{"meta":{"query_hash":"9d0c7b8de570","filters":{"venue":"Statistical Modelling"},"cohort_total":12,"direct_labels_cover":0,"predictions_cover":12,"exported":12,"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/9d0c7b8de570","api":"https://metacan.xera.ac/api/v1/cohort?venue=Statistical+Modelling"},"results":[{"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":"W2041919982","doi":"10.1177/1471082x14566913","title":"The functional linear array model","year":2015,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":54,"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":"Siemens; Deutsche Forschungsgemeinschaft","keywords":"Covariate; Functional data analysis; Scalar (mathematics); Additive model; Regression analysis; Mathematics; Linear model; Generalized linear model; Linear regression; Proper linear model; Computer science; Applied mathematics; Statistics; Polynomial regression","score_opus":0.3670815074401213,"score_gpt":0.403580841897837,"score_spread":0.03649933445771569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041919982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031870855,0.0009011442,0.97083956,0.0021547775,0.0002654025,0.00012866047,0.0038452172,0.0008371234,0.017841047],"genre_scores_gemma":[0.37256983,0.0040030857,0.4869728,0.0035835346,0.0011636037,0.0026647768,0.012717914,0.0017293743,0.114595026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99506277,0.0023153524,0.00021893413,0.0011166125,0.00082656083,0.00045978022],"domain_scores_gemma":[0.9921904,0.00464458,0.00060283893,0.00080356334,0.0015520378,0.00020654759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059555247,0.0019076095,0.0018450955,0.0015861478,0.00083462097,0.0034680776,0.0044155098,0.0030382858,0.040429767],"category_scores_gemma":[0.01763093,0.00093495724,0.0021523742,0.002574185,0.0017123545,0.0036978284,0.0026852181,0.0033894726,0.0144483745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001129916,0.0000894771,0.003882437,0.00031161675,0.00019451814,0.0002982243,0.00025309678,0.26318192,0.000568219,0.6119613,0.043409336,0.07573686],"study_design_scores_gemma":[0.000042782605,0.00008480123,0.0008116866,0.000112748436,0.00007293276,0.00029907163,0.0000850167,0.5681771,0.00025293598,0.38078362,0.049214695,0.00006253441],"about_ca_topic_score_codex":0.013673366,"about_ca_topic_score_gemma":0.009089614,"teacher_disagreement_score":0.040429767,"about_ca_system_score_codex":0.0019991181,"about_ca_system_score_gemma":0.0032339904,"threshold_uncertainty_score":0.13525105},"labels":[],"label_agreement":null},{"id":"W2042186389","doi":"10.1191/1471082x06st121oa","title":"Modelling repeated ordinal reports from multiple informants","year":2006,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Survey Sampling and Estimation Techniques","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":true,"ca_institutions":"Université de Montréal","funders":"Economic and Social Research Council","keywords":"Multivariate statistics; Psychology; Multilevel model; Random effects model; Aggression; Continuation; Ordinal regression; Multivariate analysis; Mathematics; Longitudinal data; Demography; Repeated measures design; Generalized linear model; Statistics; Developmental psychology; Econometrics; Medicine; Computer science; Meta-analysis","score_opus":0.11255771401037017,"score_gpt":0.32497840660770777,"score_spread":0.2124206925973376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042186389","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46360436,0.0011818124,0.5263906,0.0013181391,0.00030958318,0.00090731075,0.0038577756,0.00038832464,0.0020421017],"genre_scores_gemma":[0.8449485,0.00053703703,0.1437404,0.0002487257,0.00015881173,0.0024475076,0.0039436235,0.000078861485,0.0038964394],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9001277,0.08210018,0.004086534,0.007206066,0.0051233754,0.0013560461],"domain_scores_gemma":[0.6328714,0.28452986,0.040236793,0.030781578,0.010594139,0.0009862264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08029693,0.0012442399,0.0018172485,0.0022128779,0.0008833892,0.00348285,0.004254949,0.0021514476,0.0038483997],"category_scores_gemma":[0.2524312,0.001463103,0.002081557,0.004853885,0.0020532326,0.00244524,0.003596438,0.0025630032,0.0009306592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015947103,0.00053824706,0.7094571,0.0012213851,0.004402943,0.002076516,0.018202452,0.07070814,0.0011530893,0.055741496,0.0036650868,0.13123892],"study_design_scores_gemma":[0.0003436451,0.0015873631,0.22476861,0.00089434825,0.0017724992,0.0016168812,0.0063768784,0.6494456,0.003204712,0.09323103,0.01634271,0.0004157303],"about_ca_topic_score_codex":0.014290509,"about_ca_topic_score_gemma":0.008950107,"teacher_disagreement_score":0.08029693,"about_ca_system_score_codex":0.0017249032,"about_ca_system_score_gemma":0.0012304737,"threshold_uncertainty_score":0.42465585},"labels":[],"label_agreement":null},{"id":"W2144666171","doi":"10.1177/1471082x1001100503","title":"Discrete-time survival trees and forests with time-varying covariates","year":2011,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","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":"HEC Montréal","funders":"","keywords":"Covariate; Statistics; Discrete time and continuous time; Econometrics; Survival analysis; Bankruptcy; Bankruptcy prediction; Tree (set theory); Mathematics; Accelerated failure time model; Computer science; Economics; Finance","score_opus":0.024052791189226055,"score_gpt":0.21120000310632303,"score_spread":0.187147211917097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144666171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051830104,0.00031458604,0.99371696,0.0000958985,0.000050401395,0.000020742827,0.0001970496,0.00021130561,0.00021004488],"genre_scores_gemma":[0.20437959,0.0014004848,0.7882012,0.00019142467,0.0004621291,0.00047691396,0.0023794645,0.00024112305,0.002267667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618345,0.0026303001,0.00016397209,0.0005972444,0.0002743053,0.00015061197],"domain_scores_gemma":[0.9789811,0.0173553,0.0013114206,0.0011367647,0.00086684444,0.00034854942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009512556,0.0009512287,0.0013441049,0.0019780104,0.0009121117,0.0014500548,0.0022976801,0.001345009,0.0024657652],"category_scores_gemma":[0.018438099,0.00082711666,0.0019590792,0.0026202507,0.001136341,0.0025360715,0.0011767985,0.0023569916,0.000911026],"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":[0.00022538187,0.00013441645,0.013323928,0.00040077209,0.00040428014,0.0002319544,0.00044998992,0.6417922,0.0009321634,0.1512733,0.004465807,0.1863659],"study_design_scores_gemma":[0.000034045872,0.000045576795,0.0011009775,0.00005157848,0.000039598784,0.00011206069,0.000034496636,0.85939765,0.00019195648,0.13628353,0.002679489,0.000028970844],"about_ca_topic_score_codex":0.0038608229,"about_ca_topic_score_gemma":0.0058562453,"teacher_disagreement_score":0.009512556,"about_ca_system_score_codex":0.0007235842,"about_ca_system_score_gemma":0.0010029457,"threshold_uncertainty_score":0.05030781},"labels":[],"label_agreement":null},{"id":"W2166211535","doi":"10.1177/1471082x0700700406","title":"Worm plot to diagnose fit in quantile regression","year":2007,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Statistical Methods and 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":"AGE-WELL","keywords":"Quantile regression; Plot (graphics); Overfitting; Quantile; Covariate; Statistics; Econometrics; Regression; Regression analysis; Mathematics; Parametric statistics; Computer science; Artificial intelligence","score_opus":0.2188968809883266,"score_gpt":0.4481788269679603,"score_spread":0.2292819459796337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166211535","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008863706,0.00025980547,0.9783563,0.0004258853,0.00016063233,0.000121720535,0.0011912197,0.0082955,0.0023251951],"genre_scores_gemma":[0.24399622,0.0003216263,0.74388415,0.00044517458,0.00021885912,0.0010162352,0.0024922877,0.0042864927,0.0033389716],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903234,0.006399382,0.0006291153,0.0009231602,0.0014545145,0.00027033951],"domain_scores_gemma":[0.8960483,0.0863505,0.005393434,0.006251846,0.0051926593,0.0007631954