023457208,"teacher_disagreement_score":0.99713916,"about_ca_system_score_codex":0.0024945473,"about_ca_system_score_gemma":0.006151855,"threshold_uncertainty_score":0.766298},"labels":[],"label_agreement":null},{"id":"W4398235233","doi":"10.1177/09622802241254197","title":"Demystifying estimands in cluster-randomised trials","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"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 Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; Medical Research Council; National Institutes of Health; Patient-Centered Outcomes Research Institute","keywords":"Estimator; Cluster (spacecraft); Cluster randomised controlled trial; Consistency (knowledge bases); Econometrics; Statistics; Odds; Psychology; Computer science; Logistic regression; Mathematics; Artificial intelligence; Intervention (counseling); Psychiatry","score_opus":0.4900046249090602,"score_gpt":0.6860493952461615,"score_spread":0.19604477033710127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398235233","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":[0.0016494504,0.0056117186,0.98298395,0.0056091794,0.00085112476,0.0010180725,0.00021463091,0.00028185674,0.0017799282],"genre_scores_gemma":[0.08326042,0.0034574769,0.8982137,0.0047753965,0.0010268642,0.007818195,0.00030296153,0.00032273005,0.0008224086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.36055985,0.5604565,0.039620515,0.011773546,0.026120095,0.0014695683],"domain_scores_gemma":[0.20323025,0.7036929,0.03101403,0.050219405,0.011085919,0.00075749506],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.48826525,0.0023207357,0.0057734107,0.0065091425,0.0020789367,0.010798194,0.006848787,0.0065762354,0.00304871],"category_scores_gemma":[0.74871737,0.0027844526,0.006591957,0.007236928,0.014161032,0.017869147,0.00926322,0.016765827,0.00096155226],"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":[0.0005995094,0.00005474649,0.003133778,0.00596445,0.0018021311,0.0002598661,0.0030439673,0.013447937,0.00038477094,0.8324956,0.0059730764,0.13284011],"study_design_scores_gemma":[0.00031772768,0.0002187172,0.00075334375,0.0031904408,0.0005563795,0.00019225413,0.0002397879,0.019348633,0.0010497004,0.95518786,0.018814068,0.00013111504],"about_ca_topic_score_codex":0.0024840601,"about_ca_topic_score_gemma":0.0019770411,"teacher_disagreement_score":0.5117347,"about_ca_system_score_codex":0.0069510294,"about_ca_system_score_gemma":0.01153259,"threshold_uncertainty_score":0.63106006},"labels":[],"label_agreement":null},{"id":"W4398857059","doi":"10.7910/dvn/suv8dk","title":"Replication Data for: Assessing the Validity of Prevalence Estimates in Double List Experiments","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University","funders":"","keywords":"Replication (statistics); Computer science; Statistics; Psychology; Mathematics","score_opus":0.3021980780227468,"score_gpt":0.48335800031387827,"score_spread":0.18115992229113148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398857059","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017554262,0.00015624515,0.006213385,0.00045304932,0.0003010363,0.0016061157,0.98504454,0.0013973225,0.0030728246],"genre_scores_gemma":[0.014458119,0.00014461867,0.026701236,0.0012707276,0.00016797513,0.030731572,0.91408765,0.002382612,0.0100554945],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9841979,0.006615479,0.0027792149,0.003142123,0.0025653455,0.0006999573],"domain_scores_gemma":[0.8717507,0.062115785,0.0059367884,0.046256904,0.012305506,0.0016342883],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0278218,0.0024409043,0.0020892136,0.0024218939,0.0025910484,0.0030445145,0.0050308164,0.0036030656,0.18492755],"category_scores_gemma":[0.1883865,0.0014203712,0.0025571247,0.0042957864,0.0016953723,0.0017647679,0.0024585675,0.0038450197,0.078852914],"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":[0.00050841935,0.00014419392,0.0023550473,0.0014644412,0.00022407113,0.00007574632,0.00012125671,0.0005078614,0.0002858083,0.0023289549,0.9858159,0.006168324],"study_design_scores_gemma":[0.01274422,0.0003280652,0.019515907,0.0014598797,0.0009526831,0.00044706775,0.00021744231,0.0018172674,0.0022112988,0.02796257,0.9320542,0.00028933157],"about_ca_topic_score_codex":0.009118983,"about_ca_topic_score_gemma":0.020676825,"teacher_disagreement_score":0.9721782,"about_ca_system_score_codex":0.0017795287,"about_ca_system_score_gemma":0.0052862335,"threshold_uncertainty_score":0.6186443},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4399131742","doi":"10.1177/09622802241248382","title":"Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","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":"Ottawa Hospital; University of Ottawa","funders":"National Center for Advancing Translational Sciences; Patient-Centered Outcomes Research Institute; National Institute on Aging; National Institutes of Health; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Estimator; Random effects model; Statistics; Linear model; Mathematics; Mixed model; Robust statistics; Variance (accounting); Cluster (spacecraft); Computer science; Econometrics","score_opus":0.46295632730046754,"score_gpt":0.6140813913553269,"score_spread":0.15112506405485937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399131742","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":[0.0039357822,0.0013371357,0.99052304,0.0015999353,0.0001558528,0.0005541082,0.00031528654,0.00028210264,0.0012968408],"genre_scores_gemma":[0.21006708,0.002120715,0.7765323,0.0032450145,0.00044034887,0.0050923037,0.00082859,0.00043578434,0.0012378837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.73898304,0.21930675,0.010490582,0.015629834,0.014021653,0.0015681447],"domain_scores_gemma":[0.27451232,0.659463,0.01996073,0.03523287,0.010153524,0.00067760947],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2822117,0.0025264146,0.0054146014,0.0028707844,0.001725534,0.006643208,0.007861287,0.0061389417,0.0045368983],"category_scores_gemma":[0.7019325,0.0024941901,0.0058629503,0.003710583,0.00869045,0.008996081,0.00557448,0.008961902,0.001486008],"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":[0.0012484288,0.00021298422,0.016113218,0.0053136237,0.004932742,0.0011499799,0.0030269374,0.14445068,0.0011257074,0.6006798,0.0076594222,0.21408655],"study_design_scores_gemma":[0.00073399185,0.0005250638,0.0028467174,0.0017082316,0.0009437755,0.00045147273,0.00038352862,0.2264386,0.002164093,0.7546214,0.009003225,0.00017988306],"about_ca_topic_score_codex":0.0039803847,"about_ca_topic_score_gemma":0.0024664598,"teacher_disagreement_score":0.71778834,"about_ca_system_score_codex":0.0034929698,"about_ca_system_score_gemma":0.009136275,"threshold_uncertainty_score":0.8851608},"labels":[],"label_agreement":null},{"id":"W4399171742","doi":"10.5539/ijsp.v13n2p69","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 2","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability and statistics; Statistics; Mathematics","score_opus":0.058618638839225634,"score_gpt":0.39536225617823295,"score_spread":0.3367436173390073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399171742","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011874128,0.0025212013,0.0016762435,0.11886165,0.8730337,0.00053373934,0.0007232072,0.00057328155,0.0019582652],"genre_scores_gemma":[0.004221817,0.0058397776,0.004288021,0.1570958,0.7779361,0.0033057265,0.0016322745,0.0018110941,0.04386939],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9223212,0.015053039,0.015851904,0.005421337,0.03893143,0.002421127],"domain_scores_gemma":[0.1319398,0.03874792,0.01112092,0.0066861673,0.80268615,0.008819103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059127748,0.0036625615,0.010400655,0.012952451,0.0050020064,0.011940518,0.0060876347,0.018389842,0.0931521],"category_scores_gemma":[0.5353253,0.00205166,0.0063674613,0.0057443394,0.004075563,0.0073746997,0.0043501053,0.014198986,0.06551869],"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":[0.000035761346,0.0000043200653,0.000072071925,0.0003758407,0.000012308811,0.000053789037,0.000029790323,0.000011020181,0.000036412843,0.000108971486,0.99625504,0.0030046515],"study_design_scores_gemma":[0.00037535094,0.000076491044,0.0015302002,0.0045863106,0.00017173268,0.0015124558,0.00043372915,0.00070941716,0.00043619188,0.0029694063,0.9869495,0.00024926336],"about_ca_topic_score_codex":0.0034947642,"about_ca_topic_score_gemma":0.00519718,"teacher_disagreement_score":0.0931521,"about_ca_system_score_codex":0.0052142553,"about_ca_system_score_gemma":0.011311573,"threshold_uncertainty_score":0.31270117},"labels":[],"label_agreement":null},{"id":"W4399194852","doi":"10.5539/ijsp.v13n2p16","title":"Supplementing a Non-probability Sample With a Probability Sample to Predict the Finite Population Mean","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Sample (material); Estimator; Population; Sample size determination; Variance (accounting); Robustness (evolution); Random variable","score_opus":0.05169110544697883,"score_gpt":0.3607411924665252,"score_spread":0.3090500870195464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399194852","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":[0.008624365,0.000053587006,0.9903743,0.00020382724,0.000030574447,0.00005872587,0.0000628902,0.00012736925,0.0004642213],"genre_scores_gemma":[0.27354097,0.00030404906,0.72015727,0.00046199257,0.00022764556,0.0007737946,0.0006770772,0.00023707768,0.0036200657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968246,0.001796706,0.000117838106,0.0005323756,0.00055960927,0.00016886872],"domain_scores_gemma":[0.9596881,0.034231585,0.0011775013,0.0031163965,0.001410851,0.0003754923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0125948,0.0008017019,0.0014107045,0.0014699202,0.00076942967,0.0017557782,0.0025661795,0.00132463,0.0063753733],"category_scores_gemma":[0.060339406,0.00079777377,0.0013913183,0.0011957985,0.0022679693,0.003927734,0.0028310497,0.0027046867,0.00074217183],"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":[0.00031980642,0.00022889845,0.015682101,0.00032534025,0.00020594684,0.00057321537,0.0008241378,0.3716278,0.003263902,0.4837048,0.0033615027,0.11988257],"study_design_scores_gemma":[0.00002738433,0.000059204664,0.000994874,0.000034094555,0.000030280718,0.000062822975,0.00003801394,0.8932209,0.0010397197,0.10243225,0.0020373233,0.00002311212],"about_ca_topic_score_codex":0.0066698473,"about_ca_topic_score_gemma":0.0068899677,"teacher_disagreement_score":0.0125948,"about_ca_system_score_codex":0.0010576335,"about_ca_system_score_gemma":0.0021860402,"threshold_uncertainty_score":0.06660849},"labels":[],"label_agreement":null},{"id":"W4399262027","doi":"10.1002/sim.10126","title":"The effect of number of clusters and magnitude of within‐cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","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":"Sunnybrook Hospital; University of Toronto","funders":"Ministry of Long-Term Care; Canadian Institutes of Health Research; Ministry of Health, Ontario","keywords":"Estimator; Statistics; Homogeneity (statistics); Mathematics; Covariate; Confidence interval; Cluster (spacecraft); Econometrics; Proportional hazards model; Regression; Standard error; Regression analysis; Hazard ratio; Computer science","score_opus":0.25730499408334184,"score_gpt":0.5027874771247031,"score_spread":0.24548248304136122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399262027","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":[0.61097366,0.0036677802,0.3787345,0.0015154917,0.00023547692,0.0009817071,0.00042556165,0.0007657769,0.0027000192],"genre_scores_gemma":[0.9283232,0.00029348518,0.06985425,0.00019967566,0.000042399828,0.00050293043,0.00032699163,0.00015891469,0.0002981154],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.79176915,0.17567582,0.008968146,0.013442382,0.0078598885,0.0022846742],"domain_scores_gemma":[0.16419528,0.77987164,0.016752917,0.027335139,0.010239424,0.0016055264],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27790016,0.0017992097,0.0025045422,0.0030128474,0.0018521775,0.0035570308,0.0026314163,0.0038388239,0.0012710086],"category_scores_gemma":[0.60987854,0.0013011285,0.0043807495,0.0026159238,0.0049653444,0.0046563717,0.004469447,0.0033850232,0.0002847049],"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":[0.013057812,0.00066466565,0.5379377,0.001451137,0.013514752,0.00069915265,0.006144278,0.23056097,0.0030286016,0.022808857,0.0026968657,0.1674352],"study_design_scores_gemma":[0.002282951,0.0059200223,0.26943377,0.0012022151,0.006588152,0.00093469897,0.0028141309,0.6447001,0.011715782,0.04963664,0.0038797623,0.00089185685],"about_ca_topic_score_codex":0.0074714557,"about_ca_topic_score_gemma":0.0041558174,"teacher_disagreement_score":0.72209984,"about_ca_system_score_codex":0.0019428743,"about_ca_system_score_gemma":0.004193131,"threshold_uncertainty_score":0.89047766},"labels":[],"label_agreement":null},{"id":"W4399739403","doi":"10.5772/intechopen.115069","title":"Decorrelation and Imputation Methods for Multivariate Modeling","year":2024,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Statistical Methods and Bayesian 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 Alberta","funders":"","keywords":"Decorrelation; Multivariate statistics; Computer science; Imputation (statistics); Statistics; Mathematics; Algorithm; Missing data; Machine learning","score_opus":0.1312166180916879,"score_gpt":0.4611152190800255,"score_spread":0.3298986009883376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399739403","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":[0.00020230717,0.0017063654,0.9942456,0.00039913057,0.00013681258,0.000028594477,0.000191256,0.00037513627,0.0027147622],"genre_scores_gemma":[0.013876169,0.007587403,0.96436954,0.00060702785,0.00075400225,0.00057811936,0.0013390174,0.00089476473,0.009993939],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942226,0.003261217,0.00029928496,0.0008534445,0.0012316921,0.00013178393],"domain_scores_gemma":[0.9900752,0.0069836318,0.000548572,0.0012754322,0.0010093073,0.000107779546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007632174,0.0019560533,0.0019504767,0.0021354295,0.0009484754,0.002965759,0.003278642,0.002046763,0.016291944],"category_scores_gemma":[0.022385387,0.0011326084,0.0031831765,0.0047151926,0.0016771794,0.002725632,0.003929755,0.0048012673,0.009015878],"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":[0.000043429598,0.00006126103,0.0010438852,0.0008725715,0.00037770043,0.00026188703,0.00036566387,0.0612126,0.0008279362,0.5293255,0.042337652,0.3632699],"study_design_scores_gemma":[0.000021174692,0.000031023817,0.0005894317,0.00038119793,0.000072914074,0.0003697039,0.00007404762,0.22132978,0.0010298858,0.6470534,0.12895997,0.00008748471],"about_ca_topic_score_codex":0.0023637929,"about_ca_topic_score_gemma":0.0031699047,"teacher_disagreement_score":0.016291944,"about_ca_system_score_codex":0.0011696501,"about_ca_system_score_gemma":0.00196705,"threshold_uncertainty_score":0.05450201},"labels":[],"label_agreement":null},{"id":"W4400013741","doi":"10.1007/s13171-024-00362-w","title":"Inferences for Fixed Effects Based Regression Parameters in a Finite Population Setup Using Two-stage Cluster Sample","year":2024,"lang":"en","type":"article","venue":"Sankhya A","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Stage (stratigraphy); Statistics; Sample (material); Cluster (spacecraft); Cluster sampling; Population; Mathematics; Regression analysis; Regression; Econometrics; Computer science; Demography; Physics; Biology; Sociology","score_opus":0.11840943709129342,"score_gpt":0.44082442889246787,"score_spread":0.32241499180117444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400013741","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":[0.028874667,0.00021545158,0.9693206,0.00029525257,0.000049477927,0.00016162806,0.00025195302,0.00018999324,0.00064104376],"genre_scores_gemma":[0.42794544,0.0005182111,0.56463563,0.00043381605,0.00016245266,0.0011242507,0.0011428103,0.00026033382,0.003777143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9721833,0.021246672,0.0005836212,0.0043424396,0.0010951569,0.000548808],"domain_scores_gemma":[0.72556776,0.25422186,0.003868801,0.013035648,0.0022950368,0.0010108621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041648485,0.0016244086,0.004458339,0.00269073,0.0017787971,0.0033682191,0.0065723923,0.0037294908,0.0064528943],"category_scores_gemma":[0.15326263,0.0026362701,0.004445861,0.002441817,0.0044589527,0.0058348775,0.00334329,0.0068142596,0.00062499545],"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":[0.0015012743,0.0007338017,0.02015296,0.0009397432,0.0033059,0.0012330723,0.0018679039,0.24556652,0.0021336605,0.64330995,0.0045358213,0.07471938],"study_design_scores_gemma":[0.00031629417,0.0002018967,0.0044447384,0.000101223726,0.0004942881,0.0002378625,0.0002147658,0.58818823,0.0009799126,0.403367,0.0013572588,0.00009656311],"about_ca_topic_score_codex":0.012969362,"about_ca_topic_score_gemma":0.011282578,"teacher_disagreement_score":0.041648485,"about_ca_system_score_codex":0.00215362,"about_ca_system_score_gemma":0.0027390213,"threshold_uncertainty_score":0.22026092},"labels":[],"label_agreement":null},{"id":"W4400033337","doi":"10.1007/s00180-024-01518-w","title":"Multiple imputation with competing risk outcomes","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian 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":"Institute for Work & Health; Institute for Clinical Evaluative Sciences; Institute of Health Services and Policy Research; Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Missing data; Imputation (statistics); Covariate; Multivariate statistics; Hazard ratio; Statistics; Proportional hazards model; Computer science; Data mining; Econometrics; Mathematics; Confidence interval","score_opus":0.039303665338239,"score_gpt":0.3648368038649195,"score_spread":0.32553313852668053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400033337","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":[0.0013183492,0.0020818315,0.98594946,0.00082813797,0.00037663462,0.00075219636,0.004847238,0.002307018,0.0015390788],"genre_scores_gemma":[0.05045425,0.0019073504,0.9259025,0.0010630937,0.00048021824,0.005222443,0.010273014,0.0016995537,0.0029974985],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91649777,0.06911945,0.004550263,0.0053421883,0.0036643075,0.0008259889],"domain_scores_gemma":[0.84413326,0.12610526,0.0077741793,0.015506758,0.0056592296,0.0008213292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07599575,0.0027176025,0.0055822195,0.0041243993,0.0011505339,0.0048940205,0.007481856,0.0046356926,0.040095452],"category_scores_gemma":[0.20252332,0.002375163,0.010532927,0.0091361385,0.0012259954,0.0027847188,0.004908502,0.006940656,0.008048081],"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":[0.0021437,0.00047334222,0.023849117,0.008588942,0.013165598,0.0010726353,0.0010767502,0.11721524,0.00056709157,0.15882307,0.088976376,0.5840481],"study_design_scores_gemma":[0.00201866,0.0007415973,0.0057290955,0.0028658868,0.0033797505,0.0014597202,0.00020114603,0.5178634,0.0017227508,0.35160175,0.112001434,0.00041479626],"about_ca_topic_score_codex":0.0040579094,"about_ca_topic_score_gemma":0.004129579,"teacher_disagreement_score":0.07599575,"about_ca_system_score_codex":0.001655408,"about_ca_system_score_gemma":0.0062975427,"threshold_uncertainty_score":0.40190876},"labels":[],"label_agreement":null},{"id":"W4400148214","doi":"10.1002/cjs.11812","title":"Order‐restricted hypothesis tests for nonlinear mixed‐effects models with measurement errors in covariates","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","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":true,"route_about_ca":false,"ca_institutions":"University of British Columbia; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Nonlinear system; Econometrics; Statistics; Mathematics; Order (exchange); Economics; Physics","score_opus":0.1008204416341227,"score_gpt":0.3169984502068436,"score_spread":0.2161780085727209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400148214","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":[0.02152736,0.0003092516,0.97605735,0.0005336517,0.00006689553,0.00022534029,0.0001869616,0.00021958668,0.00087352796],"genre_scores_gemma":[0.45481518,0.0003947488,0.5406732,0.00039012847,0.00020766401,0.0015921278,0.00048465186,0.00010740639,0.0013349622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8983264,0.091071546,0.00191756,0.003679153,0.0041853013,0.0008199929],"domain_scores_gemma":[0.38592747,0.58729666,0.009444726,0.011718988,0.0045367097,0.0010754184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.105974756,0.0014820012,0.0031238727,0.0033259366,0.0013559388,0.0029261003,0.0049562403,0.0025411607,0.0082291635],"category_scores_gemma":[0.34032658,0.0010515908,0.0032208767,0.0033023707,0.0058142208,0.0051169423,0.0035848473,0.004520586,0.0006050166],"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":[0.0014150444,0.0005349148,0.024834784,0.0012651769,0.002701439,0.0012340837,0.0015738964,0.2199774,0.0013408965,0.55582654,0.0042302106,0.18506554],"study_design_scores_gemma":[0.00034050486,0.00046522458,0.0035278099,0.00014350336,0.00023150112,0.00018801827,0.00022589073,0.62207085,0.0009072919,0.37014705,0.0016769101,0.0000754914],"about_ca_topic_score_codex":0.00333291,"about_ca_topic_score_gemma":0.0034208829,"teacher_disagreement_score":0.105974756,"about_ca_system_score_codex":0.001850758,"about_ca_system_score_gemma":0.004225626,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4400387057","doi":"10.1002/cjs.11810","title":"Estimating the mean squared prediction error of the observed best predictor associated with small area counts: A computationally oriented approach","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Linearization; Mean squared error; Estimation; Computer science; Context (archaeology); Mean squared prediction error; Small area estimation; Statistics; Mathematics; Algorithm; Nonlinear system","score_opus":0.10128979901806785,"score_gpt":0.29022013890078996,"score_spread":0.18893033988272212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400387057","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":[0.028873105,0.00043924002,0.9694158,0.00045766844,0.000035135563,0.000029935758,0.000069434594,0.00013205367,0.00054755295],"genre_scores_gemma":[0.743119,0.0008633386,0.25279304,0.00023440171,0.00024776818,0.00020642683,0.0004568538,0.000108182365,0.0019710343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99613804,0.0025499947,0.00016435068,0.00050435856,0.0005065714,0.00013666949],"domain_scores_gemma":[0.9392508,0.055142056,0.0020021265,0.0016883326,0.0015572995,0.0003593127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009958947,0.000929524,0.0017065232,0.001522465,0.00047544533,0.0012863232,0.0021960398,0.0012973049,0.0018380949],"category_scores_gemma":[0.058517545,0.0006704358,0.0006752264,0.0016409259,0.001649713,0.0019177729,0.002285386,0.0018328992,0.00041635634],"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":[0.00023248896,0.00011270567,0.010909567,0.00024310833,0.00020161942,0.000208958,0.00016264015,0.853678,0.0015436248,0.044958126,0.0016876543,0.08606145],"study_design_scores_gemma":[0.00000981719,0.00003554427,0.0009115918,0.000025266296,0.000013548885,0.000031257157,0.000030224544,0.9797416,0.00035713532,0.018570336,0.0002599367,0.00001380426],"about_ca_topic_score_codex":0.004381126,"about_ca_topic_score_gemma":0.0029696275,"teacher_disagreement_score":0.009958947,"about_ca_system_score_codex":0.0007451735,"about_ca_system_score_gemma":0.0014885553,"threshold_uncertainty_score":0.05266863},"labels":[],"label_agreement":null},{"id":"W4400672891","doi":"10.1002/sim.10172","title":"Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Nonparametric statistics; Computer science; Receiver operating characteristic; Software; Multivariate statistics; Algorithm; Statistics; Data mining; Mathematics; Machine learning","score_opus":0.15550918662417176,"score_gpt":0.44116230951500207,"score_spread":0.28565312289083034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400672891","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":[0.0019749794,0.00021734514,0.9963911,0.00015190829,0.000048885428,0.00029141022,0.00016939257,0.00031669013,0.00043835893],"genre_scores_gemma":[0.031537108,0.00025556626,0.96439636,0.00024872637,0.000065588036,0.0017299249,0.00045195705,0.00040495498,0.00090984843],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9318371,0.055872437,0.0031456996,0.0040276325,0.004428896,0.0006882421],"domain_scores_gemma":[0.809582,0.15437475,0.010417666,0.013389254,0.0113180075,0.0009183594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06604241,0.0014927542,0.0028321568,0.006082928,0.001139928,0.0030118693,0.0031694514,0.0015430945,0.0073099886],"category_scores_gemma":[0.2144182,0.0013621438,0.0029309206,0.006600841,0.0023907674,0.003274464,0.004280478,0.0044431435,0.0018431006],"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":[0.0012134351,0.00033844987,0.018973725,0.0016489988,0.0019792921,0.00047309036,0.001703368,0.057400014,0.0015572116,0.14051422,0.015055062,0.7591431],"study_design_scores_gemma":[0.0006976869,0.0009919866,0.00997146,0.0007291464,0.0005430892,0.0013806206,0.0005892912,0.53082824,0.003446192,0.39848924,0.05196308,0.0003700362],"about_ca_topic_score_codex":0.0064399573,"about_ca_topic_score_gemma":0.0056370576,"teacher_disagreement_score":0.06604241,"about_ca_system_score_codex":0.0015906685,"about_ca_system_score_gemma":0.0061995755,"threshold_uncertainty_score":0.3492698},"labels":[],"label_agreement":null},{"id":"W4400797224","doi":"10.1007/s00180-024-01532-y","title":"The root-Gaussian Cox Process for spatial-temporal disease mapping with aggregated data","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Gaussian process; Cox process; Process (computing); Computer science; Gaussian; Mathematics; Artificial intelligence; Data mining; Statistics; Poisson process; Physics","score_opus":0.10298312571674444,"score_gpt":0.40186985875700587,"score_spread":0.2988867330402614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400797224","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":[0.003003474,0.00030898038,0.9959805,0.00018913006,0.00004561113,0.000035341207,0.0001509318,0.000096375414,0.00018967249],"genre_scores_gemma":[0.384932,0.0030846852,0.60209405,0.0004687505,0.0007012403,0.0012460533,0.0021630302,0.00023310444,0.0050770803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9933524,0.004269113,0.00027659695,0.001141345,0.00069835695,0.0002622366],"domain_scores_gemma":[0.9640937,0.028945427,0.0020816836,0.0027970017,0.001603123,0.0004790486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021095179,0.00095544383,0.0016780205,0.0024911691,0.00076469703,0.0019041328,0.0033814686,0.0016613266,0.002734798],"category_scores_gemma":[0.04297506,0.00072275166,0.0022458972,0.0036553612,0.002430796,0.0028446093,0.0026385428,0.003526512,0.0006109323],"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":[0.00015538913,0.000050539536,0.0083966805,0.00035102872,0.00036600282,0.00046593384,0.00039703245,0.4051458,0.00078455074,0.53141314,0.0030948666,0.04937901],"study_design_scores_gemma":[0.000019757224,0.000042542564,0.00078427687,0.000027246926,0.000042059524,0.000066542656,0.000036546546,0.83305913,0.00022636382,0.1629833,0.0026830453,0.000029174484],"about_ca_topic_score_codex":0.009271043,"about_ca_topic_score_gemma":0.0057593375,"teacher_disagreement_score":0.021095179,"about_ca_system_score_codex":0.0014270874,"about_ca_system_score_gemma":0.0026270545,"threshold_uncertainty_score":0.111563265},"labels":[],"label_agreement":null},{"id":"W4401083508","doi":"10.1007/978-3-031-40846-5_104","title":"How Markov’s Little Idea Transformed Statistics","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Markov chain; Statistics; Mathematics; Computer science; Econometrics","score_opus":0.058535890323343893,"score_gpt":0.3383073654084962,"score_spread":0.2797714750851523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401083508","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":[0.0019152674,0.006776003,0.8364644,0.01059867,0.0022959395,0.000032976473,0.00035835095,0.00048310272,0.14107524],"genre_scores_gemma":[0.21579607,0.013827302,0.42902148,0.010090254,0.005611761,0.00040142928,0.00062047143,0.002547153,0.3220841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790823,0.001100667,0.00007338658,0.0002486489,0.0006028668,0.00006613707],"domain_scores_gemma":[0.9949156,0.0041182507,0.00010899574,0.00040904304,0.0003807831,0.0000672251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029714648,0.00088975864,0.00092779024,0.0016290506,0.0010522853,0.0035786955,0.0011168342,0.0020361247,0.013833465],"category_scores_gemma":[0.01363638,0.00094390113,0.0008128858,0.0016551717,0.0057060616,0.005822189,0.001373942,0.0057539525,0.0048137372],"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":[0.000003302069,0.000004163527,0.000023358738,0.000025541378,0.0000060917187,0.00000921876,0.00006374694,0.00097016286,0.00005925746,0.98026454,0.0075647314,0.0110058],"study_design_scores_gemma":[0.0000016583391,0.0000020220275,0.000019067818,0.00001651237,0.0000027515919,0.000019082863,0.00000891208,0.0024918613,0.00007678471,0.977816,0.019538648,0.000006795372],"about_ca_topic_score_codex":0.0033839182,"about_ca_topic_score_gemma":0.0032901075,"teacher_disagreement_score":0.013833465,"about_ca_system_score_codex":0.0027308161,"about_ca_system_score_gemma":0.0017161964,"threshold_uncertainty_score":0.046277523},"labels":[],"label_agreement":null},{"id":"W4401382024","doi":"10.1139/cjfr-2024-0039","title":"Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Estimation; Sample (material); Stage (stratigraphy); Mathematics; Econometrics; Forestry; Environmental science; Geography; Geology; Engineering","score_opus":0.31091349005835994,"score_gpt":0.4711122492831865,"score_spread":0.16019875922482657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401382024","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":[0.045319043,0.000051117826,0.95399654,0.000026159973,0.0000097704215,0.00030825465,0.000063681444,0.00007133616,0.00015413946],"genre_scores_gemma":[0.3212407,0.00008815944,0.6761193,0.00005526034,0.0000151298855,0.0016458357,0.00027019542,0.000021590417,0.00054384285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96587735,0.029512882,0.00062978145,0.0021255957,0.0015348993,0.00031943037],"domain_scores_gemma":[0.93552774,0.050237745,0.0045414576,0.0069396053,0.0024060488,0.00034735948],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.043312173,0.00097298226,0.0015949741,0.0009660555,0.0004164808,0.0010781992,0.001981617,0.0013560047,0.0008573699],"category_scores_gemma":[0.08458079,0.0016859495,0.0017554848,0.00094352034,0.0014364148,0.001982882,0.0018722498,0.0010869773,0.00015280549],"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":[0.0022438387,0.0007229981,0.04810306,0.00057811494,0.0012941685,0.00015735617,0.00082272914,0.7077505,0.008730967,0.07631843,0.0006245598,0.15265323],"study_design_scores_gemma":[0.0003673424,0.0015632478,0.008647321,0.000040973027,0.00019178735,0.00007977347,0.0000452466,0.9514967,0.0029632668,0.03350553,0.0010024082,0.00009649313],"about_ca_topic_score_codex":0.0024041976,"about_ca_topic_score_gemma":0.0042005484,"teacher_disagreement_score":0.9566878,"about_ca_system_score_codex":0.001067437,"about_ca_system_score_gemma":0.001886022,"threshold_uncertainty_score":0.2290594},"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,"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":"Computer science; Sampling (signal processing); Weighting; Population; Credibility; Econometrics; Field (mathematics); Estimator; Data mining; Statistics; Economics; Mathematics","score_opus":0.12848584978147398,"score_gpt":0.4582514481752856,"score_spread":0.32976559839381164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401399991","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":[0.016721776,0.00009355663,0.9813409,0.0002553654,0.00002831538,0.0000493643,0.000020244195,0.00006930712,0.0014212182],"genre_scores_gemma":[0.6915467,0.00023694642,0.30580428,0.00015143825,0.000107517466,0.00017064177,0.00007751516,0.000064519925,0.0018404635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99291986,0.004864998,0.00021344206,0.0006292303,0.0011400953,0.00023244273],"domain_scores_gemma":[0.9804529,0.014824196,0.001325908,0.0017554458,0.0013157672,0.00032572137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014438966,0.0004050377,0.00085605157,0.001102369,0.00052281027,0.0012310105,0.0018186192,0.0011177969,0.0028276048],"category_scores_gemma":[0.044250388,0.0003593279,0.0006534321,0.0009678999,0.0019184293,0.0017533922,0.0026996892,0.0019689146,0.0002313906],"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":[0.00013093621,0.00011344193,0.00520764,0.000096818316,0.00007336463,0.00030308586,0.00041787376,0.5489096,0.0013640238,0.34936297,0.0012586827,0.09276153],"study_design_scores_gemma":[0.000013854398,0.0000343972,0.00040393957,0.000020716852,0.000008143881,0.000046003785,0.00004861684,0.9143888,0.0006965745,0.08324529,0.0010814308,0.000012342916],"about_ca_topic_score_codex":0.0022287162,"about_ca_topic_score_gemma":0.0016969759,"teacher_disagreement_score":0.014438966,"about_ca_system_score_codex":0.0010111169,"about_ca_system_score_gemma":0.0011327276,"threshold_uncertainty_score":0.07636148},"labels":[],"label_agreement":null},{"id":"W4401592608","doi":"10.3329/jsr.v58i1.75414","title":"Joint models for longitudinal data","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Inference; Variables; Computer science; Statistics; Multivariate statistics; Econometrics; Statistical model; Joint probability distribution; Statistical inference; Focus (optics); Variable (mathematics); Joint (building); Mixed model; Event (particle physics); Mathematics; Artificial intelligence","score_opus":0.7488066637660351,"score_gpt":0.6167895005344516,"score_spread":0.13201716323158352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401592608","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":[0.0029483915,0.002092888,0.98875856,0.0017960118,0.0002447427,0.0002653269,0.0015374764,0.00048931333,0.001867196],"genre_scores_gemma":[0.21357623,0.008437847,0.73948765,0.0025163086,0.0018387438,0.0070676985,0.008016393,0.00084031955,0.018218849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95069623,0.03416959,0.0026443594,0.0070067984,0.003952926,0.0015301182],"domain_scores_gemma":[0.8290328,0.14499539,0.009359796,0.009956659,0.0054538744,0.0012014244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06550532,0.0036461998,0.006303564,0.0064753788,0.0021174045,0.0075691165,0.009916015,0.0067364946,0.016260725],"category_scores_gemma":[0.15899183,0.0034223641,0.006689739,0.008534618,0.005643514,0.014017051,0.006233988,0.009992113,0.0040474567],"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":[0.00016012159,0.00009270096,0.0041051544,0.00063085306,0.000681293,0.00033426032,0.001001612,0.08374502,0.00015230406,0.86658514,0.006557926,0.03595364],"study_design_scores_gemma":[0.00010009438,0.00007952156,0.00080054055,0.00022696401,0.00020600884,0.00021928355,0.00012021891,0.29105735,0.000089269444,0.6973948,0.009630469,0.00007547541],"about_ca_topic_score_codex":0.016496398,"about_ca_topic_score_gemma":0.014684773,"teacher_disagreement_score":0.06550532,"about_ca_system_score_codex":0.005095859,"about_ca_system_score_gemma":0.00427149,"threshold_uncertainty_score":0.34642935},"labels":[],"label_agreement":null},{"id":"W4403203326","doi":"10.1177/09622802241268466","title":"Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Generalized linear mixed model; Random effects model; Event (particle physics); Computer science; Count data; Poisson distribution; Statistics; Mixed model; Bayesian probability; Markov chain Monte Carlo; Negative binomial distribution; Marginal model; Linear model; Econometrics; Mathematics; Regression analysis; Medicine","score_opus":0.28591415939095105,"score_gpt":0.5747006451452177,"score_spread":0.28878648575426663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403203326","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":[0.008484526,0.00042483234,0.9896597,0.0004161823,0.000055013024,0.0000465711,0.00021482057,0.00016585442,0.00053246267],"genre_scores_gemma":[0.58557314,0.001990208,0.40029588,0.0005883413,0.0004459776,0.0011826527,0.0020562685,0.00024951622,0.0076180394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9901878,0.0054525053,0.00044862804,0.0023288482,0.0010561397,0.00052607036],"domain_scores_gemma":[0.9664283,0.025724405,0.0033159894,0.002518577,0.001491843,0.00052097364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01843882,0.0013760666,0.0027337645,0.001829368,0.0009049446,0.0024221982,0.0044049346,0.0019754244,0.0029549624],"category_scores_gemma":[0.053093795,0.001284472,0.002440923,0.0025387276,0.0020453786,0.0030104623,0.0033741214,0.0038379505,0.0005695131],"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":[0.00020369737,0.00012972945,0.019335357,0.0002548829,0.0005141676,0.0005343138,0.00073863746,0.60640144,0.0005984573,0.29525852,0.0020072092,0.074023545],"study_design_scores_gemma":[0.00001740393,0.000064981265,0.0015337502,0.00005500234,0.0000747122,0.000098607416,0.000054670883,0.8690026,0.00021952187,0.12709211,0.0017422598,0.000044313667],"about_ca_topic_score_codex":0.009774826,"about_ca_topic_score_gemma":0.0070857825,"teacher_disagreement_score":0.01843882,"about_ca_system_score_codex":0.0016752947,"about_ca_system_score_gemma":0.0028387387,"threshold_uncertainty_score":0.09751493},"labels":[],"label_agreement":null},{"id":"W4403365232","doi":"10.48550/arxiv.2410.07996","title":"Smoothed pseudo-population bootstrap methods with applications to finite population quantiles","year":2024,"lang":"en","type":"preprint","venue":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"Alliance de recherche numérique du Canada","keywords":"Quantile; Population; Econometrics; Statistics; Computer science; Mathematics; Medicine","score_opus":0.09020472033228723,"score_gpt":0.4232970007969331,"score_spread":0.3330922804646459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403365232","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":[0.0014244246,0.00015357413,0.99790144,0.000048319867,0.000018171953,0.000016042903,0.000020607491,0.00013825014,0.00027920262],"genre_scores_gemma":[0.17055765,0.0009287823,0.8247943,0.00023997168,0.00018766096,0.0006167389,0.00034319423,0.00037952905,0.0019522922],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954502,0.0032825628,0.00012798638,0.0002858426,0.0007573472,0.00009606824],"domain_scores_gemma":[0.9789494,0.016602427,0.00094460975,0.0018254354,0.0014808465,0.00019732666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00708999,0.0006740913,0.0010253956,0.0020810775,0.00050952693,0.0012033834,0.0019080889,0.0010594826,0.0041128774],"category_scores_gemma":[0.04169678,0.0006014523,0.0011368181,0.0020653326,0.0014981384,0.0016195894,0.001975429,0.0023565488,0.0010620048],"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":[0.00015438808,0.00009445603,0.0028960526,0.00037542236,0.00021350037,0.00034221786,0.00046693513,0.24990821,0.002969606,0.5421983,0.003295595,0.19708535],"study_design_scores_gemma":[0.00004525305,0.00007189999,0.0010510893,0.00006981411,0.000030122987,0.00012926098,0.00005102242,0.71645784,0.0015354081,0.27174473,0.008777232,0.000036267676],"about_ca_topic_score_codex":0.0013777537,"about_ca_topic_score_gemma":0.0011481828,"teacher_disagreement_score":0.00708999,"about_ca_system_score_codex":0.0006014225,"about_ca_system_score_gemma":0.00088312034,"threshold_uncertainty_score":0.03749591},"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":"W4403691889","doi":"10.1177/09622802241282091","title":"Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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 Health Network; Institute for Work & Health; Western University; Public Health Ontario; University of Toronto","funders":"Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; University of Toronto; Instituto de Salud Carlos III; Alliance de recherche numérique du Canada; Generalitat de Catalunya; Innovation, Science and Economic Development Canada","keywords":"Statistics; Ordinal regression; Mathematics; Econometrics; Logistic regression; Ordinal data; Ordered logit; Logit; Outcome (game theory); Regression analysis; Sampling bias; Sample size determination","score_opus":0.4117070033392049,"score_gpt":0.6242060080290859,"score_spread":0.21249900468988098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403691889","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":[0.0065306136,0.0004898166,0.9911412,0.00027203702,0.00014004909,0.00043134863,0.00023471552,0.00038654744,0.0003735871],"genre_scores_gemma":[0.14731057,0.00088988745,0.84630513,0.0005104398,0.00021821567,0.0028240988,0.00079131435,0.00027168106,0.00087855675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.80349505,0.17767158,0.005079777,0.007356644,0.005362288,0.001034648],"domain_scores_gemma":[0.63903254,0.31323045,0.012839398,0.026059097,0.008017847,0.0008206783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1782217,0.0032016037,0.004051018,0.0062034056,0.0014680044,0.0042417645,0.005495294,0.0030896934,0.0056266063],"category_scores_gemma":[0.43218112,0.0023581132,0.005492483,0.0090307025,0.0027489827,0.0058547105,0.0055589434,0.0071108034,0.0009743032],"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":[0.0009830003,0.00044151145,0.039476976,0.0024838466,0.005483998,0.0007878227,0.0023885076,0.43614954,0.0016181453,0.18732204,0.005347419,0.3175172],"study_design_scores_gemma":[0.00033121443,0.0004129776,0.0041189534,0.00047346912,0.00060525525,0.00015875512,0.00024060445,0.780439,0.0009827653,0.20499071,0.007103754,0.00014255126],"about_ca_topic_score_codex":0.011129647,"about_ca_topic_score_gemma":0.010527626,"teacher_disagreement_score":0.1782217,"about_ca_system_score_codex":0.0022783352,"about_ca_system_score_gemma":0.003394921,"threshold_uncertainty_score":0.9425377},"labels":[],"label_agreement":null},{"id":"W4403764486","doi":"10.1080/02664763.2024.2418473","title":"Evaluating the median <i>p</i> -value method for assessing the statistical significance of tests when using multiple imputation","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Statistical Methods and Bayesian 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":"Institute for Clinical Evaluative Sciences; Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Statistics; Mathematics; Wilcoxon signed-rank test; Test statistic; Statistical significance; Statistic; Statistical hypothesis testing; Logistic regression; Student's t-test; p-value; F-test; Type I and type II errors; Pearson's chi-squared test; Linear regression; Pooling; Nominal level; Imputation (statistics); Missing data; Mann–Whitney U test; Computer science; Artificial intelligence","score_opus":0.15242597773040908,"score_gpt":0.5050042235663473,"score_spread":0.3525782458359382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403764486","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":[0.0046591656,0.001880666,0.98607606,0.00072837627,0.0004116376,0.0008273226,0.001202545,0.0016071975,0.0026070778],"genre_scores_gemma":[0.08338178,0.0008359567,0.90707695,0.000952997,0.00038019003,0.0036216574,0.0014234174,0.0011437021,0.0011832548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8497927,0.10928937,0.008306933,0.014663083,0.016544998,0.0014027918],"domain_scores_gemma":[0.45476842,0.48181292,0.018167373,0.028036596,0.015632235,0.0015824158],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15200576,0.0024423026,0.0048585352,0.008549628,0.002238934,0.00607126,0.0060272887,0.005165371,0.012437904],"category_scores_gemma":[0.4841022,0.0013753303,0.0063190185,0.008681114,0.005165964,0.004550032,0.0044956026,0.00910665,0.0029135454],"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":[0.0027492694,0.0006722549,0.04732412,0.007508279,0.010027985,0.0017620428,0.001949548,0.041991677,0.0046768654,0.1422543,0.05899676,0.68008685],"study_design_scores_gemma":[0.0012204963,0.0031379708,0.031668913,0.004885089,0.0050133844,0.004854445,0.0013883025,0.3757039,0.025870841,0.4057844,0.13948067,0.0009915801],"about_ca_topic_score_codex":0.0021939948,"about_ca_topic_score_gemma":0.0015174486,"teacher_disagreement_score":0.8479942,"about_ca_system_score_codex":0.002098812,"about_ca_system_score_gemma":0.0066321474,"threshold_uncertainty_score":0.8038929},"labels":[],"label_agreement":null},{"id":"W4403893991","doi":"10.1186/s13063-024-08404-2","title":"Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias","year":2024,"lang":"en","type":"article","venue":"Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Public Health; Ottawa Hospital; Institute for Clinical Evaluative Sciences; Public Health Ontario","funders":"National Center for Advancing Translational Sciences; National Institute on Aging; National Institutes of Health; Dartmouth College","keywords":"Medicine; Cluster (spacecraft); Correlation; Gerontology; Dementia; Research design; Randomized controlled trial; Statistics; Computer science; Internal medicine; Disease; Mathematics","score_opus":0.3953827012212012,"score_gpt":0.48504128723843976,"score_spread":0.08965858601723853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403893991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12923251,0.0057291877,0.8201653,0.003034442,0.0010476989,0.017162936,0.01098055,0.0013641069,0.011283333],"genre_scores_gemma":[0.5303665,0.00078353134,0.4327984,0.0011751793,0.00021319187,0.029507993,0.004008189,0.00036924108,0.0007778032],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.4132343,0.5233786,0.02860648,0.012148165,0.020785535,0.0018468688],"domain_scores_gemma":[0.14240928,0.7168399,0.06803007,0.04258792,0.02913451,0.0009983392],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.45212525,0.0017508917,0.002986632,0.0072928118,0.00097911,0.0032519286,0.002397228,0.0018491133,0.0034866866],"category_scores_gemma":[0.7583189,0.0017403922,0.00835577,0.006946959,0.002449651,0.002863792,0.004332195,0.0048227203,0.00062324456],"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":[0.010465466,0.000930571,0.40605578,0.010057367,0.047821347,0.00042627033,0.0073798103,0.111663125,0.0014187869,0.046971675,0.04604759,0.31076226],"study_design_scores_gemma":[0.007073277,0.007712021,0.4565663,0.008481258,0.022904404,0.00079967873,0.0014869788,0.3096014,0.0073072477,0.09867352,0.07857752,0.00081635744],"about_ca_topic_score_codex":0.005956877,"about_ca_topic_score_gemma":0.005610489,"teacher_disagreement_score":0.54787475,"about_ca_system_score_codex":0.0034925535,"about_ca_system_score_gemma":0.00823498,"threshold_uncertainty_score":0.6756271},"labels":[],"label_agreement":null},{"id":"W4403906640","doi":"10.5539/ijsp.v13n3p66","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 3","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.05947685874834614,"score_gpt":0.39548106721956316,"score_spread":0.33600420847121704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403906640","genre_codex":"editorial","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":[0.00013720174,0.0025687919,0.0019882908,0.12875392,0.86239284,0.00060305157,0.00076033705,0.0006522809,0.0021433004],"genre_scores_gemma":[0.0051757484,0.006580377,0.0051446785,0.17681791,0.7548301,0.0035912856,0.0016884105,0.002042039,0.04412938],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9268178,0.013820069,0.015270893,0.004893009,0.036841743,0.00235648],"domain_scores_gemma":[0.13231955,0.03757039,0.011396218,0.006431364,0.80361557,0.008666909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05805981,0.0033332948,0.010045524,0.01178295,0.0049828794,0.011449416,0.0060603134,0.018334564,0.08951686],"category_scores_gemma":[0.5247545,0.0018901186,0.006807519,0.00570864,0.0042568664,0.006589185,0.00410548,0.014398155,0.06401627],"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":[0.000036917525,0.0000042973484,0.000081520455,0.00037625126,0.000012827971,0.000058666214,0.00003196463,0.000012107139,0.000037222046,0.00012421687,0.9958669,0.0033570926],"study_design_scores_gemma":[0.00036996006,0.0000714097,0.0015353536,0.0046910476,0.00018578753,0.0015703543,0.00044122283,0.0007557088,0.000463655,0.0035443467,0.9861205,0.00025057036],"about_ca_topic_score_codex":0.0038230126,"about_ca_topic_score_gemma":0.005538901,"teacher_disagreement_score":0.08951686,"about_ca_system_score_codex":0.005525045,"about_ca_system_score_gemma":0.012080583,"threshold_uncertainty_score":0.30705333},"labels":[],"label_agreement":null},{"id":"W4403955442","doi":"10.1002/sim.10260","title":"A Comparison of Variance Estimators for Logistic Regression Models Estimated Using Generalized Estimating Equations (<scp>GEE</scp>) in the Context of Observational Health Services Research","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Institute for Clinical Evaluative Sciences; Sunnybrook Hospital; University of Toronto","funders":"Ministry of Long-Term Care; Canadian Institutes of Health Research; Ministry of Health, Ontario","keywords":"Estimator; Covariate; Statistics; Generalized estimating equation; Mathematics; Context (archaeology); Standard error; Estimating equations; Logistic regression; Econometrics; Variance (accounting); Generalized linear model; Cluster (spacecraft); Linear regression; Computer science","score_opus":0.5887665530378992,"score_gpt":0.6053092357592396,"score_spread":0.016542682721340407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403955442","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":[0.046149403,0.014084921,0.9330342,0.0015998189,0.00064980413,0.00040750802,0.0005203368,0.000657058,0.0028969974],"genre_scores_gemma":[0.30025667,0.009312473,0.6832806,0.000851194,0.0004714626,0.0016523754,0.002035138,0.0008988103,0.0012412787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8672487,0.11590016,0.0041389745,0.0041153017,0.007745184,0.00085160305],"domain_scores_gemma":[0.5564286,0.40980387,0.0071171685,0.012875985,0.013124689,0.0006496359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14054963,0.0012646465,0.0029117023,0.00332478,0.00054346345,0.0029382424,0.0030542181,0.002460809,0.0023679514],"category_scores_gemma":[0.4037508,0.00083891576,0.0037919835,0.0046879747,0.0016536319,0.0064477785,0.0029740725,0.0035756936,0.0005521431],"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":[0.003459196,0.00038310682,0.045068387,0.003675387,0.008235554,0.00033963888,0.0025556448,0.10294487,0.0011105848,0.20642999,0.012440067,0.61335754],"study_design_scores_gemma":[0.0017316003,0.0025528516,0.05189445,0.0043646935,0.0033643905,0.0009626109,0.0028723786,0.68498313,0.002700918,0.20808484,0.035819042,0.0006691524],"about_ca_topic_score_codex":0.004329064,"about_ca_topic_score_gemma":0.0029698662,"teacher_disagreement_score":0.14054963,"about_ca_system_score_codex":0.0015652335,"about_ca_system_score_gemma":0.0030635726,"threshold_uncertainty_score":0.7433064},"labels":[],"label_agreement":null},{"id":"W4404223073","doi":"10.1007/s13571-024-00348-6","title":"Effect of Missing Responses on the $$C(\\alpha )$$ or Score Tests in One-way Layout of Count Data","year":2024,"lang":"en","type":"article","venue":"Sankhya B","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Alpha (finance); Count data; Statistics; Computer science; Mathematics; Psychometrics","score_opus":0.2601914122401281,"score_gpt":0.4666803650189446,"score_spread":0.20648895277881651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404223073","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":[0.21734996,0.0013936558,0.76221657,0.0030120423,0.0014646827,0.0008525912,0.003310038,0.004690741,0.0057097967],"genre_scores_gemma":[0.6831819,0.0002484989,0.30469027,0.0011305675,0.00021690712,0.0019948178,0.0014513972,0.0016797897,0.0054058405],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.64217603,0.2833023,0.013400788,0.042360738,0.0131496545,0.0056105033],"domain_scores_gemma":[0.16873582,0.756066,0.011584505,0.054190177,0.007027537,0.0023959216],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20119084,0.002094824,0.0051253526,0.0023685992,0.0030545609,0.005037514,0.0067861546,0.00663276,0.019497527],"category_scores_gemma":[0.5264585,0.002609964,0.0060294685,0.0034413761,0.009311143,0.007470826,0.0044796555,0.010673557,0.0023017686],"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":[0.04553692,0.002768676,0.17526427,0.0059626405,0.008777227,0.0034521953,0.018861445,0.040894795,0.02501214,0.14484087,0.030929612,0.49769917],"study_design_scores_gemma":[0.0037696352,0.009151338,0.21069485,0.002492796,0.009213519,0.002895063,0.0056649675,0.2876726,0.055958744,0.38575765,0.025382314,0.0013465662],"about_ca_topic_score_codex":0.004876572,"about_ca_topic_score_gemma":0.00581195,"teacher_disagreement_score":0.20119084,"about_ca_system_score_codex":0.0026537233,"about_ca_system_score_gemma":0.0036115216,"threshold_uncertainty_score":0.9850739},"labels":[],"label_agreement":null},{"id":"W4404668027","doi":"10.6000/1929-6029.2024.13.24","title":"Sample Size and Statistical Power Calculation in Multivariable Analyses: Development and Implementation of \"SampleSizeMulti\" Packages in R","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Sample size determination; Sample (material); Power (physics); Statistical power; Statistics; Computer science; Econometrics; Mathematics; Engineering; Control engineering; Physics; Chemistry; Chromatography","score_opus":0.16341242151178634,"score_gpt":0.5796363033632727,"score_spread":0.4162238818514863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404668027","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":[0.0014279948,0.0002805821,0.97981334,0.0007395541,0.00021527756,0.0019035547,0.0027401529,0.010850515,0.0020289898],"genre_scores_gemma":[0.010952667,0.000255826,0.96998626,0.00043744117,0.00010312901,0.011167385,0.0012087229,0.0052131326,0.0006754139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92662865,0.054406606,0.005891144,0.0040138727,0.008186216,0.0008735027],"domain_scores_gemma":[0.7681616,0.18660997,0.012593636,0.01677735,0.014574568,0.001282838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07359876,0.002773345,0.0022968047,0.0042396924,0.0008547295,0.003534173,0.004113356,0.0016419815,0.022848012],"category_scores_gemma":[0.31123218,0.0020664965,0.0034819185,0.004228942,0.0022599667,0.0024848,0.00531219,0.0052829776,0.01055615],"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":[0.0011061013,0.0002807707,0.010773943,0.006510678,0.0018150382,0.0005213395,0.0029327795,0.030726751,0.003962329,0.09001481,0.19088374,0.66047174],"study_design_scores_gemma":[0.0018852638,0.0010623705,0.016489541,0.0038683123,0.0012729069,0.0014051251,0.00063860946,0.20432423,0.028628428,0.24783891,0.4918477,0.00073858694],"about_ca_topic_score_codex":0.001904895,"about_ca_topic_score_gemma":0.0020567176,"teacher_disagreement_score":0.07359876,"about_ca_system_score_codex":0.0013596573,"about_ca_system_score_gemma":0.0067736446,"threshold_uncertainty_score":0.3892321},"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":"W4405550336","doi":"10.1186/s13063-024-08653-1","title":"Re-analysis of data from cluster randomised trials to explore the impact of model choice on estimates of odds ratios: study protocol","year":2024,"lang":"en","type":"article","venue":"Trials","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"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":"Department of Health and Social Care; Medical Research Council; National Institute for Health and Care Research","keywords":"Protocol (science); Odds; Medicine; Cluster (spacecraft); Cluster analysis; Odds ratio; Research design; Statistics; Computer science; Data mining; Econometrics; Logistic regression; Alternative medicine; Machine learning; Mathematics","score_opus":0.7170500445969801,"score_gpt":0.6253028921699856,"score_spread":0.0917471524269945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405550336","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009991194,0.0009478787,0.030857535,0.0008991615,0.0017157623,0.95815766,0.004176217,0.000638045,0.0016086047],"genre_scores_gemma":[0.0009370873,0.00015503187,0.0104368655,0.00026770454,0.000044478165,0.9876123,0.00020700261,0.000048331123,0.00029124555],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6468974,0.2381334,0.069531865,0.012902404,0.02782187,0.0047131074],"domain_scores_gemma":[0.59539205,0.20073049,0.05628684,0.08908,0.054127794,0.0043828744],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.31239405,0.006513961,0.010856137,0.0068824086,0.002646698,0.00604741,0.0060930783,0.013598841,0.053668067],"category_scores_gemma":[0.56493163,0.004661776,0.015074334,0.0088103805,0.0048158914,0.0071310573,0.005138431,0.015043087,0.016577398],"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":[0.117999665,0.0019377897,0.0036386922,0.31527793,0.01898375,0.0010747864,0.005913231,0.012962619,0.0036655315,0.067579664,0.17441228,0.2765541],"study_design_scores_gemma":[0.19005264,0.011511815,0.010100535,0.13958648,0.012696184,0.0008335286,0.001076157,0.024473527,0.009303947,0.13207024,0.46650702,0.0017879944],"about_ca_topic_score_codex":0.0022800418,"about_ca_topic_score_gemma":0.0034644064,"teacher_disagreement_score":0.687606,"about_ca_system_score_codex":0.010679733,"about_ca_system_score_gemma":0.032835897,"threshold_uncertainty_score":0.84794056},"labels":[],"label_agreement":null},{"id":"W4406024725","doi":"10.5539/ijsp.v13n4p81","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 4","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Probability and statistics; Mathematics; Mathematical economics; Computer science","score_opus":0.05066828196489285,"score_gpt":0.394346623650287,"score_spread":0.34367834168539413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406024725","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013361647,0.0024106225,0.0017320805,0.12614161,0.8659001,0.00053339,0.00070774165,0.00055322226,0.0018876909],"genre_scores_gemma":[0.0048142076,0.006235971,0.004892922,0.16218226,0.76788706,0.0035080016,0.0016440088,0.0017364832,0.0470991],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93000954,0.01367185,0.014885467,0.004590863,0.034589045,0.002253213],"domain_scores_gemma":[0.13680069,0.038220685,0.011412381,0.0064165266,0.798661,0.008488771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05582187,0.0034244577,0.009979429,0.012305856,0.004862161,0.010825028,0.0063835587,0.018300617,0.09078896],"category_scores_gemma":[0.5326755,0.0019393592,0.0065383143,0.0054472545,0.0041891467,0.0068910606,0.00424984,0.013811021,0.06168838],"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":[0.000037948306,0.0000044272088,0.000085557476,0.00039732884,0.000013560765,0.00006116991,0.000030905143,0.000012780071,0.00003769212,0.00012401248,0.99585295,0.0033416783],"study_design_scores_gemma":[0.00036868674,0.000073906245,0.0015051927,0.0044217403,0.00019022582,0.001452512,0.00044251292,0.0007191121,0.00044663824,0.0033900153,0.9867423,0.00024717007],"about_ca_topic_score_codex":0.0037926391,"about_ca_topic_score_gemma":0.005429227,"teacher_disagreement_score":0.09078896,"about_ca_system_score_codex":0.005531195,"about_ca_system_score_gemma":0.011697578,"threshold_uncertainty_score":0.30371934},"labels":[],"label_agreement":null},{"id":"W4406388952","doi":"10.1016/j.endend.2012.09.006","title":"10.1016/j.endend.2012.09.006","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.027696939970220085,"score_gpt":0.28225240378141747,"score_spread":0.25455546381119737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406388952","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":[0.0035380272,0.0072063403,0.03896764,0.0070276135,0.0016904097,0.00009182114,0.0045658653,0.0049593984,0.93195283],"genre_scores_gemma":[0.018857453,0.0033216334,0.018289188,0.0012426339,0.0004267818,0.00010428127,0.0032025022,0.00081889966,0.95373654],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995615,0.00005996461,0.000034448247,0.00014095483,0.00014156925,0.00006164258],"domain_scores_gemma":[0.9983553,0.00071608264,0.00019240715,0.00022253477,0.00023425474,0.0002794216],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001434107,0.0013626744,0.00075088796,0.0020310606,0.0008865573,0.004018791,0.0012700738,0.0041558016,0.8949649],"category_scores_gemma":[0.0034667512,0.00054404495,0.0006965731,0.0017035883,0.0015522372,0.0035821183,0.0016749295,0.0016397388,0.86041427],"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":[0.00015934199,0.00022149488,0.0032189963,0.00043748002,0.000058211077,0.00020747141,0.00010427533,0.0023119354,0.00090024585,0.036401857,0.26888967,0.6870891],"study_design_scores_gemma":[0.00007545291,0.00007399253,0.0023198817,0.00075804995,0.00005429893,0.0009788765,0.00024778355,0.004618914,0.0007988991,0.042702608,0.94732183,0.00004943026],"about_ca_topic_score_codex":0.002539643,"about_ca_topic_score_gemma":0.0028003345,"teacher_disagreement_score":0.105035126,"about_ca_system_score_codex":0.0009461634,"about_ca_system_score_gemma":0.001253852,"threshold_uncertainty_score":0.14981985},"labels":[],"label_agreement":null},{"id":"W4406713780","doi":"10.1093/bioadv/vbae209","title":"<u>Imp</u>utation for <u>Li</u>pidomics and <u>Met</u>abolomics (ImpLiMet): a web-based application for optimization and method selection for missing data imputation","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","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":"Occupational Cancer Research Centre; University of Toronto; McGill Genome Centre; National Research Council Canada; McGill University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Computer science; Mathematics; Statistics","score_opus":0.05619849722513385,"score_gpt":0.42300350110061274,"score_spread":0.3668050038754789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406713780","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041795373,0.001112062,0.21593738,0.0021750992,0.001523721,0.00039473665,0.20397492,0.55490434,0.01579824],"genre_scores_gemma":[0.04078936,0.0016694855,0.35266653,0.0040873117,0.0007821752,0.0026887082,0.31540343,0.26038772,0.021525245],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632996,0.00085126056,0.00029163528,0.0009123504,0.0012834071,0.00033144417],"domain_scores_gemma":[0.9897612,0.005305578,0.0009947664,0.0021491207,0.001240365,0.0005489194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008406925,0.003169103,0.00304155,0.0032121378,0.0019926974,0.0047057946,0.004611419,0.0028769225,0.2249493],"category_scores_gemma":[0.031309277,0.0022536253,0.0035635824,0.0043432005,0.0012875801,0.0035673196,0.0063043525,0.0038364758,0.13203765],"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":[0.00091093,0.00011918346,0.0039112517,0.0020164412,0.00039426968,0.0006342753,0.00029283515,0.0035479534,0.004601014,0.008136324,0.89799494,0.07744048],"study_design_scores_gemma":[0.00092440576,0.00023504811,0.0061697676,0.0009632919,0.00025307445,0.0012961619,0.00016606353,0.065936275,0.038545046,0.06098047,0.824046,0.00048435142],"about_ca_topic_score_codex":0.0031164286,"about_ca_topic_score_gemma":0.0050461516,"teacher_disagreement_score":0.2249493,"about_ca_system_score_codex":0.0014780851,"about_ca_system_score_gemma":0.0034033484,"threshold_uncertainty_score":0.7525304},"labels":[],"label_agreement":null},{"id":"W4406715877","doi":"10.1111/bmsp.12381","title":"Efficient and accurate variational inference for multilevel threshold autoregressive models in intensive longitudinal data","year":2025,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Statistical Methods and Bayesian Inference","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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Markov chain Monte Carlo; Inference; Autoregressive model; Bayes' theorem; Bayesian inference; Bayesian probability; Computer science; Algorithm; Data set; Mathematics; Statistics; Artificial intelligence","score_opus":0.17965977955819226,"score_gpt":0.4642400169581189,"score_spread":0.28458023739992666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406715877","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":[0.006703662,0.00022470136,0.99245024,0.00018901912,0.000014224808,0.00002213066,0.000052507337,0.00007452343,0.0002689813],"genre_scores_gemma":[0.24106276,0.00064247096,0.75524926,0.0002333341,0.000098548335,0.00030621956,0.00053840346,0.00019327187,0.0016757132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976113,0.0015899335,0.000116115814,0.00030710717,0.00026231914,0.00011325435],"domain_scores_gemma":[0.97810304,0.019887097,0.00062718475,0.0005303427,0.0006051914,0.00024717968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008957706,0.0007046088,0.0013938594,0.0011675111,0.0006702879,0.0013058181,0.0022876705,0.0015363195,0.0019304382],"category_scores_gemma":[0.03568288,0.001105256,0.0014661449,0.0011071981,0.001218282,0.001985851,0.0019955568,0.0030646315,0.0002600198],"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":[0.000068999005,0.000048299797,0.0027825658,0.0001657435,0.000176823,0.00009523079,0.00024389825,0.7971346,0.001158099,0.14459461,0.0013761872,0.052155036],"study_design_scores_gemma":[0.00000688508,0.000005621384,0.00015276289,0.000011537543,0.000007303028,0.0000090808635,0.000008387027,0.964795,0.00009627598,0.03460341,0.00029798454,0.0000057923417],"about_ca_topic_score_codex":0.016542755,"about_ca_topic_score_gemma":0.018884316,"teacher_disagreement_score":0.016542755,"about_ca_system_score_codex":0.0015636099,"about_ca_system_score_gemma":0.0024722456,"threshold_uncertainty_score":0.047373414},"labels":[],"label_agreement":null},{"id":"W4406962834","doi":"10.1007/s11222-024-10561-y","title":"Empirical investigations of boosting with pseudo-outcome imputation for missing responses","year":2025,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Western University","funders":"","keywords":"Missing data; Imputation (statistics); Boosting (machine learning); Mathematics; Computer science; Artificial intelligence; Econometrics; Statistics","score_opus":0.1109151853844736,"score_gpt":0.4478529732857973,"score_spread":0.3369377879013237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406962834","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":[0.064037904,0.0052669486,0.9214435,0.003342354,0.00017452335,0.00024969675,0.00024556197,0.0003334309,0.0049060434],"genre_scores_gemma":[0.6111271,0.0024494564,0.37817445,0.0013834374,0.0006035986,0.0004847669,0.000632,0.00032689053,0.0048183813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8734919,0.11819122,0.0010371868,0.003295681,0.0028271526,0.001156805],"domain_scores_gemma":[0.28921035,0.6697098,0.009056641,0.025649581,0.0051409793,0.0012326613],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15879951,0.0018304775,0.0047598146,0.0012976774,0.0020073412,0.0037401791,0.008176305,0.0031433082,0.007201741],"category_scores_gemma":[0.45803547,0.0018190797,0.0031271398,0.0041694287,0.0059547457,0.007804936,0.0035158892,0.008357276,0.0012990742],"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":[0.0022674154,0.0009870656,0.03836102,0.0013684238,0.0019838254,0.00035110625,0.00248381,0.14101675,0.0005166759,0.62542456,0.010480223,0.1747592],"study_design_scores_gemma":[0.00021179274,0.00035868364,0.0059708273,0.00034732732,0.0004449668,0.0004018273,0.00035233545,0.46678603,0.00051800255,0.5183038,0.0062404308,0.00006404312],"about_ca_topic_score_codex":0.004020446,"about_ca_topic_score_gemma":0.0033704482,"teacher_disagreement_score":0.8412005,"about_ca_system_score_codex":0.0016466477,"about_ca_system_score_gemma":0.0030018317,"threshold_uncertainty_score":0.8398222},"labels":[],"label_agreement":null},{"id":"W4407258044","doi":"10.1002/bimj.70035","title":"Mediation Analysis With Exposure–Mediator Interaction and Covariate Measurement Error Under the Additive Hazards Model","year":2025,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Covariate; Mediation; Mediator; Statistics; Econometrics; Psychology; Mathematics; Medicine; Sociology; Internal medicine","score_opus":0.10696754191525205,"score_gpt":0.3899049935573956,"score_spread":0.28293745164214357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407258044","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":[0.018190106,0.001350211,0.9766623,0.0011500469,0.00017769646,0.00048302542,0.00033501646,0.00020212702,0.0014495013],"genre_scores_gemma":[0.63285446,0.0029448373,0.3531121,0.0011090888,0.0005260693,0.0039004963,0.0007432703,0.00012338084,0.00468633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95075995,0.039875973,0.0013898335,0.003411414,0.0030470814,0.0015157242],"domain_scores_gemma":[0.925963,0.06417695,0.002545343,0.00485069,0.0020174945,0.00044662066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05658287,0.0019296318,0.0033154797,0.0026013486,0.0013192275,0.0019305741,0.0047442717,0.0024799812,0.008204889],"category_scores_gemma":[0.113731675,0.00078715006,0.004756561,0.003599664,0.0024181998,0.002985333,0.004807767,0.0042363517,0.0005917476],"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":[0.0013743092,0.0005293568,0.052342243,0.002270485,0.0066821347,0.0034302797,0.0025313878,0.09724593,0.0017605692,0.5866685,0.0053636553,0.2398011],"study_design_scores_gemma":[0.0005833173,0.00079320034,0.010318427,0.00034009592,0.0031007046,0.00091826305,0.0005856777,0.37109986,0.0016127963,0.60194325,0.008558933,0.00014548539],"about_ca_topic_score_codex":0.0053223562,"about_ca_topic_score_gemma":0.002662867,"teacher_disagreement_score":0.05658287,"about_ca_system_score_codex":0.0013047863,"about_ca_system_score_gemma":0.0046113227,"threshold_uncertainty_score":0.29924238},"labels":[],"label_agreement":null},{"id":"W4407348889","doi":"10.1002/ijop.70018","title":"Modelling Count Data in Psychological Research: An Applied Tutorial","year":2025,"lang":"en","type":"article","venue":"International Journal of Psychology","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":"York University","funders":"","keywords":"Count data; Linear regression; Regression analysis; Psychology; Statistics; Regression; Computer science; Mathematics","score_opus":0.5358199338526047,"score_gpt":0.6299182002046492,"score_spread":0.09409826635204444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407348889","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":[0.0011292684,0.053400245,0.9063438,0.010207103,0.0034076232,0.00050414965,0.0015493828,0.0024558508,0.02100261],"genre_scores_gemma":[0.017875426,0.13320701,0.77673966,0.0080537805,0.008245576,0.0032099963,0.0031227332,0.0014757958,0.04807006],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987626,0.0006452303,0.00010325737,0.0001734888,0.0002645641,0.000050893905],"domain_scores_gemma":[0.9944548,0.0047116056,0.0001857124,0.000119611424,0.00041952397,0.00010863773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031489788,0.0023544736,0.0012774068,0.0019567227,0.0006171083,0.0025555126,0.001585811,0.0028856338,0.028783854],"category_scores_gemma":[0.011602217,0.0010212585,0.001956027,0.0021619906,0.0007544646,0.004029478,0.0019661384,0.0042515155,0.014177958],"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":[0.00005318745,0.00019800276,0.00070438883,0.0031129625,0.00013049126,0.0009072521,0.0009004542,0.01578654,0.0023677947,0.30391863,0.2787894,0.3931309],"study_design_scores_gemma":[0.0000193484,0.00010844453,0.00078174786,0.0020600066,0.000038302052,0.0010898318,0.00013764926,0.022318404,0.0005811593,0.18700154,0.78578436,0.00007911576],"about_ca_topic_score_codex":0.0017741296,"about_ca_topic_score_gemma":0.0022628026,"teacher_disagreement_score":0.028783854,"about_ca_system_score_codex":0.0010708465,"about_ca_system_score_gemma":0.0012778504,"threshold_uncertainty_score":0.0962916},"labels":[],"label_agreement":null},{"id":"W4408338912","doi":"10.3390/e27030289","title":"Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models","year":2025,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Laplace's method; Marginal likelihood; Markov chain Monte Carlo; Posterior probability; Applied mathematics; Marginal distribution; Mathematics; Likelihood function; Laplace transform; Bayesian probability; Mathematical optimization; Computer science; Algorithm; Statistical physics; Statistics; Estimation theory; Random variable; Mathematical analysis; Physics","score_opus":0.12808640491375903,"score_gpt":0.41733099836881105,"score_spread":0.28924459345505205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338912","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":[0.024859838,0.0021290646,0.9638019,0.0031623687,0.00016182195,0.000019028726,0.000108540415,0.0002469842,0.005510408],"genre_scores_gemma":[0.62987316,0.0040930333,0.3577382,0.0015354307,0.00081646576,0.00020130465,0.00031152848,0.0005233355,0.004907527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945905,0.0033149943,0.00020803763,0.000517112,0.0011742022,0.00019522334],"domain_scores_gemma":[0.9211675,0.072813205,0.0017911412,0.0024280255,0.0014124126,0.00038777525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011083794,0.00058873056,0.0010204581,0.0008829041,0.0007611818,0.0017497644,0.0013182629,0.0016837841,0.0033110485],"category_scores_gemma":[0.093823396,0.0005339643,0.00046608504,0.0010882014,0.0029166888,0.0037481063,0.0028826718,0.0037812393,0.0007076838],"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":[0.00014541311,0.000048158116,0.0035433879,0.00024587248,0.000046321114,0.00054587604,0.00050412485,0.067328975,0.0028323308,0.8757697,0.0042184945,0.04477134],"study_design_scores_gemma":[0.000026428335,0.00002510924,0.0007636081,0.000076099335,0.000017091266,0.0003758207,0.00005230141,0.36011776,0.0016241046,0.6334644,0.0034208049,0.00003636836],"about_ca_topic_score_codex":0.0022513887,"about_ca_topic_score_gemma":0.0017633556,"teacher_disagreement_score":0.011083794,"about_ca_system_score_codex":0.0015166743,"about_ca_system_score_gemma":0.0010110085,"threshold_uncertainty_score":0.058617413},"labels":[],"label_agreement":null},{"id":"W4408388570","doi":"10.2196/64354","title":"Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute on Aging","keywords":"Missing data; Imputation (statistics); Covariate; Statistics; Logistic regression; Computer science; Receiver operating characteristic; Data mining; Multivariate statistics; Mathematics","score_opus":0.13208768079548958,"score_gpt":0.48460790030432715,"score_spread":0.3525202195088376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408388570","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":[0.24114722,0.00091019453,0.7501162,0.0016412457,0.0001840081,0.0010904922,0.0017236387,0.00074359257,0.0024433183],"genre_scores_gemma":[0.72702146,0.0004756988,0.26807854,0.00033036372,0.00006712495,0.0019885062,0.0013561533,0.00010150471,0.00058060756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9771508,0.020100698,0.0005962346,0.0009522414,0.000818006,0.00038202485],"domain_scores_gemma":[0.80735314,0.1725311,0.0075068143,0.00736565,0.00415713,0.0010861692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039732985,0.00095472124,0.001760267,0.0016287054,0.0010075661,0.0016603938,0.0030519322,0.0023633263,0.002473731],"category_scores_gemma":[0.12911183,0.0011072234,0.0025886304,0.0018171316,0.0013120383,0.0022514155,0.0019332847,0.0029715425,0.00025800936],"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":[0.00035878384,0.00015566047,0.014346099,0.00018172295,0.0003780384,0.00012779249,0.00032699088,0.95372486,0.00015405501,0.020335704,0.0009970928,0.008913216],"study_design_scores_gemma":[0.00011347237,0.00008203939,0.0007641099,0.00006897357,0.00005346368,0.000028993612,0.00004039898,0.9880612,0.00016594236,0.010147347,0.00045126982,0.000022802253],"about_ca_topic_score_codex":0.012965241,"about_ca_topic_score_gemma":0.008010304,"teacher_disagreement_score":0.039732985,"about_ca_system_score_codex":0.0021176734,"about_ca_system_score_gemma":0.0026928857,"threshold_uncertainty_score":0.21013057},"labels":[],"label_agreement":null},{"id":"W4408655878","doi":"10.1080/02664763.2025.2481458","title":"Zero-inflated Poisson mixed model for longitudinal count data with informative dropouts","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Statistics","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Zero-inflated model; Zero (linguistics); Statistics; Poisson distribution; Mathematics; Poisson regression; Overdispersion; Longitudinal data; Mixed model; Quasi-likelihood; Econometrics; Computer science; Medicine; Data mining; Population","score_opus":0.0772638870620557,"score_gpt":0.3879022313273999,"score_spread":0.3106383442653442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408655878","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":[0.0068326863,0.0005714171,0.99100935,0.0003602807,0.00006732223,0.00011226414,0.00033149708,0.00017790918,0.0005372412],"genre_scores_gemma":[0.34005734,0.0032651497,0.6382504,0.0009128417,0.0005580103,0.002961249,0.0029931527,0.00026235927,0.010739514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98923755,0.007878618,0.0004242376,0.001115257,0.00095161016,0.00039272194],"domain_scores_gemma":[0.96534526,0.027768029,0.0026611122,0.0019915134,0.0018033077,0.00043084534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026787985,0.0014885822,0.0032368677,0.0027867444,0.0012955045,0.0023523665,0.0066470196,0.0029918347,0.0053053033],"category_scores_gemma":[0.051096883,0.0011710564,0.0027219907,0.0041633113,0.002163948,0.003189539,0.0025011648,0.0039561796,0.0011929373],"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":[0.00043177084,0.00015760577,0.012427762,0.00072568306,0.0004937085,0.0015431962,0.0010295537,0.2677753,0.001200885,0.64865124,0.0049510393,0.060612295],"study_design_scores_gemma":[0.00006179386,0.00012591551,0.0013539776,0.000089999434,0.00013779601,0.00026796295,0.0001280953,0.84221864,0.00033771183,0.15109305,0.0041226693,0.00006233971],"about_ca_topic_score_codex":0.0052307197,"about_ca_topic_score_gemma":0.004113034,"teacher_disagreement_score":0.026787985,"about_ca_system_score_codex":0.0017210207,"about_ca_system_score_gemma":0.0020884601,"threshold_uncertainty_score":0.14167011},"labels":[],"label_agreement":null},{"id":"W4409109033","doi":"10.5539/ijsp.v14n1p78","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 14, No. 1","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Probability and statistics; Mathematics","score_opus":0.05154300521348103,"score_gpt":0.3950209545766753,"score_spread":0.34347794936319426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409109033","genre_codex":"editorial","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":[0.00012316463,0.002837111,0.0015495744,0.13849524,0.8532579,0.00055752473,0.00080291065,0.00053649367,0.0018400135],"genre_scores_gemma":[0.0042832005,0.006865506,0.0045220023,0.16635995,0.77218586,0.0035397047,0.0017641295,0.0017168276,0.038762823],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.91905266,0.016670236,0.017125148,0.0059014605,0.03877056,0.0024799479],"domain_scores_gemma":[0.121548675,0.045815118,0.012731707,0.006889062,0.80440485,0.008610589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0632551,0.0033363,0.010307079,0.013434112,0.005164418,0.011522885,0.006176205,0.017868502,0.08912261],"category_scores_gemma":[0.57183117,0.0019391249,0.0061510853,0.0059150965,0.004149456,0.0073870467,0.0044303252,0.014527822,0.06313241],"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":[0.000035601977,0.000004519451,0.00007905547,0.00044125548,0.0000121938865,0.00004896948,0.000036251124,0.000011353017,0.000033671287,0.00012124155,0.9958462,0.0033297692],"study_design_scores_gemma":[0.000326779,0.000073189294,0.001472947,0.005086271,0.00016443612,0.0012742097,0.00046091748,0.00057684816,0.00037304513,0.0029843424,0.9869741,0.0002328832],"about_ca_topic_score_codex":0.0035068216,"about_ca_topic_score_gemma":0.005376611,"teacher_disagreement_score":0.08912261,"about_ca_system_score_codex":0.0056642094,"about_ca_system_score_gemma":0.012249384,"threshold_uncertainty_score":0.33452892},"labels":[],"label_agreement":null},{"id":"W4409109163","doi":"10.5539/ijsp.v14n1p65","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 14, No. 1","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.05154300521348103,"score_gpt":0.3950209545766753,"score_spread":0.34347794936319426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409109163","genre_codex":"editorial","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":[0.00012316463,0.002837111,0.0015495744,0.13849524,0.8532579,0.00055752473,0.00080291065,0.00053649367,0.0018400135],"genre_scores_gemma":[0.0042832005,0.006865506,0.0045220023,0.16635995,0.77218586,0.0035397047,0.0017641295,0.0017168276,0.038762823],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.91905266,0.016670236,0.017125148,0.0059014605,0.03877056,0.0024799479],"domain_scores_gemma":[0.121548675,0.045815118,0.012731707,0.006889062,0.80440485,0.008610589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0632551,0.0033363,0.010307079,0.013434112,0.005164418,0.011522885,0.006176205,0.017868502,0.08912261],"category_scores_gemma":[0.57183117,0.0019391249,0.0061510853,0.0059150965,0.004149456,0.0073870467,0.0044303252,0.014527822,0.06313241],"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":[0.000035601977,0.000004519451,0.00007905547,0.00044125548,0.0000121938865,0.00004896948,0.000036251124,0.000011353017,0.000033671287,0.00012124155,0.9958462,0.0033297692],"study_design_scores_gemma":[0.000326779,0.000073189294,0.001472947,0.005086271,0.00016443612,0.0012742097,0.00046091748,0.00057684816,0.00037304513,0.0029843424,0.9869741,0.0002328832],"about_ca_topic_score_codex":0.0035068216,"about_ca_topic_score_gemma":0.005376611,"teacher_disagreement_score":0.08912261,"about_ca_system_score_codex":0.0056642094,"about_ca_system_score_gemma":0.012249384,"threshold_uncertainty_score":0.33452892},"labels":[],"label_agreement":null},{"id":"W4409237699","doi":"10.1080/03610926.2025.2479650","title":"A unified Bayesian approach for modeling zero-inflated count and continuous outcomes","year":2025,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Bayesian Inference","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":"McMaster University","funders":"","keywords":"Zero (linguistics); Count data; Bayesian probability; Econometrics; Statistics; Computer science; Mathematics; Poisson distribution","score_opus":0.07412835554320561,"score_gpt":0.450434350824333,"score_spread":0.37630599528112735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409237699","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":[0.0025299934,0.00037338032,0.99585813,0.00025849565,0.000026088788,0.000044453034,0.00021225464,0.00010126485,0.00059597817],"genre_scores_gemma":[0.15642828,0.0022540065,0.8327917,0.00049413193,0.0003620691,0.0011659657,0.0018273601,0.00023145082,0.0044449894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9898491,0.0069468617,0.00038469362,0.0012965865,0.0011922999,0.00033050677],"domain_scores_gemma":[0.97982234,0.0155442245,0.0015121052,0.001493873,0.0012794295,0.00034789593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020544467,0.001286532,0.0026004247,0.003807067,0.00096427463,0.0030158954,0.0051675155,0.0022047637,0.0050177136],"category_scores_gemma":[0.047819197,0.0013118995,0.002770046,0.0043135043,0.0020700986,0.003294786,0.0038160924,0.003669844,0.0011735125],"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":[0.00013246248,0.00010267506,0.0069214585,0.00040719684,0.00045862494,0.0004206611,0.0007822956,0.19857955,0.00068829313,0.67059565,0.005270205,0.11564103],"study_design_scores_gemma":[0.000043431275,0.00006748833,0.0016368929,0.00017803303,0.00013569769,0.00024772948,0.000090688685,0.5899724,0.00024520248,0.39997396,0.00734078,0.00006778595],"about_ca_topic_score_codex":0.007906356,"about_ca_topic_score_gemma":0.009548927,"teacher_disagreement_score":0.020544467,"about_ca_system_score_codex":0.0018587825,"about_ca_system_score_gemma":0.0032545463,"threshold_uncertainty_score":0.10865086},"labels":[],"label_agreement":null},{"id":"W4409280262","doi":"10.1016/j.biopsych.2025.02.536","title":"298. Predicting TMS Response Using an Exponential Decay Model","year":2025,"lang":"en","type":"article","venue":"Biological Psychiatry","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto","funders":"","keywords":"Exponential decay; Exponential function; Physics; Mathematics; Nuclear physics; Mathematical analysis","score_opus":0.17274693635029784,"score_gpt":0.437463797793581,"score_spread":0.2647168614432832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409280262","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":[0.06032286,0.0005317167,0.9202794,0.0033552626,0.00020630259,0.000060492774,0.00059812365,0.00059252774,0.014053361],"genre_scores_gemma":[0.73480415,0.00073115365,0.22840261,0.00059384777,0.00013965489,0.00009826128,0.0005809215,0.00023378228,0.034415655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984133,0.00006315807,0.000007734072,0.000033131826,0.000042156487,0.000012421734],"domain_scores_gemma":[0.9988619,0.00087125716,0.000045659275,0.000060392395,0.00013568462,0.000025086043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010054762,0.00029031414,0.0002656367,0.0003553351,0.00032615394,0.00049753254,0.00064948987,0.0009764313,0.0065890164],"category_scores_gemma":[0.0064700735,0.00019586415,0.00041464524,0.00032931028,0.00028470112,0.00073358126,0.0002551762,0.00070856215,0.0021242748],"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":[0.00048756623,0.00019689642,0.007827912,0.00025092435,0.00010774037,0.00020738892,0.0002127097,0.41645908,0.019261576,0.23657808,0.023823528,0.29458648],"study_design_scores_gemma":[0.000033132394,0.000037992668,0.0017216502,0.000018336887,0.000020079775,0.000104056584,0.000023419552,0.88908553,0.0055192066,0.09969616,0.0037151591,0.000025355102],"about_ca_topic_score_codex":0.00795966,"about_ca_topic_score_gemma":0.006338279,"teacher_disagreement_score":0.00795966,"about_ca_system_score_codex":0.00068039284,"about_ca_system_score_gemma":0.00058422046,"threshold_uncertainty_score":0.022042513},"labels":[],"label_agreement":null},{"id":"W4409746284","doi":"10.1002/asmb.70012","title":"Assessing Latent Risk Based on Joint Modelling of Multiple Health Insurance Outcomes of Mixed Types","year":2025,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Statistical Methods and Bayesian Inference","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Actuarial science; Relevance (law); Health insurance; Disease; Latent class model; Health care; Econometrics; Medicine; Computer science; Business; Economics; Machine learning","score_opus":0.16753174942159374,"score_gpt":0.35249636489227565,"score_spread":0.1849646154706819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409746284","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":[0.488234,0.00017649683,0.50983053,0.0005821475,0.000024710942,0.00010473629,0.00023571926,0.00008490511,0.00072671764],"genre_scores_gemma":[0.9659588,0.000082037244,0.032668628,0.000041150015,0.000023643406,0.00014317212,0.00016516159,0.000014833303,0.0009026765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99370486,0.0045344247,0.0002214817,0.00075934787,0.0004046865,0.00037524704],"domain_scores_gemma":[0.94479626,0.047581002,0.00418248,0.00175813,0.0009130567,0.0007691026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015053336,0.0007660369,0.0014514149,0.0016339523,0.00056254957,0.0024526243,0.0014473358,0.001265061,0.001810791],"category_scores_gemma":[0.047020663,0.0007649864,0.0018850404,0.0011458462,0.001607791,0.0018062872,0.0020497844,0.0015988353,0.00017324637],"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":[0.0006041398,0.00038671805,0.14472987,0.00009944621,0.0007155526,0.00041899894,0.0010727968,0.7302697,0.0011616886,0.09605041,0.00046027097,0.02403046],"study_design_scores_gemma":[0.00001588361,0.00006854483,0.0056287562,0.00001790689,0.00005602811,0.00003710624,0.0001185371,0.96647036,0.00021422414,0.027190525,0.00015935782,0.000022780903],"about_ca_topic_score_codex":0.010797456,"about_ca_topic_score_gemma":0.0084646875,"teacher_disagreement_score":0.015053336,"about_ca_system_score_codex":0.0016425538,"about_ca_system_score_gemma":0.0014515014,"threshold_uncertainty_score":0.079610586},"labels":[],"label_agreement":null},{"id":"W4409771786","doi":"10.6000/1929-6029.2025.14.22","title":"Raking Method as a Tool for Improving Representativeness in Non-Probability Studies","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Statistics; Econometrics; Computer science; Data science; Psychology; Mathematics","score_opus":0.22934198842755865,"score_gpt":0.6295877522867993,"score_spread":0.40024576385924066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409771786","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":[0.0014480167,0.025358211,0.96702987,0.0016448552,0.00057878584,0.0013568399,0.00021815952,0.0004163411,0.00194889],"genre_scores_gemma":[0.040574756,0.015507815,0.93348646,0.0013828246,0.0006570199,0.0069518997,0.00029414476,0.00033815164,0.00080705027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.67775047,0.26520216,0.023473792,0.012155462,0.02058054,0.0008376138],"domain_scores_gemma":[0.44262153,0.48759332,0.022993267,0.026869802,0.019105839,0.00081622007],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22946557,0.0023594396,0.006641802,0.011159031,0.0021074393,0.0066042435,0.006112364,0.0037159068,0.0064289705],"category_scores_gemma":[0.51396894,0.0019038336,0.00786736,0.011287025,0.0060788933,0.005676038,0.0051272865,0.0057162237,0.0014435758],"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":[0.00038099667,0.00008891082,0.009110756,0.027069602,0.0074883704,0.0003170015,0.0039730286,0.019618647,0.0008056328,0.15542668,0.0104828775,0.76523745],"study_design_scores_gemma":[0.0007390296,0.0011058729,0.012878363,0.028238192,0.010433305,0.00210033,0.0015190333,0.08810694,0.00502462,0.6673264,0.18173215,0.0007957924],"about_ca_topic_score_codex":0.0031830932,"about_ca_topic_score_gemma":0.0031800899,"teacher_disagreement_score":0.22946557,"about_ca_system_score_codex":0.0031356674,"about_ca_system_score_gemma":0.009515054,"threshold_uncertainty_score":0.9502061},"labels":[],"label_agreement":null},{"id":"W4409800482","doi":"10.1002/sim.70074","title":"A Variance Estimator for Marginal Cox Regression Models Fit to Non‐Nested Multilevel Data","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","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":"Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Marginal model; Statistics; Estimator; Generalized linear model; Multilevel model; Generalized estimating equation; Mathematics; Regression analysis; Proportional hazards model; Covariate; Censored regression model; Variance (accounting); Econometrics","score_opus":0.18844865111631784,"score_gpt":0.4953526763331343,"score_spread":0.3069040252168165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409800482","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":[0.0011028851,0.00015374135,0.997971,0.0001515112,0.0000552499,0.00008819854,0.00009023174,0.00018853246,0.00019873516],"genre_scores_gemma":[0.0895064,0.00058705354,0.90450925,0.0004639306,0.00026088857,0.0015968077,0.0009059046,0.00046096538,0.0017087191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97625345,0.018095085,0.0007683353,0.002027913,0.0023079866,0.0005472218],"domain_scores_gemma":[0.94392854,0.0441467,0.0025000584,0.0047763847,0.004311077,0.0003372709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03705213,0.0012157358,0.0020930744,0.002071707,0.0006370315,0.0015411635,0.003507368,0.002222305,0.004377301],"category_scores_gemma":[0.1252724,0.0009950788,0.0034851877,0.0024054593,0.0012989047,0.002830513,0.0025529794,0.004529393,0.001159413],"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":[0.00034719618,0.00020709286,0.02196972,0.00089542137,0.001858471,0.00040730325,0.00067291997,0.16888548,0.0021733437,0.43721735,0.021485237,0.34388044],"study_design_scores_gemma":[0.00018114185,0.0003351414,0.0044795796,0.00029055413,0.00038316587,0.00038929007,0.00017036806,0.7324506,0.0014021507,0.23998149,0.019757012,0.00017958453],"about_ca_topic_score_codex":0.005078225,"about_ca_topic_score_gemma":0.0049360036,"teacher_disagreement_score":0.03705213,"about_ca_system_score_codex":0.0011658698,"about_ca_system_score_gemma":0.0031492782,"threshold_uncertainty_score":0.19595271},"labels":[],"label_agreement":null},{"id":"W4410143381","doi":"10.31219/osf.io/wzqxg_v2","title":"Bayesian estimation in multiple comparisons","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"Social Sciences and Humanities Research Council of Canada; Université Laval","keywords":"Estimation; Bayesian probability; Bayes estimator; Statistics; Econometrics; Computer science; Mathematics; Artificial intelligence; Economics","score_opus":0.11910840791339748,"score_gpt":0.4191065727002673,"score_spread":0.2999981647868698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410143381","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":[0.001029233,0.00063821743,0.9954776,0.0005956411,0.00023672807,0.00026331368,0.00015668377,0.00024162815,0.0013610288],"genre_scores_gemma":[0.040369716,0.0010807246,0.9528204,0.0005467546,0.00041326365,0.0027444141,0.0004522263,0.00029045925,0.0012821151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.82776606,0.14167514,0.004705286,0.01379687,0.01083128,0.0012252846],"domain_scores_gemma":[0.6912382,0.26570815,0.010212597,0.021015948,0.010883812,0.0009412872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13321899,0.003860555,0.006301706,0.005022382,0.002281165,0.0073493426,0.0077461563,0.0049996576,0.013633655],"category_scores_gemma":[0.40903237,0.0032029096,0.0052166525,0.0066571534,0.00667874,0.007415839,0.006177941,0.010518014,0.003305758],"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":[0.0005154316,0.00022536745,0.0047901515,0.002067449,0.0025885182,0.00039918997,0.001592637,0.07015052,0.00085867627,0.5938704,0.015318724,0.307623],"study_design_scores_gemma":[0.00015332832,0.00015143321,0.0012547773,0.00043020127,0.00025602055,0.00012652972,0.00020015107,0.13819687,0.00076038734,0.8444291,0.013953583,0.00008772051],"about_ca_topic_score_codex":0.0062774587,"about_ca_topic_score_gemma":0.005551091,"teacher_disagreement_score":0.13321899,"about_ca_system_score_codex":0.004364294,"about_ca_system_score_gemma":0.007902673,"threshold_uncertainty_score":0.70453775},"labels":[],"label_agreement":null},{"id":"W4410331985","doi":"10.1080/00949655.2025.2502547","title":"Comparison of computationally efficient approximate methods for nonlinear and generalized linear mixed effects models","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian Inference","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalized linear mixed model; Applied mathematics; Generalized linear model; Nonlinear system; Mixed model; Mathematical optimization; Statistics","score_opus":0.09282241199729441,"score_gpt":0.49996552272875705,"score_spread":0.40714311073146264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410331985","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":[0.013233038,0.0029263722,0.980357,0.0005455995,0.000074642805,0.0001787919,0.00027996782,0.00047418557,0.0019303983],"genre_scores_gemma":[0.09494485,0.0028562052,0.89801675,0.00030960114,0.000083260886,0.0012113752,0.00085838896,0.00042769045,0.0012919001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98404986,0.012996322,0.00055210054,0.00054239406,0.0016561556,0.00020321435],"domain_scores_gemma":[0.9242751,0.06766249,0.0017000156,0.0028956598,0.0031041256,0.0003625937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021824619,0.0011789736,0.0017933847,0.0017412952,0.00066678465,0.0019505463,0.003348329,0.0015550316,0.0041382615],"category_scores_gemma":[0.094992265,0.00064471585,0.0017902871,0.002714571,0.0010475852,0.002917869,0.0024468817,0.0021074554,0.0010634875],"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":[0.00089038647,0.0002361885,0.0048262035,0.0014875532,0.0007696333,0.00023442267,0.00072418706,0.5502329,0.0008707907,0.14284101,0.005638396,0.29124832],"study_design_scores_gemma":[0.00016714973,0.00013677408,0.0012080552,0.00022845568,0.00013387226,0.00018034696,0.00019884307,0.9349001,0.0005444163,0.056078847,0.0061677718,0.000055442462],"about_ca_topic_score_codex":0.00998169,"about_ca_topic_score_gemma":0.012987974,"teacher_disagreement_score":0.021824619,"about_ca_system_score_codex":0.0016719605,"about_ca_system_score_gemma":0.00415853,"threshold_uncertainty_score":0.11542094},"labels":[],"label_agreement":null},{"id":"W4410351772","doi":"10.1177/09622802251338387","title":"Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints on different scales","year":2025,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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":"Robarts Clinical Trials; Western University","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Estimator; Statistics; Wilcoxon signed-rank test; Confidence interval; Sample size determination; Mathematics; Fraction (chemistry); Variance (accounting); Coverage probability; Point estimation; Cluster (spacecraft); Rank (graph theory); Interval (graph theory); Econometrics; Computer science; Mann–Whitney U test","score_opus":0.19560503028694087,"score_gpt":0.6096647113663234,"score_spread":0.4140596810793825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410351772","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":[0.0009801699,0.00085055217,0.99562055,0.00019221104,0.00006889046,0.00087301276,0.000312593,0.00039735093,0.00070466904],"genre_scores_gemma":[0.055296253,0.0010824318,0.93207586,0.0006129546,0.00022012347,0.008177585,0.0010236298,0.00041454533,0.0010966182],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83272314,0.14836228,0.0046597407,0.0049811825,0.008536232,0.00073743094],"domain_scores_gemma":[0.6623189,0.2897534,0.018400053,0.020179635,0.008629505,0.00071844197],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15559705,0.0021058917,0.0047060535,0.0049891295,0.00060158677,0.0030838065,0.003683262,0.002436006,0.0108564235],"category_scores_gemma":[0.38192895,0.0010746584,0.0045193094,0.0053346287,0.0022255331,0.0031155755,0.0032097492,0.005680413,0.002205521],"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":[0.0017909771,0.0003140449,0.0065702237,0.0069532352,0.004827945,0.00019248822,0.00081353256,0.09634864,0.0014420663,0.3266417,0.023714844,0.53039026],"study_design_scores_gemma":[0.0019681335,0.0021105877,0.0068574464,0.0025350857,0.0024765118,0.00047658297,0.00029347176,0.40678063,0.004034773,0.529716,0.042466715,0.0002840392],"about_ca_topic_score_codex":0.0010419689,"about_ca_topic_score_gemma":0.001268722,"teacher_disagreement_score":0.84440297,"about_ca_system_score_codex":0.001811084,"about_ca_system_score_gemma":0.0036985008,"threshold_uncertainty_score":0.82288563},"labels":[],"label_agreement":null},{"id":"W4410784164","doi":"10.1186/s12874-025-02594-2","title":"Comparison of methods to handle missing values in a continuous index test in a diagnostic accuracy study – a simulation study","year":2025,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McGill University","funders":"Universitätsklinikum Hamburg-Eppendorf; Deutsche Forschungsgemeinschaft","keywords":"Missing data; Statistics; Covariate; Imputation (statistics); Sample size determination; Inverse probability weighting; Weighting; Correlation; Mathematics; Medicine; Estimator","score_opus":0.6339960035583913,"score_gpt":0.6978124382894738,"score_spread":0.06381643473108256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410784164","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":[0.4859695,0.007046556,0.49664804,0.0017583644,0.00029716152,0.0027556787,0.001062376,0.00057069084,0.003891723],"genre_scores_gemma":[0.8248186,0.00130059,0.16935502,0.00038816707,0.00011079495,0.0027926783,0.00074774196,0.00009093204,0.0003954717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9439428,0.050953086,0.0014750194,0.00143702,0.0016456961,0.0005463748],"domain_scores_gemma":[0.40690872,0.56535715,0.008363576,0.009452133,0.00849014,0.0014282869],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09983845,0.0013182542,0.0019977754,0.003082852,0.00071347604,0.0016421049,0.002960096,0.0028601314,0.0026284445],"category_scores_gemma":[0.23561038,0.0009817128,0.0045840987,0.0021392687,0.0012187405,0.0020795974,0.0017884618,0.002687409,0.00025932977],"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":[0.008290622,0.0010779346,0.06996684,0.0017958864,0.0042279596,0.0004243614,0.0008736577,0.8241999,0.00062249735,0.01334753,0.0022268712,0.07294586],"study_design_scores_gemma":[0.0011957476,0.0013903291,0.0068410966,0.00042983107,0.00072942954,0.00027529502,0.00013258879,0.97963834,0.00063622463,0.007641908,0.0009994044,0.00008989167],"about_ca_topic_score_codex":0.007539819,"about_ca_topic_score_gemma":0.0036702189,"teacher_disagreement_score":0.90016156,"about_ca_system_score_codex":0.0025786073,"about_ca_system_score_gemma":0.002652802,"threshold_uncertainty_score":0.5280025},"labels":[],"label_agreement":null},{"id":"W4411085620","doi":"10.1007/s42519-025-00461-3","title":"Analyzing