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0150362775,0.0015793127,0.0014115329,0.007112372,0.00092882104,0.0027383284,0.0019799976,0.0022292603,0.023668218],"category_scores_gemma":[0.14211252,0.0007720109,0.0013031295,0.0053556906,0.0020293612,0.004981546,0.0039973687,0.004075344,0.0034862044],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009385796,0.00029380305,0.04505597,0.0011168808,0.0004969705,0.002057398,0.0027942546,0.096514426,0.0043408303,0.36525068,0.10318961,0.37795055],"study_design_scores_gemma":[0.0001368951,0.00023543662,0.00983384,0.00034073464,0.00008397569,0.0011473658,0.000748413,0.66284645,0.0049494663,0.2670094,0.0525086,0.00015938508],"about_ca_topic_score_codex":0.0026323975,"about_ca_topic_score_gemma":0.0017599259,"teacher_disagreement_score":0.023668218,"about_ca_system_score_codex":0.0008543419,"about_ca_system_score_gemma":0.0010865787,"threshold_uncertainty_score":0.079520345},"labels":[],"label_agreement":null},{"id":"W2167286520","doi":"10.1177/1471082x0700700204","title":"A measure of partial association for generalized estimating equations","year":2007,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"St. Stephen's University; St. Michael's Hospital","funders":"National Institutes of Health","keywords":"Mathematics; Generalized estimating equation; Statistics; Measure (data warehouse); Estimating equations; Linear regression; Covariate; Regression analysis; Partial correlation; Ordinary least squares; Outcome (game theory); Applied mathematics; Correlation; Estimator","score_opus":0.3184001615592276,"score_gpt":0.5005314592625004,"score_spread":0.1821312977032728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167286520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011112627,0.0006634849,0.9967069,0.00037721326,0.00009327578,0.00016268005,0.00020815808,0.00019695215,0.000480011],"genre_scores_gemma":[0.06325335,0.0015530463,0.9284864,0.0006375164,0.00040781262,0.0032837458,0.0010051861,0.000371173,0.0010017843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.82426894,0.15297318,0.0061197034,0.008090795,0.007661029,0.0008863431],"domain_scores_gemma":[0.6515168,0.30699393,0.012142925,0.020683106,0.008000902,0.00066228455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11846287,0.0032308318,0.0055602035,0.00583256,0.0010695257,0.0040555606,0.0044749603,0.004292076,0.004868487],"category_scores_gemma":[0.31729442,0.0016837728,0.005518214,0.009650313,0.0043751583,0.0056926347,0.0048946985,0.007673049,0.0016103021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021259868,0.00014310176,0.01156561,0.0018221774,0.005324231,0.00041544472,0.00095408683,0.10208333,0.0008955571,0.66513366,0.008979942,0.20247021],"study_design_scores_gemma":[0.00015452302,0.0004469837,0.0042320034,0.00044830283,0.00065688725,0.0004147122,0.0002376582,0.2682235,0.00084937183,0.69519126,0.028898623,0.00024619026],"about_ca_topic_score_codex":0.0029737628,"about_ca_topic_score_gemma":0.0021227773,"teacher_disagreement_score":0.11846287,"about_ca_system_score_codex":0.001890378,"about_ca_system_score_gemma":0.004227011,"threshold_uncertainty_score":0.62649906},"labels":[],"label_agreement":null},{"id":"W2286729808","doi":"10.1177/1471082x1001100304","title":"Variable selection in additive models by non-negative garrote","year":2011,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Marine and fisheries research","field":"Environmental Science","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":"Dalhousie University","funders":"","keywords":"Selection (genetic algorithm); Feature selection; Variable (mathematics); Computer science; Model selection; Mathematical optimization; Parametric statistics; Series (stratigraphy); Econometrics; Machine