Longitudinal Data with Nonignorable Missing Continuous Responses and Covariate Measurement Errors","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Mathematics; Missing data; Statistics; Longitudinal data; Econometrics; Data mining; Computer science","score_opus":0.1732997479902833,"score_gpt":0.4449640889402768,"score_spread":0.2716643409499935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411085620","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":[0.04100577,0.0011552944,0.9547766,0.0016734179,0.00019442715,0.00010667036,0.00049592205,0.00024425567,0.00034771214],"genre_scores_gemma":[0.5609543,0.0025270313,0.425575,0.0010360832,0.0008775619,0.0015154583,0.0023070227,0.00026824075,0.0049394155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9581747,0.03147677,0.002094408,0.004686768,0.002653459,0.00091392687],"domain_scores_gemma":[0.5858771,0.37735948,0.014546587,0.017252635,0.003985828,0.0009783502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.093508184,0.0016157066,0.0039608027,0.002345501,0.001294132,0.0028727849,0.005481005,0.0039007824,0.0043958095],"category_scores_gemma":[0.2792961,0.0022241645,0.0030748188,0.0039462857,0.0035153483,0.0048221783,0.0033573299,0.0043866825,0.0006010944],"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":[0.0020204645,0.0010183266,0.10500125,0.0025420925,0.005763942,0.0025030868,0.002695393,0.16920523,0.0024431588,0.42722365,0.008470494,0.271113],"study_design_scores_gemma":[0.00019395901,0.00038589732,0.010087857,0.00027424042,0.0007182931,0.00052001403,0.0003561079,0.38952607,0.0010245042,0.5936565,0.0031749767,0.00008170065],"about_ca_topic_score_codex":0.0041843415,"about_ca_topic_score_gemma":0.0030818982,"teacher_disagreement_score":0.093508184,"about_ca_system_score_codex":0.0013629828,"about_ca_system_score_gemma":0.0048922603,"threshold_uncertainty_score":0.49452448},"labels":[],"label_agreement":null},{"id":"W4411370832","doi":"10.31219/osf.io/wzqxg_v3","title":"Bayesian estimation in multiple comparisons","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"Social Sciences and Humanities Research Council of Canada; Université Laval","keywords":"Estimation; Bayesian probability; Bayes estimator; Econometrics; Statistics; Computer science; Artificial intelligence; Mathematics; Economics","score_opus":0.11910840791339748,"score_gpt":0.4191065727002673,"score_spread":0.2999981647868698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411370832","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":[0.001029233,0.00063821743,0.9954776,0.0005956411,0.00023672807,0.00026331368,0.00015668377,0.00024162815,0.0013610288],"genre_scores_gemma":[0.040369716,0.0010807246,0.9528204,0.0005467546,0.00041326365,0.0027444141,0.0004522263,0.00029045925,0.0012821151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.82776606,0.14167514,0.004705286,0.01379687,0.01083128,0.0012252846],"domain_scores_gemma":[0.6912382,0.26570815,0.010212597,0.021015948,0.010883812,0.0009412872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13321899,0.003860555,0.006301706,0.005022382,0.002281165,0.0073493426,0.0077461563,0.0049996576,0.013633655],"category_scores_gemma":[0.40903237,0.0032029096,0.0052166525,0.0066571534,0.00667874,0.007415839,0.006177941,0.010518014,0.003305758],"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":[0.0005154316,0.00022536745,0.0047901515,0.002067449,0.0025885182,0.00039918997,0.001592637,0.07015052,0.00085867627,0.5938704,0.015318724,0.307623],"study_design_scores_gemma":[0.00015332832,0.00015143321,0.0012547773,0.00043020127,0.00025602055,0.00012652972,0.00020015107,0.13819687,0.00076038734,0.8444291,0.013953583,0.00008772051],"about_ca_topic_score_codex":0.0062774587,"about_ca_topic_score_gemma":0.005551091,"teacher_disagreement_score":0.13321899,"about_ca_system_score_codex":0.004364294,"about_ca_system_score_gemma":0.007902673,"threshold_uncertainty_score":0.70453775},"labels":[],"label_agreement":null},{"id":"W4411643745","doi":"10.1017/s0272263125100922","title":"Bayesian estimation in multiple comparisons","year":2025,"lang":"en","type":"article","venue":"Studies in Second Language Acquisition","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":"Université Laval","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Bayesian probability; Estimation; Bayes estimator; Statistics; Computer science; Econometrics; Mathematics; Economics","score_opus":0.06067515629988454,"score_gpt":0.42147563300672614,"score_spread":0.36080047670684157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411643745","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":[0.0019799408,0.00038546746,0.99528545,0.0005079768,0.00018368037,0.000326646,0.00008873854,0.00018221482,0.0010599548],"genre_scores_gemma":[0.0756594,0.0004957273,0.91959095,0.00040048498,0.0002505547,0.0023841637,0.00022416096,0.00013813753,0.00085643056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.69827735,0.26473862,0.0060773147,0.015633317,0.013928119,0.0013452441],"domain_scores_gemma":[0.52335745,0.42675728,0.013649925,0.024728708,0.010567969,0.0009386717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18878794,0.0032737711,0.0059764194,0.005680227,0.0023925514,0.006140318,0.008238996,0.0047697937,0.009534082],"category_scores_gemma":[0.50852036,0.0029513864,0.004939377,0.0071207816,0.008251197,0.0062228967,0.0057839444,0.01113709,0.0016579175],"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":[0.0006108815,0.0002839154,0.0065567624,0.0016861551,0.0032841768,0.00039990214,0.0019359632,0.092896536,0.0008049921,0.5780587,0.008763649,0.30471843],"study_design_scores_gemma":[0.00016625815,0.00019578416,0.001670279,0.00043895814,0.0002724293,0.00011877063,0.00021417382,0.23574318,0.000851884,0.753367,0.006868412,0.000092914976],"about_ca_topic_score_codex":0.0064609237,"about_ca_topic_score_gemma":0.005881462,"teacher_disagreement_score":0.18878794,"about_ca_system_score_codex":0.0049418313,"about_ca_system_score_gemma":0.0061756433,"threshold_uncertainty_score":0.998418},"labels":[],"label_agreement":null},{"id":"W4411730200","doi":"10.1016/j.spl.2025.110494","title":"Hypothetical versus real life predictions for clusters based finite population total using count and binary survey data","year":2025,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Bayesian Inference","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":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Count data; Statistics; Binary data; Binary number; Population; Demography; Poisson distribution","score_opus":0.1923379074008679,"score_gpt":0.4073272211415165,"score_spread":0.21498931374064859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411730200","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":[0.65372545,0.0006752829,0.3228786,0.008110582,0.0002588561,0.00013827531,0.0016209533,0.0005231336,0.012068892],"genre_scores_gemma":[0.9841526,0.00020782376,0.012603255,0.00044484495,0.0001342862,0.00008749151,0.0005359605,0.00006858924,0.0017649863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958605,0.0023958066,0.00013544694,0.00095494016,0.00031169062,0.0003415162],"domain_scores_gemma":[0.83381736,0.1495419,0.0058366973,0.005972017,0.002353891,0.00247817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01665954,0.00063742965,0.0014835035,0.001975556,0.0009408748,0.0039816024,0.004463544,0.002541904,0.0074813417],"category_scores_gemma":[0.1261035,0.0007325896,0.0013109153,0.0015010358,0.00547074,0.007409341,0.0019541653,0.0027235667,0.0005460352],"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":[0.0005080837,0.00011561604,0.021928608,0.00024140142,0.00016615368,0.000308389,0.0014481752,0.3005844,0.00024170321,0.65637,0.0057993424,0.0122881],"study_design_scores_gemma":[0.000044049648,0.00004042755,0.004700722,0.00006330386,0.000046262445,0.00014178884,0.00047215127,0.5421312,0.00013290987,0.45162067,0.00056600804,0.000040505656],"about_ca_topic_score_codex":0.010240276,"about_ca_topic_score_gemma":0.007191687,"teacher_disagreement_score":0.01665954,"about_ca_system_score_codex":0.002151861,"about_ca_system_score_gemma":0.0009527498,"threshold_uncertainty_score":0.08810514},"labels":[],"label_agreement":null},{"id":"W4411835771","doi":"10.1017/psy.2025.10016","title":"Item Response Models for Rating Relational Data","year":2025,"lang":"en","type":"article","venue":"Psychometrika","topic":"Statistical Methods and Bayesian 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":"Kellogg's (Canada)","funders":"National Science and Technology Council","keywords":"Computer science; Markov chain Monte Carlo; Cluster analysis; Data mining; Curse of dimensionality; Item response theory; Bayesian probability; Markov chain; Relational model; Bayesian network; Relational database; Machine learning; Econometrics; Artificial intelligence; Mathematics; Statistics; Psychometrics","score_opus":0.35311617672425644,"score_gpt":0.4910224467680542,"score_spread":0.13790627004379774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411835771","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":[0.0068036993,0.0003541955,0.988436,0.0007223738,0.000063984335,0.0005087675,0.0014278797,0.00036538852,0.0013177836],"genre_scores_gemma":[0.24782287,0.0012548416,0.7308944,0.000959526,0.00034021586,0.0077851093,0.0068814037,0.00025026815,0.003811349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9353864,0.053893536,0.0017804044,0.00479419,0.003503045,0.0006423775],"domain_scores_gemma":[0.84898657,0.12280539,0.0080836825,0.013987545,0.005463213,0.00067350105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04732907,0.0022776772,0.002687998,0.003487734,0.0010777988,0.003504757,0.006560252,0.004359885,0.010494432],"category_scores_gemma":[0.15903318,0.0014837295,0.0026549487,0.007379072,0.0021238574,0.005645468,0.002457687,0.0059430636,0.0048580975],"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":[0.00027846333,0.0005123976,0.020390715,0.0010115346,0.0010611874,0.0003700915,0.0018540999,0.18933235,0.00094632077,0.65803623,0.01305734,0.11314926],"study_design_scores_gemma":[0.000106816435,0.00016881588,0.004810305,0.00017964428,0.00012922878,0.00024109383,0.00023459656,0.52432764,0.0003279242,0.45990846,0.009447144,0.00011834906],"about_ca_topic_score_codex":0.0045218845,"about_ca_topic_score_gemma":0.003665789,"teacher_disagreement_score":0.04732907,"about_ca_system_score_codex":0.0030177443,"about_ca_system_score_gemma":0.0014224463,"threshold_uncertainty_score":0.25030303},"labels":[],"label_agreement":null},{"id":"W4411873900","doi":"10.5539/ijsp.v14n2p55","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 14, No. 2","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.04997058575948823,"score_gpt":0.3943879030869853,"score_spread":0.3444173173274971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411873900","genre_codex":"editorial","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":[0.000119606615,0.0025191393,0.0016253243,0.12133128,0.870694,0.00053673435,0.0007168255,0.000561701,0.0018954988],"genre_scores_gemma":[0.0041413335,0.0058019366,0.004261521,0.15859964,0.77820677,0.0033141414,0.001576802,0.0017452625,0.04235261],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92305726,0.015178664,0.015659522,0.0054404205,0.038305655,0.0023584568],"domain_scores_gemma":[0.13318126,0.040337138,0.011503097,0.0068157753,0.7992271,0.008935633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05862579,0.0036227782,0.010375116,0.012954225,0.004917533,0.011910094,0.0060911155,0.01838198,0.09007652],"category_scores_gemma":[0.5355007,0.0020131797,0.006260361,0.0056694862,0.0041147177,0.0074160807,0.004352006,0.0142857535,0.06442106],"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":[0.000036271846,0.000004468447,0.00007247243,0.00038449574,0.000012368338,0.000053569514,0.000030585987,0.00001090837,0.00003673011,0.000108000706,0.99621874,0.0030313286],"study_design_scores_gemma":[0.0003753496,0.00007860076,0.0015204052,0.004586253,0.00016820662,0.0014697978,0.00043850584,0.0006941744,0.00042542664,0.002920775,0.98707783,0.00024464235],"about_ca_topic_score_codex":0.0034208584,"about_ca_topic_score_gemma":0.0050424566,"teacher_disagreement_score":0.09007652,"about_ca_system_score_codex":0.0051841424,"about_ca_system_score_gemma":0.011148717,"threshold_uncertainty_score":0.3100465},"labels":[],"label_agreement":null},{"id":"W4413298197","doi":"10.1177/09622802251362642","title":"Imputation of incomplete ordinal and nominal data by predictive mean matching","year":2025,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian 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":"Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Categorical variable; Missing data; Imputation (statistics); Statistics; Multinomial logistic regression; Mathematics; Ordinal data; Logistic regression; Ordinal regression; Regression analysis; Econometrics; Computer science","score_opus":0.23644908515090432,"score_gpt":0.619632349005499,"score_spread":0.38318326385459467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413298197","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":[0.00873054,0.0005763416,0.98702884,0.00029644553,0.00019461854,0.0002623906,0.00093462155,0.0008374188,0.0011388323],"genre_scores_gemma":[0.17974582,0.0007494167,0.8106193,0.00058972614,0.0002147297,0.0015123374,0.004229957,0.0004920385,0.0018466541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9668032,0.023961117,0.0018017363,0.0028115402,0.0039422205,0.00068026426],"domain_scores_gemma":[0.91394067,0.059354056,0.005395551,0.014238064,0.0065132515,0.0005584132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04089844,0.0011880555,0.0031912266,0.0030981663,0.0011390119,0.0030024168,0.003972653,0.0017573861,0.009889618],"category_scores_gemma":[0.16127473,0.0011501889,0.0034312326,0.006468124,0.001273631,0.002802102,0.0039819805,0.004511255,0.0026938925],"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":[0.0017382224,0.00034262933,0.051166825,0.001933707,0.002207045,0.00080058875,0.001216948,0.1590863,0.0020512405,0.10759936,0.027348842,0.6445083],"study_design_scores_gemma":[0.00041829338,0.0003081121,0.008710013,0.0006695148,0.0004854774,0.00062282925,0.00018325457,0.72836936,0.003295367,0.23416278,0.022601731,0.00017330579],"about_ca_topic_score_codex":0.0033646133,"about_ca_topic_score_gemma":0.003063938,"teacher_disagreement_score":0.04089844,"about_ca_system_score_codex":0.0014111021,"about_ca_system_score_gemma":0.0043187975,"threshold_uncertainty_score":0.21629423},"labels":[],"label_agreement":null},{"id":"W4413306953","doi":"10.1002/cjs.70015","title":"How to measure statistical evidence and its strength: Bayes factors or relative belief ratios?","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Measure (data warehouse); Bayes' theorem; Statistics; Bayes factor; Mathematics; Econometrics; Computer science; Bayesian probability; Data mining","score_opus":0.11475130780137843,"score_gpt":0.35713759361814135,"score_spread":0.24238628581676291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413306953","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":[0.008925694,0.02599422,0.9220733,0.026980704,0.0018513862,0.00025758526,0.00050599675,0.00033175029,0.013079443],"genre_scores_gemma":[0.45288026,0.009953042,0.51911926,0.008274325,0.0060325186,0.001048919,0.00024980996,0.00037317173,0.0020686202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8442515,0.108499594,0.012047269,0.010543253,0.023257878,0.0014004962],"domain_scores_gemma":[0.41293013,0.50839424,0.02828576,0.026421187,0.021002503,0.0029661157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14937967,0.0026920086,0.0063854856,0.01845299,0.0022402809,0.016627068,0.0069692386,0.008918875,0.005630866],"category_scores_gemma":[0.5392921,0.002108946,0.0036687914,0.010656245,0.027927,0.034626137,0.008321605,0.014045939,0.0016275303],"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":[0.00017382352,0.00004873818,0.0025983136,0.0011969015,0.00065945956,0.000097300486,0.0007120479,0.00466184,0.0004349865,0.9085178,0.0051443004,0.07575449],"study_design_scores_gemma":[0.000028174285,0.000038772057,0.0005208678,0.0005918845,0.00010690638,0.00016874404,0.00015057852,0.006791907,0.00034045288,0.9867143,0.004470956,0.00007642127],"about_ca_topic_score_codex":0.0021444182,"about_ca_topic_score_gemma":0.0010695935,"teacher_disagreement_score":0.14937967,"about_ca_system_score_codex":0.0039978754,"about_ca_system_score_gemma":0.002648782,"threshold_uncertainty_score":0.7900047},"labels":[],"label_agreement":null},{"id":"W4413626650","doi":"10.1111/jebm.70058","title":"A Systematic Survey of the Optimal Strategy for Dealing With Missing Binary Outcomes in Simulation Studies of Randomized Controlled Trials","year":2025,"lang":"en","type":"article","venue":"Journal of Evidence-Based Medicine","topic":"Statistical Methods and Bayesian 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":"McMaster University; Impact","funders":"National Natural Science Foundation of China","keywords":"Missing data; Randomized controlled trial; Statistics; Imputation (statistics); Meta-analysis; Statistical power; Computer science; MEDLINE; Descriptive statistics; Psychology; Econometrics; Medicine; Mathematics","score_opus":0.484340746084108,"score_gpt":0.5428199664858457,"score_spread":0.05847922040173775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413626650","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":[0.001146722,0.9754252,0.015515688,0.0038628748,0.00056213525,0.0019352733,0.00065447105,0.00009351719,0.0008041392],"genre_scores_gemma":[0.042985532,0.86950845,0.07079429,0.004306961,0.0004791321,0.010740184,0.000867817,0.00012829927,0.0001893894],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.5361184,0.29659525,0.13750133,0.007942986,0.020847142,0.0009948594],"domain_scores_gemma":[0.1483331,0.786617,0.03512926,0.009879897,0.019243337,0.00079735403],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.31727886,0.003211208,0.015176622,0.019658366,0.0014365489,0.009897031,0.0050872676,0.006596493,0.005911999],"category_scores_gemma":[0.73009175,0.003970553,0.018294698,0.014980787,0.0040584086,0.010319865,0.0046966537,0.0054407557,0.0009225409],"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":[0.0008202936,0.000036821464,0.001601571,0.8140092,0.015631843,0.00007368203,0.00076218945,0.0019005563,0.00015429509,0.0061485483,0.0026837462,0.15617731],"study_design_scores_gemma":[0.0007802923,0.00034668576,0.0011404188,0.9429342,0.02541695,0.0002685042,0.00039765806,0.0017300879,0.00041044742,0.009033251,0.017428983,0.000112532754],"about_ca_topic_score_codex":0.0053202235,"about_ca_topic_score_gemma":0.007643208,"teacher_disagreement_score":0.68272114,"about_ca_system_score_codex":0.011357093,"about_ca_system_score_gemma":0.027520347,"threshold_uncertainty_score":0.8419167},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["metaresearch"],"domain":"methods","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4413855990","doi":"10.5539/ijsp.v14n3p94","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 14, No. 3","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Probability and statistics; Mathematical economics","score_opus":0.050709953823087184,"score_gpt":0.3945072903970635,"score_spread":0.3437973365739763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413855990","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013706795,0.002561706,0.0019127462,0.13052832,0.8607799,0.000608399,0.00075576664,0.0006368762,0.002079179],"genre_scores_gemma":[0.005011811,0.0064991675,0.005083946,0.17603171,0.7573061,0.0035897647,0.0016334917,0.0019445943,0.042899385],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9272747,0.013981666,0.015108592,0.0049236193,0.036418196,0.0022932794],"domain_scores_gemma":[0.13287923,0.03887508,0.011733621,0.0065476066,0.8011707,0.008793735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057653055,0.00329989,0.010041693,0.0118655525,0.0048750825,0.011407848,0.0060627754,0.018344745,0.08697535],"category_scores_gemma":[0.5250291,0.0018605731,0.006656352,0.005631082,0.004273168,0.0066182544,0.0040971944,0.014434916,0.06326804],"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":[0.00003715483,0.000004431752,0.00008088026,0.00038366677,0.000012823001,0.000057541045,0.000032462453,0.00001186367,0.000037435067,0.00012180905,0.9958544,0.003365667],"study_design_scores_gemma":[0.0003709584,0.00007353721,0.0015252689,0.0046908325,0.00018085139,0.0015113358,0.00044274892,0.00073808053,0.00044806788,0.003464519,0.98630667,0.00024700508],"about_ca_topic_score_codex":0.003727144,"about_ca_topic_score_gemma":0.005380121,"teacher_disagreement_score":0.08697535,"about_ca_system_score_codex":0.005443205,"about_ca_system_score_gemma":0.011863826,"threshold_uncertainty_score":0.30490214},"labels":[],"label_agreement":null},{"id":"W4414159953","doi":"10.1016/j.sste.2025.100742","title":"Variable Screening Methods in Conditional Logistic Individual Level Models of Disease Spread","year":2025,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Overfitting; Akaike information criterion; Feature selection; Logistic regression; Information Criteria; Variable (mathematics); Selection (genetic algorithm)","score_opus":0.32417506934308643,"score_gpt":0.4696500327162242,"score_spread":0.14547496337313776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414159953","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":[0.0071013025,0.00023457855,0.9912971,0.00031518223,0.000023566752,0.00004996824,0.00013403769,0.00023673402,0.00060763664],"genre_scores_gemma":[0.3979253,0.0009573333,0.5932821,0.0005468842,0.00019313183,0.00088683865,0.0012209644,0.00025092036,0.0047364915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946844,0.004066769,0.00015059156,0.00051530456,0.00042011216,0.00016285671],"domain_scores_gemma":[0.9774156,0.019683642,0.0011361224,0.0007374229,0.0008316176,0.00019559804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011445936,0.0010625791,0.00093750795,0.0014571595,0.0004580028,0.0011804467,0.002514643,0.0012210134,0.0035073715],"category_scores_gemma":[0.033464286,0.0005555263,0.0017810169,0.0015508557,0.0015421176,0.0015785423,0.0020462312,0.0025718366,0.00059316884],"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":[0.00016755713,0.00008977381,0.010635141,0.00030965867,0.00033961772,0.00023079712,0.00033150663,0.6836162,0.00081134203,0.18538211,0.0043471935,0.11373903],"study_design_scores_gemma":[0.000020259908,0.000039070244,0.0007865646,0.000042036794,0.000025183079,0.00004332195,0.000023179922,0.94768625,0.00024656986,0.04967319,0.0013934209,0.000021043257],"about_ca_topic_score_codex":0.0066659222,"about_ca_topic_score_gemma":0.005103337,"teacher_disagreement_score":0.011445936,"about_ca_system_score_codex":0.0010502568,"about_ca_system_score_gemma":0.0018103187,"threshold_uncertainty_score":0.06053263},"labels":[],"label_agreement":null},{"id":"W4415331273","doi":"10.18690/rei.4907","title":"New Polystochastic Statistical Inference in Social Sciences - Defining new Rules and Thresholds","year":2025,"lang":"en","type":"article","venue":"Revija za elementarno izobraževanje","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"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":"University of Sydney; Egg Farmers of Canada","keywords":"Statistical inference; Inference; Fiducial inference; Null hypothesis; Statistical hypothesis testing; Frequentist inference; Bayesian inference; Bayesian probability; Type I and type II errors","score_opus":0.08013739530444743,"score_gpt":0.443433400421961,"score_spread":0.3632960051175136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415331273","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":[0.002140054,0.0013866128,0.9903351,0.0031289654,0.00027448114,0.00013118955,0.00011424706,0.00009424741,0.0023950422],"genre_scores_gemma":[0.12448151,0.0028510254,0.8649835,0.003022694,0.0012606232,0.0015751781,0.00022040546,0.00022552723,0.0013795543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85012007,0.10871578,0.009064518,0.012247414,0.01879974,0.001052449],"domain_scores_gemma":[0.6097612,0.34002718,0.013085925,0.022606833,0.01242369,0.0020951605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16875161,0.0015823367,0.0043993904,0.0056418446,0.0020782077,0.0098628905,0.0054644574,0.005034687,0.0030269467],"category_scores_gemma":[0.36955693,0.0019095878,0.0036294698,0.0044952016,0.024202142,0.014045218,0.010486839,0.01629997,0.0010112976],"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":[0.000048746322,0.000028477249,0.001339982,0.00039464099,0.00016957927,0.00008467202,0.00073487335,0.0048166835,0.00026616277,0.95748824,0.0011639756,0.03346401],"study_design_scores_gemma":[0.000018860825,0.000029303847,0.00026018245,0.00015171504,0.000024782581,0.00005078765,0.000043854696,0.012949838,0.00023424675,0.9835788,0.0026317362,0.000025992242],"about_ca_topic_score_codex":0.0027595665,"about_ca_topic_score_gemma":0.0017415092,"teacher_disagreement_score":0.16875161,"about_ca_system_score_codex":0.004646973,"about_ca_system_score_gemma":0.0069310036,"threshold_uncertainty_score":0.8924545},"labels":[],"label_agreement":null},{"id":"W4415506485","doi":"10.1136/bmj-2025-084194","title":"Covariate adjustment in cluster randomised trials: a practical guide","year":2025,"lang":"en","type":"article","venue":"BMJ","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Ottawa Hospital; University of Ottawa","funders":"UC Berkeley College of Chemistry; Collaboration for Leadership in Applied Health Research and Care - Greater Manchester; Medical Research Council; National Institute for Health and Care Research","keywords":"Covariate; Missing data; Outcome (game theory); Imputation (statistics); Cluster (spacecraft); Confidence interval; Identification (biology)","score_opus":0.2000691977266834,"score_gpt":0.5172433151332554,"score_spread":0.317174117406572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415506485","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":[0.00037991247,0.021276955,0.903129,0.02884136,0.0044870926,0.012868732,0.0060540293,0.007600079,0.015362835],"genre_scores_gemma":[0.0021054868,0.009158311,0.95847446,0.005757604,0.0017017408,0.015092399,0.0011215723,0.0011975091,0.0053908424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9209364,0.06072406,0.010382233,0.0019013151,0.0056133317,0.0004426729],"domain_scores_gemma":[0.78134954,0.17994119,0.009186522,0.008074432,0.018881502,0.0025667404],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07162493,0.0037621579,0.004886284,0.00814309,0.0010040356,0.0055063437,0.005937117,0.008282526,0.05124707],"category_scores_gemma":[0.25116572,0.0042317584,0.0045367344,0.0064745625,0.003047984,0.004920705,0.0035429767,0.013709851,0.036508415],"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":[0.00051101216,0.00020339768,0.0004360233,0.012518829,0.00062833907,0.00050344673,0.00096988276,0.007870239,0.0006876279,0.047070317,0.5577069,0.37089399],"study_design_scores_gemma":[0.001977048,0.00044069902,0.0009313423,0.012461686,0.0004129107,0.001542909,0.0003050341,0.016157633,0.0007666524,0.1826072,0.7820788,0.00031799538],"about_ca_topic_score_codex":0.0028461246,"about_ca_topic_score_gemma":0.004854818,"teacher_disagreement_score":0.92837507,"about_ca_system_score_codex":0.0025845452,"about_ca_system_score_gemma":0.011114909,"threshold_uncertainty_score":0.37879336},"labels":[],"label_agreement":null},{"id":"W4415740629","doi":"10.31223/x5kf39","title":"A Weighted Fitting Approach for Diameter Distributions from Horizontal Point Sampling","year":2025,"lang":"","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","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":true,"ca_institutions":"Western Forest Products; University of British Columbia; Natural Resources Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Probability distribution; Probability density function; Sampling distribution; Equivalence (formal languages); Sampling (signal processing); Software; Gamma distribution; Point (geometry)","score_opus":0.0672699832812723,"score_gpt":0.3729454148385282,"score_spread":0.3056754315572559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415740629","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":[0.0023849308,0.000033826786,0.9964516,0.00003722622,0.000013141028,0.00003086418,0.00007743915,0.00041022617,0.0005608023],"genre_scores_gemma":[0.09720429,0.00015684494,0.89787364,0.00016460707,0.00007517698,0.00034396234,0.0007443366,0.0009110624,0.0025260232],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99644935,0.0013110405,0.00018985373,0.00086718146,0.0010602236,0.00012238907],"domain_scores_gemma":[0.98755497,0.007184693,0.00091699493,0.0024178785,0.0017877397,0.00013771067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066908766,0.0011717131,0.00082228216,0.0023675314,0.00083286635,0.0018992561,0.0027705692,0.0012097561,0.007463437],"category_scores_gemma":[0.03906877,0.0009796489,0.0013942643,0.0027955715,0.0011341117,0.0032012856,0.0027654448,0.0021425385,0.0022273667],"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":[0.00015426654,0.00011242765,0.0099489,0.00042445477,0.00024281039,0.00029481202,0.0009795997,0.26674688,0.010312118,0.19151844,0.009691651,0.5095737],"study_design_scores_gemma":[0.000021551183,0.00007454935,0.0022342552,0.00008909677,0.000037269827,0.00023892519,0.00015666675,0.8352534,0.0041025896,0.14366272,0.014057479,0.00007146398],"about_ca_topic_score_codex":0.0056966464,"about_ca_topic_score_gemma":0.007612527,"teacher_disagreement_score":0.007463437,"about_ca_system_score_codex":0.0010828731,"about_ca_system_score_gemma":0.0014310401,"threshold_uncertainty_score":0.03538513},"labels":[],"label_agreement":null},{"id":"W4415966503","doi":"10.1101/2025.11.03.25339124","title":"A Generalizable Distribution Structure Analysis Algorithm with Audit-Ready Framework for Medical Research","year":2025,"lang":"","type":"preprint","venue":"medRxiv","topic":"Statistical Methods and Bayesian 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":"L'Alliance Boviteq","funders":"","keywords":"Audit; Statistical inference; Parametric statistics; Identification (biology); Causal inference; Inference; Statistical hypothesis testing; Data quality","score_opus":0.07896302758485607,"score_gpt":0.44981059063320467,"score_spread":0.3708475630483486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415966503","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":[0.0016361807,0.000047155314,0.99473816,0.00012210516,0.000011334157,0.00018141101,0.00009944231,0.0029229044,0.0002413179],"genre_scores_gemma":[0.042781133,0.000047382575,0.9557516,0.00009401481,0.000019203644,0.00047308434,0.00031784305,0.00019898229,0.00031671408],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99176264,0.0040882453,0.00087118556,0.0011818262,0.0018221056,0.000273942],"domain_scores_gemma":[0.9586733,0.028287798,0.0023637125,0.0038392572,0.0061290455,0.00070684013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016956998,0.0012893326,0.0013254447,0.0039166166,0.00084436644,0.0030241078,0.0029712883,0.0014526974,0.004932784],"category_scores_gemma":[0.07648479,0.00092593924,0.0016117916,0.0023887076,0.0014127984,0.0027036758,0.004176701,0.0025856367,0.0016669316],"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":[0.0007496287,0.0003000802,0.01065261,0.0005117565,0.00020346795,0.00041850566,0.0006486271,0.23623697,0.0037828907,0.077912025,0.010719687,0.65786374],"study_design_scores_gemma":[0.00011611861,0.00004930021,0.00033728674,0.000050317223,0.000026086514,0.00009330641,0.000034786975,0.94525844,0.0015688515,0.049658634,0.0027853295,0.000021492548],"about_ca_topic_score_codex":0.005365309,"about_ca_topic_score_gemma":0.0048312093,"teacher_disagreement_score":0.016956998,"about_ca_system_score_codex":0.0019725647,"about_ca_system_score_gemma":0.007560854,"threshold_uncertainty_score":0.08967829},"labels":[],"label_agreement":null},{"id":"W4416051414","doi":"10.48550/arxiv.2508.15665","title":"Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"European and Developing Countries Clinical Trials Partnership; Medical Research Council; National Institutes of Health; Foreign, Commonwealth and Development Office; Engineering and Physical Sciences Research Council; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; European Commission; Bill and Melinda Gates Foundation","keywords":"Inference; Bayes' theorem; Bayesian inference; Bayesian probability; Human immunodeficiency virus (HIV); Gaussian process; Markov chain Monte Carlo; Monte Carlo method","score_opus":0.09788856410248475,"score_gpt":0.3799266372202439,"score_spread":0.28203807311775914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416051414","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":[0.002345796,0.00010202829,0.9961409,0.000112556765,0.000019181301,0.00002950509,0.00017466233,0.0003609765,0.0007144418],"genre_scores_gemma":[0.1256087,0.0003928948,0.86745644,0.00020050631,0.00011870207,0.00034161165,0.0011520843,0.0005001799,0.004228955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786013,0.0011806953,0.000118066084,0.00023222809,0.00046875028,0.00014004258],"domain_scores_gemma":[0.98852473,0.009165351,0.00038034294,0.0007478841,0.0010227547,0.00015884786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007108127,0.00071783166,0.0015261016,0.00159127,0.0006128489,0.0018979098,0.0023381305,0.0011495304,0.008178424],"category_scores_gemma":[0.035658605,0.0012299715,0.0012524298,0.0019687281,0.0009771432,0.0018442256,0.0021414256,0.002737308,0.0017929348],"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":[0.00010428661,0.00007641576,0.002786566,0.00020381423,0.0001277918,0.0001102781,0.00014222109,0.71527183,0.00081812934,0.16245885,0.007529119,0.11037062],"study_design_scores_gemma":[0.000014726954,0.000005275703,0.00019602223,0.000018226649,0.000005792044,0.000012525331,0.000009333263,0.951914,0.00020335459,0.046357173,0.0012558572,0.000007689378],"about_ca_topic_score_codex":0.015262505,"about_ca_topic_score_gemma":0.022078315,"teacher_disagreement_score":0.015262505,"about_ca_system_score_codex":0.0016283472,"about_ca_system_score_gemma":0.0035742943,"threshold_uncertainty_score":0.037591815},"labels":[],"label_agreement":null},{"id":"W4416182859","doi":"10.1002/cjs.70025","title":"Receiver operating characteristic curve analysis with non‐ignorable missing disease status","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Receiver operating characteristic; Identifiability; Estimator; Missing data; Confidence interval; Point estimation; Logistic regression; Maximum likelihood","score_opus":0.02853840166451193,"score_gpt":0.3159132295253869,"score_spread":0.287374827860875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416182859","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":[0.04716967,0.0033211485,0.94589615,0.0011978841,0.000096541706,0.00012727814,0.0005343332,0.00047856884,0.0011784048],"genre_scores_gemma":[0.8052205,0.0012801613,0.19079599,0.0003755933,0.00025750048,0.00026084608,0.00097496266,0.00009757095,0.0007368727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9735493,0.020389048,0.0009076869,0.0022284982,0.0024465187,0.00047902885],"domain_scores_gemma":[0.8368637,0.13377693,0.010995749,0.011431311,0.006154052,0.00077831896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047039405,0.0009367955,0.002094208,0.004109783,0.0005192779,0.0023288901,0.0022453107,0.0021037424,0.0009991328],"category_scores_gemma":[0.16843773,0.00045065914,0.0016625435,0.003557952,0.0017075243,0.0022561173,0.001709998,0.0021560343,0.00039772614],"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":[0.0013264031,0.0003996971,0.1383642,0.0013358505,0.0025061413,0.0019804349,0.0009409662,0.4507142,0.0037026645,0.091673754,0.008545289,0.2985104],"study_design_scores_gemma":[0.00007809558,0.00044271658,0.020930586,0.00019268198,0.0003561372,0.0012545167,0.0001438746,0.8864628,0.0017864122,0.08279653,0.0054459875,0.00010974951],"about_ca_topic_score_codex":0.0026396234,"about_ca_topic_score_gemma":0.0009433194,"teacher_disagreement_score":0.047039405,"about_ca_system_score_codex":0.0011772846,"about_ca_system_score_gemma":0.0019290679,"threshold_uncertainty_score":0.24877113},"labels":[],"label_agreement":null},{"id":"W4416205280","doi":"10.1016/j.jtbi.2025.112290","title":"Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature","year":2025,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Statistical Methods and Bayesian Inference","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":"University of Waterloo","funders":"Medical Research Council; National Institutes of Health; Foreign, Commonwealth and Development Office; Engineering and Physical Sciences Research Council; University of Waterloo; Joint United Nations Programme on HIV/AIDS; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; European Commission; Bill and Melinda Gates Foundation","keywords":"Inference; Bayes' theorem; Bayesian inference; Gaussian process; Human immunodeficiency virus (HIV); Bayesian probability; Monte Carlo method; Markov chain Monte Carlo","score_opus":0.028974863909663976,"score_gpt":0.3738171154192822,"score_spread":0.3448422515096182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416205280","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":[0.0024679091,0.000077013974,0.99624074,0.00009253636,0.000016309621,0.000027429842,0.00012350077,0.00032136013,0.00063309714],"genre_scores_gemma":[0.12699258,0.0003044222,0.86752063,0.000167188,0.00009691799,0.0003098201,0.00076234457,0.0003869604,0.0034590482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980646,0.0010721622,0.00010502726,0.00019222277,0.00043250946,0.00013352445],"domain_scores_gemma":[0.9887998,0.009096054,0.00035053468,0.00065547443,0.0009458449,0.00015230147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006856618,0.0006337116,0.001437793,0.0014109416,0.00061418535,0.0017505469,0.0022721782,0.0010699052,0.0074867937],"category_scores_gemma":[0.03412882,0.0011378461,0.0011508808,0.0017500459,0.00088526955,0.0016941273,0.0019272034,0.0025642444,0.0014969662],"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":[0.000093137845,0.00007029696,0.0023644927,0.00016194543,0.000105682324,0.00009849126,0.00013036504,0.74203455,0.00077460584,0.14214164,0.005499168,0.10652559],"study_design_scores_gemma":[0.000012999754,0.0000048173642,0.0001523085,0.000013328102,0.00000481521,0.000010546112,0.000007639654,0.9651027,0.00017772158,0.03361788,0.0008887613,0.0000064268197],"about_ca_topic_score_codex":0.016054165,"about_ca_topic_score_gemma":0.022591334,"teacher_disagreement_score":0.016054165,"about_ca_system_score_codex":0.0016028275,"about_ca_system_score_gemma":0.003557766,"threshold_uncertainty_score":0.036261678},"labels":[],"label_agreement":null},{"id":"W4416322124","doi":"10.3390/stats8040110","title":"Prediction Inferences for Finite Population Totals Using Longitudinal Survey Data","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Regression analysis; Sampling design; Sampling (signal processing); Regression; Cluster sampling; Correlation; Sample (material); Poisson sampling","score_opus":0.5194947094376512,"score_gpt":0.5077778827261674,"score_spread":0.011716826711483774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416322124","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":[0.011229843,0.00014899863,0.98764545,0.00009147109,0.000016185722,0.000045548008,0.00011119643,0.00018307335,0.0005282725],"genre_scores_gemma":[0.5067856,0.00096467824,0.48720637,0.0003603497,0.00015672501,0.00076053606,0.0015086515,0.00014675052,0.0021103278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98984104,0.007852702,0.0002428039,0.0010308851,0.0008665092,0.0001660814],"domain_scores_gemma":[0.94440365,0.047676615,0.0021777416,0.0037501955,0.0016618411,0.0003299223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0211361,0.0012535881,0.0014632264,0.0011930844,0.00040547387,0.0014291513,0.0020088397,0.0009601488,0.0021781258],"category_scores_gemma":[0.058713894,0.000687548,0.0014038045,0.0010528127,0.0010367107,0.0022817142,0.0022226477,0.00206962,0.0004514918],"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":[0.00041860243,0.00020818102,0.015768742,0.0006333672,0.0004626969,0.00021183059,0.00048724035,0.6709969,0.00174282,0.123515934,0.0021624058,0.1833913],"study_design_scores_gemma":[0.00002713864,0.0001319796,0.0013239726,0.00005350445,0.00006214816,0.000030184181,0.00005595226,0.949071,0.0007664963,0.047061354,0.0013985165,0.000017749679],"about_ca_topic_score_codex":0.002691115,"about_ca_topic_score_gemma":0.0025960428,"teacher_disagreement_score":0.0211361,"about_ca_system_score_codex":0.0008924059,"about_ca_system_score_gemma":0.0015266496,"threshold_uncertainty_score":0.11177975},"labels":[],"label_agreement":null},{"id":"W4416524821","doi":"10.1016/b978-0-323-90509-1.00036-9","title":"Distributions","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Statistical Methods and Bayesian Inference","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":"University of Calgary","funders":"","keywords":"Probability distribution; Event (particle physics); Measure (data warehouse); Population; Rare events; Relevance (law); Statistical model; Probabilistic logic; Empirical probability","score_opus":0.0448158937610448,"score_gpt":0.34294204868390843,"score_spread":0.29812615492286365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416524821","genre_codex":"other","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":[0.0019750195,0.007366015,0.18666853,0.0032372144,0.0010631047,0.00006901395,0.0026432236,0.0018095734,0.7951683],"genre_scores_gemma":[0.039274745,0.010306566,0.047025878,0.001153084,0.001148937,0.0002304071,0.0040967003,0.0011017758,0.8956619],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994068,0.0000919042,0.000023506942,0.00017290741,0.00026860225,0.000036291367],"domain_scores_gemma":[0.9993162,0.0002149892,0.00003364212,0.000219788,0.00017977519,0.000035680878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004999552,0.0008799784,0.0008460198,0.0015529979,0.00078186294,0.0038908601,0.0009970065,0.0016739276,0.172845],"category_scores_gemma":[0.0029231163,0.00046996752,0.00050913903,0.0020097157,0.0012527513,0.0033146841,0.0015413,0.0022241909,0.08830944],"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":[0.000017256567,0.00002260809,0.00016460624,0.00013562318,0.000011461222,0.000068832946,0.00010611393,0.0018632812,0.0007718454,0.6892479,0.120312184,0.18727824],"study_design_scores_gemma":[0.00000982047,0.000009031394,0.0003002516,0.0001033144,0.0000072382395,0.00028046602,0.00004703816,0.0053871092,0.000624353,0.4130539,0.5801618,0.000015775897],"about_ca_topic_score_codex":0.0016864233,"about_ca_topic_score_gemma":0.0016461578,"teacher_disagreement_score":0.172845,"about_ca_system_score_codex":0.0010946034,"about_ca_system_score_gemma":0.00083310925,"threshold_uncertainty_score":0.5782242},"labels":[],"label_agreement":null},{"id":"W4417107648","doi":"10.6000/1929-6029.2025.14.70","title":"A New Robust Imputation Method for Longitudinal Data with Non-Normal Continuous Outcomes","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Imputation (statistics); Longitudinal data; Multivariate statistics; Normality; Robustness (evolution); Regression","score_opus":0.1668352769701048,"score_gpt":0.5590105407798045,"score_spread":0.3921752638096997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417107648","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":[0.00054953713,0.00018993,0.9986442,0.00010719037,0.00006252347,0.000023859448,0.00010792377,0.00018189597,0.00013292547],"genre_scores_gemma":[0.03165555,0.0006699489,0.9629323,0.00024364701,0.0003022818,0.0004807622,0.0010786504,0.00022233833,0.0024144256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946343,0.0028807658,0.0002969681,0.0009732265,0.0010427876,0.00017189884],"domain_scores_gemma":[0.9939276,0.003290356,0.0006677003,0.0007215741,0.0012339706,0.00015872557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075599025,0.0008755186,0.0021691923,0.001747438,0.00072872755,0.0012186662,0.0038484146,0.0018758872,0.004180765],"category_scores_gemma":[0.018111076,0.00070969714,0.0032639957,0.0031866888,0.0005419,0.0018352107,0.0016674846,0.0029512362,0.001897804],"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":[0.0005327939,0.00020984972,0.005934746,0.00093066576,0.0013995757,0.0007423446,0.00041378706,0.18740998,0.007051157,0.08189422,0.025227943,0.6882529],"study_design_scores_gemma":[0.00011175297,0.00015754103,0.0013742594,0.000114024384,0.00020616356,0.0006777259,0.000039616636,0.94666505,0.002102749,0.03303507,0.015413375,0.00010269259],"about_ca_topic_score_codex":0.0022467107,"about_ca_topic_score_gemma":0.002037629,"teacher_disagreement_score":0.0075599025,"about_ca_system_score_codex":0.0005549991,"about_ca_system_score_gemma":0.0019999233,"threshold_uncertainty_score":0.039981008},"labels":[],"label_agreement":null},{"id":"W4417524754","doi":"10.1016/j.annepidem.2025.12.005","title":"Comparing intraclass correlation coefficient estimators for binary outcomes in sample size calculations in twin pregnancies","year":2025,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Statistical Methods and Bayesian 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":"McGill University; McGill University Health Centre","funders":"","keywords":"Intraclass correlation; Sample size determination; Estimator; Logistic regression; Correlation; Sample (material)","score_opus":0.27687282416907116,"score_gpt":0.49726207254350846,"score_spread":0.2203892483744373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417524754","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":[0.075015746,0.0044198493,0.9134608,0.0013538591,0.00042078295,0.0013560319,0.00028903031,0.0002941,0.0033898437],"genre_scores_gemma":[0.4902977,0.0010910616,0.5021898,0.0006758707,0.00018967286,0.00471033,0.00027972096,0.00024348765,0.0003224039],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.62625,0.3378567,0.011713493,0.007979213,0.015421145,0.0007793787],"domain_scores_gemma":[0.25887418,0.6614833,0.034347646,0.027768033,0.01658051,0.0009463054],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.26652938,0.0010407899,0.0020448344,0.0046362486,0.0011354636,0.0028173365,0.002540717,0.0022239995,0.0020974022],"category_scores_gemma":[0.70779276,0.0011632732,0.003817595,0.0035990942,0.0043160054,0.0030405717,0.0034710567,0.0037132844,0.0002798492],"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":[0.00445978,0.00054945,0.26816952,0.006175718,0.01198944,0.0004724451,0.0072157476,0.08437401,0.003228704,0.1048516,0.009225563,0.4992879],"study_design_scores_gemma":[0.0029550043,0.00653156,0.17690708,0.00866465,0.0066908593,0.0022851322,0.0020341792,0.29546785,0.014975201,0.4552063,0.027441744,0.000840488],"about_ca_topic_score_codex":0.0019759734,"about_ca_topic_score_gemma":0.0017285042,"teacher_disagreement_score":0.7334706,"about_ca_system_score_codex":0.0019687966,"about_ca_system_score_gemma":0.0038967526,"threshold_uncertainty_score":0.9044999},"labels":[],"label_agreement":null},{"id":"W56489066","doi":"10.1007/978-1-4613-0141-7_9","title":"Bayesian and Likelihood Inference for the Generalized Fieller—Creasy Problem","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","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":"University of Toronto; University of Regina","funders":"","keywords":"Frequentist inference; Prior probability; Markov chain Monte Carlo; Mathematics; Bayesian inference; Bayesian probability; Inference; Bayes factor; Matching (statistics); Bayesian statistics; Applied mathematics; Algorithm; Computer science; Statistics; Artificial intelligence","score_opus":0.04676580047843084,"score_gpt":0.3477503579115554,"score_spread":0.3009845574331246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W56489066","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":[0.0064134905,0.0020775856,0.9782065,0.003569834,0.00017136714,0.00003784627,0.00022532468,0.000089438494,0.009208589],"genre_scores_gemma":[0.32670966,0.008246196,0.61420107,0.0023365992,0.002537857,0.0006195042,0.002079289,0.0006887173,0.04258115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99516284,0.0030135843,0.00020206824,0.000731512,0.00064357056,0.00024644987],"domain_scores_gemma":[0.96511924,0.030884799,0.0011167452,0.0012539941,0.0011583086,0.0004668513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010904734,0.001485805,0.002819407,0.0024941545,0.0017923982,0.004305927,0.0043825596,0.004534078,0.012349871],"category_scores_gemma":[0.07261051,0.0017782688,0.0024440296,0.0032751022,0.006996831,0.008994188,0.004148221,0.00784882,0.0015579291],"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":[0.000038362276,0.000025095715,0.00030752708,0.000116079704,0.000054738437,0.00007190917,0.00013481233,0.028384764,0.0001039871,0.94655716,0.0048827217,0.019322917],"study_design_scores_gemma":[0.000011204337,0.000003445816,0.000100036166,0.000019965319,0.0000071226154,0.000027698463,0.000017545586,0.046240862,0.000031749023,0.95224524,0.0012790934,0.00001605362],"about_ca_topic_score_codex":0.00922021,"about_ca_topic_score_gemma":0.0072380006,"teacher_disagreement_score":0.012349871,"about_ca_system_score_codex":0.0028202385,"about_ca_system_score_gemma":0.0026915746,"threshold_uncertainty_score":0.057670414},"labels":[],"label_agreement":null},{"id":"W568201293","doi":"10.1017/cbo9780511611131","title":"Applied Asymptotics","year":2007,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":115,"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":"Inference; Computer science; Range (aeronautics); Bayesian inference; Statistical inference; Bayesian probability; Code (set theory); Face (sociological concept); Econometrics; Data science; Mathematics; Statistics; Artificial intelligence; Sociology; Social science; Engineering; Programming language","score_opus":0.05183101028587162,"score_gpt":0.283831213022963,"score_spread":0.23200020273709138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W568201293","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":[0.0013535701,0.021498755,0.77673995,0.0053198836,0.0037606598,0.00008290413,0.0004567614,0.0010765058,0.18971111],"genre_scores_gemma":[0.18974526,0.05611194,0.5417559,0.012115048,0.015839107,0.0017286872,0.0022921823,0.0032702966,0.17714155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931678,0.0030605912,0.00035724824,0.0008560967,0.0023136798,0.0002446049],"domain_scores_gemma":[0.9807179,0.0141023295,0.0005562135,0.0025333334,0.0018848537,0.00020527095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008123441,0.0016767469,0.0019370312,0.0031052462,0.0013417215,0.0043782755,0.002412679,0.003156622,0.037251655],"category_scores_gemma":[0.048781626,0.0007789462,0.0016982964,0.0035360677,0.0048236223,0.0058187293,0.0033734865,0.0070247026,0.0152026145],"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":[0.000012908377,0.000020176438,0.00025522965,0.00030061073,0.000031215714,0.00007972687,0.00018999355,0.002081246,0.00019537118,0.90821666,0.025932,0.062685],"study_design_scores_gemma":[0.000008723984,0.000018890081,0.00023309229,0.00019149361,0.000014040486,0.00020824213,0.000046452253,0.008079727,0.00018255379,0.88701236,0.10398939,0.000015022845],"about_ca_topic_score_codex":0.0021323408,"about_ca_topic_score_gemma":0.0015025015,"teacher_disagreement_score":0.037251655,"about_ca_system_score_codex":0.0025276851,"about_ca_system_score_gemma":0.0017168875,"threshold_uncertainty_score":0.124619186},"labels":[],"label_agreement":null},{"id":"W571773678","doi":"10.1007/978-1-4419-8342-8","title":"Dynamic Mixed Models for Familial Longitudinal Data","year":2011,"lang":"en","type":"book","venue":"Springer series in statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Longitudinal data; Class (philosophy); Computer science; Count data; Binary number; Foundation (evidence); Binary data; Longitudinal study; Econometrics; Data mining; Statistics; Mathematics; Artificial intelligence; Geography; Arithmetic","score_opus":0.17015404874413284,"score_gpt":0.3847776976944932,"score_spread":0.21462364895036037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571773678","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":[0.0006112617,0.01043402,0.98128223,0.0012809016,0.0004134337,0.000018482511,0.0006547363,0.0005932467,0.0047116657],"genre_scores_gemma":[0.061813217,0.03660605,0.8231827,0.0019661088,0.0030450453,0.0009312553,0.0063780067,0.0017145451,0.06436303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984415,0.0009372214,0.00008915775,0.00024034904,0.0002465298,0.000045266617],"domain_scores_gemma":[0.9926455,0.005982833,0.00028806043,0.00062388496,0.00036542237,0.00009432135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005005188,0.0023045384,0.0019993342,0.0018428322,0.00042050448,0.0017911094,0.0029942591,0.0022337926,0.015985323],"category_scores_gemma":[0.012564641,0.0017508868,0.0018802152,0.0030287893,0.0009774533,0.0029743707,0.0013881207,0.003908555,0.006244054],"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":[0.00004407729,0.000045498964,0.0008380208,0.00039486028,0.00031265954,0.00025815616,0.00025553562,0.043469753,0.00044448435,0.69158465,0.07260907,0.18974322],"study_design_scores_gemma":[0.000014510491,0.000016038719,0.0003563587,0.00010827412,0.000066804234,0.00025925404,0.000024224742,0.08770727,0.000113646056,0.86640376,0.04489123,0.000038595204],"about_ca_topic_score_codex":0.003304658,"about_ca_topic_score_gemma":0.0053769345,"teacher_disagreement_score":0.015985323,"about_ca_system_score_codex":0.0010407006,"about_ca_system_score_gemma":0.0011063162,"threshold_uncertainty_score":0.053476214},"labels":[],"label_agreement":null},{"id":"W67682558","doi":"10.1007/978-3-642-33042-1_53","title":"Inferences in Binary Regression Models for Independent Data with Measurement Errors in Covariates","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Covariate; Statistics; Regression analysis; Regression; Observational error; Mathematics; Likelihood function; Econometrics; Binary data; Binary number; Maximum likelihood","score_opus":0.19080656576707636,"score_gpt":0.3895248834745548,"score_spread":0.19871831770747841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W67682558","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":[0.00095810083,0.0019159084,0.9935448,0.00096269365,0.0001591755,0.000023428398,0.00014097865,0.00015817056,0.0021366961],"genre_scores_gemma":[0.078425534,0.008292646,0.898037,0.0014511532,0.0015344373,0.0004977227,0.0011467739,0.00040525838,0.010209403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98118883,0.013025666,0.0008568553,0.0016807279,0.0030057621,0.00024203956],"domain_scores_gemma":[0.9193232,0.075198136,0.0017316386,0.00217224,0.0013442307,0.00023050563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025415288,0.0017384198,0.002439971,0.0024994677,0.00081541215,0.0039699744,0.0059717363,0.002728847,0.0068815905],"category_scores_gemma":[0.12609157,0.0024915542,0.0029933054,0.0039174818,0.003288935,0.007132942,0.0027923007,0.007892171,0.0022030207],"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":[0.000066552784,0.00006619123,0.0011094142,0.00087061967,0.00029314103,0.00014275793,0.00043562174,0.036051944,0.00032088844,0.82840675,0.009764568,0.12247155],"study_design_scores_gemma":[0.000014155198,0.000010749571,0.00024119929,0.000090905814,0.000048202335,0.00004876802,0.000021348002,0.070140384,0.0001668236,0.9258889,0.0033113915,0.000017228],"about_ca_topic_score_codex":0.0025358615,"about_ca_topic_score_gemma":0.002205897,"teacher_disagreement_score":0.025415288,"about_ca_system_score_codex":0.0020582646,"about_ca_system_score_gemma":0.0015444967,"threshold_uncertainty_score":0.1344105},"labels":[],"label_agreement":null},{"id":"W6902004466","doi":"10.6084/m9.figshare.26625385.v1","title":"Additional file 1 of Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Ottawa; BC Cancer Agency; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Hessian matrix; Logistic regression; Poisson regression; Poisson distribution; Regression; Mathematical proof","score_opus":0.15747238500188263,"score_gpt":0.4068937866330571,"score_spread":0.24942140163117446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902004466","genre_codex":"dataset","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":[0.00033364724,0.000041112235,0.0017344439,0.00028291225,0.00010180685,0.0003216528,0.9945392,0.00039842326,0.0022467838],"genre_scores_gemma":[0.03388983,0.0004917116,0.022702662,0.002577481,0.0007898355,0.0144335525,0.8680081,0.005044225,0.052062538],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980482,0.00071112614,0.0003018095,0.0003893559,0.0003380332,0.00021145448],"domain_scores_gemma":[0.9059091,0.08108953,0.0026592165,0.0044447435,0.0048702997,0.0010271095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0046746354,0.0011345883,0.0015910161,0.0022379968,0.00090945704,0.0016253515,0.002529205,0.001502702,0.9262737],"category_scores_gemma":[0.12490161,0.0009556326,0.0013251897,0.004685097,0.00034498947,0.0022424445,0.0012778151,0.0015501545,0.20665786],"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":[0.000270383,0.00009436893,0.0019419829,0.0012379426,0.000065756605,0.00004685174,0.000053830754,0.0005040843,0.000027819662,0.001837076,0.9816207,0.012299114],"study_design_scores_gemma":[0.012272258,0.00081428187,0.04761774,0.006440557,0.00062770705,0.0012254061,0.0006456725,0.006379835,0.0008367333,0.050468262,0.87236285,0.00030871984],"about_ca_topic_score_codex":0.010841159,"about_ca_topic_score_gemma":0.01356014,"teacher_disagreement_score":0.9262737,"about_ca_system_score_codex":0.0013030817,"about_ca_system_score_gemma":0.0029686703,"threshold_uncertainty_score":0.10516155},"labels":[],"label_agreement":null},{"id":"W6902018597","doi":"10.6084/m9.figshare.12560585","title":"Additional file 1 of Explaining the variation in the attained power of a stepped-wedge trial with unequal cluster sizes","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Statistical Methods and Bayesian 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":"Centre for Advancing Health Outcomes; University of British Columbia","funders":"","keywords":"Distribution (mathematics); Cluster (spacecraft); Power (physics); Coefficient of variation; Variation (astronomy); Approximation error; Statistical power","score_opus":0.11447789606508973,"score_gpt":0.36781869146243207,"score_spread":0.25334079539734233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902018597","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001677935,0.00025884528,0.0103000775,0.0009972197,0.00044708076,0.0051081483,0.9710533,0.0022163098,0.0079411585],"genre_scores_gemma":[0.12366972,0.0015230323,0.13484232,0.007570456,0.0013560839,0.12992159,0.48921913,0.00981711,0.102080576],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960672,0.002058288,0.0004892697,0.00048422295,0.0006368858,0.00026413077],"domain_scores_gemma":[0.82711476,0.1560919,0.004567809,0.006013397,0.005259196,0.00095289864],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010983985,0.0017222442,0.0018811845,0.0018215341,0.0008543159,0.0018670148,0.0024724961,0.0023869902,0.8846045],"category_scores_gemma":[0.13911615,0.0009022678,0.001929753,0.0019516505,0.0005155464,0.0018719514,0.0010354789,0.0019909097,0.09894085],"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":[0.0036982764,0.0003119512,0.001858409,0.008555583,0.00027334565,0.00012552422,0.00015458101,0.0032496313,0.00013995578,0.005583946,0.93432176,0.04172711],"study_design_scores_gemma":[0.05346687,0.0036220355,0.02575101,0.016773213,0.0014696101,0.00142263,0.0005859835,0.033879243,0.002399385,0.08182538,0.7783389,0.00046569572],"about_ca_topic_score_codex":0.0035405299,"about_ca_topic_score_gemma":0.007631203,"teacher_disagreement_score":0.989016,"about_ca_system_score_codex":0.0018852736,"about_ca_system_score_gemma":0.0035121592,"threshold_uncertainty_score":0.16459769},"labels":[],"label_agreement":null},{"id":"W6902060255","doi":"10.6084/m9.figshare.25201939.v1","title":"Additional file 1 of Model-based standardization using multiple imputation","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; University of Toronto","funders":"","keywords":"Standardization; Imputation (statistics); Missing data; Data file","score_opus":0.13506589631637078,"score_gpt":0.3860245751603788,"score_spread":0.250958678844008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902060255","genre_codex":"dataset","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":[0.00021187107,0.000046200494,0.0030546712,0.0001909055,0.000055370107,0.00015632079,0.9936707,0.0008734795,0.0017405051],"genre_scores_gemma":[0.017412081,0.0004309522,0.030777832,0.0015695997,0.00026894538,0.006007983,0.9151908,0.007832458,0.020509265],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976204,0.0009721939,0.0002723029,0.00058087707,0.0003600486,0.00019402755],"domain_scores_gemma":[0.8992355,0.087809995,0.0020299465,0.005205653,0.0047552534,0.0009636109],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005900786,0.0014666085,0.0015759704,0.002764321,0.0011253393,0.0028233405,0.0030316978,0.002059248,0.8958865],"category_scores_gemma":[0.097445644,0.0011939593,0.001593982,0.00522049,0.00055806275,0.0024050854,0.0016081746,0.0021708533,0.2176154],"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":[0.00033832237,0.0000739204,0.0016042244,0.002043336,0.00011290546,0.00008147234,0.00008306736,0.0010903893,0.0000714378,0.00227662,0.97923124,0.012992997],"study_design_scores_gemma":[0.0057835327,0.00029164652,0.012690804,0.0040923874,0.00051896105,0.0007433724,0.0003455643,0.0053049275,0.0008220696,0.05063721,0.9185385,0.00023090048],"about_ca_topic_score_codex":0.008216557,"about_ca_topic_score_gemma":0.011684345,"teacher_disagreement_score":0.9940992,"about_ca_system_score_codex":0.001137229,"about_ca_system_score_gemma":0.0033414694,"threshold_uncertainty_score":0.14850533},"labels":[],"label_agreement":null},{"id":"W6902162664","doi":"10.6084/m9.figshare.22792859.v1","title":"Additional file 1 of Multiple imputation methods for missing multilevel ordinal outcomes","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Missing data; Imputation (statistics); Ordinal data; Categorical variable","score_opus":0.24019378156943577,"score_gpt":0.47512831407102,"score_spread":0.23493453250158425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902162664","genre_codex":"dataset","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":[0.00023499849,0.000044819073,0.0041608936,0.00019565155,0.00005689985,0.00022176674,0.9921421,0.001081227,0.0018616096],"genre_scores_gemma":[0.018003667,0.0004466829,0.04616466,0.0017612703,0.0003115025,0.009534004,0.886834,0.0094045475,0.027539693],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764794,0.0009835318,0.00030846868,0.0004849574,0.000373473,0.00020161612],"domain_scores_gemma":[0.8898722,0.097707845,0.002420357,0.0043850294,0.004599165,0.0010154921],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006310533,0.0013587129,0.0016528559,0.0028209276,0.0010984401,0.0021555463,0.0027021295,0.001835908,0.91306067],"category_scores_gemma":[0.102430716,0.0012152657,0.0014501136,0.005239734,0.00048368212,0.0021489891,0.0014510895,0.0021916055,0.23882072],"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":[0.0003136208,0.00008880086,0.0013828635,0.0019219144,0.00009343662,0.000057544046,0.000082533756,0.00082446286,0.000056338933,0.0020297149,0.9791861,0.013962677],"study_design_scores_gemma":[0.0059710396,0.00034924006,0.012958296,0.004804406,0.0005045612,0.0007673853,0.00036570858,0.0060377237,0.0009469557,0.050501417,0.91654015,0.000253206],"about_ca_topic_score_codex":0.0054837195,"about_ca_topic_score_gemma":0.009660805,"teacher_disagreement_score":0.91306067,"about_ca_system_score_codex":0.0010500046,"about_ca_system_score_gemma":0.0031016984,"threshold_uncertainty_score":0.12400836},"labels":[],"label_agreement":null},{"id":"W6902291090","doi":"10.6084/m9.figshare.26265015.v1","title":"Additional file 1 of The spectrum of health conditions in community-based cross-sectional surveys in Southeast Asia 2010-21: a scoping review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Southeast asia; Population; Identification (biology); Government (linguistics); Public health","score_opus":0.20467456228732137,"score_gpt":0.44197109086538755,"score_spread":0.23729652857806618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902291090","genre_codex":"dataset","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":[0.00011680656,0.0001342959,0.0003008259,0.00012453254,0.000023328672,0.00021055923,0.9983758,0.000137499,0.0005762834],"genre_scores_gemma":[0.009322636,0.001616078,0.013057602,0.00097588135,0.00017099171,0.010812416,0.952893,0.0007015522,0.010449838],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9977719,0.0006220381,0.0007674204,0.00035005965,0.00034725363,0.00014133965],"domain_scores_gemma":[0.90617853,0.08191452,0.0047346307,0.0017396086,0.0048265904,0.0006061711],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0058095483,0.0013200665,0.0019404654,0.008244839,0.0006410797,0.0017288151,0.0024372463,0.0016135381,0.83613086],"category_scores_gemma":[0.08347548,0.00104958,0.0019420044,0.013790442,0.0003429338,0.0024238233,0.0015846979,0.0009175547,0.07038624],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039982377,0.000105583174,0.002918301,0.054096468,0.0002992375,0.00007032685,0.00017322353,0.0005946374,0.00007302117,0.0020550187,0.90768415,0.031530276],"study_design_scores_gemma":[0.009314516,0.00045150518,0.081162736,0.063051075,0.0022680548,0.00091845955,0.0009963912,0.0026363127,0.00065836863,0.022419367,0.8157993,0.0003238764],"about_ca_topic_score_codex":0.011129216,"about_ca_topic_score_gemma":0.017380077,"teacher_disagreement_score":0.83613086,"about_ca_system_score_codex":0.0017359557,"about_ca_system_score_gemma":0.0043161693,"threshold_uncertainty_score":0.23373938},"labels":[],"label_agreement":null},{"id":"W6911361317","doi":"10.5281/zenodo.1193222","title":"Bayes Is On The Way","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayes' theorem; Bayesian probability; Feature (linguistics); Bayesian inference","score_opus":0.1158679350895252,"score_gpt":0.34097013951218996,"score_spread":0.22510220442266476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911361317","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":[0.007124058,0.076084666,0.14426865,0.16056712,0.012902943,0.00008179305,0.006112189,0.0016258017,0.5912328],"genre_scores_gemma":[0.25843894,0.05844796,0.07615587,0.011409115,0.0035295726,0.00020589189,0.0028351895,0.0012923375,0.58768517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99618065,0.0011498779,0.0002463488,0.001109467,0.0010431339,0.00027059604],"domain_scores_gemma":[0.99326175,0.003048343,0.00041971015,0.0011832673,0.0016919934,0.00039491343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002987964,0.0009961838,0.0017956885,0.0015915487,0.0037130024,0.008997848,0.0013517889,0.0035777027,0.052560918],"category_scores_gemma":[0.016538993,0.0011904751,0.0009254911,0.0022408254,0.008084246,0.0075496878,0.0022212276,0.006275327,0.012871863],"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":[0.000069816924,0.0000089950345,0.00045313957,0.00018189357,0.000038505714,0.00006947135,0.0005831784,0.00089421595,0.00018868466,0.86833584,0.07902161,0.050154664],"study_design_scores_gemma":[0.000016740463,0.0000128098245,0.0005710902,0.0003265968,0.000034361914,0.000071173876,0.00025429626,0.0011419315,0.00032148798,0.48174113,0.51543814,0.00007022201],"about_ca_topic_score_codex":0.1792998,"about_ca_topic_score_gemma":0.20930552,"teacher_disagreement_score":0.1792998,"about_ca_system_score_codex":0.008227763,"about_ca_system_score_gemma":0.011650254,"threshold_uncertainty_score":0.3565123},"labels":[],"label_agreement":null},{"id":"W6920515283","doi":"10.60692/gw2kv-47t09","title":"Paleoseismological Findings at a New Trench Indicate the 1714 M8.1 Earthquake Ruptured the Main Frontal Thrust Over all the Bhutan Himalaya","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Statistical Methods and Bayesian 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":"Dalhousie University","funders":"","keywords":"Trench; Foreland basin; Slip (aerodynamics); Thrust fault; Slow earthquake; Seismic gap; Aftershock; Epicenter","score_opus":0.075542638112698,"score_gpt":0.2851973170542358,"score_spread":0.20965467894153778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920515283","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":[0.98472667,0.00011572457,0.005721905,0.00010793404,0.0000057275,0.00001694381,0.00077889545,0.000042594416,0.0084836725],"genre_scores_gemma":[0.99667126,0.000049578357,0.0016490949,0.0000115721705,0.0000040203895,0.0000044704216,0.00018925293,0.0000033302813,0.0014174518],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998266,0.000049049115,0.0000062767713,0.00005509846,0.00003625084,0.000026723397],"domain_scores_gemma":[0.9995648,0.00014079084,0.000091773334,0.00004268947,0.00008890464,0.000071030176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834939,0.0001958961,0.00010951695,0.0008227371,0.0007219301,0.00049598934,0.00034623695,0.00017516923,0.004439117],"category_scores_gemma":[0.0012857517,0.000115781586,0.00006993363,0.0010010484,0.0006592344,0.0001920474,0.00048184465,0.00018912231,0.0003643055],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013375019,0.00006669603,0.87043226,0.00009136206,0.00009697083,0.0012978384,0.0047451225,0.0058848774,0.017971998,0.004443422,0.0007220028,0.094113685],"study_design_scores_gemma":[0.000004964272,0.000036205493,0.98802894,0.000009423804,0.000019091332,0.00025960212,0.0011019772,0.004329798,0.0012280109,0.0015962995,0.0033679907,0.000017738113],"about_ca_topic_score_codex":0.036239292,"about_ca_topic_score_gemma":0.14569858,"teacher_disagreement_score":0.036239292,"about_ca_system_score_codex":0.00065326935,"about_ca_system_score_gemma":0.00038160337,"threshold_uncertainty_score":0.07205671},"labels":[],"label_agreement":null},{"id":"W6920668901","doi":"10.6084/m9.figshare.14516296","title":"Additional file 2 of Dynamic model updating (DMU) approach for statistical learning model building with missing data","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Missing data; Table (database); Model building; Statistical model; Regression analysis; Data modeling","score_opus":0.17686627813692232,"score_gpt":0.3901808655635539,"score_spread":0.2133145874266316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920668901","genre_codex":"dataset","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":[0.00027883757,0.000044288314,0.002890922,0.00016205787,0.000053177057,0.00011075601,0.9941864,0.0014520672,0.0008213578],"genre_scores_gemma":[0.016617922,0.00024170232,0.027579913,0.00093434827,0.00021562167,0.0038211017,0.9342277,0.0057151117,0.010646669],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984652,0.00051920285,0.00021129502,0.00038273504,0.00029983144,0.00012184221],"domain_scores_gemma":[0.91968733,0.07190727,0.0012762048,0.003037898,0.003561749,0.0005296458],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004888376,0.0015520894,0.0015915063,0.002017844,0.0007551818,0.0020718588,0.0031095834,0.001857257,0.8690601],"category_scores_gemma":[0.075951464,0.00095530896,0.0014234532,0.0036551945,0.00041827362,0.0021967974,0.0012967669,0.0017698895,0.20058544],"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":[0.00036282762,0.000114150374,0.0014546653,0.0030506116,0.00012144062,0.00006830384,0.000046171397,0.0021519174,0.00012912268,0.0014775053,0.97464323,0.0163802],"study_design_scores_gemma":[0.008820398,0.00051303237,0.014072599,0.003269711,0.0005246789,0.0006429616,0.00031444174,0.020400707,0.0024191572,0.046505373,0.9022046,0.00031224417],"about_ca_topic_score_codex":0.004623826,"about_ca_topic_score_gemma":0.009285946,"teacher_disagreement_score":0.8690601,"about_ca_system_score_codex":0.0008787459,"about_ca_system_score_gemma":0.0022729468,"threshold_uncertainty_score":0.1867699},"labels":[],"label_agreement":null},{"id":"W6920707360","doi":"10.6084/m9.figshare.24130184.v1","title":"Additional file 1 of Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Table (database); Mean squared error; Approximation error; Random error; Efficiency; Root mean square","score_opus":0.36232715526400705,"score_gpt":0.45418288957903147,"score_spread":0.09185573431502442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920707360","genre_codex":"dataset","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":[0.003257729,0.00037164503,0.03098568,0.0014814413,0.00033024861,0.012789593,0.93393576,0.003823496,0.013024469],"genre_scores_gemma":[0.1365566,0.001551612,0.24051164,0.005215054,0.0006281773,0.21302086,0.351483,0.006035217,0.044997796],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99187565,0.005553712,0.0008480732,0.0007241089,0.00067466864,0.0003238372],"domain_scores_gemma":[0.69667685,0.28227365,0.005988736,0.0076088808,0.0061039017,0.0013480075],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.022476554,0.0016534383,0.0019337981,0.0019776246,0.00083111273,0.0019421135,0.0029573825,0.0023849378,0.8220349],"category_scores_gemma":[0.21013547,0.0011652274,0.0020009456,0.0034875865,0.0005442068,0.0019973011,0.0011930532,0.0019334549,0.07443072],"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":[0.006962061,0.00072595617,0.0033854484,0.014295744,0.000948028,0.00024946823,0.00034026307,0.016816927,0.0001646006,0.014518536,0.88058335,0.061009746],"study_design_scores_gemma":[0.12589833,0.004728382,0.015952175,0.014386051,0.0033681064,0.0017613844,0.0005127918,0.11044443,0.0016438613,0.1181771,0.60269606,0.00043142255],"about_ca_topic_score_codex":0.003536313,"about_ca_topic_score_gemma":0.006392039,"teacher_disagreement_score":0.97752345,"about_ca_system_score_codex":0.0024020977,"about_ca_system_score_gemma":0.0053681387,"threshold_uncertainty_score":0.25384563},"labels":[],"label_agreement":null},{"id":"W6920964694","doi":"10.6084/m9.figshare.12872405","title":"Additional file 1 of LEVEL (Logical Explanations &amp; Visualizations of Estimates in Linear mixed models): recommendations for reporting multilevel data and analyses","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Statistical Methods and Bayesian 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":"Population Health Research Institute; McMaster University","funders":"","keywords":"Logistic regression; Logistic model tree; Regression diagnostic; Factor regression model; Regression analysis; Log-linear model; Proper linear model; Linear regression","score_opus":0.8120494787764482,"score_gpt":0.5767028517575817,"score_spread":0.23534662701886644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920964694","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025854676,0.000042460906,0.004462108,0.0003496917,0.00007852902,0.00033572738,0.98892885,0.0027309635,0.0028129981],"genre_scores_gemma":[0.019445943,0.00046376121,0.0816567,0.0018516955,0.00042758862,0.014679135,0.8215569,0.020736191,0.039182056],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99751854,0.0010872987,0.00042271233,0.00032822418,0.00045405785,0.00018926448],"domain_scores_gemma":[0.8899671,0.09470576,0.0031410255,0.004028148,0.0070294375,0.0011284777],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0073317126,0.0013107293,0.0016508161,0.0036891827,0.0010441849,0.0026744327,0.0030785408,0.0014927868,0.90916646],"category_scores_gemma":[0.10503472,0.001280365,0.001412643,0.005343656,0.0004138369,0.0027711818,0.001996194,0.0016528945,0.25216144],"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":[0.00013778651,0.00005030851,0.0010055515,0.0016645073,0.00003319094,0.000027066413,0.00011800946,0.00033429495,0.000042615364,0.0012772667,0.9836488,0.0116606755],"study_design_scores_gemma":[0.0024554466,0.00015233338,0.010501519,0.0038859814,0.0001944264,0.00026596134,0.0006176287,0.0033206956,0.0008523722,0.02583413,0.9517466,0.0001730005],"about_ca_topic_score_codex":0.010230306,"about_ca_topic_score_gemma":0.016593007,"teacher_disagreement_score":0.9926683,"about_ca_system_score_codex":0.0015296974,"about_ca_system_score_gemma":0.0038460644,"threshold_uncertainty_score":0.12956297},"labels":[],"label_agreement":null},{"id":"W6920977159","doi":"10.6084/m9.figshare.16625213","title":"Additional file 1 of Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial","year":2021,"lang":"en","type":"article","venue":"Open MIND","topic":"Statistical Methods and Bayesian 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 Guelph; Western University; Institute for Clinical Evaluative Sciences; McMaster University; Lawson Health Research Institute","funders":"","keywords":"Randomization; Resampling; Population; Baseline (sea); Principal component analysis; Varimax rotation; A priori and a posteriori; Principal (computer security)","score_opus":0.0974726872439021,"score_gpt":0.4229673230007822,"score_spread":0.3254946357568801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920977159","genre_codex":"dataset","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":[0.0026491494,0.00019920843,0.008286981,0.0017251665,0.0003750578,0.015615372,0.95771056,0.0015977322,0.011840658],"genre_scores_gemma":[0.124464795,0.0011011413,0.11761,0.0071953055,0.0010529563,0.35419488,0.3187091,0.0041270736,0.071544744],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99195695,0.0044854498,0.0014101433,0.000668902,0.00097435317,0.0005041724],"domain_scores_gemma":[0.7898156,0.18373542,0.008727836,0.008347418,0.0073287943,0.0020449301],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.013762143,0.0010988818,0.001730869,0.0020054795,0.0011289084,0.0020275512,0.002798371,0.002270434,0.8669249],"category_scores_gemma":[0.18843895,0.0008521874,0.0014324309,0.0036462347,0.00041733246,0.00234001,0.0011603086,0.0015100763,0.06751471],"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":[0.010142597,0.00051242387,0.0028683022,0.010266193,0.00035384131,0.00022778739,0.0002432381,0.0016645212,0.000080581885,0.005366378,0.9116586,0.05661554],"study_design_scores_gemma":[0.16326408,0.0061041266,0.035407223,0.022591261,0.0019433388,0.0018943825,0.0010641598,0.022594221,0.0012998964,0.054592803,0.6887968,0.00044768822],"about_ca_topic_score_codex":0.0029459847,"about_ca_topic_score_gemma":0.006473523,"teacher_disagreement_score":0.8669249,"about_ca_system_score_codex":0.0024151062,"about_ca_system_score_gemma":0.005294833,"threshold_uncertainty_score":0.1898154},"labels":[],"label_agreement":null},{"id":"W6929138079","doi":"10.4230/lipics.giscience.2023.9","title":"Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures","year":2023,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","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":"Anonymity; Popularity; Set (abstract data type); Location data; Field (mathematics); Spatial analysis; Location-based service; k-anonymity","score_opus":0.06392384411763485,"score_gpt":0.3668328807835996,"score_spread":0.30290903666596475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929138079","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":[0.026492955,0.0003186781,0.96748483,0.00076967466,0.00010498631,0.00012114555,0.001175796,0.00086886063,0.0026631407],"genre_scores_gemma":[0.6571877,0.0007300901,0.33424214,0.00046691683,0.00039672395,0.00038055834,0.0020628525,0.0002587134,0.004274326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98514044,0.006575166,0.0010742951,0.0026471966,0.0037484502,0.0008142994],"domain_scores_gemma":[0.9423778,0.022287155,0.008041986,0.02167105,0.004712623,0.0009094042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008595346,0.00090779265,0.0013316363,0.0023569928,0.0022299148,0.0037692972,0.0031044052,0.0015032836,0.0024193747],"category_scores_gemma":[0.052490078,0.0006370962,0.0013765463,0.0058193333,0.0028419206,0.011791025,0.006248577,0.0027717575,0.0013239105],"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":[0.001641911,0.000342685,0.022415964,0.000598462,0.00042966628,0.00062174676,0.0021396826,0.21799889,0.013655627,0.3529264,0.015204071,0.37202492],"study_design_scores_gemma":[0.00008526996,0.00030232378,0.0030790097,0.00009369717,0.00011792519,0.000992129,0.00066154706,0.60962486,0.016686428,0.34849194,0.019645771,0.0002191357],"about_ca_topic_score_codex":0.0021563382,"about_ca_topic_score_gemma":0.002487542,"teacher_disagreement_score":0.008595346,"about_ca_system_score_codex":0.0018189662,"about_ca_system_score_gemma":0.0034664941,"threshold_uncertainty_score":0.045457065},"labels":[],"label_agreement":null},{"id":"W6929325080","doi":"10.48336/4vby-tr77","title":"Building a solid foundation for reading: an analysis of curriculum, policy, and instructional material documents","year":2023,"lang":"en","type":"article","venue":"Memorial University Research Repository (Memorial University)","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reading (process); Resource (disambiguation); Foundation (evidence); Content analysis; Nova scotia; Scientific writing; Instructional design","score_opus":0.05937176191765978,"score_gpt":0.38617166642905276,"score_spread":0.32679990451139296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929325080","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":[0.97596776,0.00042208607,0.010641169,0.0003102728,0.000013877428,0.00049786543,0.0010658012,0.000066581684,0.011014729],"genre_scores_gemma":[0.98609054,0.00018555648,0.011619849,0.00004443335,0.0000060496955,0.00016203782,0.0007876815,0.000036007114,0.001068007],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9923334,0.004000211,0.00042084223,0.0006133773,0.0021424782,0.00048963533],"domain_scores_gemma":[0.93390566,0.04774714,0.006590715,0.0025402736,0.008704741,0.0005114494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007065769,0.00026552708,0.0004229679,0.0063944585,0.0013235172,0.003118871,0.00045632463,0.0003902596,0.001446885],"category_scores_gemma":[0.066556774,0.0003122959,0.00040278002,0.007842825,0.0017197529,0.0013497447,0.0015152689,0.0007139359,0.00017575362],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013849981,0.0006634149,0.5646281,0.0013466907,0.00036026989,0.0012753373,0.0892304,0.0066232285,0.0094466,0.046505038,0.0029247743,0.2756111],"study_design_scores_gemma":[0.00007915726,0.00037492387,0.89756936,0.0007480303,0.0004958666,0.00022132369,0.041123834,0.021990327,0.0059285886,0.011475477,0.019911598,0.00008152361],"about_ca_topic_score_codex":0.13293168,"about_ca_topic_score_gemma":0.122772604,"teacher_disagreement_score":0.13293168,"about_ca_system_score_codex":0.009138202,"about_ca_system_score_gemma":0.009894325,"threshold_uncertainty_score":0.26431584},"labels":[],"label_agreement":null},{"id":"W6929514952","doi":"10.5061/dryad.4s9m2","title":"Data from: Nutritional geometry and fitness consequences in Drosophila suzukii, the Spotted-Wing Drosophila","year":2017,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Statistical Methods and Bayesian 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":"Wilfrid Laurier University","funders":"","keywords":"Drosophila suzukii; Drosophila (subgenus); Offspring; Larva; Evolutionary ecology; Ripening","score_opus":0.10433742758610955,"score_gpt":0.3768724420319782,"score_spread":0.2725350144458687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929514952","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020098875,0.00009824537,0.00021230918,0.00012911254,0.000030489615,0.000021352436,0.99656725,0.0004646835,0.00046665862],"genre_scores_gemma":[0.0028732473,0.00006775938,0.0007801433,0.000052437044,0.0000056948347,0.00014559831,0.9955142,0.000089298424,0.00047161308],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928856,0.000121768135,0.000093370174,0.00020541604,0.00018797742,0.00010291171],"domain_scores_gemma":[0.9981585,0.00067093596,0.00029077512,0.00034270537,0.0003538286,0.00018320812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014672539,0.0014255749,0.0009255048,0.001811478,0.00068281323,0.0012974006,0.0022305015,0.001515836,0.021513386],"category_scores_gemma":[0.005965506,0.00037399985,0.0010183923,0.0022100937,0.0006507104,0.0006978961,0.0016290458,0.0015426067,0.020571357],"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":[0.00028933762,0.00015141227,0.011003844,0.001988318,0.00018935543,0.00013862897,0.00017277368,0.0019341563,0.0009482727,0.0015762512,0.973768,0.007839632],"study_design_scores_gemma":[0.0010877898,0.00007824452,0.041745067,0.0005516173,0.00013072998,0.00022759328,0.00029581995,0.002860932,0.0017421532,0.0035829255,0.9475972,0.00010001749],"about_ca_topic_score_codex":0.018446164,"about_ca_topic_score_gemma":0.040032562,"teacher_disagreement_score":0.021513386,"about_ca_system_score_codex":0.0011944142,"about_ca_system_score_gemma":0.0019457104,"threshold_uncertainty_score":0.07196939},"labels":[],"label_agreement":null},{"id":"W6929722490","doi":"10.5061/dryad.1978","title":"Data from: Complex phylogeographic patterns in the freshwater alga Synura provide new insights on ubiquity versus endemism in microbial eukaryotes","year":2010,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Alberta","funders":"","keywords":"Endemism; Phylogeography; Biological dispersal; Biogeography; Species complex; Taxon; Abiotic component; Population","score_opus":0.19507967933220313,"score_gpt":0.36048916329550085,"score_spread":0.16540948396329772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929722490","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98646915,0.00025843896,0.0011369233,0.00006599096,0.000004003854,0.0000066967254,0.011142408,0.000026585265,0.0008899078],"genre_scores_gemma":[0.9746644,0.0001940852,0.0018242351,0.00002984394,0.000005634007,0.000019282392,0.022922203,0.000018840277,0.00032159392],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996973,0.00005473241,0.000031726722,0.00011598448,0.00005805275,0.000042194657],"domain_scores_gemma":[0.99926525,0.00018114813,0.0002861213,0.00012823148,0.00008270384,0.000056518813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036424006,0.00017954408,0.0003094896,0.0013842816,0.00042674592,0.0005891703,0.0001832659,0.00023519344,0.0025550507],"category_scores_gemma":[0.0011457627,0.00013131561,0.0003505562,0.0022669835,0.00029102026,0.000441826,0.0008200809,0.0003343898,0.0006449798],"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":[0.0004348484,0.00005007433,0.91842055,0.0003908764,0.00047352648,0.00016931818,0.0009472435,0.0026078878,0.03565846,0.000683685,0.00178288,0.0383807],"study_design_scores_gemma":[0.00000852095,0.000017142023,0.9914589,0.000022141028,0.000052784533,0.0001428177,0.00027093562,0.0009123251,0.0011292172,0.00020998792,0.0057656737,0.000009496701],"about_ca_topic_score_codex":0.0033065244,"about_ca_topic_score_gemma":0.008014098,"teacher_disagreement_score":0.0033065244,"about_ca_system_score_codex":0.00021420777,"about_ca_system_score_gemma":0.00030513474,"threshold_uncertainty_score":0.008547485},"labels":[],"label_agreement":null},{"id":"W6929837957","doi":"10.5281/zenodo.10797986","title":"Diagnosis and Treatments with Perceived Mentally Disturbed Persons: The Case of Traditional Healers in South Cotabato","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Checklist; Pupil; Action (physics); Action plan; Section (typography)","score_opus":0.10276662757495159,"score_gpt":0.32265047285313714,"score_spread":0.21988384527818555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929837957","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":[0.99375534,0.0007757237,0.00012494747,0.002491308,0.00004395813,0.000039914707,0.00001660429,0.0000025610966,0.0027495997],"genre_scores_gemma":[0.9970004,0.00097190705,0.00018569923,0.00049290835,0.000022295058,0.000010848546,0.000008204162,0.0000019202419,0.0013058324],"study_design_codex":"case_report","study_design_gemma":"qualitative","domain_scores_codex":[0.9996309,0.000087413304,0.000023054345,0.000028560964,0.000044518285,0.00018549427],"domain_scores_gemma":[0.9993994,0.000116517134,0.000189557,0.000016923022,0.000038443508,0.00023913647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028394398,0.00029302735,0.00020998518,0.00067387667,0.0066153803,0.00075033365,0.0007253295,0.0016538115,0.0037293122],"category_scores_gemma":[0.0016018181,0.00040664678,0.00022225418,0.0006676265,0.002215641,0.00059035694,0.0014620055,0.001794184,0.00022654558],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010099356,0.0004979088,0.13256459,0.00036268574,0.000018839086,0.7202901,0.12666672,0.00012426305,0.0022421074,0.0021328924,0.0013108364,0.013688047],"study_design_scores_gemma":[0.000025071817,0.00047675992,0.18478344,0.00064112735,0.000040439958,0.35692632,0.44725373,0.00042722668,0.0006634829,0.0012083723,0.007495663,0.00005830339],"about_ca_topic_score_codex":0.050217718,"about_ca_topic_score_gemma":0.13677357,"teacher_disagreement_score":0.050217718,"about_ca_system_score_codex":0.0024243896,"about_ca_system_score_gemma":0.0026594554,"threshold_uncertainty_score":0.09985083},"labels":[],"label_agreement":null},{"id":"W6930248539","doi":"10.5281/zenodo.11789603","title":"Iso 811 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Limiting; Oscillograph; Work (physics); Point (geometry)","score_opus":0.07501432771234481,"score_gpt":0.33592783020086575,"score_spread":0.26091350248852097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930248539","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":[0.00038071774,0.0012007772,0.0053680986,0.000802191,0.0023040215,0.0004286947,0.00955112,0.004377733,0.97558665],"genre_scores_gemma":[0.0027425722,0.0023609307,0.0037484122,0.0007886571,0.0004595334,0.00022856402,0.014849645,0.0031270164,0.97169465],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99561113,0.00021379333,0.00024164627,0.00030878658,0.0033416795,0.00028293315],"domain_scores_gemma":[0.9922633,0.00030257972,0.00018323619,0.0006015514,0.006268453,0.00038083774],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0024992146,0.0022925974,0.0012853158,0.0061500035,0.0019043629,0.008610747,0.004554438,0.0032587366,0.6427518],"category_scores_gemma":[0.008022787,0.0012655829,0.001475858,0.0051164734,0.0011844819,0.0070283352,0.0033075556,0.0028576916,0.69224906],"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":[0.000082798244,0.00007543907,0.00009036059,0.000659819,0.000006172305,0.00008416808,0.00007156985,0.00028520872,0.001949866,0.007246023,0.8590989,0.13034968],"study_design_scores_gemma":[0.0000068972026,0.00001781164,0.00013483084,0.0001022711,0.0000033646998,0.00004870706,0.00003661545,0.000052119925,0.00075136946,0.00073414814,0.99810314,0.00000870861],"about_ca_topic_score_codex":0.009616571,"about_ca_topic_score_gemma":0.009416636,"teacher_disagreement_score":0.3572482,"about_ca_system_score_codex":0.0029369688,"about_ca_system_score_gemma":0.004468518,"threshold_uncertainty_score":0.5095712},"labels":[],"label_agreement":null},{"id":"W6930334284","doi":"10.5281/zenodo.12244840","title":"goal 4 quality education pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lifelong learning; Quality (philosophy); Mainstreaming; Sustainable development; Basic education; Action (physics); Education for sustainable development; Higher education; Adult education; Informal education","score_opus":0.09016905447337442,"score_gpt":0.38506958182226636,"score_spread":0.29490052734889194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930334284","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":[0.00043496425,0.00031451424,0.0018118508,0.0043963087,0.0024260024,0.00036946105,0.0034154202,0.0028891908,0.98394233],"genre_scores_gemma":[0.0021020372,0.0003464457,0.0012773372,0.0018742776,0.000598737,0.00012784357,0.0019266313,0.0013707893,0.99037594],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99624383,0.00024670226,0.00012307646,0.00026517085,0.002632359,0.0004889832],"domain_scores_gemma":[0.98969287,0.00088441063,0.00035710083,0.0007287066,0.005809348,0.0025275184],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0023332525,0.0011835091,0.0009240153,0.0031750544,0.0025439928,0.011282939,0.0021239503,0.0054509784,0.8738539],"category_scores_gemma":[0.011869574,0.0007433546,0.0019361178,0.0023886985,0.00083631586,0.006033725,0.005679465,0.0034648161,0.7650966],"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":[0.000019733381,0.00006450684,0.00007410268,0.000121659614,0.0000018732903,0.000020856183,0.000026104366,0.000047130212,0.00013855155,0.0026849543,0.96347857,0.033322085],"study_design_scores_gemma":[0.000012874269,0.000021799535,0.00042051615,0.000083547195,0.0000022461356,0.000034803317,0.00004464941,0.00004173949,0.00015426736,0.0009674252,0.99820757,0.00000864316],"about_ca_topic_score_codex":0.0062118685,"about_ca_topic_score_gemma":0.010502958,"teacher_disagreement_score":0.12614608,"about_ca_system_score_codex":0.0030354648,"about_ca_system_score_gemma":0.0048124446,"threshold_uncertainty_score":0.179932},"labels":[],"label_agreement":null},{"id":"W6930418627","doi":"10.5281/zenodo.12234861","title":"Golf 1 cabrio reparaturanleitung pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Launched; Class (philosophy); Test (biology); Download; Automotive industry; Service (business)","score_opus":0.056699005633360604,"score_gpt":0.3212366334391554,"score_spread":0.26453762780579476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930418627","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":[0.00084828044,0.00053676666,0.00023919673,0.0007431487,0.0011216166,0.00012617698,0.006410012,0.0023333456,0.9876414],"genre_scores_gemma":[0.0016346554,0.00030250513,0.00013765067,0.00020158089,0.00014495198,0.00004796777,0.0027373603,0.0008048676,0.9939885],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99966395,0.000018054116,0.000009380781,0.0000416121,0.0001778438,0.000089152396],"domain_scores_gemma":[0.99929976,0.00005812642,0.000030850493,0.0000663503,0.00028290722,0.0002619145],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029893298,0.0010990006,0.0005489947,0.0015254326,0.0018633894,0.003228023,0.00082010985,0.0013117965,0.91490734],"category_scores_gemma":[0.0012396147,0.00042401932,0.0004585836,0.0015516668,0.00026851945,0.0020724493,0.002613857,0.0014334735,0.84289837],"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":[0.000014532187,0.000015216232,0.00006282255,0.000032977045,6.653425e-7,0.000026516658,0.000029905881,0.000010311942,0.00008159632,0.00028369232,0.97629625,0.023145441],"study_design_scores_gemma":[0.0000048826437,0.000010089804,0.00050488784,0.000032088887,6.8877875e-7,0.00002638334,0.000063328866,0.000014250537,0.000080837024,0.00006457218,0.9991948,0.0000032289263],"about_ca_topic_score_codex":0.009569875,"about_ca_topic_score_gemma":0.026670134,"teacher_disagreement_score":0.085092664,"about_ca_system_score_codex":0.00097559905,"about_ca_system_score_gemma":0.0007361475,"threshold_uncertainty_score":0.12137425},"labels":[],"label_agreement":null},{"id":"W6930480582","doi":"10.5281/zenodo.12861305","title":"discovering psychology the science of mind 1st canadian edition","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"History of psychology; Experimental psychology; Focus (optics); Psychological science; Sociology of scientific knowledge","score_opus":0.08753101914842225,"score_gpt":0.36289118489218514,"score_spread":0.2753601657437629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930480582","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":[0.00048075287,0.08975879,0.078533985,0.01971428,0.018034657,0.00015293549,0.022814095,0.0068113278,0.76369923],"genre_scores_gemma":[0.0044309967,0.0321501,0.025592811,0.0012597548,0.0032986298,0.00009460142,0.0066115605,0.0035291933,0.9230325],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987859,0.00009859255,0.00006773543,0.00018582099,0.0007963625,0.00006567422],"domain_scores_gemma":[0.99610364,0.00087345013,0.00012023485,0.000488432,0.0021275522,0.00028659462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015525047,0.0022965707,0.0014899508,0.0049432786,0.0014880416,0.006921636,0.0020885544,0.0021002477,0.22690026],"category_scores_gemma":[0.0064623323,0.0013856508,0.0011435939,0.006565808,0.0031501406,0.004320282,0.0016834538,0.0031255884,0.10386556],"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":[0.000014275288,0.000009259354,0.00008466193,0.0002686812,0.000008685302,0.000025671907,0.00007539379,0.00062348,0.00013729765,0.061878935,0.8406321,0.09624162],"study_design_scores_gemma":[0.000003929093,0.0000032540352,0.00019420822,0.00012338553,0.0000061565042,0.000047786878,0.000019062622,0.00032261238,0.00009272895,0.023136139,0.9760414,0.000009327178],"about_ca_topic_score_codex":0.22963858,"about_ca_topic_score_gemma":0.386311,"teacher_disagreement_score":0.22963858,"about_ca_system_score_codex":0.009317119,"about_ca_system_score_gemma":0.013900234,"threshold_uncertainty_score":0.759057},"labels":[],"label_agreement":null},{"id":"W6930623742","doi":"10.5281/zenodo.15019654","title":"Figs 97–100 in West Palaearctic species of Euura Newman, 1837 (Hymenoptera, Tenthredinidae)","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Guelph","funders":"","keywords":"Assemblage (archaeology); Taxonomy (biology); Fauna; Subgenus","score_opus":0.08049096077721524,"score_gpt":0.3222068520026016,"score_spread":0.24171589122538634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930623742","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":[0.004268413,0.0052449,0.00529582,0.00041234528,0.0016370794,0.0002547936,0.031802174,0.001113397,0.94997114],"genre_scores_gemma":[0.08334412,0.008119181,0.021616623,0.0005390052,0.0012908367,0.00066827814,0.0898793,0.002140022,0.79240257],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998417,0.000034868935,0.00001231304,0.000044724544,0.000040893672,0.000025504773],"domain_scores_gemma":[0.99984324,0.00004377415,0.00003010617,0.000019827832,0.000043144926,0.000019835585],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013383554,0.00096331467,0.00046463916,0.0022829259,0.00072429684,0.00063968875,0.00086427375,0.00054391794,0.32997712],"category_scores_gemma":[0.00053601706,0.00028614743,0.0004515133,0.0033447598,0.0006920485,0.0010116152,0.00053129846,0.00084701716,0.14902668],"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":[0.0001631428,0.000057817168,0.0020380984,0.00073888415,0.000028101515,0.0002680655,0.00037841004,0.00077525614,0.0016095046,0.014660715,0.7794634,0.19981864],"study_design_scores_gemma":[0.00002534266,0.000011437156,0.013601818,0.0001395328,0.000008155912,0.0002528753,0.00011453446,0.00022580194,0.00017347609,0.0029381453,0.9825004,0.00000846066],"about_ca_topic_score_codex":0.008815086,"about_ca_topic_score_gemma":0.018946981,"teacher_disagreement_score":0.32997712,"about_ca_system_score_codex":0.0008619624,"about_ca_system_score_gemma":0.00027304137,"threshold_uncertainty_score":0.9557063},"labels":[],"label_agreement":null},{"id":"W6930642019","doi":"10.5281/zenodo.14517008","title":"Autostronomy/AutoProf: v1.3.3","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"Université de Montréal","funders":"","keywords":"Action (physics); Set (abstract data type); Work (physics)","score_opus":0.07613384458231377,"score_gpt":0.33757940055675706,"score_spread":0.2614455559744433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930642019","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005187935,0.00028373775,0.046835974,0.00044444067,0.0004347506,0.00011232646,0.09119575,0.84225625,0.01791801],"genre_scores_gemma":[0.00727027,0.00024259344,0.030937538,0.0007839179,0.00022036982,0.00045178458,0.09926311,0.83437735,0.0264531],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996405,0.0006163906,0.0002689462,0.00075356645,0.0014740936,0.00048205993],"domain_scores_gemma":[0.9915241,0.0026587206,0.00040852238,0.002727799,0.0022014447,0.00047944335],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052213715,0.0033311965,0.002962654,0.0039530336,0.0016605242,0.0061911503,0.0061305887,0.0034542307,0.5681787],"category_scores_gemma":[0.026217742,0.0039825877,0.0030629958,0.0031769276,0.00107792,0.007036896,0.006113555,0.0046994872,0.64152735],"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":[0.00014043383,0.000022404598,0.0003524024,0.0003875042,0.000043996202,0.00004108601,0.00005484143,0.00041197185,0.00055871025,0.0019645803,0.9816175,0.01440443],"study_design_scores_gemma":[0.00019087394,0.000035795776,0.0011021213,0.00021815687,0.00004615851,0.0002906996,0.00004540018,0.0051808404,0.00864451,0.01919701,0.9648506,0.00019782687],"about_ca_topic_score_codex":0.004331321,"about_ca_topic_score_gemma":0.0044740112,"teacher_disagreement_score":0.5681787,"about_ca_system_score_codex":0.0016659335,"about_ca_system_score_gemma":0.002563019,"threshold_uncertainty_score":0.61594063},"labels":[],"label_agreement":null},{"id":"W6930720208","doi":"10.5281/zenodo.15141639","title":"Table 1 in Walaphyllium subgen. nov., the dancing leaf insects from Australia and Papua New Guinea with