learning; Mathematics; Statistics","score_opus":0.03270197921753465,"score_gpt":0.24048484824284386,"score_spread":0.2077828690253092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286729808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032387606,0.00019291067,0.99598026,0.00006989987,0.000033964225,0.000023483786,0.0000255672,0.00018943401,0.00024572652],"genre_scores_gemma":[0.11272605,0.0005782152,0.88383955,0.00014974485,0.00019167861,0.00035994538,0.00029863793,0.00037256762,0.0014836721],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96895313,0.027170436,0.00065598096,0.0015561223,0.0013001107,0.00036418476],"domain_scores_gemma":[0.9506874,0.040105738,0.0015844455,0.005632923,0.0016639753,0.0003255543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028361656,0.0026326342,0.0030817841,0.005100176,0.0014241142,0.0021020893,0.0029740087,0.0021874749,0.004309371],"category_scores_gemma":[0.056533445,0.0013392505,0.0034088274,0.0048987186,0.0035622383,0.0035845137,0.0029069202,0.003127104,0.0022419451],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000593181,0.00036446567,0.0065473947,0.0005966794,0.0013408802,0.0011736644,0.0007624015,0.2923143,0.006255005,0.13671258,0.0043852325,0.5489543],"study_design_scores_gemma":[0.00010251921,0.00031759625,0.001603743,0.00012475261,0.00013411703,0.0004157996,0.00008122499,0.82728356,0.0027403021,0.16111454,0.0059592393,0.00012262596],"about_ca_topic_score_codex":0.0009962968,"about_ca_topic_score_gemma":0.002189197,"teacher_disagreement_score":0.028361656,"about_ca_system_score_codex":0.0003463946,"about_ca_system_score_gemma":0.0011902835,"threshold_uncertainty_score":0.14999259},"labels":[],"label_agreement":null},{"id":"W2724598028","doi":"10.1177/1471082x17705993","title":"Estimation of partly linear additive hazards model with left-truncated and right-censored data","year":2017,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Statistical Methods and 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 Calgary","funders":"","keywords":"Mathematics; Covariate; Estimator; Spline (mechanical); Linear model; Semiparametric model; Applied mathematics; Parametric statistics; Nonlinear system; Additive model; Semiparametric regression; Inference; Statistics; Econometrics; Computer science; Artificial intelligence","score_opus":0.14440374555615648,"score_gpt":0.40027549152921776,"score_spread":0.2558717459730613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724598028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022561556,0.00018628742,0.97648287,0.00021692827,0.00001881953,0.000039410486,0.00014378739,0.000107825996,0.00024244474],"genre_scores_gemma":[0.6883129,0.0007526302,0.3049223,0.00028614758,0.0001304031,0.00043163588,0.0012366788,0.00010125273,0.0038261248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99625707,0.00242443,0.00014230616,0.0005383202,0.00044285876,0.00019504181],"domain_scores_gemma":[0.9855134,0.011326574,0.0010352471,0.0012474859,0.0006846169,0.00019269665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008769499,0.0006768636,0.0016149362,0.0009776447,0.00034704973,0.0011923789,0.0026020044,0.0012468327,0.0018283318],"category_scores_gemma":[0.021592302,0.00058982597,0.0019066418,0.0011574737,0.0010268638,0.0014735934,0.0018363694,0.0021395609,0.00033119306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003912778,0.000103628576,0.019551715,0.0004344847,0.00041738426,0.0008094872,0.00057707506,0.7482647,0.002349844,0.11902554,0.0018145421,0.10626039],"study_design_scores_gemma":[0.000030446885,0.00006009833,0.0017562646,0.00002913561,0.000044416884,0.00012724735,0.000052429194,0.9487856,0.00049262936,0.04771769,0.0008763123,0.000027730297],"about_ca_topic_score_codex":0.0032204816,"about_ca_topic_score_gemma":0.0024380046,"teacher_disagreement_score":0.008769499,"about_ca_system_score_codex":0.0005835159,"about_ca_system_score_gemma":0.0015372033,"threshold_uncertainty_score":0.046378076},"labels":[],"label_agreement":null},{"id":"W2782124212","doi":"10.1177/1471082x17746538","title":"Frailty modelling for multitype recurrent events in clinical trials","year":2018,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","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":"Canadian VIGOUR Centre; University of Alberta","funders":"","keywords":"Event (particle physics); Computer science; Feature (linguistics); Event data; Data science; Medicine; Machine