description of a new species (Phasmatodea, Phylliidae)","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"Espace pour la vie","funders":"","keywords":"New guinea; RADIUS; Table (database); Vein; Margin (machine learning)","score_opus":0.14548855467766228,"score_gpt":0.3093646103494249,"score_spread":0.16387605567176264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930720208","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44606367,0.041694082,0.0066603827,0.0013618288,0.0031303635,0.0017723556,0.14661896,0.001098,0.3516004],"genre_scores_gemma":[0.7108899,0.013303093,0.031150162,0.0017156444,0.0004334537,0.0007854343,0.15424061,0.00023138814,0.087250285],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999094,0.000007700859,0.000014418968,0.000031654534,0.00001870803,0.00001812247],"domain_scores_gemma":[0.9998975,0.000015093974,0.00004188407,0.000008827862,0.000024003004,0.000012775368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008240918,0.00060534425,0.0002854322,0.0026352284,0.00081036834,0.00029763507,0.00041863942,0.00027757304,0.027716514],"category_scores_gemma":[0.0002430034,0.00015676038,0.00025050898,0.0025019264,0.000255211,0.00059061195,0.00040188388,0.00038698476,0.007918244],"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":[0.00042351382,0.00016031148,0.08881986,0.005339027,0.00014318572,0.0025328435,0.0028952062,0.00037031097,0.0359278,0.0022030072,0.09474497,0.76644003],"study_design_scores_gemma":[0.000022322847,0.00020197933,0.4173904,0.0007290973,0.00011660262,0.0037311004,0.0013537065,0.00019842967,0.0013459268,0.0003682882,0.57452345,0.000018683588],"about_ca_topic_score_codex":0.009332214,"about_ca_topic_score_gemma":0.021079704,"teacher_disagreement_score":0.027716514,"about_ca_system_score_codex":0.00028705734,"about_ca_system_score_gemma":0.0003699291,"threshold_uncertainty_score":0.092720985},"labels":[],"label_agreement":null},{"id":"W6930886275","doi":"10.5281/zenodo.16613778","title":"Replication package for: \"Are Cities Losing Innovation Advantages? Online versus Face-to-face Interactions\"","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Replication (statistics); Key (lock); The Internet; Context (archaeology)","score_opus":0.12994140865270729,"score_gpt":0.4088848476892223,"score_spread":0.278943439036515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930886275","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004108344,0.00009579033,0.0012207625,0.00043173696,0.00025770985,0.0004594412,0.9950689,0.0007339763,0.001320843],"genre_scores_gemma":[0.0078368,0.00015977163,0.008626488,0.00068092364,0.00013837266,0.016564356,0.9543696,0.0011721409,0.010451573],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951462,0.00174322,0.00075264677,0.0013280743,0.0006055853,0.00042424185],"domain_scores_gemma":[0.97487193,0.010325196,0.001819453,0.008162046,0.004031311,0.0007900122],"candidate_categories":["metaresearch","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011355171,0.0025989607,0.0024692137,0.0020979473,0.0021298837,0.0032912677,0.005365945,0.0029533375,0.31174752],"category_scores_gemma":[0.06660757,0.0015179659,0.0037907714,0.0037439012,0.001209361,0.0018654658,0.0026609192,0.0047306404,0.106863685],"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":[0.0003179939,0.00005926741,0.00076865783,0.0009878874,0.00018470171,0.0000301653,0.000060367234,0.00027862727,0.00006711665,0.0011966868,0.9929213,0.0031272012],"study_design_scores_gemma":[0.011132333,0.00016700498,0.012671931,0.0013024029,0.0006796368,0.00014462217,0.00030157,0.0012966098,0.0005031042,0.012716112,0.9588791,0.00020560503],"about_ca_topic_score_codex":0.029019436,"about_ca_topic_score_gemma":0.037178535,"teacher_disagreement_score":0.99463403,"about_ca_system_score_codex":0.0019798928,"about_ca_system_score_gemma":0.004559352,"threshold_uncertainty_score":0.98170865},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W6931099070","doi":"10.5281/zenodo.4578159","title":"FIG. 4 in Recent Brachiopoda from the Mozambique-Madagascar area, western Indian Ocean","year":2016,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 New Brunswick","funders":"","keywords":"Dorsum; Point (geometry); Scale (ratio); Oblique case; Indian ocean","score_opus":0.06681650847916924,"score_gpt":0.3196715081260817,"score_spread":0.25285499964691244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931099070","genre_codex":"empirical","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":[0.87735605,0.008023771,0.0013895237,0.0007493506,0.0003473291,0.0002668108,0.0064816545,0.00030271622,0.10508285],"genre_scores_gemma":[0.9729802,0.0021750988,0.004130711,0.00022155135,0.000079223355,0.000114961025,0.0030873036,0.00005327089,0.01715774],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.999902,0.000009085227,0.00000919382,0.000031020238,0.000018719385,0.000030028194],"domain_scores_gemma":[0.9998604,0.0000101932055,0.000055735632,0.000012585261,0.000043311193,0.00001778846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001534968,0.00047262933,0.00015396926,0.0028249987,0.0021142,0.0007869221,0.00042342534,0.00024866642,0.010834979],"category_scores_gemma":[0.00037483347,0.00022069408,0.00026396263,0.0038838289,0.0007389886,0.00033901693,0.0007963572,0.00046051393,0.0012402702],"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":[0.00063702476,0.00004177812,0.6506372,0.0014837949,0.00025576487,0.0044305325,0.015475061,0.00073034765,0.03112724,0.0023871544,0.025585707,0.2672085],"study_design_scores_gemma":[0.000007743857,0.000024242696,0.9273858,0.00011373819,0.000043089858,0.0007755576,0.0020190198,0.00006451471,0.00044399823,0.00005573499,0.06905652,0.000010059837],"about_ca_topic_score_codex":0.16287889,"about_ca_topic_score_gemma":0.40289325,"teacher_disagreement_score":0.16287889,"about_ca_system_score_codex":0.0015074852,"about_ca_system_score_gemma":0.0007116999,"threshold_uncertainty_score":0.3238616},"labels":[],"label_agreement":null},{"id":"W6931189728","doi":"10.5281/zenodo.4298311","title":"Fig. 80 in The 'red-tailed' Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Manitoba","funders":"","keywords":"Nearctic ecozone; Principle of maximum entropy; Entropy (arrow of time); Niche; Taxonomy (biology)","score_opus":0.06590269534730174,"score_gpt":0.3108555089502742,"score_spread":0.24495281360297244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931189728","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":[0.2008102,0.0018490744,0.005011958,0.00076790125,0.00039407908,0.00014481095,0.10819469,0.0015159077,0.6813114],"genre_scores_gemma":[0.66727376,0.0011411603,0.008712971,0.00020002094,0.0000668934,0.000099130164,0.07521548,0.0003160291,0.2469746],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99993646,0.000012907394,0.0000026787025,0.000020728638,0.000010446478,0.000016755006],"domain_scores_gemma":[0.9999361,0.000009638945,0.000017298964,0.000006347326,0.000015138757,0.000015492828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000093778624,0.00020946209,0.00007302656,0.0014451101,0.0004548567,0.00040881598,0.00021029192,0.00014944673,0.07282596],"category_scores_gemma":[0.0001584486,0.00006855192,0.00014192252,0.0014785645,0.00031325786,0.00023879932,0.0003030231,0.00017601425,0.019413674],"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":[0.00040316768,0.00009253246,0.13495512,0.0004889405,0.000041150328,0.0008363626,0.0023350131,0.0012008171,0.008552688,0.007769175,0.5074918,0.3358333],"study_design_scores_gemma":[0.000020831387,0.00005220764,0.5762999,0.000081788974,0.000020589128,0.00068930554,0.0016812253,0.0006860612,0.0007110074,0.000806072,0.41893932,0.000011735405],"about_ca_topic_score_codex":0.042378534,"about_ca_topic_score_gemma":0.12602672,"teacher_disagreement_score":0.07282596,"about_ca_system_score_codex":0.00037202807,"about_ca_system_score_gemma":0.00017442787,"threshold_uncertainty_score":0.24362713},"labels":[],"label_agreement":null},{"id":"W6931204686","doi":"10.5281/zenodo.4298302","title":"What can DDI do for you? An introduction to the DDI","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"Carleton University","funders":"","keywords":"Conceptualization; Metadata; Interoperability; Focus (optics); Component (thermodynamics); Variable (mathematics); Conceptual model","score_opus":0.10369191064946573,"score_gpt":0.3376337473168657,"score_spread":0.23394183666739995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931204686","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":[0.002437647,0.07826118,0.47886437,0.10495129,0.01412152,0.0014628276,0.0057632914,0.007644283,0.3064936],"genre_scores_gemma":[0.025030004,0.12230342,0.48290932,0.032355044,0.010885164,0.0029928023,0.009196199,0.0062780576,0.30804995],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957867,0.0018284873,0.00039552597,0.00041154385,0.0013299279,0.00024784807],"domain_scores_gemma":[0.98946196,0.0068178684,0.00037216506,0.0006232342,0.0020289063,0.0006958418],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008682066,0.0011215978,0.0008426749,0.0031149935,0.0012933237,0.0061353752,0.0018083337,0.0027452386,0.054354757],"category_scores_gemma":[0.024765754,0.0007331518,0.0009261819,0.0033549247,0.0025810087,0.01134555,0.0035625296,0.004956485,0.036849],"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":[0.000039117374,0.00007654815,0.0005418979,0.0011904758,0.0000109472585,0.0001079397,0.0017717858,0.00035485325,0.0008590735,0.17498837,0.49894044,0.32111847],"study_design_scores_gemma":[0.0000035987546,0.000017160528,0.00028361104,0.0005063213,0.0000020383695,0.00017481235,0.00035037455,0.0002742011,0.00021861069,0.02410815,0.9740442,0.000017024793],"about_ca_topic_score_codex":0.0026743438,"about_ca_topic_score_gemma":0.0031746589,"teacher_disagreement_score":0.9938646,"about_ca_system_score_codex":0.0034302366,"about_ca_system_score_gemma":0.0033486024,"threshold_uncertainty_score":0.18183476},"labels":[],"label_agreement":null},{"id":"W6931301622","doi":"10.5281/zenodo.3767638","title":"Figure 4 in Prospects for using DNA barcoding to identify spiders in species-rich genera","year":2009,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Guelph","funders":"","keywords":"DNA barcoding; Spider; Monophyly; Intraspecific competition; Taxonomy (biology); Clade; Molecular taxonomy","score_opus":0.12482653637731421,"score_gpt":0.3718209284757961,"score_spread":0.2469943920984819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931301622","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":[0.014682684,0.0052562985,0.18165351,0.02303343,0.006406659,0.00038056533,0.03892671,0.021654936,0.7080052],"genre_scores_gemma":[0.10683669,0.0051350403,0.32577908,0.0025990258,0.0012551249,0.0002612147,0.031691633,0.0042398586,0.5222024],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996903,0.0000656565,0.000013520228,0.00009658791,0.00010094745,0.0000331296],"domain_scores_gemma":[0.99858105,0.00032705473,0.000108988774,0.00025571202,0.0005385525,0.00018866966],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011868643,0.00063423876,0.00032501447,0.0024246997,0.0005998688,0.0017826799,0.0011543119,0.0008231147,0.2993003],"category_scores_gemma":[0.0024480226,0.00032166645,0.00047065745,0.0017704606,0.000638462,0.0019271995,0.0010028544,0.00072651386,0.10884094],"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":[0.00017066702,0.000047142694,0.004793158,0.00037563377,0.000019946743,0.0001323271,0.00011852734,0.0009494408,0.0053316206,0.032554623,0.57137465,0.3841323],"study_design_scores_gemma":[0.00005323793,0.000037908136,0.024719696,0.00030287128,0.000024699073,0.00057653565,0.00020344874,0.0057286,0.0050963545,0.04568956,0.9175194,0.000047735695],"about_ca_topic_score_codex":0.0059109456,"about_ca_topic_score_gemma":0.013230969,"teacher_disagreement_score":0.2993003,"about_ca_system_score_codex":0.0006155094,"about_ca_system_score_gemma":0.00048810552,"threshold_uncertainty_score":0.9994631},"labels":[],"label_agreement":null},{"id":"W6931391559","doi":"10.5281/zenodo.8290037","title":"Lasioglossum (Dialictus) exiguum","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Manitoba","funders":"","keywords":"Holotype; Margin (machine learning); Peninsula; Type (biology); Carboniferous","score_opus":0.12871441089599178,"score_gpt":0.35588076034650845,"score_spread":0.22716634945051667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931391559","genre_codex":"empirical","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":[0.86370295,0.0038240051,0.0015211502,0.00047452442,0.00011301565,0.0000972724,0.0016036803,0.0003572941,0.128306],"genre_scores_gemma":[0.9878199,0.0008247496,0.000777761,0.00019191379,0.00003773594,0.000055282555,0.00063572725,0.000012685664,0.009644323],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999275,0.000009255823,0.0000062828617,0.000025700176,0.00001581964,0.000015377173],"domain_scores_gemma":[0.99992764,0.000010659831,0.000032418837,0.000004988849,0.000014860228,0.000009341558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102908525,0.000335698,0.00014621823,0.0008656651,0.0009196996,0.00028722655,0.0003383624,0.00021126262,0.007762387],"category_scores_gemma":[0.00020455293,0.00010563737,0.00007045937,0.0006452205,0.00061623286,0.00043005662,0.00091042026,0.00026461526,0.0015795737],"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":[0.00089667714,0.0001368521,0.19708988,0.0007874431,0.0000702078,0.0044082804,0.005849426,0.0010099415,0.04542933,0.00765624,0.02466504,0.7120008],"study_design_scores_gemma":[0.00012762824,0.0003411018,0.77986056,0.00031886058,0.00012997605,0.0039401785,0.0038664604,0.000671713,0.0028946702,0.0016971994,0.20611647,0.0000351272],"about_ca_topic_score_codex":0.021531496,"about_ca_topic_score_gemma":0.0649248,"teacher_disagreement_score":0.021531496,"about_ca_system_score_codex":0.00087320287,"about_ca_system_score_gemma":0.0002964598,"threshold_uncertainty_score":0.042812347},"labels":[],"label_agreement":null},{"id":"W6931427742","doi":"10.5281/zenodo.5598297","title":"Data from: Does pollen limitation limit plant ranges? Evidence and implications","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"McGill University","funders":"","keywords":"Pollen; Pollination; Range (aeronautics); Plant reproduction; Pollen source; Sexual reproduction; Plant species; Leverage (statistics)","score_opus":0.2921712920991475,"score_gpt":0.38526801155424334,"score_spread":0.09309671945509584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931427742","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3230159,0.20043536,0.15615258,0.03405685,0.001911335,0.00037685066,0.21393335,0.0018617569,0.06825602],"genre_scores_gemma":[0.88247705,0.024989424,0.038054362,0.005897552,0.0007134602,0.0004881559,0.042699777,0.00082793535,0.003852338],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9901911,0.005497466,0.0012259674,0.0021019585,0.000847697,0.00013586214],"domain_scores_gemma":[0.89095634,0.08584693,0.009352969,0.010216704,0.00283423,0.0007928089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01552584,0.0006524303,0.0014315412,0.0027216764,0.00055896747,0.0019671163,0.001555704,0.0012199123,0.026654463],"category_scores_gemma":[0.08368671,0.00035313514,0.0018861074,0.0058234534,0.0013590823,0.0018465195,0.0018526089,0.0010919835,0.0028359257],"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":[0.0058917264,0.00020969116,0.33112562,0.054724023,0.026294123,0.00088498945,0.001963593,0.0074397256,0.006206018,0.036155377,0.08796009,0.441145],"study_design_scores_gemma":[0.0012452184,0.0005735797,0.49160898,0.015567636,0.015600221,0.0016099202,0.0017292302,0.00987066,0.006698115,0.14679518,0.30836323,0.00033804623],"about_ca_topic_score_codex":0.0035999732,"about_ca_topic_score_gemma":0.003294733,"teacher_disagreement_score":0.026654463,"about_ca_system_score_codex":0.00055542274,"about_ca_system_score_gemma":0.000671793,"threshold_uncertainty_score":0.08916801},"labels":[],"label_agreement":null},{"id":"W6931467152","doi":"10.5281/zenodo.6585406","title":"Canola Genetic Engineering for Long-Term Agriculture and Global Food Security","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Canola; Agriculture; Food security; Crop; Abiotic component; Global warming; Sustainability; Sustainable agriculture","score_opus":0.03770289482596898,"score_gpt":0.28401032210650484,"score_spread":0.24630742728053587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931467152","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":[0.016591173,0.023088217,0.9459891,0.0021697134,0.00039722887,0.00006607777,0.00041535636,0.0009794493,0.010303785],"genre_scores_gemma":[0.28193206,0.051099017,0.64883864,0.00087717676,0.00028554068,0.0004068159,0.0012547518,0.0007262701,0.014579847],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995154,0.0001731868,0.000028880131,0.00009119126,0.000165115,0.00002625747],"domain_scores_gemma":[0.99927574,0.0003754851,0.000115468276,0.00008452868,0.00010938637,0.000039457696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012852296,0.0004945401,0.00040548545,0.00084534934,0.000267219,0.00081324717,0.0006225425,0.0005265052,0.0026108504],"category_scores_gemma":[0.0018588755,0.00020257977,0.00045503536,0.00080529705,0.0007009937,0.0007196444,0.00057464186,0.0013425517,0.0007903041],"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":[0.00017068899,0.00012885989,0.0019149324,0.0017255704,0.00019813587,0.00033623303,0.00023378638,0.021138232,0.2019978,0.30077535,0.007876875,0.46350345],"study_design_scores_gemma":[0.00007683182,0.00035844548,0.0035940497,0.0009228567,0.00027664454,0.0009509562,0.00016011858,0.09650145,0.11336256,0.40767193,0.3759727,0.00015140738],"about_ca_topic_score_codex":0.0010316422,"about_ca_topic_score_gemma":0.0017755639,"teacher_disagreement_score":0.0026108504,"about_ca_system_score_codex":0.00077922945,"about_ca_system_score_gemma":0.0008297699,"threshold_uncertainty_score":0.008734226},"labels":[],"label_agreement":null},{"id":"W6931569374","doi":"10.5281/zenodo.8176707","title":"UKRAINIAN-CANADIAN PARLIAMENTARY DIPLOMACY: NAVIGATING THE CHALLENGES OF THE ONGOING WAR WITH RUSSIA","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Parliament; Ukrainian; Politics; Democracy; Sovereignty; Accountability; Convention","score_opus":0.08539364137559569,"score_gpt":0.32192642282392997,"score_spread":0.23653278144833428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931569374","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":[0.40466562,0.02064722,0.0026947993,0.10234159,0.0019685305,0.00011630085,0.00046395182,0.000090048954,0.46701202],"genre_scores_gemma":[0.9625,0.004128188,0.000764216,0.0047040093,0.000062759485,0.000020180672,0.00011027182,0.000038350274,0.027671909],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945374,0.0013524938,0.00009966337,0.00034105268,0.0014608124,0.0022084992],"domain_scores_gemma":[0.99691015,0.00073651975,0.00020170791,0.00006592302,0.0010904382,0.0009952604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003842584,0.00032464686,0.00031066913,0.0015395685,0.035586424,0.01215697,0.0013335268,0.0021584067,0.0051642098],"category_scores_gemma":[0.0061621047,0.00023542387,0.00036132697,0.002478723,0.007415853,0.002428086,0.0057167443,0.0030069104,0.00053552905],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019378093,0.00004774067,0.023377014,0.00073107966,0.00007582704,0.004227581,0.50383157,0.0009707092,0.0019955726,0.27449888,0.085979715,0.104070574],"study_design_scores_gemma":[0.0000077150635,0.000012287857,0.019020146,0.00033978702,0.00003244581,0.00035531368,0.3353242,0.0002604599,0.0005670981,0.002728212,0.64128864,0.00006374064],"about_ca_topic_score_codex":0.9733666,"about_ca_topic_score_gemma":0.98960054,"teacher_disagreement_score":0.09387642,"about_ca_system_score_codex":0.09387642,"about_ca_system_score_gemma":0.15540233,"threshold_uncertainty_score":0.6811243},"labels":[],"label_agreement":null},{"id":"W6931586835","doi":"10.5281/zenodo.7109414","title":"Australian preparatory scholars' illustration in their expertise of the character of wisdom","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"Victoria Park","funders":"","keywords":"Character (mathematics); sort; Moral character; The Internet","score_opus":0.09962608456105179,"score_gpt":0.3229655873822605,"score_spread":0.2233395028212087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931586835","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":[0.046466537,0.03589686,0.016817417,0.49633443,0.020251783,0.00016274114,0.00019341486,0.00021966879,0.38365716],"genre_scores_gemma":[0.6961396,0.01863005,0.014193194,0.07723283,0.004943397,0.00022422655,0.000054355747,0.00026240453,0.18831992],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98363805,0.009435884,0.0007937181,0.0011141752,0.003932835,0.0010853373],"domain_scores_gemma":[0.9669358,0.016227918,0.0023746744,0.004074468,0.006788692,0.003598474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01980401,0.00045353532,0.0006493163,0.0027122058,0.013353402,0.010520968,0.0023705394,0.0037096753,0.012274009],"category_scores_gemma":[0.034404416,0.0004637175,0.00046153372,0.004019287,0.023501283,0.0068958527,0.012081095,0.010242026,0.0021345406],"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":[0.000050447983,0.000040443225,0.0027079491,0.0008235039,0.000013730791,0.0017963399,0.4032973,0.00013415412,0.0007023919,0.3713611,0.16121608,0.05785657],"study_design_scores_gemma":[0.0000029117095,0.000023540188,0.002371546,0.00060036697,0.0000054455304,0.0010608558,0.05053982,0.00011550391,0.00018489023,0.028180858,0.9168895,0.00002486178],"about_ca_topic_score_codex":0.022033004,"about_ca_topic_score_gemma":0.04437678,"teacher_disagreement_score":0.022033004,"about_ca_system_score_codex":0.008466882,"about_ca_system_score_gemma":0.012912405,"threshold_uncertainty_score":0.1047349},"labels":[],"label_agreement":null},{"id":"W6931608299","doi":"10.5281/zenodo.7456684","title":"FIGURE 2 in Two new unique hibiscus-inhabiting species of the plant bug genus Sejanus Distant from Japan and Taiwan (Hemiptera: Heteroptera: Miridae: Phylinae)","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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":"Agriculture and Agri-Food Canada","funders":"","keywords":"Instar; Genus; Thrips; Host (biology); Habitat; Habitus; Type locality; Taxonomy (biology)","score_opus":0.04928376300342528,"score_gpt":0.28784698527512037,"score_spread":0.23856322227169507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931608299","genre_codex":"empirical","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":[0.95359856,0.0002619103,0.0034591532,0.00022108799,0.00013078697,0.000086519125,0.0006038764,0.00018845234,0.041449543],"genre_scores_gemma":[0.9765629,0.000102598045,0.0065352293,0.00010297515,0.000020914964,0.000035616253,0.00079106784,0.00003976025,0.015808938],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99993384,0.000006735274,0.0000035406765,0.00003578157,0.000008031984,0.000012054297],"domain_scores_gemma":[0.9999409,0.000007908949,0.000017721786,0.0000102014465,0.000008604467,0.00001464236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000044908975,0.00026658358,0.00009764424,0.0006073146,0.00074899016,0.00034045987,0.0002139918,0.00034108115,0.010123959],"category_scores_gemma":[0.0001672153,0.00014143724,0.00019909255,0.00029325593,0.00046428517,0.00026218232,0.0005391838,0.0004153017,0.0014859748],"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":[0.0009043255,0.00032082287,0.56544024,0.00074949703,0.00011858968,0.024376266,0.015577624,0.0007490258,0.1652839,0.008618706,0.019906607,0.1979544],"study_design_scores_gemma":[0.000032111748,0.00017350595,0.8528661,0.00010512001,0.00010015711,0.03565138,0.0036726147,0.0012652661,0.006951702,0.001311207,0.09784089,0.000029974693],"about_ca_topic_score_codex":0.0026857276,"about_ca_topic_score_gemma":0.015221629,"teacher_disagreement_score":0.98987603,"about_ca_system_score_codex":0.00014186393,"about_ca_system_score_gemma":0.000086669235,"threshold_uncertainty_score":0.033868074},"labels":[],"label_agreement":null},{"id":"W6931643163","doi":"10.5281/zenodo.7226143","title":"QianfengClarkShen/Tbps_CRC: initial release","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Action (physics); Work (physics); Component (thermodynamics); Context (archaeology)","score_opus":0.08494667458745114,"score_gpt":0.34291819725233985,"score_spread":0.2579715226648887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931643163","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022874272,0.0011877166,0.14638697,0.0033957318,0.0017044444,0.000599703,0.17031564,0.29335648,0.38076594],"genre_scores_gemma":[0.02347171,0.0010597341,0.075273655,0.0018914653,0.0009102328,0.0011325708,0.21627212,0.21566391,0.4643246],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99814653,0.00031882827,0.00006956963,0.00022965236,0.0010341589,0.0002012962],"domain_scores_gemma":[0.99637055,0.00073987205,0.0001414009,0.0010535814,0.0012777997,0.00041691237],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037794902,0.002114695,0.001568884,0.0019542342,0.00089836086,0.0036757959,0.003867616,0.0022162746,0.4074845],"category_scores_gemma":[0.007720524,0.0011642355,0.0010995399,0.0018195187,0.0008100777,0.0032546592,0.0026275543,0.0027508747,0.47777385],"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":[0.00021879846,0.00004896896,0.00016914854,0.00021567292,0.00002688685,0.000035872472,0.000044548407,0.0008035072,0.0010914683,0.010690803,0.9390764,0.04757784],"study_design_scores_gemma":[0.00013743113,0.00004425486,0.00042944096,0.00006983939,0.000011119979,0.000066909124,0.0000144281385,0.0036325112,0.0037237199,0.011646644,0.9801681,0.000055714492],"about_ca_topic_score_codex":0.008410841,"about_ca_topic_score_gemma":0.0068447925,"teacher_disagreement_score":0.4074845,"about_ca_system_score_codex":0.0015287639,"about_ca_system_score_gemma":0.0020048774,"threshold_uncertainty_score":0.8451514},"labels":[],"label_agreement":null},{"id":"W6931704808","doi":"10.5281/zenodo.6265521","title":"Phryxe pecosensis Townsend 1926","year":2005,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Choristoneura fumiferana; Spruce budworm; Host (biology); Tortricidae","score_opus":0.08933433150052338,"score_gpt":0.33275997043279143,"score_spread":0.24342563893226804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931704808","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":[0.28762653,0.004830242,0.0040482036,0.00052835373,0.00034918918,0.00024131121,0.0023938264,0.00030985757,0.69967246],"genre_scores_gemma":[0.9209149,0.0014573975,0.0019716849,0.0003615333,0.000100025376,0.00004227807,0.0012951543,0.000018741448,0.07383834],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99990726,0.00000498346,0.000004480943,0.000043136744,0.000026750773,0.000013329844],"domain_scores_gemma":[0.99993646,0.00001649062,0.000015464255,0.000009435654,0.000013777402,0.000008286281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006522299,0.00045460812,0.00014220008,0.0005521946,0.0008353179,0.00035313782,0.0003670253,0.00028787847,0.028812153],"category_scores_gemma":[0.0002982097,0.00017170722,0.00007383082,0.00041798205,0.0004226114,0.00097054645,0.00090515666,0.00057058374,0.0036572963],"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":[0.00035666718,0.00014772176,0.030937904,0.0005819409,0.0000623321,0.0064245267,0.0025520357,0.001242168,0.039642278,0.0142698595,0.022886608,0.8808959],"study_design_scores_gemma":[0.000076668824,0.00042426298,0.4713503,0.00025592238,0.00006793324,0.016048744,0.0018188946,0.000858703,0.008904557,0.0032878811,0.49687284,0.000033295782],"about_ca_topic_score_codex":0.011276733,"about_ca_topic_score_gemma":0.015738498,"teacher_disagreement_score":0.028812153,"about_ca_system_score_codex":0.00035895128,"about_ca_system_score_gemma":0.00016558898,"threshold_uncertainty_score":0.096386194},"labels":[],"label_agreement":null},{"id":"W6931765555","doi":"10.5683/sp3/c0e3lb","title":"Molecular dynamics simulations of the plant-specific insert monomer at pH 4.5","year":2014,"lang":"en","type":"dataset","venue":"Borealis","topic":"Statistical Methods and Bayesian 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 Guelph","funders":"","keywords":"Molecular dynamics; Monomer; Dissociation (chemistry); Protein tertiary structure; Umbrella sampling; Protein structure; In silico; Insert (composites)","score_opus":0.05025700750970921,"score_gpt":0.3244584253987321,"score_spread":0.27420141788902286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931765555","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7184376,0.00209583,0.015608142,0.002822797,0.00034245622,0.00016348387,0.23626849,0.006826552,0.01743467],"genre_scores_gemma":[0.6541236,0.00050444534,0.031307712,0.0005992505,0.000059378952,0.0004867256,0.30897224,0.00084414304,0.0031025412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997936,0.000061405335,0.000009806235,0.000060621107,0.000040167117,0.000034479286],"domain_scores_gemma":[0.99943453,0.0003311912,0.00003796273,0.000056126093,0.00008980856,0.000050321665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071649015,0.0009287941,0.0006571023,0.00047939754,0.00066840864,0.0005113527,0.0017828787,0.0013767469,0.0035289023],"category_scores_gemma":[0.0021389734,0.00033828954,0.0008399596,0.0007004063,0.00046322093,0.0004752872,0.00046433255,0.0013155688,0.0010944026],"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":[0.0009832043,0.0006866039,0.0146030625,0.0007815435,0.00041923928,0.00035394475,0.00022842716,0.8488359,0.004271325,0.013500595,0.101313256,0.014022837],"study_design_scores_gemma":[0.0006290822,0.000086648775,0.0061557437,0.000048720933,0.00005170158,0.00006833375,0.000060254533,0.96239674,0.001947573,0.006062204,0.022454748,0.000038196882],"about_ca_topic_score_codex":0.020670716,"about_ca_topic_score_gemma":0.03648079,"teacher_disagreement_score":0.020670716,"about_ca_system_score_codex":0.0011453349,"about_ca_system_score_gemma":0.0012967983,"threshold_uncertainty_score":0.0411008},"labels":[],"label_agreement":null},{"id":"W6939214132","doi":"10.60692/ajq38-d7070","title":"Tests for homogeneity of risk differences in stratified design with correlated bilateral data","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Homogeneity (statistics); Statistic; Tangent; Statistical hypothesis testing; Inference; Inverse","score_opus":0.17231750381244615,"score_gpt":0.31465801442375785,"score_spread":0.1423405106113117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939214132","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":[0.13290513,0.00073260994,0.8580585,0.0005248325,0.00015188567,0.0024092202,0.0016349303,0.00054462685,0.003038183],"genre_scores_gemma":[0.71719366,0.0002351697,0.27457914,0.00034464116,0.000118835334,0.005351955,0.0013151809,0.00010187563,0.00075952016],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86288905,0.10364295,0.0073314747,0.012306115,0.012167478,0.0016629185],"domain_scores_gemma":[0.58687276,0.3608651,0.016210727,0.029783553,0.005145759,0.001122181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.112136014,0.0010877242,0.0024288173,0.0029980964,0.0012677532,0.0019515193,0.0021498655,0.0018892032,0.008073947],"category_scores_gemma":[0.3586499,0.0005817484,0.0043295324,0.0031674919,0.003761593,0.0023660585,0.0021726678,0.0020899663,0.0006866182],"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":[0.007876871,0.00078003405,0.2727323,0.0023335447,0.015028026,0.0016994905,0.0035672279,0.04054126,0.008319705,0.17590086,0.008564506,0.46265617],"study_design_scores_gemma":[0.002544049,0.011301676,0.26862878,0.0008162911,0.004637787,0.0015935935,0.0016655759,0.23164889,0.013403662,0.44467494,0.018635718,0.00044908296],"about_ca_topic_score_codex":0.0013925133,"about_ca_topic_score_gemma":0.0011593224,"teacher_disagreement_score":0.112136014,"about_ca_system_score_codex":0.0012592287,"about_ca_system_score_gemma":0.0029872751,"threshold_uncertainty_score":0.59303904},"labels":[],"label_agreement":null},{"id":"W6939531629","doi":"10.6084/m9.figshare.14570720.v1","title":"Additional file 1 of Using random forests to model 90-day hometime in people with stroke","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"Institute for Clinical Evaluative Sciences; University of Toronto; University of Calgary","funders":"","keywords":"Table (database); Demographics; Random forest; Cohort; Stroke (engine); Pairwise comparison","score_opus":0.08026284222746047,"score_gpt":0.33560423712250315,"score_spread":0.2553413948950427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939531629","genre_codex":"dataset","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":[0.00041904484,0.000023636541,0.00095366576,0.0001197583,0.000021551858,0.000096576114,0.9969644,0.00036431523,0.0010370753],"genre_scores_gemma":[0.02704226,0.00021387178,0.011883943,0.0007550501,0.00018582036,0.0032896667,0.94159853,0.0013567347,0.013674128],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999529,0.00014390441,0.00006992731,0.00013553967,0.00006457254,0.00005708787],"domain_scores_gemma":[0.9904585,0.00794402,0.00037243648,0.00047598887,0.0005841607,0.00016489363],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017918233,0.0008147601,0.000879797,0.0011605871,0.00056958286,0.0011063126,0.0016373551,0.0010950952,0.7888614],"category_scores_gemma":[0.025746418,0.0004792552,0.00097014336,0.0018355603,0.00015461526,0.0010594275,0.0007339599,0.0009006878,0.12364837],"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":[0.0002945876,0.00007499403,0.004286766,0.00088854774,0.000083377134,0.00006804726,0.000054492706,0.0017641826,0.00002729656,0.0011905548,0.9799311,0.011336143],"study_design_scores_gemma":[0.0113733,0.00070270157,0.047196414,0.0055300044,0.00071798457,0.0010743743,0.00056293,0.035592638,0.0006170495,0.04242131,0.85397434,0.0002369695],"about_ca_topic_score_codex":0.013076199,"about_ca_topic_score_gemma":0.02352637,"teacher_disagreement_score":0.7888614,"about_ca_system_score_codex":0.00065295905,"about_ca_system_score_gemma":0.0012961895,"threshold_uncertainty_score":0.30116355},"labels":[],"label_agreement":null},{"id":"W6939587128","doi":"10.6084/m9.figshare.14516296.v1","title":"Additional file 2 of Dynamic model updating (DMU) approach for statistical learning model building with missing data","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Missing data; Table (database); Model building; Statistical model; Regression analysis; Data modeling","score_opus":0.17686627813692232,"score_gpt":0.3901808655635539,"score_spread":0.2133145874266316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939587128","genre_codex":"dataset","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":[0.00027883757,0.000044288314,0.002890922,0.00016205787,0.000053177057,0.00011075601,0.9941864,0.0014520672,0.0008213578],"genre_scores_gemma":[0.016617922,0.00024170232,0.027579913,0.00093434827,0.00021562167,0.0038211017,0.9342277,0.0057151117,0.010646669],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984652,0.00051920285,0.00021129502,0.00038273504,0.00029983144,0.00012184221],"domain_scores_gemma":[0.91968733,0.07190727,0.0012762048,0.003037898,0.003561749,0.0005296458],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004888376,0.0015520894,0.0015915063,0.002017844,0.0007551818,0.0020718588,0.0031095834,0.001857257,0.8690601],"category_scores_gemma":[0.075951464,0.00095530896,0.0014234532,0.0036551945,0.00041827362,0.0021967974,0.0012967669,0.0017698895,0.20058544],"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":[0.00036282762,0.000114150374,0.0014546653,0.0030506116,0.00012144062,0.00006830384,0.000046171397,0.0021519174,0.00012912268,0.0014775053,0.97464323,0.0163802],"study_design_scores_gemma":[0.008820398,0.00051303237,0.014072599,0.003269711,0.0005246789,0.0006429616,0.00031444174,0.020400707,0.0024191572,0.046505373,0.9022046,0.00031224417],"about_ca_topic_score_codex":0.004623826,"about_ca_topic_score_gemma":0.009285946,"teacher_disagreement_score":0.8690601,"about_ca_system_score_codex":0.0008787459,"about_ca_system_score_gemma":0.0022729468,"threshold_uncertainty_score":0.1867699},"labels":[],"label_agreement":null},{"id":"W6945043939","doi":"10.25384/sage.21454222","title":"sj-pdf-1-smm-10.1177_09622802221134172 - Supplemental material for Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations","year":2022,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Covariate; Bayesian probability; Proportional hazards model; Inference; Hazard; Bayesian inference; Statistical inference; Nonlinear system","score_opus":0.12010396792548117,"score_gpt":0.3829423885821843,"score_spread":0.26283842065670315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945043939","genre_codex":"dataset","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":[0.0007352953,0.00067874027,0.19529867,0.0037170553,0.0022163768,0.00055547006,0.64185834,0.07269504,0.08224496],"genre_scores_gemma":[0.017541938,0.002103385,0.19286582,0.0057326136,0.0023266023,0.0027166288,0.4851557,0.11931895,0.17223829],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99674356,0.000786911,0.00029016778,0.00048252576,0.0014750643,0.00022180947],"domain_scores_gemma":[0.969933,0.022321006,0.0012441191,0.0019628294,0.0037704085,0.0007685876],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0058953706,0.0016538855,0.0019110342,0.0037191636,0.0008105974,0.0032901042,0.0037911877,0.0036888148,0.84143215],"category_scores_gemma":[0.046573207,0.0027295959,0.0015368228,0.00449769,0.00070128235,0.0030649127,0.0024734666,0.0034372003,0.6464733],"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":[0.000065906,0.00006856827,0.0004279232,0.00047950726,0.00004377823,0.000050187773,0.000036709756,0.0011127676,0.00049169036,0.0073667974,0.95652384,0.033332247],"study_design_scores_gemma":[0.000514881,0.00009979271,0.0029317134,0.0007572722,0.0000730246,0.00037532335,0.000063051004,0.011372295,0.0024255114,0.071049556,0.9101895,0.000147956],"about_ca_topic_score_codex":0.0051588817,"about_ca_topic_score_gemma":0.0094677415,"teacher_disagreement_score":0.84143215,"about_ca_system_score_codex":0.0018736717,"about_ca_system_score_gemma":0.00290924,"threshold_uncertainty_score":0.22617781},"labels":[],"label_agreement":null},{"id":"W6957909940","doi":"10.60692/9x9dc-f1a92","title":"On Predictive Distribution of K-Inflated Poisson Models with and Without Additional Information","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Statistical Methods and Bayesian 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":"Brock University","funders":"","keywords":"Poisson distribution; Random variable; Estimator; Bayesian probability; Variable (mathematics); Observable; Distribution (mathematics); Term (time); Prior information; Compound probability distribution","score_opus":0.052892577624547604,"score_gpt":0.257768961403232,"score_spread":0.20487638377868442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957909940","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":[0.036102902,0.00050206436,0.9606196,0.00069246505,0.0000378408,0.000058293637,0.00014638242,0.00020377996,0.001636692],"genre_scores_gemma":[0.64711756,0.0022794767,0.34244302,0.0006328125,0.00031547973,0.00045870827,0.0013326512,0.00031839235,0.0051018745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99468416,0.003548604,0.00014916148,0.0007535793,0.00060714665,0.00025739067],"domain_scores_gemma":[0.9232114,0.068044506,0.0029438797,0.0031517202,0.0020850878,0.00056337117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02484783,0.0010447457,0.0023507087,0.0025887846,0.0010838639,0.0022489703,0.004678047,0.0022382447,0.0032740547],"category_scores_gemma":[0.09897369,0.0011086779,0.0019632156,0.002499551,0.003762029,0.0067027933,0.0030369225,0.004662057,0.0005403612],"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":[0.000226739,0.000073310366,0.006363059,0.00023282283,0.00017818098,0.00032099825,0.0005647356,0.59235543,0.00079216214,0.36448514,0.0017528135,0.032654677],"study_design_scores_gemma":[0.000017065335,0.000023935536,0.0007253726,0.000057969562,0.000029772893,0.0000724979,0.000052399864,0.9066813,0.00033378688,0.09140948,0.0005638671,0.000032569904],"about_ca_topic_score_codex":0.01002248,"about_ca_topic_score_gemma":0.0064356076,"teacher_disagreement_score":0.02484783,"about_ca_system_score_codex":0.0023316017,"about_ca_system_score_gemma":0.0019255768,"threshold_uncertainty_score":0.13140947},"labels":[],"label_agreement":null},{"id":"W6958009777","doi":"10.6084/m9.figshare.24130187","title":"Additional file 2 of Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Cluster (spacecraft); Code (set theory); Data file; Key (lock); Table (database)","score_opus":0.35441663374967525,"score_gpt":0.45349387384399886,"score_spread":0.09907724009432362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958009777","genre_codex":"dataset","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":[0.0017653605,0.00021355649,0.016044248,0.00091425265,0.00020220877,0.0041782535,0.9671249,0.0024896227,0.0070675835],"genre_scores_gemma":[0.13907371,0.0012727429,0.20186815,0.00559336,0.00066971465,0.10862371,0.47430798,0.009818296,0.058772348],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563074,0.0026325637,0.00052186166,0.00047962513,0.0004966757,0.00023844744],"domain_scores_gemma":[0.7836142,0.20237395,0.0042481204,0.0040316437,0.004724126,0.0010079682],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0121391555,0.0012764655,0.0018485888,0.0016291699,0.0006679247,0.0016585167,0.0025769828,0.0017533195,0.85498184],"category_scores_gemma":[0.1501751,0.0010073608,0.001972457,0.0028250848,0.0004997184,0.0016743371,0.00094852503,0.0019709808,0.07410986],"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":[0.0031288804,0.00033980262,0.003070458,0.009880662,0.0006492886,0.0002039256,0.00016720526,0.011314894,0.000114913346,0.008128265,0.93045217,0.032549515],"study_design_scores_gemma":[0.100825384,0.0028967694,0.019700773,0.012231865,0.0029854183,0.0019431027,0.0004642103,0.10532775,0.0017348496,0.12909913,0.6223233,0.00046748386],"about_ca_topic_score_codex":0.004717252,"about_ca_topic_score_gemma":0.007943249,"teacher_disagreement_score":0.98786086,"about_ca_system_score_codex":0.001828084,"about_ca_system_score_gemma":0.004471699,"threshold_uncertainty_score":0.20685083},"labels":[],"label_agreement":null},{"id":"W6958161179","doi":"10.6084/m9.figshare.24130184","title":"Additional file 1 of Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Table (database); Mean squared error; Approximation error; Random error; Efficiency; Root mean square","score_opus":0.36232715526400705,"score_gpt":0.45418288957903147,"score_spread":0.09185573431502442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958161179","genre_codex":"dataset","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":[0.003257729,0.00037164503,0.03098568,0.0014814413,0.00033024861,0.012789593,0.93393576,0.003823496,0.013024469],"genre_scores_gemma":[0.1365566,0.001551612,0.24051164,0.005215054,0.0006281773,0.21302086,0.351483,0.006035217,0.044997796],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99187565,0.005553712,0.0008480732,0.0007241089,0.00067466864,0.0003238372],"domain_scores_gemma":[0.69667685,0.28227365,0.005988736,0.0076088808,0.0061039017,0.0013480075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.022476554,0.0016534383,0.0019337981,0.0019776246,0.00083111273,0.0019421135,0.0029573825,0.0023849378,0.8220349],"category_scores_gemma":[0.21013547,0.0011652274,0.0020009456,0.0034875865,0.0005442068,0.0019973011,0.0011930532,0.0019334549,0.07443072],"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":[0.006962061,0.00072595617,0.0033854484,0.014295744,0.000948028,0.00024946823,0.00034026307,0.016816927,0.0001646006,0.014518536,0.88058335,0.061009746],"study_design_scores_gemma":[0.12589833,0.004728382,0.015952175,0.014386051,0.0033681064,0.0017613844,0.0005127918,0.11044443,0.0016438613,0.1181771,0.60269606,0.00043142255],"about_ca_topic_score_codex":0.003536313,"about_ca_topic_score_gemma":0.006392039,"teacher_disagreement_score":0.8220349,"about_ca_system_score_codex":0.0024020977,"about_ca_system_score_gemma":0.0053681387,"threshold_uncertainty_score":0.25384563},"labels":[],"label_agreement":null},{"id":"W6958231004","doi":"10.6084/m9.figshare.12872405.v1","title":"Additional file 1 of LEVEL (Logical Explanations &amp; Visualizations of Estimates in Linear mixed models): recommendations for reporting multilevel data and analyses","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"Population Health Research Institute; McMaster University","funders":"","keywords":"Logistic regression; Logistic model tree; Regression diagnostic; Factor regression model; Regression analysis; Log-linear model; Proper linear model; Linear regression","score_opus":0.8208900910738924,"score_gpt":0.5472691949162407,"score_spread":0.2736208961576517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958231004","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024490553,0.000046009314,0.0050666514,0.00034193214,0.00008416653,0.00031565625,0.98779196,0.0029718091,0.0031368795],"genre_scores_gemma":[0.019996438,0.00047355576,0.087364696,0.0018935031,0.0004400432,0.013509737,0.8094179,0.024546169,0.042357974],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975139,0.001126925,0.0003823622,0.000348963,0.00043852822,0.00018936596],"domain_scores_gemma":[0.88535756,0.099438876,0.003062843,0.00430622,0.006696356,0.0011381562],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0074802446,0.0013465547,0.0016617823,0.003671089,0.0010696164,0.0027826484,0.0031173711,0.0015441227,0.9142909],"category_scores_gemma":[0.10438748,0.001362197,0.0014931237,0.005329379,0.00045275627,0.0028565002,0.0019961703,0.0017322723,0.28197932],"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":[0.00013564543,0.00004784252,0.00097651524,0.0015998317,0.000037485544,0.000028232043,0.00012032851,0.00038879024,0.000041503914,0.0014363746,0.9836142,0.011573343],"study_design_scores_gemma":[0.0022707644,0.0001404215,0.009541147,0.0037471224,0.00019378851,0.00026430874,0.0005463411,0.00343149,0.0007445233,0.028109223,0.95084214,0.00016870053],"about_ca_topic_score_codex":0.009521726,"about_ca_topic_score_gemma":0.015630187,"teacher_disagreement_score":0.99251974,"about_ca_system_score_codex":0.0014861652,"about_ca_system_score_gemma":0.003566321,"threshold_uncertainty_score":0.12225354},"labels":[],"label_agreement":null},{"id":"W6958244672","doi":"10.6084/m9.figshare.24130187.v1","title":"Additional file 2 of Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Cluster (spacecraft); Code (set theory); Data file; Key (lock); Table (database)","score_opus":0.35441663374967525,"score_gpt":0.45349387384399886,"score_spread":0.09907724009432362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958244672","genre_codex":"dataset","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":[0.0017653605,0.00021355649,0.016044248,0.00091425265,0.00020220877,0.0041782535,0.9671249,0.0024896227,0.0070675835],"genre_scores_gemma":[0.13907371,0.0012727429,0.20186815,0.00559336,0.00066971465,0.10862371,0.47430798,0.009818296,0.058772348],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563074,0.0026325637,0.00052186166,0.00047962513,0.0004966757,0.00023844744],"domain_scores_gemma":[0.7836142,0.20237395,0.0042481204,0.0040316437,0.004724126,0.0010079682],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0121391555,0.0012764655,0.0018485888,0.0016291699,0.0006679247,0.0016585167,0.0025769828,0.0017533195,0.85498184],"category_scores_gemma":[0.1501751,0.0010073608,0.001972457,0.0028250848,0.0004997184,0.0016743371,0.00094852503,0.0019709808,0.07410986],"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":[0.0031288804,0.00033980262,0.003070458,0.009880662,0.0006492886,0.0002039256,0.00016720526,0.011314894,0.000114913346,0.008128265,0.93045217,0.032549515],"study_design_scores_gemma":[0.100825384,0.0028967694,0.019700773,0.012231865,0.0029854183,0.0019431027,0.0004642103,0.10532775,0.0017348496,0.12909913,0.6223233,0.00046748386],"about_ca_topic_score_codex":0.004717252,"about_ca_topic_score_gemma":0.007943249,"teacher_disagreement_score":0.98786086,"about_ca_system_score_codex":0.001828084,"about_ca_system_score_gemma":0.004471699,"threshold_uncertainty_score":0.20685083},"labels":[],"label_agreement":null},{"id":"W6958403963","doi":"10.6084/m9.figshare.26981816","title":"Additional file 1 of Exploration of different statistical approaches in the comparison of dopamine and norepinephrine in the treatment of shock: SOAP II","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Statistical Methods and Bayesian 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 Alberta","funders":"","keywords":"Dopamine; Norepinephrine; Statistical analysis; Catecholamine","score_opus":0.267417721058561,"score_gpt":0.4180412586857668,"score_spread":0.15062353762720582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958403963","genre_codex":"dataset","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":[0.00045871784,0.00008374345,0.003946555,0.00026496762,0.00009392453,0.00029778018,0.9915359,0.0010862066,0.0022321336],"genre_scores_gemma":[0.038817756,0.0007719764,0.071680374,0.002442016,0.000540286,0.01297785,0.8347817,0.009972817,0.028015213],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837923,0.0006967726,0.00020718187,0.0003005465,0.00030275615,0.000113444476],"domain_scores_gemma":[0.88145053,0.1091422,0.0017204718,0.0024814433,0.004436207,0.0007691127],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0047084973,0.0012303753,0.0012018484,0.0016630414,0.0007952099,0.0016306908,0.0020392498,0.0013080505,0.88943136],"category_scores_gemma":[0.091041625,0.00071771437,0.0014827782,0.0024129555,0.0003713826,0.0013401116,0.0010605712,0.0012018501,0.16024616],"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":[0.00076042453,0.00019961329,0.0019022238,0.006278524,0.00015317994,0.00008094402,0.0001061425,0.0013959883,0.00016015444,0.002097001,0.96138644,0.025479455],"study_design_scores_gemma":[0.011872451,0.0010132858,0.027291836,0.0074359835,0.0008428376,0.00086026604,0.0005071181,0.01118067,0.0020653936,0.05588114,0.8807234,0.00032561677],"about_ca_topic_score_codex":0.0049458994,"about_ca_topic_score_gemma":0.009483746,"teacher_disagreement_score":0.88943136,"about_ca_system_score_codex":0.0011262536,"about_ca_system_score_gemma":0.002413293,"threshold_uncertainty_score":0.1577127},"labels":[],"label_agreement":null},{"id":"W6958555164","doi":"10.6084/m9.figshare.26265111.v1","title":"Additional file 2 of The spectrum of health conditions in community-based cross-sectional surveys in Southeast Asia 2010-21: a scoping review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Southeast asia; Population; Identification (biology); Public health; Government (linguistics)","score_opus":0.19881756200783782,"score_gpt":0.4412424282857868,"score_spread":0.24242486627794899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958555164","genre_codex":"dataset","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":[0.00011072958,0.00010528975,0.0002663649,0.00011383515,0.000022541995,0.00020493455,0.9984086,0.00015664715,0.00061110314],"genre_scores_gemma":[0.008250955,0.0011926211,0.010798534,0.0008741884,0.00015309431,0.009699464,0.9573841,0.00073545036,0.010911663],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9979279,0.00053530495,0.00071100245,0.000332367,0.0003458586,0.00014756041],"domain_scores_gemma":[0.9218908,0.06726259,0.0041805906,0.0016143778,0.004459047,0.00059259235],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0051538623,0.0013472063,0.00197482,0.00819479,0.0006330274,0.0018293607,0.0023282631,0.0016831957,0.84987587],"category_scores_gemma":[0.07323899,0.0010493057,0.0020144312,0.012553636,0.0003289174,0.0023887656,0.0015755084,0.00089289376,0.076618664],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041988757,0.000103612874,0.0029088205,0.05025489,0.00030263304,0.00007984813,0.00016344288,0.00062778196,0.000078725134,0.0019640708,0.9143519,0.028744366],"study_design_scores_gemma":[0.009343452,0.0004072479,0.074488506,0.05260571,0.0019651498,0.0008453761,0.0008970081,0.002525629,0.0006916447,0.021368517,0.834561,0.0003008314],"about_ca_topic_score_codex":0.010979474,"about_ca_topic_score_gemma":0.016942108,"teacher_disagreement_score":0.84987587,"about_ca_system_score_codex":0.0016566371,"about_ca_system_score_gemma":0.00411462,"threshold_uncertainty_score":0.21413386},"labels":[],"label_agreement":null},{"id":"W6958651033","doi":"10.6084/m9.figshare.17084225.v1","title":"Additional file 1 of Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"Dalhousie University; University of Toronto; University of Saskatchewan","funders":"","keywords":"Generalized linear mixed model; Count data; Mixed model; Log-linear model; Quantile; Generalized linear model; R package; Linear model","score_opus":0.16456260767899808,"score_gpt":0.382078968276449,"score_spread":0.21751636059745091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958651033","genre_codex":"dataset","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":[0.00055474683,0.000058317994,0.013745693,0.00019472858,0.00008010829,0.0001663727,0.9772568,0.0061549456,0.0017883058],"genre_scores_gemma":[0.026227929,0.00029876098,0.08780158,0.0014555998,0.00024227891,0.0053345533,0.8345259,0.025426142,0.018687192],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99839777,0.0005562807,0.00016122489,0.00039786223,0.00033405426,0.00015276538],"domain_scores_gemma":[0.95861524,0.03488575,0.0013514765,0.0025166257,0.0020841723,0.00054677046],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004537206,0.0014811223,0.0014850682,0.0019411137,0.000950409,0.0019392245,0.0032390547,0.0019275673,0.81535184],"category_scores_gemma":[0.051996347,0.0011410726,0.001397734,0.0029669264,0.0006015271,0.001939063,0.0016374766,0.0018529095,0.19844718],"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":[0.00032335165,0.00011290856,0.0032455965,0.0021356277,0.00011477524,0.00010461791,0.000102529,0.0035351003,0.00036073668,0.003646268,0.96661705,0.019701418],"study_design_scores_gemma":[0.004266444,0.00035660516,0.0147155,0.002427195,0.0003727437,0.00094115076,0.00029492192,0.030190175,0.0040616672,0.061984643,0.8800928,0.00029622816],"about_ca_topic_score_codex":0.005689205,"about_ca_topic_score_gemma":0.011723937,"teacher_disagreement_score":0.81535184,"about_ca_system_score_codex":0.001092078,"about_ca_system_score_gemma":0.0022477359,"threshold_uncertainty_score":0.26337808},"labels":[],"label_agreement":null},{"id":"W6958671397","doi":"10.6084/m9.figshare.22792859","title":"Additional file 1 of Multiple imputation methods for missing multilevel ordinal outcomes","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Missing data; Imputation (statistics); Ordinal data; Categorical variable","score_opus":0.24019378156943577,"score_gpt":0.47512831407102,"score_spread":0.23493453250158425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958671397","genre_codex":"dataset","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":[0.00023499849,0.000044819073,0.0041608936,0.00019565155,0.00005689985,0.00022176674,0.9921421,0.001081227,0.0018616096],"genre_scores_gemma":[0.018003667,0.0004466829,0.04616466,0.0017612703,0.0003115025,0.009534004,0.886834,0.0094045475,0.027539693],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99764794,0.0009835318,0.00030846868,0.0004849574,0.000373473,0.00020161612],"domain_scores_gemma":[0.8898722,0.097707845,0.002420357,0.0043850294,0.004599165,0.0010154921],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006310533,0.0013587129,0.0016528559,0.0028209276,0.0010984401,0.0021555463,0.0027021295,0.001835908,0.91306067],"category_scores_gemma":[0.102430716,0.0012152657,0.0014501136,0.005239734,0.00048368212,0.0021489891,0.0014510895,0.0021916055,0.23882072],"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":[0.0003136208,0.00008880086,0.0013828635,0.0019219144,0.00009343662,0.000057544046,0.000082533756,0.00082446286,0.000056338933,0.0020297149,0.9791861,0.013962677],"study_design_scores_gemma":[0.0059710396,0.00034924006,0.012958296,0.004804406,0.0005045612,0.0007673853,0.00036570858,0.0060377237,0.0009469557,0.050501417,0.91654015,0.000253206],"about_ca_topic_score_codex":0.0054837195,"about_ca_topic_score_gemma":0.009660805,"teacher_disagreement_score":0.91306067,"about_ca_system_score_codex":0.0010500046,"about_ca_system_score_gemma":0.0031016984,"threshold_uncertainty_score":0.12400836},"labels":[],"label_agreement":null},{"id":"W6958727589","doi":"10.6084/m9.figshare.26265111","title":"Additional file 2 of The spectrum of health conditions in community-based cross-sectional surveys in Southeast Asia 2010-21: a scoping review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Southeast asia; Population; Identification (biology); Public health; Government (linguistics)","score_opus":0.19881756200783782,"score_gpt":0.4412424282857868,"score_spread":0.24242486627794899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958727589","genre_codex":"dataset","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":[0.00011072958,0.00010528975,0.0002663649,0.00011383515,0.000022541995,0.00020493455,0.9984086,0.00015664715,0.00061110314],"genre_scores_gemma":[0.008250955,0.0011926211,0.010798534,0.0008741884,0.00015309431,0.009699464,0.9573841,0.00073545036,0.010911663],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9979279,0.00053530495,0.00071100245,0.000332367,0.0003458586,0.00014756041],"domain_scores_gemma":[0.9218908,0.06726259,0.0041805906,0.0016143778,0.004459047,0.00059259235],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0051538623,0.0013472063,0.00197482,0.00819479,0.0006330274,0.0018293607,0.0023282631,0.0016831957,0.84987587],"category_scores_gemma":[0.07323899,0.0010493057,0.0020144312,0.012553636,0.0003289174,0.0023887656,0.0015755084,0.00089289376,0.076618664],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041988757,0.000103612874,0.0029088205,0.05025489,0.00030263304,0.00007984813,0.00016344288,0.00062778196,0.000078725134,0.0019640708,0.9143519,0.028744366],"study_design_scores_gemma":[0.009343452,0.0004072479,0.074488506,0.05260571,0.0019651498,0.0008453761,0.0008970081,0.002525629,0.0006916447,0.021368517,0.834561,0.0003008314],"about_ca_topic_score_codex":0.010979474,"about_ca_topic_score_gemma":0.016942108,"teacher_disagreement_score":0.84987587,"about_ca_system_score_codex":0.0016566371,"about_ca_system_score_gemma":0.00411462,"threshold_uncertainty_score":0.21413386},"labels":[],"label_agreement":null},{"id":"W6976841567","doi":"10.6084/m9.figshare.26265015","title":"Additional file 1 of The spectrum of health conditions in community-based cross-sectional surveys in Southeast Asia 2010-21: a scoping review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Southeast asia; Population; Identification (biology); Government (linguistics); Public health","score_opus":0.20467456228732137,"score_gpt":0.44197109086538755,"score_spread":0.23729652857806618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976841567","genre_codex":"dataset","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":[0.00011680656,0.0001342959,0.0003008259,0.00012453254,0.000023328672,0.00021055923,0.9983758,0.000137499,0.0005762834],"genre_scores_gemma":[0.009322636,0.001616078,0.013057602,0.00097588135,0.00017099171,0.010812416,0.952893,0.0007015522,0.010449838],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9977719,0.0006220381,0.0007674204,0.00035005965,0.00034725363,0.00014133965],"domain_scores_gemma":[0.90617853,0.08191452,0.0047346307,0.0017396086,0.0048265904,0.0006061711],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0058095483,0.0013200665,0.0019404654,0.008244839,0.0006410797,0.0017288151,0.0024372463,0.0016135381,0.83613086],"category_scores_gemma":[0.08347548,0.00104958,0.0019420044,0.013790442,0.0003429338,0.0024238233,0.0015846979,0.0009175547,0.07038624],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039982377,0.000105583174,0.002918301,0.054096468,0.0002992375,0.00007032685,0.00017322353,0.0005946374,0.00007302117,0.0020550187,0.90768415,0.031530276],"study_design_scores_gemma":[0.009314516,0.00045150518,0.081162736,0.063051075,0.0022680548,0.00091845955,0.0009963912,0.0026363127,0.00065836863,0.022419367,0.8157993,0.0003238764],"about_ca_topic_score_codex":0.011129216,"about_ca_topic_score_gemma":0.017380077,"teacher_disagreement_score":0.83613086,"about_ca_system_score_codex":0.0017359557,"about_ca_system_score_gemma":0.0043161693,"threshold_uncertainty_score":0.23373938},"labels":[],"label_agreement":null},{"id":"W6976995008","doi":"10.6084/m9.figshare.23519680","title":"Comparing estimation approaches for generalized additive mixed models with binary outcomes","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"McGill University; McGill University Health Centre","funders":"","keywords":"Covariate; Multicollinearity; Bayesian probability; Prior probability; Mixed model; Generalized linear mixed model; Estimation; Binary data; Variance (accounting); Bayes estimator","score_opus":0.4383214593159911,"score_gpt":0.39563412623455535,"score_spread":0.04268733308143574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976995008","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":[0.0053909696,0.0014559046,0.9912226,0.0005602026,0.00008013327,0.0001772123,0.00014124757,0.00029199236,0.00067979895],"genre_scores_gemma":[0.061170295,0.001792038,0.93417704,0.00038102074,0.00011137044,0.0010379724,0.00050112495,0.00029680776,0.0005323884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91457796,0.077699974,0.0016445116,0.0025878635,0.003106646,0.000382985],"domain_scores_gemma":[0.6775036,0.3077335,0.0043146014,0.005525974,0.0043651024,0.0005572234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09567933,0.0018672317,0.0021630249,0.003930519,0.00095836807,0.0029858283,0.0041741612,0.002722598,0.004345923],"category_scores_gemma":[0.25125822,0.0015091283,0.00386775,0.0038062276,0.0018029887,0.00412666,0.0041738497,0.0042236927,0.00082996994],"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":[0.0009865697,0.00032423827,0.0135523835,0.0024944434,0.005272864,0.00022496884,0.0017691221,0.28994375,0.0008594909,0.27751064,0.00595241,0.40110913],"study_design_scores_gemma":[0.00029019525,0.0003261709,0.0034350734,0.00071997277,0.00054854766,0.00017680376,0.00037896234,0.69641525,0.00074330554,0.2886577,0.008124226,0.00018379075],"about_ca_topic_score_codex":0.008323612,"about_ca_topic_score_gemma":0.009352876,"teacher_disagreement_score":0.09567933,"about_ca_system_score_codex":0.0024051447,"about_ca_system_score_gemma":0.0032927166,"threshold_uncertainty_score":0.5060067},"labels":[],"label_agreement":null},{"id":"W6977150329","doi":"10.6084/m9.figshare.26557820.v1","title":"Additional file 1 of Knowledge of HIV/AIDS among married women in Bangladesh: analysis of three consecutive multiple indicator cluster surveys (MICS)","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Manitoba","funders":"","keywords":"Logistic regression; Calibration; Cluster (spacecraft); Table (database); Sensitivity (control systems); Receiver operating characteristic","score_opus":0.05310892120800074,"score_gpt":0.3190133561340549,"score_spread":0.26590443492605415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977150329","genre_codex":"dataset","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":[0.0004853152,0.000010518193,0.00016531312,0.00007191795,0.0000100642865,0.000100246725,0.9985164,0.000047078258,0.0005931919],"genre_scores_gemma":[0.032121215,0.00018944434,0.0049428847,0.00047331987,0.00009457454,0.0053637056,0.94533086,0.00028546268,0.011198502],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9992487,0.00020950391,0.0001438539,0.00015189596,0.00013739667,0.00010869148],"domain_scores_gemma":[0.9821691,0.013107468,0.0014463208,0.0008689713,0.0020038204,0.00040438175],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018088071,0.000808612,0.0008446214,0.0020622066,0.00084898307,0.0007961015,0.0015827015,0.0007550397,0.81236506],"category_scores_gemma":[0.029596426,0.0004726717,0.0006574237,0.0045576426,0.00021956909,0.0013651138,0.0009795495,0.0008348564,0.080130845],"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":[0.00022525429,0.00011396318,0.008859395,0.001842813,0.00004583324,0.00006608653,0.00014779596,0.00054625486,0.000046504247,0.000833284,0.9771803,0.01009253],"study_design_scores_gemma":[0.0083456505,0.0009095195,0.24800514,0.008328866,0.0005416762,0.0011708058,0.0033593676,0.0071169916,0.0010149544,0.01321874,0.70770264,0.0002855702],"about_ca_topic_score_codex":0.019788919,"about_ca_topic_score_gemma":0.02770132,"teacher_disagreement_score":0.81236506,"about_ca_system_score_codex":0.000991606,"about_ca_system_score_gemma":0.0018438858,"threshold_uncertainty_score":0.2676384},"labels":[],"label_agreement":null},{"id":"W6977272035","doi":"10.6084/m9.figshare.9202541","title":"Additional file 1: of Regional variation of premature mortality in Ontario, Canada: a spatial analysis","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Table (database); Bayesian probability; Linear model; Sensitivity (control systems); Generalized linear model; Variation (astronomy); Life table; Population; Sample (material)","score_opus":0.05779462240525704,"score_gpt":0.29748531848242177,"score_spread":0.23969069607716473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977272035","genre_codex":"dataset","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":[0.0011217936,0.000022039188,0.0005350972,0.00009375587,0.000013791233,0.00011648105,0.99632025,0.00013406466,0.0016427982],"genre_scores_gemma":[0.064919166,0.00033231324,0.011151363,0.00023556787,0.000042188254,0.0028514958,0.8911253,0.0007296895,0.028612964],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9993037,0.00009274043,0.000079665035,0.00011901965,0.0002624376,0.00014242354],"domain_scores_gemma":[0.9916889,0.003168502,0.0004597794,0.0006086776,0.0037641695,0.00031000903],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015484006,0.000721959,0.00080560945,0.0023446332,0.00148779,0.0012510748,0.0022223464,0.00057363877,0.41907895],"category_scores_gemma":[0.015688334,0.00056092086,0.0014367541,0.006271994,0.00033544272,0.00070246926,0.0008849078,0.0006749739,0.025388768],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011122301,0.0000413502,0.02594235,0.00092177966,0.00010790986,0.00009231322,0.00036510485,0.0037939306,0.000078316974,0.0013611034,0.954367,0.012817658],"study_design_scores_gemma":[0.0013130911,0.0000985948,0.26436114,0.0021322866,0.00046117185,0.00028420537,0.0021703488,0.016797336,0.00055216317,0.004134038,0.70750195,0.00019361576],"about_ca_topic_score_codex":0.9666023,"about_ca_topic_score_gemma":0.9777363,"teacher_disagreement_score":0.41907895,"about_ca_system_score_codex":0.01216696,"about_ca_system_score_gemma":0.027384069,"threshold_uncertainty_score":0.8286134},"labels":[],"label_agreement":null},{"id":"W6977304029","doi":"10.6084/m9.figshare.25201939","title":"Additional file 1 of Model-based standardization using multiple imputation","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"SickKids Foundation; University of Toronto","funders":"","keywords":"Standardization; Imputation (statistics); Missing data; Data file","score_opus":0.13506589631637078,"score_gpt":0.3860245751603788,"score_spread":0.250958678844008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977304029","genre_codex":"dataset","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":[0.00021187107,0.000046200494,0.0030546712,0.0001909055,0.000055370107,0.00015632079,0.9936707,0.0008734795,0.0017405051],"genre_scores_gemma":[0.017412081,0.0004309522,0.030777832,0.0015695997,0.00026894538,0.006007983,0.9151908,0.007832458,0.020509265],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976204,0.0009721939,0.0002723029,0.00058087707,0.0003600486,0.00019402755],"domain_scores_gemma":[0.8992355,0.087809995,0.0020299465,0.005205653,0.0047552534,0.0009636109],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005900786,0.0014666085,0.0015759704,0.002764321,0.0011253393,0.0028233405,0.0030316978,0.002059248,0.8958865],"category_scores_gemma":[0.097445644,0.0011939593,0.001593982,0.00522049,0.00055806275,0.0024050854,0.0016081746,0.0021708533,0.2176154],"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":[0.00033832237,0.0000739204,0.0016042244,0.002043336,0.00011290546,0.00008147234,0.00008306736,0.0010903893,0.0000714378,0.00227662,0.97923124,0.012992997],"study_design_scores_gemma":[0.0057835327,0.00029164652,0.012690804,0.0040923874,0.00051896105,0.0007433724,0.0003455643,0.0053049275,0.0008220696,0.05063721,0.9185385,0.00023090048],"about_ca_topic_score_codex":0.008216557,"about_ca_topic_score_gemma":0.011684345,"teacher_disagreement_score":0.8958865,"about_ca_system_score_codex":0.001137229,"about_ca_system_score_gemma":0.0033414694,"threshold_uncertainty_score":0.14850533},"labels":[],"label_agreement":null},{"id":"W6977320762","doi":"10.6084/m9.figshare.22599472","title":"Additional file 1 of Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights","year":2023,"lang":"en","type":"article","venue":"Open MIND","topic":"Statistical Methods and Bayesian 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 Toronto","funders":"","keywords":"Estimation; Calibration; Inverse; Estimation theory; Maximum likelihood; Bayesian probability","score_opus":0.12609262284571746,"score_gpt":0.36150887201632326,"score_spread":0.2354162491706058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977320762","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005595414,0.000042115113,0.009148011,0.0002498754,0.000054142718,0.00019815448,0.9855419,0.000935612,0.0032706545],"genre_scores_gemma":[0.035435434,0.00037400107,0.058550883,0.001110628,0.00025961068,0.0052270163,0.86147714,0.004767993,0.0327972],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910516,0.00038729346,0.00009773597,0.00020349343,0.00014085742,0.00006538882],"domain_scores_gemma":[0.947358,0.04702167,0.001036515,0.0017787192,0.0024797472,0.0003252407],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028748508,0.0009737427,0.0009718307,0.0014079658,0.0005428456,0.00111285,0.0020543907,0.0011938706,0.866476],"category_scores_gemma":[0.06297249,0.0007190176,0.00061549357,0.0024803046,0.00026282552,0.0011515092,0.00083703495,0.0011250125,0.18016876],"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":[0.00013549518,0.0000791778,0.001799669,0.00092746294,0.00004692714,0.000054373188,0.000058044516,0.0023349938,0.000065887376,0.003761041,0.97168475,0.019052131],"study_design_scores_gemma":[0.00291855,0.00023768011,0.014696188,0.0022703432,0.00023842091,0.00069700886,0.00036583433,0.021397443,0.0009795671,0.08177269,0.87427723,0.00014896014],"about_ca_topic_score_codex":0.009661074,"about_ca_topic_score_gemma":0.012738493,"teacher_disagreement_score":0.866476,"about_ca_system_score_codex":0.000974365,"about_ca_system_score_gemma":0.0019287227,"threshold_uncertainty_score":0.1904558},"labels":[],"label_agreement":null},{"id":"W6977381049","doi":"10.6084/m9.figshare.14570720","title":"Additional file 1 of Using random forests to model 90-day hometime in people with stroke","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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":"Institute for Clinical Evaluative Sciences; University of Toronto; University of Calgary","funders":"","keywords":"Table (database); Demographics; Random forest; Cohort; Stroke (engine); Pairwise comparison","score_opus":0.08026284222746047,"score_gpt":0.33560423712250315,"score_spread":0.2553413948950427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977381049","genre_codex":"dataset","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":[0.00041904484,0.000023636541,0.00095366576,0.0001197583,0.000021551858,0.000096576114,0.9969644,0.00036431523,0.0010370753],"genre_scores_gemma":[0.02704226,0.00021387178,0.011883943,0.0007550501,0.00018582036,0.0032896667,0.94159853,0.0013567347,0.013674128],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999529,0.00014390441,0.00006992731,0.00013553967,0.00006457254,0.00005708787],"domain_scores_gemma":[0.9904585,0.00794402,0.00037243648,0.00047598887,0.0005841607,0.00016489363],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017918233,0.0008147601,0.000879797,0.0011605871,0.00056958286,0.0011063126,0.0016373551,0.0010950952,0.7888614],"category_scores_gemma":[0.025746418,0.0004792552,0.00097014336,0.0018355603,0.00015461526,0.0010594275,0.0007339599,0.0009006878,0.12364837],"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":[0.0002945876,0.00007499403,0.004286766,0.00088854774,0.000083377134,0.00006804726,0.000054492706,0.0017641826,0.00002729656,0.0011905548,0.9799311,0.011336143],"study_design_scores_gemma":[0.0113733,0.00070270157,0.047196414,0.0055300044,0.00071798457,0.0010743743,0.00056293,0.035592638,0.0006170495,0.04242131,0.85397434,0.0002369695],"about_ca_topic_score_codex":0.013076199,"about_ca_topic_score_gemma":0.02352637,"teacher_disagreement_score":0.7888614,"about_ca_system_score_codex":0.00065295905,"about_ca_system_score_gemma":0.0012961895,"threshold_uncertainty_score":0.30116355},"labels":[],"label_agreement":null},{"id":"W6977392437","doi":"10.6084/m9.figshare.22495657","title":"Immunofluorescent staining for differentiated cell markers in intestinal monolayer cultures.","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 British Columbia","funders":"","keywords":"Staining; Mucin; Monolayer; Epithelium; Brush border; Goblet cell; Cell culture; Positive staining","score_opus":0.09068333317247566,"score_gpt":0.3725636788918498,"score_spread":0.28188034571937415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977392437","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":[0.026694266,0.002454379,0.76659656,0.001602463,0.0010561224,0.0004603936,0.11409219,0.018364891,0.06867874],"genre_scores_gemma":[0.08322046,0.0021886048,0.7386254,0.0011359542,0.0001760549,0.0013946289,0.09421097,0.0076777376,0.07137016],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994217,0.00007939009,0.000035233876,0.00020572281,0.0001746185,0.000083214145],"domain_scores_gemma":[0.9990208,0.00036735355,0.00006916325,0.00026353818,0.00020519127,0.0000738962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014612833,0.0007690049,0.0007353794,0.001353586,0.0006217305,0.000820602,0.0008586821,0.00090501696,0.15785655],"category_scores_gemma":[0.0016569486,0.00064448884,0.0009841046,0.0011337197,0.00049358665,0.00093014364,0.000779596,0.0018196772,0.038084358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062751456,0.00027501016,0.0018067224,0.002946002,0.0002868941,0.0004641658,0.00028251676,0.006357768,0.5435695,0.0419434,0.267905,0.13353552],"study_design_scores_gemma":[0.0003024547,0.00033530875,0.015439719,0.0006125601,0.00019143024,0.0011563586,0.00020384292,0.035946615,0.35022488,0.019386109,0.57599944,0.0002013484],"about_ca_topic_score_codex":0.002015484,"about_ca_topic_score_gemma":0.005713094,"teacher_disagreement_score":0.15785655,"about_ca_system_score_codex":0.00077379035,"about_ca_system_score_gemma":0.00074230554,"threshold_uncertainty_score":0.5280828},"labels":[],"label_agreement":null},{"id":"W6977523246","doi":"10.6084/m9.figshare.26981816.v1","title":"Additional file 1 of Exploration of different statistical approaches in the comparison of dopamine and norepinephrine in the treatment of shock: SOAP II","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian 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 Alberta","funders":"","keywords":"Dopamine; Norepinephrine; Statistical analysis; Catecholamine","score_opus":0.2681146523589434,"score_gpt":0.38694549725259714,"score_spread":0.11883084489365375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977523246","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045538766,0.00009660445,0.007220437,0.00030170256,0.000108976375,0.0002536441,0.9866232,0.0024434726,0.0024965159],"genre_scores_gemma":[0.038387854,0.00074820616,0.098745845,0.002570967,0.0005324818,0.01024588,0.799602,0.021113101,0.028053654],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811363,0.00084343745,0.00020425537,0.00036153945,0.00033700257,0.0001401971],"domain_scores_gemma":[0.8776593,0.113096364,0.0014866275,0.0029203026,0.004027947,0.0008093973],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0059028454,0.0015266191,0.0013779105,0.0019518741,0.0008654873,0.0019913844,0.0024685487,0.0015586384,0.8897941],"category_scores_gemma":[0.09114854,0.0009440307,0.001954423,0.0026440083,0.00046005237,0.0016921503,0.0012284068,0.0015023414,0.20183037],"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":[0.0006324485,0.00016964121,0.0017567654,0.005237525,0.00018976495,0.00009218676,0.00010436291,0.0023169387,0.0002081681,0.0029729519,0.9620693,0.024249999],"study_design_scores_gemma":[0.009981124,0.00077568076,0.01773553,0.00542774,0.00082356407,0.00070340186,0.00035478472,0.01649158,0.0025523582,0.06893812,0.87586516,0.00035106306],"about_ca_topic_score_codex":0.004414313,"about_ca_topic_score_gemma":0.008239541,"teacher_disagreement_score":0.9940972,"about_ca_system_score_codex":0.0011742796,"about_ca_system_score_gemma":0.0026142,"threshold_uncertainty_score":0.15719527},"labels":[],"label_agreement":null},{"id":"W6977585400","doi":"10.6084/m9.figshare.9202541.v1","title":"Additional file 1: of Regional variation of premature mortality in Ontario, Canada: a spatial analysis","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Table (database); Bayesian probability; Linear model; Sensitivity (control systems); Generalized linear model; Variation (astronomy); Life table; Population; Sample (material)","score_opus":0.05779462240525704,"score_gpt":0.29748531848242177,"score_spread":0.23969069607716473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977585400","genre_codex":"dataset","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":[0.0011217936,0.000022039188,0.0005350972,0.00009375587,0.000013791233,0.00011648105,0.99632025,0.00013406466,0.0016427982],"genre_scores_gemma":[0.064919166,0.00033231324,0.011151363,0.00023556787,0.000042188254,0.0028514958,0.8911253,0.0007296895,0.028612964],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9993037,0.00009274043,0.000079665035,0.00011901965,0.0002624376,0.00014242354],"domain_scores_gemma":[0.9916889,0.003168502,0.0004597794,0.0006086776,0.0037641695,0.00031000903],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015484006,0.000721959,0.00080560945,0.0023446332,0.00148779,0.0012510748,0.0022223464,0.00057363877,0.41907895],"category_scores_gemma":[0.015688334,0.00056092086,0.0014367541,0.006271994,0.00033544272,0.00070246926,0.0008849078,0.0006749739,0.025388768],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011122301,0.0000413502,0.02594235,0.00092177966,0.00010790986,0.00009231322,0.00036510485,0.0037939306,0.000078316974,0.0013611034,0.954367,0.012817658],"study_design_scores_gemma":[0.0013130911,0.0000985948,0.26436114,0.0021322866,0.00046117185,0.00028420537,0.0021703488,0.016797336,0.00055216317,0.004134038,0.70750195,0.00019361576],"about_ca_topic_score_codex":0.9666023,"about_ca_topic_score_gemma":0.9777363,"teacher_disagreement_score":0.41907895,"about_ca_system_score_codex":0.01216696,"about_ca_system_score_gemma":0.027384069,"threshold_uncertainty_score":0.8286134},"labels":[],"label_agreement":null},{"id":"W6979315079","doi":"","title":"Pseudo Empirical Likelihood Inference for Non-Probability Survey Samples","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Inference; Survey sampling; Point estimation; Statistical inference; Empirical likelihood; Survey data collection; Sampling (signal processing); Field (mathematics); Statistical hypothesis testing","score_opus":0.22940209148496546,"score_gpt":0.44824079872068845,"score_spread":0.218838707235723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979315079","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":[0.001588884,0.0002084367,0.9975243,0.0001634797,0.000024481402,0.000032054002,0.000041364226,0.000041981373,0.0003750562],"genre_scores_gemma":[0.20836431,0.0018396205,0.783849,0.00077929796,0.00040405325,0.0010090166,0.0007707645,0.0001797934,0.0028040928],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9723573,0.022331828,0.0008020516,0.0017417133,0.0025415572,0.00022562189],"domain_scores_gemma":[0.8277566,0.15609832,0.004362457,0.008269228,0.003075865,0.00043752996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027849328,0.0010786852,0.0017027581,0.002397616,0.000638494,0.0026320657,0.0032385818,0.0018729486,0.0043324567],"category_scores_gemma":[0.18807407,0.0010114074,0.0014021605,0.0028463397,0.003949119,0.0061698956,0.0031958746,0.0035971692,0.00084530906],"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":[0.00008387796,0.00006751163,0.0028359897,0.00041173978,0.00019077386,0.00018739788,0.00030231493,0.057132058,0.00035291552,0.86480486,0.001619822,0.07201078],"study_design_scores_gemma":[0.000045151257,0.000053981083,0.00090818375,0.000083653475,0.000033081822,0.00014887679,0.00006373958,0.3536677,0.00044389762,0.6406985,0.0038262736,0.000026938225],"about_ca_topic_score_codex":0.0013436319,"about_ca_topic_score_gemma":0.0012121703,"teacher_disagreement_score":0.027849328,"about_ca_system_score_codex":0.0012477005,"about_ca_system_score_gemma":0.00163987,"threshold_uncertainty_score":0.14728308},"labels":[],"label_agreement":null},{"id":"W6980413880","doi":"","title":"Canada 'missed the boat' on policies to mitigate climate change: expert","year":2016,"lang":"en","type":"other","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Climate change; Public policy; Work (physics); Agency (philosophy)","score_opus":0.09685831837418284,"score_gpt":0.39174629490049295,"score_spread":0.2948879765263101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980413880","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":[0.00890711,0.011386725,0.10974963,0.16561082,0.0026736497,0.00018278947,0.012913123,0.0022100692,0.686366],"genre_scores_gemma":[0.27488124,0.014318058,0.12729515,0.027570551,0.0016473791,0.00024665683,0.005398904,0.0020014055,0.5466407],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99450666,0.001237679,0.00012553313,0.00041539693,0.0030852212,0.0006293956],"domain_scores_gemma":[0.98611605,0.0053989184,0.00039831473,0.00087974063,0.0062296125,0.0009773596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074134027,0.001057496,0.0009143388,0.002720623,0.0044419942,0.0075650844,0.0026151054,0.004322777,0.05603032],"category_scores_gemma":[0.041179504,0.00044023115,0.0006592115,0.0043070065,0.0035869754,0.0027922832,0.002252894,0.0037555615,0.004070035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004243571,0.00004170842,0.0017156716,0.00014967301,0.000045707635,0.0000972942,0.00032910908,0.0080291,0.000084493586,0.22600284,0.6503271,0.11313482],"study_design_scores_gemma":[0.00004501345,0.000010082985,0.0042294166,0.00066436006,0.00004775749,0.00006784472,0.00087667984,0.030518204,0.00047468676,0.30661407,0.6563394,0.00011240132],"about_ca_topic_score_codex":0.96377146,"about_ca_topic_score_gemma":0.9791444,"teacher_disagreement_score":0.05603032,"about_ca_system_score_codex":0.036190324,"about_ca_system_score_gemma":0.14472821,"threshold_uncertainty_score":0.2625804},"labels":[],"label_agreement":null},{"id":"W6981367028","doi":"","title":"Effets du smart shopping sur les variables relationnelles pour des produits de consommation au Québec : achat de vêtements","year":2021,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lien; Context (archaeology); Consumer behaviour","score_opus":0.03157941693554138,"score_gpt":0.25204592062903086,"score_spread":0.22046650369348947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981367028","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":[0.985297,0.000956709,0.0012620919,0.0022587043,0.00005850671,0.0000953079,0.00084380695,0.000029343422,0.009198483],"genre_scores_gemma":[0.9879026,0.0006718777,0.0012170613,0.0005532028,0.00002705629,0.00009477579,0.0004864444,0.000031180978,0.009015724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966888,0.001362328,0.00011261115,0.00044862047,0.0010480925,0.00033955942],"domain_scores_gemma":[0.9728684,0.011540984,0.0024069776,0.000985115,0.010457369,0.0017412184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061555994,0.00059273705,0.0006632335,0.0012669843,0.0022614466,0.0029300065,0.0011761945,0.000873781,0.011019613],"category_scores_gemma":[0.019483866,0.00038581618,0.0009956169,0.002528552,0.0019376294,0.0016029082,0.0011650387,0.0013910249,0.0006733756],"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":[0.00061476184,0.00029510213,0.93367916,0.00038676435,0.0004716556,0.00028678513,0.019347144,0.0005706069,0.0013132847,0.0015757907,0.0035068586,0.037952017],"study_design_scores_gemma":[0.0000143463885,0.0002019467,0.9805532,0.00018383756,0.00019244912,0.000026990525,0.013042018,0.0012140983,0.00041731726,0.00021986324,0.0038894832,0.000044566405],"about_ca_topic_score_codex":0.89901066,"about_ca_topic_score_gemma":0.93530154,"teacher_disagreement_score":0.10098934,"about_ca_system_score_codex":0.010874201,"about_ca_system_score_gemma":0.012636914,"threshold_uncertainty_score":0.20316815},"labels":[],"label_agreement":null},{"id":"W6981579226","doi":"","title":"Essays in Macroeconomics and Labour Mobility","year":2024,"lang":"en","type":"other","venue":"Spectrum Research Repository (Concordia University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Higher education; Statistics education; The arts; Public policy; Liberal arts education; Public funding","score_opus":0.049563981166591296,"score_gpt":0.3445917295006284,"score_spread":0.2950277483340371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981579226","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":[0.011816158,0.38343704,0.0064838184,0.3891292,0.032676276,0.000029355755,0.0017650919,0.00013566665,0.17452738],"genre_scores_gemma":[0.2910548,0.39695486,0.0027737995,0.030004742,0.08651858,0.00016123777,0.0017871623,0.00029423722,0.19045065],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99950016,0.0001892172,0.000026452331,0.00009215574,0.00011668456,0.00007533376],"domain_scores_gemma":[0.9971275,0.0020054365,0.00021567073,0.00015285704,0.00030165963,0.00019687504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012253567,0.0008550802,0.00084098027,0.001667195,0.0011557797,0.0030791147,0.000591143,0.002156879,0.016697992],"category_scores_gemma":[0.0052797133,0.00025688615,0.0008830892,0.0028592462,0.0018913868,0.0028679657,0.0013738496,0.0025544553,0.0034031721],"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":[0.00004642212,0.00005218847,0.0028927324,0.00033419882,0.00005602114,0.000116902476,0.00059667614,0.0026416648,0.000118495685,0.3970292,0.5568345,0.039281014],"study_design_scores_gemma":[0.000020165877,0.00003606302,0.0071090767,0.0011380913,0.000024801757,0.00007835814,0.00076262344,0.0012264911,0.000071925635,0.28931737,0.7001896,0.000025444548],"about_ca_topic_score_codex":0.003673172,"about_ca_topic_score_gemma":0.0026171545,"teacher_disagreement_score":0.016697992,"about_ca_system_score_codex":0.0027819076,"about_ca_system_score_gemma":0.0015453375,"threshold_uncertainty_score":0.0558604},"labels":[],"label_agreement":null},{"id":"W6982098538","doi":"","title":"Gravity data acquisition and potential-field data modelling along Metal Earth's Chibougamau transect using geophysical and geological constraints","year":2019,"lang":"en","type":"dissertation","venue":"Lu Zone Ul (Laurentian University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transect; Petrophysics; Precambrian; Data acquisition; Mineral exploration; Mineral resource classification","score_opus":0.06163631523058896,"score_gpt":0.30272820975039055,"score_spread":0.2410918945198016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6982098538","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":[0.86498207,0.00009649276,0.05997415,0.00031722628,0.000045864774,0.00043926924,0.045544922,0.0051879124,0.02341208],"genre_scores_gemma":[0.8817044,0.00011293074,0.078108825,0.000026689593,0.000009644857,0.0002273868,0.032671608,0.0002625819,0.006875907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99984145,0.000011799488,0.000007426204,0.000045957597,0.000057690428,0.00003573376],"domain_scores_gemma":[0.99978334,0.0000136354665,0.000015162406,0.000028455583,0.0001365765,0.000022838201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766993,0.00038907904,0.00018382049,0.0011492814,0.00065253815,0.00079787447,0.00065225776,0.00034239775,0.0036187237],"category_scores_gemma":[0.00073449063,0.00026136418,0.00040075797,0.0017430609,0.00027153493,0.00038455264,0.00045942893,0.00039567152,0.0011283578],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002427746,0.00025385484,0.26681918,0.00020750171,0.00010061927,0.0005907426,0.0015395106,0.44640723,0.020247418,0.0071758498,0.0237952,0.23262009],"study_design_scores_gemma":[0.000051968807,0.000053862066,0.21381952,0.000037072343,0.000031839263,0.00006518609,0.0008000635,0.7500014,0.005830149,0.000879342,0.028331617,0.00009799254],"about_ca_topic_score_codex":0.78073716,"about_ca_topic_score_gemma":0.87068415,"teacher_disagreement_score":0.78073716,"about_ca_system_score_codex":0.0020589414,"about_ca_system_score_gemma":0.0057433806,"threshold_uncertainty_score":0.44110823},"labels":[],"label_agreement":null},{"id":"W6982293688","doi":"","title":"How many fit all? Latent class analysis of administrative data on healthcare utilization by persons with dementia in Quebec, Canada","year":2022,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dementia; Latent class model; Health care; Class (philosophy); Data collection; MEDLINE","score_opus":0.26775254972375373,"score_gpt":0.45939543652685355,"score_spread":0.19164288680309982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6982293688","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":[0.9594052,0.0036838937,0.0046171867,0.012827458,0.000113797076,0.00021265974,0.013634295,0.00006981784,0.005435672],"genre_scores_gemma":[0.9857266,0.0018073426,0.0038304345,0.0005467079,0.00002993028,0.00008860094,0.0039294614,0.00003026917,0.0040106345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99768984,0.000925455,0.000121825404,0.00027643493,0.0005049073,0.0004815973],"domain_scores_gemma":[0.99108607,0.003916848,0.00075047364,0.00028567904,0.0030580773,0.0009028579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006025943,0.0003540011,0.00063955004,0.0016737005,0.0027986825,0.0024469215,0.0014869693,0.0005711967,0.002428033],"category_scores_gemma":[0.021568673,0.00026446418,0.00085024966,0.005375242,0.0011808544,0.0006764518,0.00079401955,0.0013344014,0.0002611582],"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":[0.0002082689,0.00014464043,0.90782195,0.000098912205,0.00025902656,0.00007943424,0.004622794,0.0037211282,0.00014607058,0.0036874712,0.03194216,0.04726813],"study_design_scores_gemma":[0.00003152625,0.000030352114,0.95855993,0.00026665974,0.00012329638,0.0000316993,0.0124322325,0.020503908,0.00013180485,0.0018534911,0.005978719,0.000056467234],"about_ca_topic_score_codex":0.9978098,"about_ca_topic_score_gemma":0.99812037,"teacher_disagreement_score":0.035579976,"about_ca_system_score_codex":0.035579976,"about_ca_system_score_gemma":0.048615772,"threshold_uncertainty_score":0.258152},"labels":[],"label_agreement":null},{"id":"W6996143130","doi":"","title":"Recurrent event studies: efficient panel designs and joint modeling of events and severities","year":2011,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Population; Event data; Term (time); Feature (linguistics); Filter (signal processing)","score_opus":0.14993586281458005,"score_gpt":0.32960555817273174,"score_spread":0.1796696953581517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996143130","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":[0.003080961,0.00038933067,0.9941468,0.0005645819,0.00009243808,0.00028580037,0.00032433923,0.000118054006,0.000997657],"genre_scores_gemma":[0.16835324,0.0028431127,0.80996656,0.00096334756,0.0007937898,0.005732198,0.0021565196,0.00019708001,0.008994231],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93745446,0.052251577,0.001383658,0.0053214566,0.0024040437,0.0011848346],"domain_scores_gemma":[0.8186661,0.14182714,0.014572464,0.018475614,0.0051066643,0.0013520548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08477079,0.0024518147,0.003818836,0.002142815,0.0014181721,0.0038788219,0.0074733547,0.0047030617,0.012215644],"category_scores_gemma":[0.16542883,0.002816154,0.0044793766,0.0036312668,0.003449943,0.006944522,0.0054979394,0.007070808,0.0021645771],"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":[0.00038562343,0.00024503557,0.010866256,0.00049485045,0.0009356107,0.0003424295,0.0013385053,0.14079475,0.00058214983,0.7675993,0.0056248293,0.07079066],"study_design_scores_gemma":[0.00025984817,0.0003505633,0.0023005393,0.00024465687,0.00030379184,0.00012084973,0.00022806454,0.35178253,0.0005476846,0.631321,0.012436619,0.000103811224],"about_ca_topic_score_codex":0.0036776527,"about_ca_topic_score_gemma":0.0040256334,"teacher_disagreement_score":0.08477079,"about_ca_system_score_codex":0.0017675406,"about_ca_system_score_gemma":0.0031483201,"threshold_uncertainty_score":0.44831616},"labels":[],"label_agreement":null},{"id":"W7001177161","doi":"","title":"Induced bias on measuring influence by length-biased sampling of failure times","year":2008,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Observational error; Noise (video); Measure (data warehouse); Statistical analysis","score_opus":0.11091262836870651,"score_gpt":0.3340880732323293,"score_spread":0.22317544486362278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001177161","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":[0.0062745363,0.0003653784,0.99199474,0.00019897883,0.000039461072,0.00008137101,0.000062172934,0.000102645536,0.00088079],"genre_scores_gemma":[0.3561801,0.0013223799,0.63798565,0.00052374834,0.00060084707,0.0011333758,0.0004486985,0.00027173184,0.0015334361],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95269555,0.036047727,0.0019246655,0.002921631,0.005645804,0.0007646866],"domain_scores_gemma":[0.49277735,0.46109822,0.01679366,0.021173086,0.0067083505,0.0014493305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.080423355,0.0015472024,0.0017809507,0.005158223,0.001122292,0.0023029188,0.0039630174,0.0032817833,0.002856458],"category_scores_gemma":[0.38828206,0.00083794945,0.0033818476,0.003721917,0.0066887033,0.0053369594,0.0050707404,0.004167222,0.00049590145],"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":[0.00021591946,0.000096401556,0.028228072,0.00062898506,0.00068014266,0.00048606374,0.001570109,0.14944765,0.0017010614,0.67912936,0.0019712332,0.13584496],"study_design_scores_gemma":[0.000039835246,0.00023577308,0.00641765,0.00026272493,0.00021966756,0.00058020424,0.00013455184,0.5506537,0.0029443784,0.43492606,0.003473348,0.000112123766],"about_ca_topic_score_codex":0.0023121366,"about_ca_topic_score_gemma":0.0015947986,"teacher_disagreement_score":0.080423355,"about_ca_system_score_codex":0.0027357321,"about_ca_system_score_gemma":0.0017669323,"threshold_uncertainty_score":0.42532444},"labels":[],"label_agreement":null},{"id":"W7008622642","doi":"","title":"Computer Modelling Group Announces Third Quarter Results","year":2017,"lang":"en","type":"other","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Group (periodic table); Computer modelling; Computer Applications","score_opus":0.09407480632609855,"score_gpt":0.3623493379725314,"score_spread":0.26827453164643283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008622642","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":[0.0030517064,0.0026183869,0.08469646,0.063605115,0.017689306,0.00037886432,0.034103956,0.014193465,0.7796627],"genre_scores_gemma":[0.021150066,0.001345963,0.016657898,0.005460133,0.0036704002,0.0003299835,0.018916057,0.007796339,0.92467314],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99491936,0.0010933538,0.00018145081,0.0005293309,0.0026606342,0.00061582914],"domain_scores_gemma":[0.9792851,0.00657353,0.0004601185,0.0049713748,0.0066066757,0.0021032717],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00902967,0.0017144629,0.0019234546,0.002652184,0.002340845,0.010050921,0.0032071976,0.004083638,0.35353446],"category_scores_gemma":[0.03135285,0.00070864684,0.0014460752,0.0027498768,0.0015540584,0.004786548,0.0032716573,0.0058435863,0.25330514],"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":[0.00011040727,0.00008094604,0.00019682205,0.000036784826,0.000013256222,0.000021037622,0.000010062005,0.00090561935,0.00008255586,0.018330129,0.95216805,0.028044248],"study_design_scores_gemma":[0.000128194,0.00006114077,0.000941437,0.000086133536,0.00003090548,0.00005132396,0.00006217862,0.018765766,0.001996178,0.12213874,0.85569936,0.00003857056],"about_ca_topic_score_codex":0.011198288,"about_ca_topic_score_gemma":0.0164703,"teacher_disagreement_score":0.35353446,"about_ca_system_score_codex":0.0045902324,"about_ca_system_score_gemma":0.0045199445,"threshold_uncertainty_score":0.92210466},"labels":[],"label_agreement":null},{"id":"W7008824033","doi":"","title":"A comparative study of some existing post-model-selection inferential methods in linear regression models","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"Division of Mathematical Sciences; McGill University","keywords":"Inference; Statistical inference; Confidence interval; Regression analysis; Regression; Linear regression; Coverage probability; Prediction interval; Confidence and prediction bands; Fiducial inference","score_opus":0.13694717366637035,"score_gpt":0.44466765309118367,"score_spread":0.3077204794248133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008824033","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":[0.0059212935,0.006441866,0.98453325,0.00065984455,0.000044615117,0.000051493214,0.000042752035,0.00016911257,0.0021358912],"genre_scores_gemma":[0.21117489,0.012856144,0.7729472,0.00043568652,0.0004287257,0.00041500732,0.00024506997,0.00026041936,0.0012368698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96651477,0.02471445,0.0012768625,0.0022775836,0.0048318286,0.0003844696],"domain_scores_gemma":[0.76257604,0.22182843,0.0031707902,0.0063110855,0.005584698,0.00052885374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05459173,0.0013309396,0.0017260802,0.0037903395,0.0009226891,0.003526641,0.0041346475,0.002064069,0.0022282382],"category_scores_gemma":[0.15718888,0.0008371545,0.0018378051,0.0041930513,0.0032563475,0.0056008906,0.0028232073,0.0040332708,0.0005381638],"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":[0.00019313005,0.0001621859,0.0047632274,0.0012965254,0.00031128287,0.00020239565,0.0014116745,0.07950572,0.0007705505,0.5478603,0.0014014303,0.36212158],"study_design_scores_gemma":[0.000045093668,0.0003329674,0.0025484643,0.0004949848,0.00014241785,0.00029388754,0.00029138048,0.6443964,0.0020712377,0.3406732,0.008608978,0.00010085693],"about_ca_topic_score_codex":0.0021174357,"about_ca_topic_score_gemma":0.0021946847,"teacher_disagreement_score":0.05459173,"about_ca_system_score_codex":0.002237013,"about_ca_system_score_gemma":0.002705295,"threshold_uncertainty_score":0.28871214},"labels":[],"label_agreement":null},{"id":"W7015588344","doi":"","title":"A study of bias in the naive estimator in longitudinal linear mixed-effects models with measurement error and misclassification in covariates","year":2014,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Observational error; Covariate; Estimator; Mean squared error; Errors-in-variables models; Linear model; Mean squared prediction error; Generalized linear model","score_opus":0.18420637320646674,"score_gpt":0.36558875595925194,"score_spread":0.1813823827527852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015588344","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":[0.025200106,0.0029055611,0.9680815,0.0020032127,0.00020684626,0.00013239145,0.000079954305,0.00014151055,0.0012489073],"genre_scores_gemma":[0.40878502,0.0038047284,0.5792474,0.002001556,0.00075462216,0.0009487835,0.0004106693,0.0002324165,0.003814816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9141817,0.07383436,0.001997456,0.004890959,0.0044883396,0.0006072184],"domain_scores_gemma":[0.46176472,0.50441647,0.010375162,0.015357859,0.007371138,0.0007146607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13692027,0.0013766551,0.0026024154,0.0021625813,0.0011787377,0.0031019773,0.0047052116,0.0034162595,0.0026446735],"category_scores_gemma":[0.42863774,0.0009941823,0.0022215056,0.002878314,0.004686286,0.0054828464,0.002978426,0.0036532711,0.0004474988],"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":[0.00042289993,0.00017525113,0.043087587,0.0011387584,0.0019609714,0.00072069134,0.0027543188,0.058041297,0.000985994,0.7464565,0.0027480768,0.1415077],"study_design_scores_gemma":[0.00020661947,0.0004964638,0.0074224323,0.00076619105,0.0007731774,0.00086869346,0.0006427357,0.37123755,0.0017529475,0.6074238,0.008254725,0.00015467913],"about_ca_topic_score_codex":0.005627017,"about_ca_topic_score_gemma":0.0034735466,"teacher_disagreement_score":0.13692027,"about_ca_system_score_codex":0.0024433692,"about_ca_system_score_gemma":0.0026987342,"threshold_uncertainty_score":0.7241123},"labels":[],"label_agreement":null},{"id":"W7017457018","doi":"","title":"Asymptotic