learning","score_opus":0.8667122139808082,"score_gpt":0.6079313804695468,"score_spread":0.25878083351126147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782124212","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012291472,0.013990734,0.9611832,0.0063167056,0.00065945106,0.00091233774,0.0013759024,0.00044737008,0.002822948],"genre_scores_gemma":[0.54506093,0.020404328,0.40263832,0.005088337,0.0023273544,0.008154351,0.0038178328,0.0003905477,0.012117987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9097562,0.0765209,0.004453961,0.004571636,0.0035148724,0.0011823294],"domain_scores_gemma":[0.719412,0.25142872,0.015082713,0.009246096,0.003596763,0.0012336313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.122997865,0.0017742341,0.0045990273,0.0031519937,0.00071236794,0.004363217,0.005089085,0.005189223,0.007254847],"category_scores_gemma":[0.29071864,0.0012213716,0.0062160497,0.004014775,0.0024374607,0.005002256,0.0034448712,0.007256774,0.0015519017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013123204,0.00022356045,0.033458862,0.0054938933,0.004151561,0.001191075,0.0018120488,0.2764758,0.0006580135,0.45697546,0.017297212,0.20095028],"study_design_scores_gemma":[0.0003276201,0.0004818729,0.0050936206,0.0015606297,0.0009580372,0.00041010536,0.00012978788,0.41965824,0.0003167601,0.55816716,0.012775399,0.00012083067],"about_ca_topic_score_codex":0.0039209886,"about_ca_topic_score_gemma":0.005034998,"teacher_disagreement_score":0.122997865,"about_ca_system_score_codex":0.002521501,"about_ca_system_score_gemma":0.0035409771,"threshold_uncertainty_score":0.65048265},"labels":[],"label_agreement":null},{"id":"W2905530915","doi":"10.1177/1471082x18810114","title":"Component-based regularization of a multivariate GLM with a thematic partitioning of the explanatory variables","year":2018,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Remote Sensing in Agriculture","field":"Environmental Science","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":"European Commission; Biodiversa+; Canadian Institute for Advanced Research","keywords":"Mathematics; Generalized linear model; Linear model; Regularization (linguistics); Linear regression; Design matrix; Contrast (vision); Statistics; Covariate; Applied mathematics; Computer science; Artificial intelligence","score_opus":0.01355865584465346,"score_gpt":0.21337092113268516,"score_spread":0.1998122652880317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905530915","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":[0.0058175605,0.00008670578,0.9932729,0.00013185722,0.000024518258,0.000028935752,0.00006975855,0.00029995697,0.00026787203],"genre_scores_gemma":[0.13539156,0.00028182246,0.8583592,0.00031645774,0.000149294,0.00045118926,0.0012696091,0.0005653248,0.0032154932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99783677,0.0013423152,0.00007849831,0.00039815015,0.00023962458,0.00010476023],"domain_scores_gemma":[0.9962805,0.0024072959,0.00026923334,0.0005056504,0.00046378438,0.000073542134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046684006,0.0015842484,0.0013359141,0.001123802,0.00049016863,0.0012733187,0.0025324817,0.0015585226,0.0027476],"category_scores_gemma":[0.011864184,0.000685156,0.0022831724,0.001583437,0.0009597345,0.0014019067,0.0017982903,0.0026806032,0.0009876635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022275108,0.00015646272,0.0031890855,0.00032016117,0.00048655953,0.00017441533,0.00031240555,0.7302102,0.0073276856,0.059894826,0.00672264,0.19098274],"study_design_scores_gemma":[0.00000867027,0.000019095934,0.00030182855,0.0000097656475,0.000017877932,0.000015642769,0.000011494986,0.9868752,0.00047057023,0.011159811,0.001097205,0.000012765699],"about_ca_topic_score_codex":0.0057288855,"about_ca_topic_score_gemma":0.00731219,"teacher_disagreement_score":0.0057288855,"about_ca_system_score_codex":0.0009304163,"about_ca_system_score_gemma":0.0018809976,"threshold_uncertainty_score":0.024689138},"labels":[],"label_agreement":null},{"id":"W3165324759","doi":"10.1177/1471082x211008011","title":"Bayesian adjustment for measurement error in an offset variable in a Poisson regression model","year":2021,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Traffic and Road Safety","field":"Engineering","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 