analysis of the one-way random effects models","year":2000,"lang":"en","type":"other","venue":"TSpace","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Asymptotic analysis; Asymptotic analysis; Stochastic process; Statistical analysis; Asymptotic distribution; Asymptotic expansion","score_opus":0.04888538072570763,"score_gpt":0.3739554757805095,"score_spread":0.3250700950548019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017457018","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":[0.0038620222,0.0024454047,0.9877145,0.00092255033,0.00017203896,0.00007754478,0.0004351992,0.00040379472,0.003966988],"genre_scores_gemma":[0.31652528,0.009757206,0.6263633,0.0012723897,0.0012681357,0.0022055712,0.0021552262,0.0012613222,0.039191566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9770874,0.018589456,0.00044258748,0.0014801683,0.0019390465,0.0004613666],"domain_scores_gemma":[0.88181645,0.10557991,0.002419077,0.006758422,0.0028660782,0.0005600367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02900966,0.0018987383,0.003942127,0.0028358502,0.0007856446,0.0022958806,0.0039575533,0.0024515092,0.015658591],"category_scores_gemma":[0.1321351,0.0013300616,0.002988251,0.0032413923,0.0036186702,0.003134879,0.002537349,0.005159261,0.001910285],"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":[0.00034298853,0.00017105333,0.0028457036,0.0009544621,0.00096002297,0.00036437926,0.000530935,0.106370516,0.00074824906,0.72449625,0.016798036,0.14541739],"study_design_scores_gemma":[0.0001472495,0.00009670544,0.0023519367,0.00025156094,0.0003718351,0.0002493485,0.00007470238,0.29704258,0.0003501783,0.6900848,0.008900003,0.00007910019],"about_ca_topic_score_codex":0.016085327,"about_ca_topic_score_gemma":0.016547348,"teacher_disagreement_score":0.02900966,"about_ca_system_score_codex":0.0030303579,"about_ca_system_score_gemma":0.004005466,"threshold_uncertainty_score":0.15341955},"labels":[],"label_agreement":null},{"id":"W7017646321","doi":"","title":"Bayesian sample size calculations for cohort and case-control studies","year":2002,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Sample (material); Sample size determination; Cohort; Order (exchange)","score_opus":0.06047715726185005,"score_gpt":0.35378009221122636,"score_spread":0.2933029349493763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017646321","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":[0.0144290365,0.0038503695,0.9616584,0.001299791,0.0008603728,0.0067931274,0.0022263294,0.0007845776,0.008098063],"genre_scores_gemma":[0.13845265,0.0020081461,0.83039206,0.00077520375,0.00048661695,0.01959305,0.003045209,0.00028314078,0.004963933],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85135704,0.12767008,0.0060388786,0.005555053,0.008622188,0.00075688073],"domain_scores_gemma":[0.6433432,0.31994843,0.009287123,0.019314418,0.006654643,0.0014522045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1532208,0.0010994152,0.0029271008,0.00809422,0.0011290938,0.0020515665,0.0035431196,0.0032855913,0.023115436],"category_scores_gemma":[0.46294767,0.0016605866,0.0030542167,0.0042757574,0.0018167305,0.0025364258,0.0030490407,0.0032874437,0.0028248376],"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":[0.006763883,0.000503934,0.05614007,0.0043409523,0.0053698905,0.00083900336,0.0017187968,0.04191631,0.0013372203,0.24701059,0.051262144,0.5827972],"study_design_scores_gemma":[0.004825292,0.0023435627,0.04893644,0.004765118,0.003807469,0.0026220588,0.0006777535,0.31607974,0.0033075039,0.49050605,0.12184927,0.00027981526],"about_ca_topic_score_codex":0.0035725608,"about_ca_topic_score_gemma":0.0034098804,"teacher_disagreement_score":0.1532208,"about_ca_system_score_codex":0.0015890064,"about_ca_system_score_gemma":0.002290999,"threshold_uncertainty_score":0.8103187},"labels":[],"label_agreement":null},{"id":"W7018281789","doi":"","title":"A COMPARISON OF STATISTICAL METHODS USED IN TRIAL-BASED ECONOMIC EVALUATIONS; DOES IT MATTER WHICH METHOD IS USED?","year":2019,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Statistical Methods and Bayesian Inference","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":"Centre for Movement Disorders","funders":"","keywords":"Statistical analysis; Term (time); Feature (linguistics); Field (mathematics); Statistical hypothesis testing","score_opus":0.10249433237264934,"score_gpt":0.4909819837542018,"score_spread":0.3884876513815525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018281789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0617047,0.19642095,0.67362726,0.024167772,0.012155532,0.014233017,0.0025291438,0.0011175459,0.014044048],"genre_scores_gemma":[0.4226138,0.03539135,0.5078629,0.00934493,0.0019575953,0.018595863,0.0012129899,0.0012158987,0.001804538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.116024375,0.8225988,0.029616222,0.005421117,0.025624368,0.00071506074],"domain_scores_gemma":[0.039974917,0.9185043,0.016374871,0.0143034635,0.00982713,0.0010153179],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.6563105,0.0034874356,0.013535415,0.006622259,0.0017248442,0.011838229,0.006483562,0.009578407,0.009614654],"category_scores_gemma":[0.86264586,0.002953958,0.018158423,0.008842777,0.007213632,0.01149451,0.0048393225,0.014190539,0.0011584454],"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":[0.12228332,0.0015242345,0.014982774,0.06465811,0.15652542,0.00030488908,0.0059213773,0.0133618,0.001116681,0.091300406,0.0155059025,0.5125151],"study_design_scores_gemma":[0.11164151,0.04609826,0.042245273,0.085028365,0.14550349,0.0017917926,0.0038347316,0.13848345,0.0055333893,0.36234152,0.05482085,0.0026774162],"about_ca_topic_score_codex":0.0026181343,"about_ca_topic_score_gemma":0.0035230485,"teacher_disagreement_score":0.3436895,"about_ca_system_score_codex":0.0053388,"about_ca_system_score_gemma":0.008763222,"threshold_uncertainty_score":0.4238304},"labels":[],"label_agreement":null},{"id":"W7018872401","doi":"","title":"On the efficiency of testing procedures in the linear model for multivariate longitudinal data","year":2001,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kronecker product; Covariance; Covariance matrix; Multivariate statistics; Product (mathematics); Linear model; Matrix (chemical analysis); Kronecker delta","score_opus":0.06478342242178489,"score_gpt":0.2758469373547043,"score_spread":0.2110635149329194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018872401","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":[0.021302601,0.0008746839,0.9750959,0.0005523941,0.000046903813,0.00022579776,0.00006974014,0.00020153592,0.0016303825],"genre_scores_gemma":[0.30736908,0.0017897947,0.68561447,0.00060644216,0.00017644821,0.0013903269,0.0006038739,0.0004717104,0.001977873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.88949156,0.09454591,0.0028372947,0.003909657,0.008382642,0.00083296234],"domain_scores_gemma":[0.27776387,0.70173043,0.0060116295,0.008690786,0.005318162,0.00048507852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13628285,0.0022218504,0.0029274377,0.0026870347,0.0011143124,0.0032000912,0.0034227674,0.0027526347,0.002575854],"category_scores_gemma":[0.46970984,0.0010920594,0.002454683,0.002589304,0.0069361716,0.0059326044,0.003949886,0.0049609784,0.0008842153],"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":[0.0011874089,0.00027768355,0.017590519,0.0009775598,0.0010386755,0.00054716534,0.0009807011,0.58157927,0.003618676,0.22208081,0.0013741516,0.16874743],"study_design_scores_gemma":[0.000099506906,0.00059797824,0.0037807196,0.00031117687,0.00012049917,0.0002962616,0.00021801254,0.86057615,0.0025212332,0.13010295,0.0012958014,0.00007967433],"about_ca_topic_score_codex":0.004722106,"about_ca_topic_score_gemma":0.003054491,"teacher_disagreement_score":0.13628285,"about_ca_system_score_codex":0.0028497023,"about_ca_system_score_gemma":0.004287599,"threshold_uncertainty_score":0.72074115},"labels":[],"label_agreement":null},{"id":"W7022147102","doi":"","title":"Ottawa charter","year":2011,"lang":"en","type":"article","venue":"Deakin Research Online (Deakin University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.2927021613456004,"score_gpt":0.4287642360324307,"score_spread":0.1360620746868303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022147102","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":[0.0006625966,0.008976546,0.0066325413,0.002433551,0.0020793383,0.00020347329,0.041096188,0.0038232356,0.93409264],"genre_scores_gemma":[0.0027221255,0.005032586,0.002954429,0.00026404872,0.00013372129,0.00018181912,0.010327417,0.0014079142,0.976976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977028,0.00032821562,0.00024026878,0.0004798939,0.0010279516,0.00022088943],"domain_scores_gemma":[0.9948344,0.0008290147,0.00036418167,0.0015616171,0.0020143127,0.0003964613],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014620111,0.0023429885,0.002385115,0.005407818,0.0021971944,0.0077430266,0.0023834286,0.0023245553,0.5841897],"category_scores_gemma":[0.007402527,0.0016204533,0.0008547177,0.012706205,0.0011539805,0.0045605754,0.0022690466,0.0029505992,0.5186521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081954306,0.00002890127,0.00021395629,0.00030909933,0.000012710392,0.00007577214,0.0000649365,0.0006604266,0.00017707651,0.02275127,0.84035224,0.13527164],"study_design_scores_gemma":[0.000018281846,0.0000148418185,0.00051685265,0.00023228844,0.000009973509,0.00006237065,0.000060529233,0.00030711095,0.00018284506,0.0052524386,0.9933155,0.000026972579],"about_ca_topic_score_codex":0.076268606,"about_ca_topic_score_gemma":0.13569808,"teacher_disagreement_score":0.9237314,"about_ca_system_score_codex":0.003775141,"about_ca_system_score_gemma":0.007741466,"threshold_uncertainty_score":0.59310293},"labels":[],"label_agreement":null},{"id":"W7022402353","doi":"","title":"sabinar","year":2025,"lang":"en","type":"other","venue":"Spectrum Research Repository (Concordia University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metaphor; Embodied cognition; Bridging (networking); Bridge (graph theory); Exhibition; Construct (python library); Process (computing); Confusion; Adaptation (eye)","score_opus":0.061092387348683615,"score_gpt":0.3576641606319367,"score_spread":0.29657177328325307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022402353","genre_codex":"other","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":[0.0041556163,0.0022491997,0.005436965,0.002275491,0.0021708477,0.0001726215,0.0054662297,0.0070999865,0.970973],"genre_scores_gemma":[0.033169642,0.0022788937,0.0063111545,0.0013795996,0.00044908456,0.00018170613,0.00843276,0.0029119628,0.94488525],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988859,0.00015170997,0.00006233496,0.00030341028,0.00044725003,0.00014939526],"domain_scores_gemma":[0.9984944,0.00018353458,0.00007529242,0.0002877393,0.00058105955,0.00037788294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542707,0.0010899507,0.000631901,0.0017406027,0.0027022567,0.0068033896,0.0017345729,0.0021137784,0.56039613],"category_scores_gemma":[0.0027656697,0.000492764,0.0005326518,0.0015178771,0.00072666135,0.003186725,0.0041818386,0.0018419579,0.3894236],"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":[0.00041486445,0.00014609349,0.0014274648,0.00064773805,0.000016012664,0.0006619142,0.0011178289,0.00038173166,0.0027578443,0.02345311,0.6530592,0.31591615],"study_design_scores_gemma":[0.000007865263,0.000014854446,0.00037638567,0.000056316017,0.000002765025,0.00018107831,0.00020993857,0.000106778556,0.00030847467,0.0009283579,0.9977986,0.000008576899],"about_ca_topic_score_codex":0.004674238,"about_ca_topic_score_gemma":0.0073075914,"teacher_disagreement_score":0.56039613,"about_ca_system_score_codex":0.0013882931,"about_ca_system_score_gemma":0.0018290264,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7023748896","doi":"","title":"Plan de exportación de panela pulverizada orgánica para la asociación de productores agropecuarios La Shita en el distrito de Salas para el mercado canadiense, Quebec 2013","year":2017,"lang":"es","type":"dissertation","venue":"Repositorio Institucional USAT (Universidad Católica Santo Toribio de Mogrovejo)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Consumer market; Work (physics)","score_opus":0.027176807841325427,"score_gpt":0.32485930655179024,"score_spread":0.2976824987104648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023748896","genre_codex":"empirical","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":[0.8999959,0.0013797388,0.014987774,0.0012258136,0.00004499247,0.0009230271,0.006865684,0.0004707494,0.07410626],"genre_scores_gemma":[0.88691145,0.0016487599,0.01509467,0.00027663284,0.000009213508,0.00032155804,0.0052395533,0.000107284235,0.09039083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967384,0.000029989094,0.000008972419,0.000069086665,0.00012586747,0.00009231954],"domain_scores_gemma":[0.99937797,0.00002732136,0.00007083204,0.000036591347,0.00038751526,0.00009962147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005837048,0.00042141438,0.00018748466,0.00074402394,0.0012841173,0.0014371381,0.00058295164,0.0003031199,0.009573829],"category_scores_gemma":[0.0005054941,0.00018806997,0.0003730936,0.0012334954,0.00045121944,0.00047190278,0.00060809735,0.00049856177,0.0010596395],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014077874,0.0008983743,0.3300658,0.0019179132,0.0002309946,0.0010678084,0.00453464,0.023906272,0.23985116,0.007172058,0.020972043,0.3679752],"study_design_scores_gemma":[0.00009264304,0.0011005595,0.7978642,0.0003043069,0.00015471144,0.00014702421,0.007136381,0.0071530743,0.027414905,0.00070403266,0.15784948,0.00007874384],"about_ca_topic_score_codex":0.7857573,"about_ca_topic_score_gemma":0.92282474,"teacher_disagreement_score":0.2142427,"about_ca_system_score_codex":0.012040834,"about_ca_system_score_gemma":0.013939657,"threshold_uncertainty_score":0.43100882},"labels":[],"label_agreement":null},{"id":"W7024814776","doi":"","title":"Sur les estimateurs doublement robustes avec sélection de modèles et de variables pour les données administratives","year":2021,"lang":"fr","type":"other","venue":"Open MIND","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economic analysis; Consumer welfare; Western europe; Sugar industry","score_opus":0.19228103576329938,"score_gpt":0.4139922499142458,"score_spread":0.22171121415094644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024814776","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":[0.006466909,0.0019648313,0.988817,0.0005476985,0.00020975697,0.0001985583,0.0004668228,0.00042911115,0.00089940923],"genre_scores_gemma":[0.1780376,0.0032849596,0.805642,0.0007900499,0.0007068258,0.001953873,0.0028706985,0.0007291095,0.005984873],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92124224,0.05539771,0.004040029,0.010609116,0.007768526,0.0009423187],"domain_scores_gemma":[0.6802638,0.27344692,0.009993432,0.025277033,0.010254782,0.00076405035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.068585165,0.0027387205,0.004829401,0.0034648064,0.0013652275,0.0071245343,0.004043474,0.0031182482,0.007335118],"category_scores_gemma":[0.27019253,0.0017627438,0.007205656,0.0034228489,0.003269565,0.0051994165,0.0039735264,0.0060496232,0.0017447677],"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":[0.0017415964,0.00022748724,0.031058174,0.003974514,0.007717939,0.0007517428,0.0013899009,0.2577139,0.007379906,0.2159062,0.0080555985,0.46408308],"study_design_scores_gemma":[0.00038819065,0.0008815416,0.015736535,0.0016212346,0.001969724,0.0008572062,0.00043074126,0.7184159,0.011883534,0.21480384,0.032640878,0.00037058984],"about_ca_topic_score_codex":0.010626476,"about_ca_topic_score_gemma":0.00652245,"teacher_disagreement_score":0.068585165,"about_ca_system_score_codex":0.0022702895,"about_ca_system_score_gemma":0.004416738,"threshold_uncertainty_score":0.36271733},"labels":[],"label_agreement":null},{"id":"W7025363327","doi":"","title":"Uudet sanat EU:n lainsäädännön unkarin- ja suomenkielisissä käännöksissä","year":2017,"lang":"fi","type":"other","venue":"Doria (University of Helsinki)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Power (physics); Quarter (Canadian coin); Relation (database)","score_opus":0.04318819899115233,"score_gpt":0.3060927659254689,"score_spread":0.2629045669343166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025363327","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":[0.05795402,0.006316978,0.007963757,0.019681884,0.0049242964,0.00027228164,0.0033474131,0.0009043961,0.898635],"genre_scores_gemma":[0.11402749,0.00432684,0.007904097,0.0039261,0.0003993615,0.00015566147,0.0026184663,0.0006196514,0.8660224],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984659,0.00020123775,0.000085686544,0.00030778203,0.00066400453,0.00027532288],"domain_scores_gemma":[0.99872917,0.00016230711,0.00011086024,0.00013366816,0.0006115793,0.00025235105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016400379,0.00046939313,0.0004087104,0.0007486184,0.0033185333,0.00724223,0.0009142948,0.0016993249,0.11634396],"category_scores_gemma":[0.0021456308,0.00032996738,0.00046020563,0.000979625,0.0012133186,0.002465065,0.0038912336,0.0017727646,0.03677231],"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":[0.0006509923,0.00037892157,0.026489109,0.001379375,0.00010194019,0.0019791706,0.013356694,0.00061379,0.029453607,0.10035797,0.4464971,0.37874132],"study_design_scores_gemma":[0.0000057416637,0.00003593561,0.00424107,0.00014560184,0.00001160192,0.0001186379,0.0019904922,0.000100728175,0.0025672729,0.0010460382,0.98972297,0.0000139375825],"about_ca_topic_score_codex":0.0196952,"about_ca_topic_score_gemma":0.06133778,"teacher_disagreement_score":0.11634396,"about_ca_system_score_codex":0.0032625378,"about_ca_system_score_gemma":0.005623627,"threshold_uncertainty_score":0.38920933},"labels":[],"label_agreement":null},{"id":"W7027166906","doi":"","title":"Bias study of the naive estimator in a longitudinal binary mixed-effects model with measurement error and misclassification in covariates.","year":2014,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"","keywords":"Estimator; Observational error; Covariate; Binary data; Binary number; Errors-in-variables models","score_opus":0.13135992984433797,"score_gpt":0.3443526239346177,"score_spread":0.2129926940902797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027166906","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":[0.021510163,0.0018207857,0.972291,0.0016133622,0.00025350336,0.00018131034,0.0001288307,0.00015158759,0.002049399],"genre_scores_gemma":[0.44655257,0.0018156007,0.54156864,0.0019729333,0.0005536485,0.0009588095,0.00052278524,0.0001741615,0.0058809277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94663,0.044144917,0.0015580243,0.003894242,0.0031536834,0.0006190207],"domain_scores_gemma":[0.653457,0.30120078,0.012679915,0.022969173,0.008600103,0.0010929352],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09822756,0.00077375345,0.0020750337,0.0017785692,0.0011200524,0.0026968431,0.0042610075,0.0026666222,0.004628804],"category_scores_gemma":[0.38392663,0.0007480069,0.0021562553,0.0022206518,0.004004342,0.004540565,0.002904377,0.0035763548,0.00060732296],"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":[0.00029114485,0.00011476455,0.030260425,0.00055667385,0.0010127344,0.00042980767,0.0016566392,0.017737214,0.000792232,0.84983283,0.003738089,0.09357738],"study_design_scores_gemma":[0.00021840255,0.00027860072,0.007611528,0.00059665076,0.00066695624,0.0007955131,0.00047562318,0.23368898,0.0020766067,0.7410273,0.0124610355,0.00010280601],"about_ca_topic_score_codex":0.004826214,"about_ca_topic_score_gemma":0.0035728642,"teacher_disagreement_score":0.90177244,"about_ca_system_score_codex":0.0021291098,"about_ca_system_score_gemma":0.0024231144,"threshold_uncertainty_score":0.5194832},"labels":[],"label_agreement":null},{"id":"W7027467615","doi":"","title":"Comparing the performance of different multiple imputation strategies for missing binary outcomes in cluster randomized trials: a simulation study","year":2012,"lang":"en","type":"other","venue":"Dove Medical Press (Taylor and Francis Group)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"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); Logistic regression; Markov chain Monte Carlo; Binary data; Markov chain; Estimator; CRTS","score_opus":0.1336811243827571,"score_gpt":0.414589068777398,"score_spread":0.2809079443946409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027467615","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":[0.7183189,0.005747572,0.26200804,0.002360508,0.00024280649,0.0047595976,0.0012051904,0.0005391163,0.004818355],"genre_scores_gemma":[0.9131661,0.0010062107,0.081441835,0.00034315235,0.000045729088,0.0028687972,0.00049171416,0.000053775457,0.00058269536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91566557,0.07794192,0.002164739,0.0016126427,0.0016359609,0.0009791583],"domain_scores_gemma":[0.39128703,0.57307494,0.014319174,0.010992506,0.008357523,0.0019688087],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.099753425,0.0017486739,0.0028033853,0.0019016983,0.00091681065,0.0019004813,0.003374957,0.0030204554,0.0033342803],"category_scores_gemma":[0.2615887,0.0009939555,0.004941379,0.0030094562,0.0013154554,0.0024841027,0.0022558202,0.0034990117,0.0002940261],"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":[0.020193407,0.0022003986,0.03177077,0.0021640484,0.0051301774,0.00034382884,0.00093581073,0.86657506,0.00039709668,0.02209963,0.0022875613,0.045902185],"study_design_scores_gemma":[0.0031295551,0.0030235243,0.0027259625,0.0003066739,0.0011694628,0.00012999563,0.00018310972,0.979658,0.0004071772,0.008633582,0.00054636155,0.00008656043],"about_ca_topic_score_codex":0.008889183,"about_ca_topic_score_gemma":0.005482063,"teacher_disagreement_score":0.90024656,"about_ca_system_score_codex":0.003037298,"about_ca_system_score_gemma":0.005059905,"threshold_uncertainty_score":0.52755284},"labels":[],"label_agreement":null},{"id":"W7035896434","doi":"","title":"Attitudes of Gratitude","year":2015,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gratitude; Worship; Forgiveness; Affect (linguistics); Helping behavior","score_opus":0.023406853256330303,"score_gpt":0.27422135294202143,"score_spread":0.25081449968569114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7035896434","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":[0.4063761,0.003015387,0.009732071,0.05744728,0.0010918749,0.00014082098,0.00035069184,0.0001895091,0.52165633],"genre_scores_gemma":[0.9661056,0.0012736298,0.0014683414,0.0059748455,0.000102104335,0.00007706255,0.000077199074,0.00004487142,0.024876239],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99490595,0.0033100916,0.00013257639,0.00024162921,0.0009058569,0.0005038863],"domain_scores_gemma":[0.9900203,0.002401986,0.0018483376,0.00076056854,0.002178061,0.0027908585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050437846,0.00023049618,0.00026059235,0.0007433546,0.0027522002,0.005097542,0.00043862328,0.0013241891,0.015257582],"category_scores_gemma":[0.02043566,0.00016936456,0.0003872042,0.00046104076,0.004117032,0.0035219574,0.0038951759,0.003946578,0.0025938805],"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":[0.00019820478,0.00048308828,0.08227454,0.00037192347,0.000153715,0.00081221375,0.36660168,0.00040443067,0.002242238,0.22929612,0.10427339,0.2128885],"study_design_scores_gemma":[0.000037220118,0.00039209783,0.09291344,0.0008504448,0.000089548295,0.0017050082,0.32691133,0.0006728073,0.0010459124,0.09381435,0.48138365,0.00018421],"about_ca_topic_score_codex":0.0017494763,"about_ca_topic_score_gemma":0.002048118,"teacher_disagreement_score":0.015257582,"about_ca_system_score_codex":0.0020069086,"about_ca_system_score_gemma":0.0013044588,"threshold_uncertainty_score":0.051041663},"labels":[],"label_agreement":null},{"id":"W7039894580","doi":"","title":"Nurses’ Experience of Family-Centered Rounds in the Intensive Care Unit","year":2023,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Population; Government (linguistics); Work (physics); Health care; Multidisciplinary approach","score_opus":0.11328643869734299,"score_gpt":0.3759251037960278,"score_spread":0.2626386650986848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039894580","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":[0.98880625,0.0005688379,0.0007889218,0.0026749833,0.000099874014,0.00005857197,0.000041655763,0.000020374482,0.0069405925],"genre_scores_gemma":[0.9964641,0.000317532,0.00037495818,0.0004989604,0.000013999737,0.000027734233,0.00001647533,0.0000102862205,0.0022759754],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99507034,0.0034387002,0.00012056527,0.00024086864,0.00042022023,0.0007092664],"domain_scores_gemma":[0.99213976,0.004095844,0.0007422602,0.0002640155,0.00079352624,0.0019646182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046222555,0.00037685732,0.0003792674,0.00033895342,0.008339996,0.002962709,0.0014902239,0.001127255,0.002499029],"category_scores_gemma":[0.0109623475,0.00038814195,0.00032185204,0.00026888016,0.0062424573,0.0020594688,0.0038348308,0.0016152022,0.00028987718],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059871625,0.00002977334,0.00651157,0.000068934685,0.000007344441,0.001706469,0.982863,0.000081685415,0.00046139388,0.00030848547,0.0016829865,0.0062184483],"study_design_scores_gemma":[0.000005834661,0.0001177994,0.005265598,0.00010878924,0.000007019324,0.0007941642,0.97939336,0.000090518435,0.00018006789,0.00014602228,0.013866337,0.000024477476],"about_ca_topic_score_codex":0.06452159,"about_ca_topic_score_gemma":0.19363494,"teacher_disagreement_score":0.06452159,"about_ca_system_score_codex":0.0068837777,"about_ca_system_score_gemma":0.007000944,"threshold_uncertainty_score":0.12829202},"labels":[],"label_agreement":null},{"id":"W7043234492","doi":"","title":"Sample Size Formulas for Estimating Risk Ratios with the Modified Poisson Model for Binary Outcomes","year":2021,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Sample size determination; Logistic regression; Poisson regression; Poisson distribution; Estimation; Binary data; Regression analysis; Regression; Sample (material)","score_opus":0.20323759265859923,"score_gpt":0.3965082956106596,"score_spread":0.19327070295206036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043234492","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":[0.0011140616,0.0006314837,0.99619675,0.0003512484,0.00014245545,0.00048249908,0.000085377746,0.00013687191,0.0008592871],"genre_scores_gemma":[0.03310696,0.0014009983,0.9583903,0.000539339,0.0003173245,0.004724457,0.00026477955,0.00019017339,0.0010657003],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96598154,0.025559396,0.0018794838,0.0017395908,0.004548197,0.0002918163],"domain_scores_gemma":[0.87739116,0.108919814,0.004021109,0.0046021435,0.0047967425,0.00026908529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055553842,0.001536814,0.001945104,0.004140872,0.00061616505,0.0016357462,0.003773316,0.0020980851,0.0075792847],"category_scores_gemma":[0.26276332,0.00093991926,0.0020383506,0.0030748246,0.0016224207,0.0036864148,0.0020191784,0.004975226,0.001551355],"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":[0.00044965497,0.00023061624,0.006687832,0.0015022228,0.00060174474,0.00045910646,0.0010048888,0.06258183,0.0018306887,0.4156293,0.020677764,0.48834434],"study_design_scores_gemma":[0.000660152,0.00059639424,0.003599495,0.0012756719,0.00058289943,0.0012766004,0.00029054767,0.4891606,0.0030980634,0.4647596,0.03453191,0.00016804233],"about_ca_topic_score_codex":0.0021129106,"about_ca_topic_score_gemma":0.0020430875,"teacher_disagreement_score":0.055553842,"about_ca_system_score_codex":0.0016425084,"about_ca_system_score_gemma":0.0021192944,"threshold_uncertainty_score":0.2938003},"labels":[],"label_agreement":null},{"id":"W7066139402","doi":"","title":"Forgotten pasts and contested futures in Vancouver <break/>(Passés oubliés et futurs contestés à Vancouver)","year":2016,"lang":"fr","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Futures contract; Narrative; Perspective (graphical); Agency (philosophy); Government (linguistics)","score_opus":0.03358197362205435,"score_gpt":0.28995042864891885,"score_spread":0.2563684550268645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066139402","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":[0.88677585,0.0015634305,0.00091693713,0.007871543,0.00011282569,0.000028536133,0.0007391267,0.000020895252,0.101970896],"genre_scores_gemma":[0.9842957,0.00026710672,0.00032532332,0.00013954907,0.000012473474,0.00001124231,0.00018038476,0.000021557946,0.01474666],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99875784,0.00032499354,0.00003596292,0.00021247982,0.00020702537,0.00046163413],"domain_scores_gemma":[0.9972289,0.0007289587,0.00025483762,0.00014972988,0.0005000296,0.0011374849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014806434,0.00023802689,0.00054255873,0.0014826067,0.01882005,0.012058885,0.0015125148,0.0023829716,0.016345302],"category_scores_gemma":[0.005803093,0.00043482298,0.00022656436,0.0043472727,0.0075642755,0.0028686994,0.00449303,0.0042664534,0.0006433811],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011114244,0.00025366733,0.09179674,0.0002777398,0.00013379804,0.0038699482,0.43785027,0.0036421912,0.00061011314,0.31185237,0.054890193,0.0937115],"study_design_scores_gemma":[0.00004104881,0.00005129032,0.13129844,0.00036538843,0.000056059525,0.00044780754,0.6237031,0.0017446976,0.00022542832,0.048487518,0.19344084,0.0001383582],"about_ca_topic_score_codex":0.89326894,"about_ca_topic_score_gemma":0.97421706,"teacher_disagreement_score":0.10673106,"about_ca_system_score_codex":0.020710241,"about_ca_system_score_gemma":0.009431384,"threshold_uncertainty_score":0.21471918},"labels":[],"label_agreement":null},{"id":"W7070684598","doi":"","title":"QUALITY OF LIFE PARAMETERS IMPROVEMENT FOLLOWING TOTAL HIP REPLACEMENT","year":2012,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"WOMAC; Quality of life (healthcare); Osteoarthritis; Rehabilitation; Arthroplasty; Total hip replacement; Orthopedic surgery; Patient satisfaction","score_opus":0.47713426637567774,"score_gpt":0.6187828499116399,"score_spread":0.14164858353596216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070684598","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":[0.9971228,0.001835017,0.00017452265,0.000046187168,0.00002011543,0.000024709248,0.000133401,0.0000048695774,0.0006383091],"genre_scores_gemma":[0.99914753,0.00032180236,0.00009054314,0.000025351263,0.00001708896,0.0000122523725,0.00020371567,0.0000010689109,0.00018069097],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99908245,0.00025380854,0.00014196058,0.00008526993,0.00028276016,0.00015380516],"domain_scores_gemma":[0.99770397,0.00034220028,0.0012881465,0.00010965326,0.00028819861,0.0002678775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087421236,0.00023118396,0.00040031408,0.00067005504,0.00023363029,0.0004894989,0.00020477637,0.00032337513,0.0016385604],"category_scores_gemma":[0.0042853365,0.00012813605,0.00045928312,0.0006391275,0.00029388652,0.00030321875,0.00051560666,0.00048964913,0.00019295615],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007925743,0.00036153826,0.96904445,0.00021888039,0.0002650636,0.0005874911,0.00033553675,0.00017885891,0.0018465869,0.000017223747,0.0003189262,0.026032712],"study_design_scores_gemma":[0.000007850481,0.0005511998,0.99810934,0.000013979002,0.000032136733,0.0006310446,0.000106404616,0.00006053116,0.0002563438,0.000015593752,0.00021020832,0.00000528985],"about_ca_topic_score_codex":0.0006037692,"about_ca_topic_score_gemma":0.00089006725,"teacher_disagreement_score":0.0016385604,"about_ca_system_score_codex":0.00027483254,"about_ca_system_score_gemma":0.00019534065,"threshold_uncertainty_score":0.005481541},"labels":[],"label_agreement":null},{"id":"W7070870209","doi":"","title":"Profile of census divisions and subdivisions in British Columbia","year":2004,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Population; Work (physics); Demographic analysis","score_opus":0.012806363849536751,"score_gpt":0.24339178560449223,"score_spread":0.23058542175495547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070870209","genre_codex":"empirical","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":[0.59371066,0.0031501646,0.00085966964,0.0015709351,0.000079262136,0.00034499945,0.28664806,0.00040762636,0.113228574],"genre_scores_gemma":[0.68330026,0.0043756235,0.0028093683,0.00035470093,0.000016209042,0.00029507445,0.093661465,0.0001431328,0.21504419],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952483,0.00003803331,0.000046075555,0.000060237526,0.0001844188,0.00014629452],"domain_scores_gemma":[0.9972312,0.00018538494,0.00022023321,0.0000858994,0.0018530178,0.00042432363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026107332,0.00026108127,0.00030191345,0.0071969046,0.0016355164,0.001346556,0.0008481478,0.00028535275,0.014264893],"category_scores_gemma":[0.0024955738,0.0003909376,0.00018610897,0.016901907,0.0003430685,0.00033272858,0.0006616798,0.0005164458,0.0025456112],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002558984,0.000074376425,0.5818254,0.000626398,0.00010573915,0.000521974,0.006448215,0.0018911795,0.0016643815,0.0055131577,0.27912349,0.12194983],"study_design_scores_gemma":[0.000007825083,0.000010314267,0.9279328,0.00013849013,0.000024376213,0.00011476877,0.005635858,0.00084542786,0.00025554714,0.00019484528,0.064810485,0.000029289418],"about_ca_topic_score_codex":0.98649746,"about_ca_topic_score_gemma":0.99633324,"teacher_disagreement_score":0.014264893,"about_ca_system_score_codex":0.008609725,"about_ca_system_score_gemma":0.01829295,"threshold_uncertainty_score":0.06246823},"labels":[],"label_agreement":null},{"id":"W7086631028","doi":"","title":"Bayesian Functional ANOVA Modeling Using Gaussian Process Prior Distributions","year":2009,"lang":"en","type":"article","venue":"UC Berkeley","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Posterior probability; Markov chain Monte Carlo; Bayesian probability; Prior probability; Variance (accounting); Graphical model; Partition (number theory); Gaussian process; Markov chain","score_opus":0.10388845380792706,"score_gpt":0.38931669959331294,"score_spread":0.2854282457853859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7086631028","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":[0.0047201174,0.00007474399,0.9943316,0.00012395502,0.000011359926,0.000041242216,0.00012172549,0.00015002649,0.000425176],"genre_scores_gemma":[0.32664713,0.0006139249,0.665424,0.00026626347,0.00012066058,0.001415208,0.0009136294,0.00022126266,0.004377844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912443,0.0060817758,0.00024475853,0.0011944658,0.00094189023,0.00029279987],"domain_scores_gemma":[0.9761243,0.019736156,0.0011740974,0.001413976,0.0013226767,0.00022883124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020786624,0.0015174045,0.002280605,0.0024643745,0.0008328152,0.0024216855,0.0037437873,0.0024512748,0.003334759],"category_scores_gemma":[0.048312828,0.0012064835,0.0026037015,0.00198988,0.0026932391,0.0030344923,0.0017466914,0.00312885,0.00061449624],"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":[0.0001450008,0.00007275976,0.0018770279,0.0001118612,0.00016685766,0.0001265348,0.00042023725,0.48548123,0.0010122455,0.476952,0.001347662,0.03228657],"study_design_scores_gemma":[0.000032688426,0.00003539956,0.0004306638,0.000023169772,0.000034166373,0.000034191246,0.000024647472,0.79065317,0.00023799702,0.2073884,0.0010793622,0.000026135054],"about_ca_topic_score_codex":0.009835905,"about_ca_topic_score_gemma":0.009198608,"teacher_disagreement_score":0.020786624,"about_ca_system_score_codex":0.0022947022,"about_ca_system_score_gemma":0.001851833,"threshold_uncertainty_score":0.10993153},"labels":[],"label_agreement":null},{"id":"W7095529186","doi":"","title":"MINIMUM CHANGE EDIT AND IMPUTATION FOR THE 2006 CANADIAN CENSUS","year":2015,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Census; Missing data; Data collection","score_opus":0.22445841168352393,"score_gpt":0.40191399735463984,"score_spread":0.1774555856711159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095529186","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":[0.019034803,0.0019171562,0.93893385,0.00230222,0.0004228259,0.0002534282,0.02444568,0.0030384932,0.009651557],"genre_scores_gemma":[0.1976957,0.001299637,0.7420914,0.00042900315,0.00023069483,0.0004230386,0.042403284,0.001304047,0.014123156],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945334,0.0015198779,0.00028676627,0.0010493156,0.0021032426,0.00050740497],"domain_scores_gemma":[0.98802525,0.0041815513,0.0005251054,0.0032202085,0.003795281,0.00025264727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007579874,0.0007377238,0.001389065,0.003884945,0.0039892346,0.0025502525,0.0040158588,0.0013855149,0.0075965854],"category_scores_gemma":[0.044396017,0.0008086224,0.0013517451,0.010447462,0.0014960648,0.0022118925,0.0018704757,0.0033424527,0.0018300794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054550625,0.000084786094,0.015900798,0.00047873132,0.00028484262,0.00025990035,0.0009897767,0.15114318,0.0007257283,0.29793897,0.14853694,0.3831108],"study_design_scores_gemma":[0.00008059758,0.000026747764,0.015518837,0.00017078154,0.00008505636,0.00020351955,0.0003125078,0.5605918,0.0017778118,0.3182913,0.10277876,0.00016225404],"about_ca_topic_score_codex":0.77460885,"about_ca_topic_score_gemma":0.85141814,"teacher_disagreement_score":0.22539115,"about_ca_system_score_codex":0.009065492,"about_ca_system_score_gemma":0.022887034,"threshold_uncertainty_score":0.45343703},"labels":[],"label_agreement":null},{"id":"W7095687119","doi":"","title":"Multivariate Mixed Normal Conditional","year":2006,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistician; Multivariate statistics; Order (exchange); Term (time); Multivariate analysis","score_opus":0.049243071292194976,"score_gpt":0.3595854580292796,"score_spread":0.31034238673708464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095687119","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":[0.010104625,0.0033019132,0.9551634,0.0025785312,0.0008907674,0.00024582224,0.008871098,0.001808628,0.017035276],"genre_scores_gemma":[0.41345516,0.008858527,0.32611495,0.0018333717,0.0029116012,0.0020020017,0.028879678,0.001782488,0.21416223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952461,0.0021062475,0.00018691497,0.001294369,0.0007194955,0.00044697124],"domain_scores_gemma":[0.986552,0.0071029156,0.0011696788,0.0026892156,0.0019621816,0.0005240941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007620983,0.0026462725,0.0028624632,0.002195348,0.0010690968,0.0030846922,0.0031645785,0.002602913,0.0828445],"category_scores_gemma":[0.023889463,0.0014862387,0.0031740416,0.0031679254,0.0024097152,0.004809924,0.0025482022,0.0045327093,0.014475983],"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":[0.0005543776,0.00016686888,0.0056827334,0.0004595667,0.00037882465,0.0005577956,0.00032295648,0.04799617,0.0009110222,0.7898387,0.051266294,0.10186477],"study_design_scores_gemma":[0.000106134314,0.00014750108,0.005976943,0.00032154532,0.0002243628,0.0005384052,0.00013964868,0.3681137,0.0010104856,0.5598201,0.06340282,0.00019842421],"about_ca_topic_score_codex":0.01503243,"about_ca_topic_score_gemma":0.01894952,"teacher_disagreement_score":0.0828445,"about_ca_system_score_codex":0.0026624962,"about_ca_system_score_gemma":0.0027293921,"threshold_uncertainty_score":0.27714247},"labels":[],"label_agreement":null},{"id":"W7095964754","doi":"","title":"LSAC RESEARCH REPORT SERIES � A Bivariate Lognormal Response-Time Model for the Detection of Collusion Between Test Takers","year":2008,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Discretion; Test (biology); Bivariate analysis; Collusion; Agency (philosophy); Series (stratigraphy); Common law","score_opus":0.2624894320574217,"score_gpt":0.44371309486649013,"score_spread":0.18122366280906843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095964754","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":[0.052206874,0.0017170954,0.9020065,0.009353063,0.0009236434,0.0011163888,0.007949103,0.00086528505,0.023862096],"genre_scores_gemma":[0.6222826,0.0044430597,0.2669248,0.002375354,0.001528171,0.002703179,0.011976501,0.00057331094,0.08719307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989945,0.0062028295,0.00033109277,0.0013859384,0.0014750452,0.00066010543],"domain_scores_gemma":[0.9206363,0.05495265,0.0064698444,0.0057384646,0.010370663,0.0018320213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02189989,0.0013772465,0.00199776,0.0026565301,0.001476629,0.003363029,0.0039605843,0.0030097507,0.024684232],"category_scores_gemma":[0.09501965,0.0011541777,0.002372762,0.0035780238,0.0024242834,0.0040674694,0.0022142394,0.0058355024,0.0048290705],"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":[0.00074779993,0.00056716683,0.04684291,0.00039690878,0.0006722925,0.00065124576,0.0011586412,0.1865694,0.0006999866,0.5741489,0.09329614,0.09424861],"study_design_scores_gemma":[0.00021971653,0.00030105445,0.011767042,0.00019682344,0.00025735894,0.0005606663,0.00035751166,0.7588178,0.00046787664,0.20040685,0.02646591,0.00018137068],"about_ca_topic_score_codex":0.08137991,"about_ca_topic_score_gemma":0.050057802,"teacher_disagreement_score":0.08137991,"about_ca_system_score_codex":0.005217378,"about_ca_system_score_gemma":0.007204438,"threshold_uncertainty_score":0.16181248},"labels":[],"label_agreement":null},{"id":"W7096259359","doi":"","title":"AN APPLICATION OF THE BOOTSTRAP VARIANCE ESTIMATION METHOD TO THE PARTICIPATION AND ACTIVITY LIMITATION SURVEY","year":2015,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Jackknife resampling; Logistic regression; Variance (accounting); Sampling (signal processing); Propensity score matching; Stratified sampling; Sampling design; Range (aeronautics)","score_opus":0.26427801629162345,"score_gpt":0.4922430272673661,"score_spread":0.22796501097574262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096259359","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":[0.002831436,0.00023110444,0.9948775,0.00030502444,0.000091031994,0.0002177202,0.00013354665,0.00024352063,0.0010690565],"genre_scores_gemma":[0.08934057,0.00071591855,0.9053017,0.00034271355,0.00024519555,0.0018655586,0.00042782017,0.00022977187,0.001530738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96175885,0.033157166,0.0006932654,0.0009769257,0.003131206,0.0002824885],"domain_scores_gemma":[0.95237494,0.038614288,0.0018245452,0.0035100074,0.0033829836,0.00029318954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030194012,0.0008228712,0.0015055893,0.0039336113,0.0008861746,0.0009995555,0.0016797292,0.0012121692,0.004910464],"category_scores_gemma":[0.12962262,0.00063716114,0.0017117563,0.004996473,0.0014488155,0.0011248796,0.0023706001,0.0021887608,0.0011166949],"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":[0.00031463336,0.00024608022,0.0155723,0.0007610066,0.0007536347,0.0005915866,0.0013763948,0.027820852,0.001692607,0.18502693,0.021100245,0.74474365],"study_design_scores_gemma":[0.00038225687,0.00083078846,0.02814295,0.0011287553,0.00040402528,0.0014790758,0.00081379234,0.36730748,0.00306118,0.5055683,0.09059535,0.0002861345],"about_ca_topic_score_codex":0.0050877044,"about_ca_topic_score_gemma":0.005727376,"teacher_disagreement_score":0.030194012,"about_ca_system_score_codex":0.0009815113,"about_ca_system_score_gemma":0.0025019024,"threshold_uncertainty_score":0.15968311},"labels":[],"label_agreement":null},{"id":"W7096743515","doi":"","title":"SUMMARY","year":2015,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Focus (optics); Control (management); Subject (documents); Presentation (obstetrics)","score_opus":0.18769929663952434,"score_gpt":0.4198102651441927,"score_spread":0.23211096850466834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096743515","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":[0.002320672,0.02219369,0.0061712726,0.287103,0.11690672,0.00052517484,0.03511125,0.0019618045,0.5277065],"genre_scores_gemma":[0.020946853,0.009707564,0.0022381886,0.05868651,0.014416909,0.00031948788,0.0134645915,0.0007637462,0.87945616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982168,0.00038554164,0.00010473518,0.00037357357,0.00070979755,0.00020947267],"domain_scores_gemma":[0.9930912,0.0014473918,0.00024840145,0.0005599362,0.0038453862,0.00080765726],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021237473,0.00060977525,0.00067015475,0.0017040662,0.0014308965,0.0024112575,0.0013043078,0.0012118511,0.4175552],"category_scores_gemma":[0.010804639,0.00024869133,0.0005531262,0.0013765516,0.00033079166,0.0021550623,0.0020971939,0.0019660252,0.2334687],"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":[0.000034717024,0.0000066033153,0.00019148854,0.000093667346,0.0000036567196,0.000037492297,0.00007390776,0.0000279736,0.000059690556,0.003191931,0.97431165,0.021967307],"study_design_scores_gemma":[0.0000029015616,0.000004638102,0.0004050563,0.00005378633,0.0000017928776,0.000028051369,0.00008151687,0.000016146088,0.000046825393,0.0005769952,0.998779,0.0000031571244],"about_ca_topic_score_codex":0.008556174,"about_ca_topic_score_gemma":0.01255985,"teacher_disagreement_score":0.5824448,"about_ca_system_score_codex":0.0023142453,"about_ca_system_score_gemma":0.002234776,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7096784435","doi":"","title":"Objective Priors for Model Selection in One-Way Random Effects Models.” Submitted to The Canadian journal of Statistics","year":2005,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prior probability; Bayes factor; Bayes' theorem; Divergence (linguistics); Bayes' rule; Context (archaeology); Model selection","score_opus":0.05832554378106482,"score_gpt":0.3399667509702713,"score_spread":0.2816412071892065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096784435","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":[0.0013723372,0.0020162351,0.9929422,0.0014282848,0.00017868915,0.000077403,0.00016361543,0.00015382214,0.0016674019],"genre_scores_gemma":[0.06664753,0.0033417535,0.9216633,0.000961679,0.0010083232,0.00090246147,0.0009810008,0.00033972686,0.004154182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9701503,0.025864152,0.00073223055,0.0011661452,0.0018713647,0.00021594757],"domain_scores_gemma":[0.91617596,0.07161243,0.0029500504,0.0049419547,0.0037287613,0.0005908679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04615701,0.0012761457,0.001815757,0.0026229424,0.0009343401,0.0026661544,0.0031417126,0.0026107794,0.009150852],"category_scores_gemma":[0.13021682,0.0017947024,0.0021362384,0.0029658447,0.003780841,0.004106646,0.0031167502,0.0052256654,0.0020051922],"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":[0.00016315539,0.000072098526,0.0016255224,0.00068925315,0.0006601148,0.00027068434,0.00045522104,0.062145203,0.00044809902,0.769069,0.03047769,0.133924],"study_design_scores_gemma":[0.000059296777,0.000045852776,0.0009230731,0.0002897084,0.00009404253,0.00010496977,0.000049870167,0.1981614,0.0004383466,0.78044873,0.019322723,0.00006193825],"about_ca_topic_score_codex":0.0052509103,"about_ca_topic_score_gemma":0.0073481947,"teacher_disagreement_score":0.04615701,"about_ca_system_score_codex":0.002531109,"about_ca_system_score_gemma":0.0028882667,"threshold_uncertainty_score":0.24410456},"labels":[],"label_agreement":null},{"id":"W7097221113","doi":"","title":"La revue canadienne de statistique Kendall’s tau for Serial Dependence","year":2008,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistical analysis; Sequence (biology); Bayesian probability; Field (mathematics)","score_opus":0.07084194821187996,"score_gpt":0.35275550407657713,"score_spread":0.2819135558646972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097221113","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":[0.0017886533,0.06019641,0.91805667,0.011687657,0.003478913,0.00003922592,0.00029162754,0.00031173343,0.004149107],"genre_scores_gemma":[0.11894188,0.09759194,0.7245085,0.009413254,0.030615391,0.0009983723,0.00093615043,0.0024182408,0.014576296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94857585,0.033102516,0.0035214515,0.0055499887,0.008129425,0.0011206972],"domain_scores_gemma":[0.6762568,0.29225248,0.0044652526,0.016707316,0.009153679,0.0011644958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06075838,0.003390961,0.007935384,0.010219747,0.0027588133,0.012070491,0.007143696,0.009243662,0.0056603993],"category_scores_gemma":[0.17807217,0.0037762455,0.005632821,0.014043291,0.019120341,0.019889316,0.0062054843,0.032029085,0.0034604294],"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":[0.000046841007,0.000031727115,0.00069067505,0.00059874094,0.00031232808,0.00012738013,0.00039839774,0.0052353987,0.00021558003,0.93558335,0.007862836,0.04889676],"study_design_scores_gemma":[0.000016895912,0.000016588341,0.0004477847,0.00021965685,0.00008066319,0.00015863504,0.000036103338,0.018870117,0.0001654435,0.9558578,0.024077773,0.00005258854],"about_ca_topic_score_codex":0.009952212,"about_ca_topic_score_gemma":0.005454681,"teacher_disagreement_score":0.06075838,"about_ca_system_score_codex":0.006959547,"about_ca_system_score_gemma":0.009365631,"threshold_uncertainty_score":0.32132488},"labels":[],"label_agreement":null},{"id":"W7097298832","doi":"","title":"University of Toronto Department of StatisticsA Limit Result for the Prior Predictive","year":2013,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Limit (mathematics); Convergence (economics); Term (time); Bayesian probability","score_opus":0.04787337818733792,"score_gpt":0.32617513925414693,"score_spread":0.27830176106680904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097298832","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":[0.03065641,0.12057505,0.34555382,0.11986952,0.011366019,0.0002202844,0.03208886,0.0040196585,0.33565038],"genre_scores_gemma":[0.42511636,0.11013343,0.09147505,0.006996741,0.006796139,0.00093522324,0.013458087,0.0018486534,0.34324032],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99602145,0.00084402063,0.00019042357,0.00092882605,0.001683441,0.00033182284],"domain_scores_gemma":[0.9732443,0.013053625,0.0020685296,0.0024043715,0.0074350717,0.0017940195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046332683,0.001611833,0.0024197511,0.0031787644,0.0027870291,0.0062480327,0.0016887344,0.00263546,0.06218142],"category_scores_gemma":[0.034133203,0.0009164547,0.001098335,0.0040287967,0.0030525853,0.0019475326,0.0026535024,0.0046488484,0.01344514],"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":[0.0004529302,0.00007760607,0.0072249947,0.0015424149,0.00022857315,0.0009156246,0.00092797435,0.017802548,0.001263018,0.51775444,0.30154738,0.15026248],"study_design_scores_gemma":[0.00014620427,0.000082021375,0.012145898,0.0015238742,0.000226953,0.00088799786,0.0003219678,0.06223977,0.002034567,0.4694155,0.4507079,0.00026733644],"about_ca_topic_score_codex":0.14989683,"about_ca_topic_score_gemma":0.17236839,"teacher_disagreement_score":0.14989683,"about_ca_system_score_codex":0.01669486,"about_ca_system_score_gemma":0.010779212,"threshold_uncertainty_score":0.29804868},"labels":[],"label_agreement":null},{"id":"W7097511534","doi":"","title":"Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics","year":2001,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; A priori and a posteriori; Inference; Conditional dependence; Bayesian inference; Conditional independence; Statistical hypothesis testing; Posterior probability; Identification (biology)","score_opus":0.35924570065254363,"score_gpt":0.37809422726797565,"score_spread":0.018848526615432015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097511534","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":[0.0028492007,0.0024083597,0.9913961,0.0015016581,0.000092737915,0.0001140276,0.0003144457,0.0001694246,0.0011539351],"genre_scores_gemma":[0.21115305,0.006115392,0.77309537,0.00096454093,0.00077704235,0.0015909524,0.0010354441,0.00017425077,0.0050939736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96982723,0.023779603,0.0008407442,0.0024468345,0.002609027,0.0004965559],"domain_scores_gemma":[0.8839069,0.10286711,0.0062293475,0.0032312437,0.0029900167,0.00077542674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043528397,0.0024331398,0.003248731,0.006252549,0.0018150391,0.0031398016,0.005439838,0.0040142643,0.0064197667],"category_scores_gemma":[0.13161112,0.0028660425,0.003225155,0.0053558457,0.00560478,0.004493245,0.0036420103,0.0063302414,0.0011218789],"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":[0.00017367218,0.00013757886,0.008276914,0.0006405842,0.0010383765,0.0004884437,0.0009658415,0.2606432,0.0004029966,0.5772416,0.006285768,0.14370511],"study_design_scores_gemma":[0.000048398368,0.000048076075,0.001957323,0.00015338871,0.00015596033,0.00021968511,0.00007691244,0.3559831,0.00013018106,0.6372279,0.0039316015,0.00006749982],"about_ca_topic_score_codex":0.02713679,"about_ca_topic_score_gemma":0.026676456,"teacher_disagreement_score":0.043528397,"about_ca_system_score_codex":0.00493032,"about_ca_system_score_gemma":0.0033649013,"threshold_uncertainty_score":0.23020291},"labels":[],"label_agreement":null},{"id":"W7097577134","doi":"","title":"Bootstrapping for variance estimation in multi-level models fitted to survey data","year":2006,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bootstrapping (finance); Survey data collection; Variance (accounting); Estimation; Sampling (signal processing); Survey methodology; Survey sampling; Linearization","score_opus":0.5388756662966123,"score_gpt":0.47479968886351,"score_spread":0.06407597743310234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097577134","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":[0.0010514621,0.0001321489,0.9984642,0.000060239523,0.000016518023,0.000030770243,0.00002151765,0.00010222306,0.00012079595],"genre_scores_gemma":[0.08982765,0.00075915613,0.90672874,0.00019118018,0.00020063753,0.00091035507,0.0003912227,0.0003330838,0.00065805775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.950882,0.042576388,0.00090406934,0.0016547694,0.003369189,0.0006135091],"domain_scores_gemma":[0.83661485,0.14201191,0.0039611496,0.013073664,0.003704431,0.00063397887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049464576,0.0014702884,0.002658851,0.0035626546,0.0011267939,0.0016396913,0.004249802,0.0027599037,0.0029570083],"category_scores_gemma":[0.23416753,0.0012654949,0.003215376,0.0049390676,0.0032210462,0.0033611804,0.003444416,0.0047711246,0.0013154165],"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":[0.00032321416,0.00027981368,0.006796516,0.00080494926,0.00091010734,0.0006553394,0.0011923907,0.2684305,0.0026357893,0.5191319,0.005750832,0.19308867],"study_design_scores_gemma":[0.0000576697,0.00007597184,0.0011394749,0.000109831395,0.00005672598,0.000111371766,0.00007434435,0.7589488,0.0008861347,0.2355672,0.002918962,0.000053599113],"about_ca_topic_score_codex":0.0046110013,"about_ca_topic_score_gemma":0.003934927,"teacher_disagreement_score":0.049464576,"about_ca_system_score_codex":0.0015536523,"about_ca_system_score_gemma":0.0020598283,"threshold_uncertainty_score":0.26159674},"labels":[],"label_agreement":null},{"id":"W7097586333","doi":"","title":"CROSS-SECTIONAL INFERENCE BASED ON","year":2016,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variance (accounting); Sample (material); Inference; Estimation; Population; Population variance; Covariance; Analysis of covariance; Sample size determination","score_opus":0.11457387148134376,"score_gpt":0.43768442363246596,"score_spread":0.32311055215112217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097586333","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":[0.008766874,0.0008908439,0.9827151,0.000884069,0.000323188,0.00017085095,0.0005766721,0.00022958586,0.005442803],"genre_scores_gemma":[0.5088874,0.0032589014,0.46500704,0.002417271,0.0013644511,0.0016444506,0.0035008902,0.00027815418,0.013641415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96838903,0.02035694,0.001420748,0.00630433,0.002730505,0.00079844444],"domain_scores_gemma":[0.83107877,0.13485956,0.009044778,0.019539822,0.0046798624,0.00079722353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050615273,0.0010804079,0.0028879838,0.003755535,0.0014684645,0.0037510935,0.0039726375,0.0022295518,0.017187951],"category_scores_gemma":[0.18385468,0.0013317289,0.003752801,0.004441473,0.004019965,0.0077811144,0.003805924,0.0056783287,0.002391548],"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":[0.00017299598,0.00016378211,0.03429653,0.000508159,0.0013644756,0.00043459283,0.0007867436,0.036290158,0.00033203934,0.8242233,0.005916502,0.09551077],"study_design_scores_gemma":[0.000088216286,0.00016770791,0.00826033,0.0004332702,0.00039635686,0.00041345487,0.00033314948,0.18466148,0.0007141656,0.78602755,0.018431898,0.00007242881],"about_ca_topic_score_codex":0.0066611115,"about_ca_topic_score_gemma":0.005479984,"teacher_disagreement_score":0.050615273,"about_ca_system_score_codex":0.0017371493,"about_ca_system_score_gemma":0.002260587,"threshold_uncertainty_score":0.2676823},"labels":[],"label_agreement":null},{"id":"W7098124464","doi":"","title":"British Columbia.","year":2010,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prior probability; Bayesian probability; Gaussian; Laplace distribution; Laplace transform; Laplace's method; Gaussian process; Conjugate prior; Distribution (mathematics)","score_opus":0.03768396561412311,"score_gpt":0.3487603013529855,"score_spread":0.3110763357388624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098124464","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":[0.0033625655,0.0045039332,0.002695657,0.0068658805,0.00111426,0.000121481,0.034357797,0.0011070137,0.9458715],"genre_scores_gemma":[0.012085182,0.0026744762,0.0024416312,0.0010013069,0.000060628776,0.00008284434,0.009793905,0.00039086744,0.97146916],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992592,0.000079623205,0.000035640212,0.00021615984,0.00029597158,0.00011335675],"domain_scores_gemma":[0.99832326,0.00025046122,0.00009174696,0.00019008583,0.0008921484,0.00025228722],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005454274,0.0010632809,0.0009878329,0.0017788216,0.0029210655,0.004129124,0.001860403,0.0021008272,0.5982506],"category_scores_gemma":[0.0028022754,0.00046977584,0.00040825285,0.003714876,0.00087190303,0.0015483239,0.0017483092,0.0018718694,0.32485884],"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":[0.00023431679,0.00010770788,0.0023352117,0.00046295807,0.000028199447,0.0005724383,0.00021568926,0.00056452275,0.0007755093,0.021842377,0.69512403,0.277737],"study_design_scores_gemma":[0.000021451151,0.000010748774,0.0032686805,0.00025588082,0.000007854497,0.00013797705,0.00021014283,0.00041074672,0.0002978358,0.0030965386,0.9922585,0.00002344334],"about_ca_topic_score_codex":0.40045145,"about_ca_topic_score_gemma":0.57893616,"teacher_disagreement_score":0.40174937,"about_ca_system_score_codex":0.006385712,"about_ca_system_score_gemma":0.008531935,"threshold_uncertainty_score":0.79624116},"labels":[],"label_agreement":null},{"id":"W7100157343","doi":"","title":"LSAC RESEARCH REPORT SERIES � Bayesian Estimation Methods for Multidimensional Models for Discrete and Continuous Responses","year":2006,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Agency (philosophy); Discretion; Accreditation; Estimation; Series (stratigraphy); Common law","score_opus":0.1594179585823253,"score_gpt":0.5168481947270754,"score_spread":0.3574302361447501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100157343","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":[0.00069388485,0.0007855617,0.9939912,0.0007568974,0.00016713393,0.00016174615,0.00064281287,0.00038670094,0.0024140303],"genre_scores_gemma":[0.032363493,0.0028233877,0.9445836,0.0006858683,0.0007706749,0.001986042,0.0052044005,0.0007673691,0.010815201],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97228944,0.02024568,0.0009322127,0.002293123,0.003831018,0.00040848178],"domain_scores_gemma":[0.86925524,0.10605619,0.0033967977,0.0106421225,0.009727002,0.00092266226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039538145,0.002640638,0.002809043,0.0048117046,0.0019054271,0.0040534814,0.0050766594,0.004274026,0.02812735],"category_scores_gemma":[0.1763516,0.0027781758,0.004901711,0.0051941015,0.0027744134,0.0063410904,0.0034362222,0.009066606,0.009538591],"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":[0.00017912469,0.00024394596,0.0049739736,0.00070743816,0.0010445515,0.00013447824,0.00053024275,0.1207729,0.0004309797,0.52713096,0.059464563,0.2843869],"study_design_scores_gemma":[0.000127709,0.000097267315,0.002039538,0.00042912425,0.00020732945,0.00019682467,0.00012141209,0.57296956,0.0006809942,0.37038985,0.05260257,0.00013780339],"about_ca_topic_score_codex":0.0398046,"about_ca_topic_score_gemma":0.032238204,"teacher_disagreement_score":0.0398046,"about_ca_system_score_codex":0.0044207955,"about_ca_system_score_gemma":0.009765041,"threshold_uncertainty_score":0.20910019},"labels":[],"label_agreement":null},{"id":"W7104261630","doi":"10.71781/4971","title":"Sur les estimateurs doublement robustes avec sélection de modèles et de variables pour les données administratives","year":2021,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economic analysis; Consumer welfare; Western europe; Sugar industry","score_opus":0.03531202781006316,"score_gpt":0.2681153558644074,"score_spread":0.23280332805434423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104261630","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":[0.006466909,0.0019648313,0.988817,0.0005476985,0.00020975697,0.0001985583,0.0004668228,0.00042911115,0.00089940923],"genre_scores_gemma":[0.1780376,0.0032849596,0.805642,0.0007900499,0.0007068258,0.001953873,0.0028706985,0.0007291095,0.005984873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92124224,0.05539771,0.004040029,0.010609116,0.007768526,0.0009423187],"domain_scores_gemma":[0.6802638,0.27344692,0.009993432,0.025277033,0.010254782,0.00076405035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.068585165,0.0027387205,0.004829401,0.0034648064,0.0013652275,0.0071245343,0.004043474,0.0031182482,0.007335118],"category_scores_gemma":[0.27019253,0.0017627438,0.007205656,0.0034228489,0.003269565,0.0051994165,0.0039735264,0.0060496232,0.0017447677],"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":[0.0017415964,0.00022748724,0.031058174,0.003974514,0.007717939,0.0007517428,0.0013899009,0.2577139,0.007379906,0.2159062,0.0080555985,0.46408308],"study_design_scores_gemma":[0.00038819065,0.0008815416,0.015736535,0.0016212346,0.001969724,0.0008572062,0.00043074126,0.7184159,0.011883534,0.21480384,0.032640878,0.00037058984],"about_ca_topic_score_codex":0.010626476,"about_ca_topic_score_gemma":0.00652245,"teacher_disagreement_score":0.068585165,"about_ca_system_score_codex":0.0022702895,"about_ca_system_score_gemma":0.004416738,"threshold_uncertainty_score":0.36271733},"labels":[],"label_agreement":null},{"id":"W7115037197","doi":"","title":"Standardizing to target populations in multisite studies using inverse odds and augmented inverse probability weighting","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Bayesian Inference","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; McGill University Health Centre","funders":"Department of Epidemiology, Biostatistics and Occupational Health, McGill University","keywords":"Weighting; Inverse probability weighting; Inverse; Odds; Bayesian probability","score_opus":0.15741035361694508,"score_gpt":0.4025476233050089,"score_spread":0.2451372696880638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115037197","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":[0.016125647,0.0003425909,0.98135614,0.0005041827,0.00010086823,0.00028761142,0.00019512388,0.00019014667,0.0008976661],"genre_scores_gemma":[0.40919262,0.000556513,0.5858757,0.00046983716,0.0002098419,0.0017773719,0.00058468163,0.00017988538,0.0011535595],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94310766,0.045282114,0.0022546507,0.006165648,0.0026784386,0.0005115254],"domain_scores_gemma":[0.87044805,0.092848085,0.011048813,0.020124944,0.0047880732,0.00074203726],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.065826796,0.0014586832,0.001797681,0.0027675615,0.0008030247,0.0030055041,0.0036449132,0.002232881,0.0031216182],"category_scores_gemma":[0.19296123,0.00082557765,0.0028100077,0.0026872542,0.0031261842,0.0034776835,0.004977943,0.0032506105,0.00043857686],"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":[0.000833287,0.00031082818,0.07961966,0.0013856413,0.003210986,0.0007814872,0.0025201633,0.18663226,0.0037158004,0.3573117,0.004327091,0.35935116],"study_design_scores_gemma":[0.00044286277,0.00070778694,0.015708527,0.00042255162,0.00090780813,0.0004415559,0.00049358245,0.46513134,0.0043163914,0.49570936,0.015561737,0.00015660227],"about_ca_topic_score_codex":0.0035589766,"about_ca_topic_score_gemma":0.001834989,"teacher_disagreement_score":0.9341732,"about_ca_system_score_codex":0.0012632802,"about_ca_system_score_gemma":0.001830859,"threshold_uncertainty_score":0.3481295},"labels":[],"label_agreement":null},{"id":"W7116726263","doi":"10.1080/00031305.2025.2606079","title":"Probabilistic Parameter Estimates that Require Less Small Print","year":2025,"lang":"en","type":"article","venue":"The American Statistician","topic":"Statistical Methods and Bayesian 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":"McGill University","funders":"","keywords":"Probabilistic logic; Estimation theory; Statistical model; Estimation; Bayesian probability","score_opus":0.10987777437965937,"score_gpt":0.3954648102331808,"score_spread":0.2855870358535214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116726263","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":[0.0018940629,0.0017019609,0.93227637,0.010427684,0.0022632545,0.00014369207,0.000641119,0.007640553,0.043011244],"genre_scores_gemma":[0.055255223,0.0023799518,0.88180804,0.0099173505,0.0030725463,0.00072375534,0.0010565807,0.008055616,0.0377309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98126364,0.008946954,0.0012124159,0.002132567,0.0062478636,0.00019644904],"domain_scores_gemma":[0.7626982,0.18790492,0.004442847,0.03501047,0.009160606,0.00078303134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023107976,0.0021678754,0.0016837207,0.0027704004,0.0014279779,0.009770598,0.0035561332,0.0031469287,0.09229179],"category_scores_gemma":[0.23666607,0.0017957311,0.0018062477,0.0023804812,0.0066372068,0.015080545,0.004527789,0.009213548,0.03295569],"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":[0.000388764,0.00014621242,0.0012856373,0.001309215,0.00018506203,0.00056754734,0.0015046455,0.010367011,0.0025783598,0.4783378,0.12925822,0.3740716],"study_design_scores_gemma":[0.00016699311,0.0001334348,0.00091662887,0.00085407647,0.0000928275,0.001041314,0.00039061153,0.02056371,0.003243428,0.6302637,0.34215808,0.00017515723],"about_ca_topic_score_codex":0.0018928924,"about_ca_topic_score_gemma":0.0025978188,"teacher_disagreement_score":0.09229179,"about_ca_system_score_codex":0.0016527403,"about_ca_system_score_gemma":0.0019459791,"threshold_uncertainty_score":0.3087468},"labels":[],"label_agreement":null},{"id":"W7116982847","doi":"10.1214/25-aos2550","title":"Semi-supervised U-statistics","year":2025,"lang":"","type":"article","venue":"The Annals of Statistics","topic":"Statistical Methods and Bayesian Inference","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":"Science North","funders":"","keywords":"","score_opus":0.1395012079858714,"score_gpt":0.42920254180872736,"score_spread":0.28970133382285596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116982847","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":[0.0065835444,0.0006365716,0.99023235,0.00038672113,0.0000991158,0.000041219148,0.00029228206,0.00042912152,0.0012990097],"genre_scores_gemma":[0.36353987,0.0017075059,0.6212347,0.00095763867,0.0013001522,0.0006879086,0.0029840756,0.0006989532,0.0068892236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879623,0.0076918504,0.00066326966,0.0018776184,0.001477796,0.00032711614],"domain_scores_gemma":[0.9222893,0.055132467,0.0039172997,0.011638521,0.0060721673,0.0009502594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010987373,0.0011526055,0.0035874797,0.0021600905,0.0012569765,0.003529008,0.0028712896,0.0025843594,0.0038442637],"category_scores_gemma":[0.05972783,0.0013273326,0.0015986231,0.002263886,0.0035063548,0.005220517,0.003110615,0.0035201637,0.0021666454],"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":[0.0006964669,0.00032218389,0.0054822476,0.0008948844,0.00056098896,0.0003181729,0.00039707054,0.16458149,0.0031245423,0.50773853,0.023486584,0.2923969],"study_design_scores_gemma":[0.000036670033,0.00009546971,0.0005987489,0.000080250546,0.000033101267,0.00015501953,0.000033824377,0.6790878,0.0014652768,0.31519932,0.0031737632,0.00004078618],"about_ca_topic_score_codex":0.0013453022,"about_ca_topic_score_gemma":0.0017972182,"teacher_disagreement_score":0.010987373,"about_ca_system_score_codex":0.0010010994,"about_ca_system_score_gemma":0.0030842903,"threshold_uncertainty_score":0.058107495},"labels":[],"label_agreement":null},{"id":"W7117121226","doi":"10.1111/bmsp.70025","title":"Power priors for latent variable mediation models under small sample sizes","year":2025,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Statistical Methods and Bayesian Inference","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":"Prior probability; Univariate; Bayesian probability; Sample size determination; Latent variable; Variable (mathematics); Bayes' theorem","score_opus":0.07205106244704894,"score_gpt":0.3891975518028085,"score_spread":0.31714648935575956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117121226","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":[0.004757041,0.00048272437,0.9912238,0.00051472307,0.000057284517,0.00048170114,0.00023593535,0.00025253624,0.001994257],"genre_scores_gemma":[0.17744346,0.00088103994,0.8148569,0.0005851384,0.0001307468,0.003723089,0.0005715543,0.000353499,0.0014546375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9554059,0.036603566,0.0015541551,0.003142132,0.0027645568,0.0005297228],"domain_scores_gemma":[0.741943,0.23063216,0.0058241202,0.015052097,0.005891212,0.0006573723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09475764,0.0019180167,0.0021134727,0.00216627,0.0012662569,0.0032410133,0.00410079,0.0030091815,0.012701164],"category_scores_gemma":[0.3831127,0.0013493198,0.0026791363,0.0026167661,0.0033851005,0.0064614257,0.0042971736,0.0076816115,0.0016844702],"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":[0.0009634441,0.00020986574,0.0077144178,0.0017322053,0.000714795,0.00041405277,0.0019435665,0.09888117,0.0025101441,0.58870864,0.007579058,0.28862876],"study_design_scores_gemma":[0.00043378113,0.0004067856,0.0038833083,0.0007328911,0.00044365524,0.0003027196,0.00026363178,0.17422539,0.002889575,0.80162907,0.01468269,0.000106512394],"about_ca_topic_score_codex":0.0023406837,"about_ca_topic_score_gemma":0.0030991049,"teacher_disagreement_score":0.09475764,"about_ca_system_score_codex":0.0020626187,"about_ca_system_score_gemma":0.002548144,"threshold_uncertainty_score":0.50113225},"labels":[],"label_agreement":null},{"id":"W7117567139","doi":"10.6000/1929-6029.2025.14.79","title":"An Empirical Comparison among Four Estimation Methods for the Laplace Distribution and Its Potential Application in Medical Research","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Laplace's method; Estimator; Monte Carlo method; Estimation theory; Sample size determination; Laplace transform; Mean squared error; Scale (ratio); Inference","score_opus":0.2159397206242709,"score_gpt":0.64746413172996,"score_spread":0.43152441110568907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117567139","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":[0.0546313,0.0054933685,0.9347594,0.0017215462,0.00014893826,0.00020315708,0.0002453194,0.0005461954,0.0022508132],"genre_scores_gemma":[0.5798349,0.0037963465,0.41308266,0.00070202816,0.00024603633,0.00063319976,0.0006262227,0.00024220305,0.00083645567],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97995114,0.014300548,0.0007838736,0.0017205697,0.0029781233,0.00026582048],"domain_scores_gemma":[0.69879305,0.27332997,0.00704451,0.010329765,0.009626711,0.0008760114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0520105,0.0011469633,0.0012604641,0.0028787558,0.00079837115,0.0025599562,0.0018549345,0.0025251661,0.0019794144],"category_scores_gemma":[0.2790437,0.0005384481,0.0011449313,0.0021553966,0.0027609593,0.003720853,0.002081862,0.002138321,0.00058503484],"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":[0.001415646,0.00024245457,0.11067337,0.0022531208,0.0012693297,0.00053624844,0.0025742087,0.25125045,0.0060981186,0.1267228,0.007372946,0.48959133],"study_design_scores_gemma":[0.00030592337,0.0009111336,0.029355898,0.0011524542,0.00045233226,0.0019853215,0.0011494928,0.8259767,0.010051295,0.11546472,0.012845429,0.0003493418],"about_ca_topic_score_codex":0.002477126,"about_ca_topic_score_gemma":0.0013590248,"teacher_disagreement_score":0.0520105,"about_ca_system_score_codex":0.0011420149,"about_ca_system_score_gemma":0.0021201205,"threshold_uncertainty_score":0.27506107},"labels":[],"label_agreement":null},{"id":"W7117883823","doi":"10.5539/ijsp.v14n4p76","title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 14, No. 4","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Official statistics; Summary statistics","score_opus":0.051057569326611596,"score_gpt":0.3949496631071003,"score_spread":0.3438920937804887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117883823","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013599666,0.0024081871,0.001678374,0.12868915,0.8634638,0.00053765,0.00070421415,0.00054205296,0.0018404736],"genre_scores_gemma":[0.0047763875,0.0061810575,0.004854189,0.16353191,0.7681309,0.0035267456,0.0016094509,0.0016818432,0.045707554],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93022615,0.01387541,0.014803206,0.004631674,0.034250226,0.0022132935],"domain_scores_gemma":[0.13798995,0.039792698,0.011737492,0.006548333,0.7953567,0.008574762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055822287,0.0033818427,0.009930936,0.012384786,0.004821517,0.010833456,0.0063534207,0.018214302,0.08805237],"category_scores_gemma":[0.53359604,0.0019160458,0.006417172,0.0053976714,0.004224318,0.0069264392,0.0042609167,0.013821208,0.060333252],"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":[0.000038671536,0.0000046004307,0.00008688028,0.00040788722,0.000013730498,0.000060814902,0.00003190927,0.000012663237,0.000038279148,0.00012335772,0.99579877,0.003382391],"study_design_scores_gemma":[0.00037263802,0.000076517514,0.0015170008,0.0044436245,0.00018765367,0.0014199975,0.0004510925,0.0007108711,0.00043992724,0.0033496416,0.9867865,0.00024447194],"about_ca_topic_score_codex":0.0037243757,"about_ca_topic_score_gemma":0.005249329,"teacher_disagreement_score":0.08805237,"about_ca_system_score_codex":0.0055036517,"about_ca_system_score_gemma":0.01155786,"threshold_uncertainty_score":0.29521996},"labels":[],"label_agreement":null},{"id":"W7119808922","doi":"10.1093/jssam/smaf043","title":"Correcting Selection Bias in Non-Probability Two-Phase Payment Survey","year":2025,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Statistical Methods and Bayesian 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":"Bank of Canada","funders":"","keywords":"Selection (genetic algorithm); Selection bias; Variance (accounting); Calibration; Payment; Estimation","score_opus":0.47385081697316495,"score_gpt":0.5186313866550564,"score_spread":0.044780569681891425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7119808922","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":[0.022521302,0.00008502519,0.9755367,0.0003346114,0.000044948283,0.00022861412,0.00009272679,0.00014770421,0.001008474],"genre_scores_gemma":[0.5543257,0.0002528156,0.44139758,0.0004304781,0.00011905593,0.0009232375,0.00034705835,0.00006841293,0.0021355662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9550615,0.038388673,0.0009245457,0.0019927467,0.003066132,0.0005662702],"domain_scores_gemma":[0.81292355,0.15450853,0.010309489,0.015442993,0.0063038464,0.0005115237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0488295,0.0006711688,0.0012137995,0.0014825439,0.0006055344,0.0017898916,0.0026223387,0.0016781634,0.0042049545],"category_scores_gemma":[0.25024796,0.00085653417,0.0010448706,0.00245824,0.0015406361,0.003672973,0.002484631,0.0018345637,0.000574153],"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":[0.00037381722,0.00037416624,0.03889026,0.00057840993,0.000297427,0.0003948653,0.000946632,0.11790386,0.00159833,0.5983975,0.004685098,0.23555979],"study_design_scores_gemma":[0.00016299692,0.00030147648,0.010135101,0.0001262406,0.000081412196,0.00021669563,0.00016472494,0.663299,0.002045761,0.3173563,0.0060531003,0.00005716707],"about_ca_topic_score_codex":0.0018391397,"about_ca_topic_score_gemma":0.0014828128,"teacher_disagreement_score":0.0488295,"about_ca_system_score_codex":0.0009561467,"about_ca_system_score_gemma":0.0018457869,"threshold_uncertainty_score":0.25823814},"labels":[],"label_agreement":null},{"id":"W7132903642","doi":"","title":"Robustness properties of some bayesian inferences","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"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":"University of Toronto; Government of Ontario","keywords":"Robustness (evolution); Bounded function; Bayesian probability; Parametric statistics; Bayesian inference; Surprise; Second derivative; Frequentist inference","score_opus":0.07788517285289182,"score_gpt":0.38565127128158105,"score_spread":0.30776609842868924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132903642","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":[0.040153917,0.0011617535,0.9494169,0.0014199663,0.00008495141,0.0001303465,0.00032795523,0.00032716116,0.006977049],"genre_scores_gemma":[0.8192332,0.0018384764,0.17312555,0.0010638894,0.00059992896,0.00038780048,0.0006012635,0.0002537828,0.0028960567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97803307,0.011235838,0.0011881803,0.004125406,0.0047665336,0.0006510113],"domain_scores_gemma":[0.766499,0.20997287,0.008994525,0.008482864,0.0051294197,0.00092119485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036306787,0.0012747585,0.0022183815,0.0034386956,0.0012455994,0.0045614433,0.00329647,0.002836291,0.0036096775],"category_scores_gemma":[0.2129596,0.0007863846,0.002636625,0.001797971,0.005740035,0.007860633,0.0031397927,0.004060515,0.00047218532],"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":[0.00030054178,0.000047816735,0.0052581634,0.00050967437,0.0006294305,0.00063744275,0.00060376653,0.13522719,0.0021054249,0.7974928,0.0014685582,0.0557192],"study_design_scores_gemma":[0.000047305322,0.00009502792,0.0014256364,0.000089162495,0.00011911546,0.00021494526,0.0000822624,0.32144207,0.0017861085,0.6730295,0.0016189392,0.000049921946],"about_ca_topic_score_codex":0.0017533246,"about_ca_topic_score_gemma":0.000777149,"teacher_disagreement_score":0.036306787,"about_ca_system_score_codex":0.0032249205,"about_ca_system_score_gemma":0.0014614265,"threshold_uncertainty_score":0.19201094},"labels":[],"label_agreement":null},{"id":"W7133017751","doi":"","title":"Joint Multistate Models for Correlated Disease Processes: Extending Approaches for Interval-Censoring, Mixed Observation Schemes, and Multiple Longitudinal Outcomes","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Statistical Methods and Bayesian Inference","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":"Public Health Ontario","funders":"National Center for Research Resources; National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Outcome (game theory); Interval (graph theory); Joint (building); Multivariate statistics; Process (computing); Joint probability distribution; Cohort; Discretization; Statistical model","score_opus":0.33786196833202564,"score_gpt":0.42699052102655316,"score_spread":0.08912855269452752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133017751","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":[0.0029467782,0.001449469,0.9937324,0.0006324502,0.00012623068,0.00006242444,0.00014700506,0.00006260413,0.0008406081],"genre_scores_gemma":[0.25257382,0.009850805,0.7203314,0.001091691,0.0013441186,0.001730258,0.0012123516,0.00029727482,0.011568346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908235,0.006063084,0.0004373383,0.0014214215,0.00084468373,0.00040999256],"domain_scores_gemma":[0.9731862,0.020038225,0.0027750588,0.0021349883,0.0013248305,0.0005406875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02175501,0.002291812,0.002746202,0.0023142155,0.00089932646,0.0034186223,0.005008316,0.0027495762,0.0040088976],"category_scores_gemma":[0.030346887,0.0014333595,0.006849488,0.0026142136,0.0027563218,0.005272519,0.004887189,0.0061814436,0.0009865129],"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":[0.00009058876,0.0001290571,0.0055268593,0.00044430746,0.00047421863,0.00030862246,0.0011426767,0.18055528,0.0005556836,0.7687224,0.00201281,0.040037524],"study_design_scores_gemma":[0.00002809437,0.00009491594,0.0008421314,0.00017116735,0.00014661232,0.00011145914,0.00011053769,0.52809197,0.00017490477,0.463532,0.006629482,0.00006669604],"about_ca_topic_score_codex":0.007412972,"about_ca_topic_score_gemma":0.0056773755,"teacher_disagreement_score":0.02175501,"about_ca_system_score_codex":0.0024457518,"about_ca_system_score_gemma":0.0029352712,"threshold_uncertainty_score":0.11505288},"labels":[],"label_agreement":null},{"id":"W7135584068","doi":"","title":"Medical statistics","year":2006,"lang":"en","type":"book-chapter","venue":"Research Portal (King's College London)","topic":"Statistical Methods and Bayesian Inference","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":"Institute of Population and Public Health","funders":"","keywords":"","score_opus":0.10689950747104751,"score_gpt":0.42563177365206556,"score_spread":0.318732266181018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135584068","genre_codex":"other","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":[0.00064459123,0.028610012,0.10174927,0.022810081,0.009011815,0.0004804814,0.026428664,0.007828129,0.8024369],"genre_scores_gemma":[0.008778916,0.02473011,0.0396153,0.009449179,0.007072558,0.0011804613,0.023293585,0.0030152611,0.88286465],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978515,0.0006052811,0.00017846022,0.00028797198,0.0009618851,0.00011495992],"domain_scores_gemma":[0.991389,0.0036405998,0.00044938797,0.0015840505,0.0022954843,0.00064156856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030453973,0.0012156727,0.0017313847,0.0041313735,0.0006081965,0.0029987053,0.0010553956,0.0016674259,0.27245352],"category_scores_gemma":[0.019782038,0.0006989091,0.0005760292,0.004283436,0.0009292942,0.0018209369,0.0015836594,0.0030309774,0.24594432],"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":[0.000020947744,0.000013222719,0.00013340026,0.0002097518,0.000010650653,0.000026082818,0.000035313722,0.00011964378,0.00007176549,0.019724956,0.8111115,0.16852273],"study_design_scores_gemma":[0.000014397203,0.000019614377,0.00042982542,0.0002086595,0.000008820075,0.00017306303,0.000022554877,0.00031589053,0.00010824442,0.03385917,0.96483153,0.000008320297],"about_ca_topic_score_codex":0.0021062072,"about_ca_topic_score_gemma":0.0037412432,"teacher_disagreement_score":0.27245352,"about_ca_system_score_codex":0.0011591988,"about_ca_system_score_gemma":0.0032776122,"threshold_uncertainty_score":0.9114478},"labels":[],"label_agreement":null},{"id":"W7161940521","doi":"10.82308/41474","title":"Covariates and length-biased sampling : is there more than meets the eye ?","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Survival function; Sampling (signal processing); Consistency (knowledge bases); Sampling bias; Poisson sampling; Poisson distribution; Survival analysis; Asymptotic distribution","score_opus":0.06598080595735017,"score_gpt":0.3983615461653219,"score_spread":0.3323807402079717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161940521","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":[0.025119362,0.044760883,0.7748576,0.1473612,0.0020495036,0.00035806975,0.0006475822,0.00031275916,0.0045330767],"genre_scores_gemma":[0.39583114,0.042704295,0.45785066,0.07857841,0.013826056,0.0013647272,0.0009541829,0.00070185814,0.008188629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92278326,0.060063563,0.0033203277,0.005823551,0.00707061,0.00093865616],"domain_scores_gemma":[0.5878654,0.34345055,0.028714865,0.02669904,0.011283527,0.0019865646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10635247,0.0010701994,0.0039722077,0.0017900347,0.0017488461,0.004112077,0.0053787585,0.008504448,0.0044485824],"category_scores_gemma":[0.44501492,0.0013436108,0.0021587557,0.005106968,0.008668226,0.014737805,0.0048221233,0.006639679,0.00095653307],"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":[0.00091074506,0.00012515522,0.06924181,0.0027951563,0.0016312696,0.001714893,0.004023599,0.00939235,0.0006946721,0.6350445,0.018005352,0.25642046],"study_design_scores_gemma":[0.00022466775,0.00016418056,0.010825738,0.0013514523,0.00029405355,0.0012739965,0.0005490745,0.02156269,0.00041242607,0.9351125,0.02806491,0.00016419089],"about_ca_topic_score_codex":0.010602765,"about_ca_topic_score_gemma":0.007711095,"teacher_disagreement_score":0.10635247,"about_ca_system_score_codex":0.0026442874,"about_ca_system_score_gemma":0.004079835,"threshold_uncertainty_score":0.5624523},"labels":[],"label_agreement":null},{"id":"W7162037528","doi":"10.82308/14352","title":"Induced bias on measuring influence by length-biased sampling of failure times","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Censoring (clinical trials); Measure (data warehouse); Cohort; Sampling (signal processing); Cohort study","score_opus":0.17975603764853715,"score_gpt":0.3890822048802137,"score_spread":0.20932616723167657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162037528","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":[0.0074075065,0.00038869175,0.9908584,0.00018886285,0.000039846585,0.00008756351,0.00006629924,0.000101178484,0.0008617741],"genre_scores_gemma":[0.37581837,0.0012890534,0.6184867,0.0005112012,0.0005968853,0.0011314978,0.00046952826,0.00027294867,0.0014237997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9537034,0.034769036,0.0019798444,0.0030173352,0.0057371287,0.00079330144],"domain_scores_gemma":[0.48381203,0.46767786,0.01743676,0.022224756,0.0072758137,0.0015728729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.079789504,0.0015612064,0.0018363206,0.005139601,0.0011257597,0.002251392,0.003924195,0.0033322303,0.0025494157],"category_scores_gemma":[0.39104748,0.00082676596,0.0035271705,0.003688275,0.0065734573,0.0052595,0.005065854,0.0041177925,0.00044731185],"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":[0.00025861428,0.00011002228,0.031206593,0.0006601651,0.00081957923,0.0005211309,0.0016707246,0.1767672,0.0019733114,0.6442668,0.0019844573,0.1397614],"study_design_scores_gemma":[0.00004284992,0.0002564819,0.006946572,0.00024878312,0.00024773076,0.0005829245,0.00013344102,0.5939985,0.0032550616,0.39093155,0.0032361343,0.00011989969],"about_ca_topic_score_codex":0.0022927283,"about_ca_topic_score_gemma":0.0015479339,"teacher_disagreement_score":0.079789504,"about_ca_system_score_codex":0.0027326844,"about_ca_system_score_gemma":0.0017509051,"threshold_uncertainty_score":0.42197227},"labels":[],"label_agreement":null},{"id":"W824864210","doi":"10.1016/j.csda.2015.05.009","title":"Using mixtures of<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si124.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>t</mml:mi></mml:math>densities to make inferences in the presence of missing data with a small number of multiply imputed data sets","year":2015,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McGill University","funders":"Economic and Social Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Inference; Stability (learning theory); Computer science; Set (abstract data type); Missing data; Algorithm; Statistics; Mathematics; Artificial intelligence; Machine learning; Programming language","score_opus":0.11865438480455953,"score_gpt":0.3682728393930382,"score_spread":0.24961845458847864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W824864210","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":[0.0016866248,0.00007606703,0.9956993,0.0002060759,0.00007480919,0.00009108668,0.00042612842,0.0008438349,0.00089601637],"genre_scores_gemma":[0.04397935,0.00022309106,0.9463142,0.0003162926,0.00015172499,0.0008955895,0.0025585212,0.0008523262,0.0047088754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98679346,0.008153224,0.00072530424,0.0022285678,0.0017897622,0.00030958618],"domain_scores_gemma":[0.9365368,0.050627813,0.0018733343,0.008071349,0.0023637535,0.0005269594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026250547,0.0016385573,0.00163992,0.004565975,0.0016626623,0.005036723,0.004234892,0.0022449412,0.025156694],"category_scores_gemma":[0.15577714,0.0023083296,0.0037006633,0.004721391,0.0021414012,0.008228382,0.0046167825,0.0067697093,0.0076013855],"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":[0.00072739,0.000343794,0.008426991,0.0007134662,0.0012101912,0.00039493348,0.0015031765,0.07959066,0.0028400763,0.54326546,0.03997106,0.32101285],"study_design_scores_gemma":[0.00014125694,0.000101375816,0.002472541,0.00028643827,0.00024042347,0.00039503272,0.00019590331,0.48085526,0.003983572,0.48734912,0.023798987,0.00018004842],"about_ca_topic_score_codex":0.008270491,"about_ca_topic_score_gemma":0.010573321,"teacher_disagreement_score":0.026250547,"about_ca_system_score_codex":0.0017579718,"about_ca_system_score_gemma":0.0025847882,"threshold_uncertainty_score":0.1388278},"labels":[],"label_agreement":null},{"id":"W87059113","doi":"10.1007/978-94-007-4537-7_5","title":"Etiologic Studies’ Essentials","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Université de Montréal; McGill University","funders":"","keywords":"Population; Base (topology); Series (stratigraphy); Moment (physics); Mathematics; Function (biology); Index (typography); Statistics; Event (particle physics); Econometrics; Sample (material); Computer science; Mathematical analysis; Demography; Physics","score_opus":0.2976644618109828,"score_gpt":0.45509170325932313,"score_spread":0.1574272414483403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W87059113","genre_codex":"other","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":[0.0018674573,0.094233185,0.26751098,0.13190062,0.010420627,0.00030304582,0.00083759945,0.00031268015,0.4926138],"genre_scores_gemma":[0.22106054,0.1393486,0.19436754,0.0668223,0.053092808,0.0016682874,0.001231348,0.0009943291,0.3214142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9940485,0.0040258323,0.00027897974,0.00036580657,0.001116823,0.00016413626],"domain_scores_gemma":[0.9777801,0.018897668,0.00036112193,0.0010213343,0.001687079,0.00025277698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01221584,0.0009302199,0.001200918,0.0036659928,0.0014067675,0.0035333503,0.0016406446,0.002372378,0.014381775],"category_scores_gemma":[0.02170245,0.00072848453,0.00050861447,0.002165798,0.011765573,0.0056840065,0.0020887225,0.00658337,0.0035500305],"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":[0.000002627688,0.000008316283,0.00007722206,0.00007406661,0.000005631674,0.000028602126,0.00026872993,0.00011779114,0.000017193443,0.9632373,0.022977613,0.013185053],"study_design_scores_gemma":[0.0000039344445,0.0000027804538,0.00010868309,0.0001303231,0.0000055939095,0.000094115334,0.000091658345,0.00018190702,0.000027507076,0.8885697,0.110779546,0.000004180453],"about_ca_topic_score_codex":0.0019146694,"about_ca_topic_score_gemma":0.0023828729,"teacher_disagreement_score":0.014381775,"about_ca_system_score_codex":0.0017831028,"about_ca_system_score_gemma":0.003587744,"threshold_uncertainty_score":0.06460428},"labels":[],"label_agreement":null},{"id":"W94208768","doi":"10.1002/jae.2508","title":"Wild Bootstrap Inference for Wildly Different Cluster Sizes","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":373,"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; Queen's University","funders":"","keywords":"Estimator; Cluster (spacecraft); Econometrics; Statistics; Variance (accounting); Monte Carlo method; Inference; Mathematics; Computer science; Economics; Artificial intelligence","score_opus":0.10018479946533848,"score_gpt":0.3560613191813742,"score_spread":0.2558765197160357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94208768","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":[0.033611674,0.00049404,0.96175355,0.00067517447,0.00017065599,0.00014762285,0.0003690003,0.0004710085,0.0023072474],"genre_scores_gemma":[0.6654067,0.000325717,0.3282446,0.00076930306,0.0002288425,0.0005620049,0.0015590093,0.00047633576,0.0024274376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9733988,0.019173939,0.0008741518,0.003924903,0.002073365,0.0005549017],"domain_scores_gemma":[0.7734295,0.18011032,0.0058784494,0.032260787,0.0071481084,0.0011727738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04326613,0.0007553246,0.0020196799,0.0023979328,0.0017877733,0.0028794897,0.0030358126,0.0023269332,0.009939196],"category_scores_gemma":[0.25509015,0.00073493033,0.0019803424,0.0021508837,0.005900602,0.00451751,0.0025794304,0.0049815536,0.0011760175],"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":[0.0007568585,0.00022410833,0.01963454,0.00042632836,0.00086285896,0.0004646053,0.0009273879,0.11328996,0.0012793778,0.6966835,0.016689727,0.1487608],"study_design_scores_gemma":[0.00011529183,0.0001148199,0.0039642286,0.00022408018,0.00010522287,0.00018182512,0.00022950087,0.298684,0.001403592,0.6892708,0.0056467126,0.000059961796],"about_ca_topic_score_codex":0.0040606293,"about_ca_topic_score_gemma":0.0031763283,"teacher_disagreement_score":0.04326613,"about_ca_system_score_codex":0.0014161714,"about_ca_system_score_gemma":0.001509875,"threshold_uncertainty_score":0.22881591},"labels":[],"label_agreement":null},{"id":"W951271046","doi":"10.1017/cbo9780511536687.019","title":"Probability and statistics","year":2008,"lang":"en","type":"other","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"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 Manitoba","funders":"","keywords":"Statistics; Probability and statistics; Mathematics","score_opus":0.10006653399109264,"score_gpt":0.36827550901588574,"score_spread":0.26820897502479313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W951271046","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":[0.0011850953,0.093378395,0.38057768,0.020970894,0.0065960614,0.00029151828,0.015743721,0.0038912997,0.47736537],"genre_scores_gemma":[0.0885939,0.27521935,0.17447568,0.010949862,0.025100723,0.0022138187,0.022564856,0.00507995,0.3958019],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99530655,0.0018833393,0.0003064628,0.0006083295,0.0017596472,0.00013557366],"domain_scores_gemma":[0.9883889,0.0075181397,0.0005478709,0.0018719822,0.0013530191,0.00032007246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042952294,0.0017704809,0.0024485793,0.0048567215,0.0009055876,0.0046251607,0.0015631722,0.002584593,0.13403642],"category_scores_gemma":[0.024710877,0.00069141237,0.00091950287,0.007307571,0.0026013597,0.004480108,0.0018409673,0.004947993,0.09534844],"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":[0.000021610318,0.000032564516,0.00038889953,0.0013686882,0.00005723754,0.00012962779,0.00015646865,0.0011791883,0.00027332973,0.3534336,0.5037169,0.13924184],"study_design_scores_gemma":[0.00001299938,0.000029520825,0.0008493282,0.0005403938,0.000019720133,0.00033787455,0.000050471845,0.0015416006,0.00023338501,0.519498,0.47685522,0.000031483665],"about_ca_topic_score_codex":0.0018120384,"about_ca_topic_score_gemma":0.001556613,"teacher_disagreement_score":0.13403642,"about_ca_system_score_codex":0.0019763175,"about_ca_system_score_gemma":0.001847418,"threshold_uncertainty_score":0.4483965},"labels":[],"label_agreement":null},{"id":"W96737634","doi":"","title":"SOME METHODS ON LONGITUDINAL DATA ANALYSIS","year":2006,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian 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":"","keywords":"Covariate; Missing data; Inference; Computer science; Standard error; Statistics; Econometrics; Longitudinal data; Data mining; Causal inference; Statistical inference; Data science; Machine learning; Artificial intelligence; Mathematics","score_opus":0.23850648825809947,"score_gpt":0.5043112235226995,"score_spread":0.2658047352646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W96737634","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":[0.00050670875,0.02159662,0.95880765,0.005061234,0.0012696637,0.00013642329,0.0010071271,0.00027596715,0.011338616],"genre_scores_gemma":[0.03394716,0.060033493,0.8620157,0.005650692,0.009322264,0.0022788942,0.002124854,0.0005786178,0.024048226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9889713,0.006671097,0.0007172416,0.0011220215,0.0022603872,0.00025787207],"domain_scores_gemma":[0.9795529,0.015404681,0.00087675964,0.0019273112,0.0019219349,0.0003163863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017589478,0.0019517916,0.0018403648,0.004516974,0.0015341596,0.0026560957,0.0029245857,0.002725374,0.011824744],"category_scores_gemma":[0.035411112,0.0009606407,0.0031766195,0.0062946514,0.003026898,0.00370012,0.0026152297,0.0065485397,0.0052678064],"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":[0.000031571795,0.00003737826,0.00083423493,0.0006416469,0.00015325655,0.00018045434,0.00030977296,0.0041844426,0.00024407159,0.84918493,0.029130341,0.11506787],"study_design_scores_gemma":[0.000025147294,0.000030186942,0.00045116985,0.00026013705,0.00005325962,0.00030392018,0.0000445312,0.011510603,0.00019827946,0.8697162,0.11736984,0.000036720812],"about_ca_topic_score_codex":0.0033239424,"about_ca_topic_score_gemma":0.002199979,"teacher_disagreement_score":0.017589478,"about_ca_system_score_codex":0.001999969,"about_ca_system_score_gemma":0.002401034,"threshold_uncertainty_score":0.09302318},"labels":[],"label_agreement":null},{"id":"W968624666","doi":"10.1007/978-1-4614-6871-4_8","title":"Response-Dependent Sampling with Clustered and Longitudinal Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Statistical Methods and Bayesian Inference","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 Waterloo","funders":"","keywords":"Sampling (signal processing); Context (archaeology); Inference; Sampling design; Statistics; Parametric statistics; Computer science; Clinical study design; Sample size determination; Longitudinal data; Econometrics; Data mining; Medicine; Artificial intelligence; Mathematics; Clinical trial; Environmental health; Population; Geography; Pathology","score_opus":0.15140007317682053,"score_gpt":0.38632894439278825,"score_spread":0.23492887121596773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W968624666","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":[0.0015603667,0.0005111517,0.9962747,0.00028311365,0.000120106386,0.00009008293,0.00017177084,0.00012243359,0.0008663303],"genre_scores_gemma":[0.070509955,0.0018765389,0.9109602,0.00069304864,0.00065836345,0.0015961239,0.0018854903,0.00027734705,0.011542849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97045004,0.022551788,0.0008398387,0.0031651913,0.0026054347,0.00038774853],"domain_scores_gemma":[0.8790085,0.10470969,0.0027210757,0.01058265,0.0023418022,0.00063634233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045296896,0.0018546229,0.003060606,0.001713209,0.0009876502,0.00237854,0.0080947215,0.0033913222,0.00975294],"category_scores_gemma":[0.1321739,0.0031567854,0.0029993255,0.0033444436,0.0034013356,0.003986893,0.0036036817,0.0054026553,0.002176019],"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":[0.00021364613,0.00015037548,0.0027197222,0.0006031551,0.00045841665,0.00039447023,0.0007103319,0.051720474,0.0004141834,0.8306837,0.010571659,0.10135976],"study_design_scores_gemma":[0.00007860156,0.0000696498,0.0006579813,0.00010463335,0.000085331514,0.00024293436,0.00006441978,0.24681431,0.00029981317,0.745853,0.0056867907,0.000042555897],"about_ca_topic_score_codex":0.002549224,"about_ca_topic_score_gemma":0.0025607757,"teacher_disagreement_score":0.045296896,"about_ca_system_score_codex":0.0016857899,"about_ca_system_score_gemma":0.0016686155,"threshold_uncertainty_score":0.23955572},"labels":[],"label_agreement":null}]}