Saskatchewan","funders":"","keywords":"Statistics; Observational error; Poisson regression; Markov chain Monte Carlo; Crash; Bayesian probability; Econometrics; Errors-in-variables models; Regression analysis; Poisson distribution; Population; Random effects model; Computer science; Mathematics; Demography; Medicine","score_opus":0.05915737857479945,"score_gpt":0.2796012220572382,"score_spread":0.22044384348243876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165324759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030212548,0.00029245453,0.96690303,0.0006039225,0.0000934337,0.0002268819,0.00023984378,0.00035775427,0.001070076],"genre_scores_gemma":[0.61660695,0.0006738775,0.37220082,0.00044604656,0.0002703988,0.0013961707,0.0011178311,0.00025460043,0.007033264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9528164,0.034667127,0.0019143,0.005483113,0.0035754198,0.0015435755],"domain_scores_gemma":[0.8838748,0.09580595,0.008205731,0.006437013,0.005024748,0.0006518603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056038376,0.0015117811,0.0033976827,0.002006412,0.0013137645,0.0028153278,0.00662928,0.0031255332,0.0032920325],"category_scores_gemma":[0.1639764,0.0016813682,0.0027193825,0.0029281767,0.0023611046,0.0036263962,0.003255063,0.0046382165,0.0007498683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045736475,0.00030173236,0.044249654,0.00043834114,0.0012203542,0.0006388232,0.0012416532,0.5943867,0.0010393191,0.25907555,0.003058435,0.09389213],"study_design_scores_gemma":[0.00010364363,0.00021499886,0.0073654917,0.00011515041,0.00028891666,0.000108374436,0.0001178764,0.92028415,0.00050188735,0.066599324,0.00419831,0.00010194944],"about_ca_topic_score_codex":0.028912934,"about_ca_topic_score_gemma":0.015848715,"teacher_disagreement_score":0.056038376,"about_ca_system_score_codex":0.0027122754,"about_ca_system_score_gemma":0.004110892,"threshold_uncertainty_score":0.29636282},"labels":[],"label_agreement":null},{"id":"W3217739957","doi":"10.1177/1471082x211059233","title":"Bayesian analysis of two-part nonlinear latent variable model: Semiparametric method","year":2021,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","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":"Covariate; Mathematics; Collinearity; Econometrics; Statistics; Semiparametric regression; Latent variable; Bayesian probability; Semiparametric model; Population; Parametric statistics","score_opus":0.03581909957611684,"score_gpt":0.3270863331790412,"score_spread":0.29126723360292434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217739957","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021422955,0.00015050173,0.99703145,0.00010987843,0.000010522431,0.000022793243,0.000060133236,0.00007643585,0.00039595578],"genre_scores_gemma":[0.26598376,0.0014792107,0.72259235,0.00046252893,0.00022714454,0.0008196707,0.0012298367,0.00036856928,0.006836996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99649745,0.0022351346,0.000120845085,0.00048400427,0.00048024525,0.00018234608],"domain_scores_gemma":[0.9913961,0.0068792733,0.00046017283,0.00048652495,0.00059771945,0.00018013634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006343127,0.0009903762,0.0020969133,0.0017101746,0.00088927976,0.0018665201,0.0031990008,0.001720051,0.0056421948],"category_scores_gemma":[0.01858649,0.0010809287,0.0020282152,0.0018066138,0.0015484684,0.002250323,0.002996411,0.0032067099,0.0010056156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016042848,0.0001360053,0.0046976227,0.00045066245,0.0003631224,0.00039910377,0.00056637696,0.48326743,0.0020926166,0.37887695,0.005055295,0.12393446],"study_design_scores_gemma":[0.000016147089,0.000014847504,0.00039541646,0.000037372316,0.00003471104,0.000058997895,0.000030099602,0.9088368,0.00026009826,0.088692166,0.0015975874,0.000025790454],"about_ca_topic_score_codex":0.0065462296,"about_ca_topic_score_gemma":0.00637098,"teacher_disagreement_score":0.0065462296,"about_ca_system_score_codex":0.0013778153,"about_ca_system_score_gemma":0.0027274406,"threshold_uncertainty_score":0.03354603},"labels":[],"label_agreement":null}]}