{"id":"W1881569770","doi":"10.1002/9781118445112.stat05243","title":"Kappa and Its Dependence on Marginal Rates","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kappa; Cohen's kappa; Statistic; Statistics; Measure (data warehouse); Mathematics; Econometrics; Computer science; Data mining; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002285355,0.0007128985,0.001000612,0.0005485315,0.0001852634,0.0003799318,0.001310515,0.0003935274,0.009830364],"category_scores_gemma":[0.003746122,0.0005163636,0.00007073601,0.0003936506,0.0002900217,0.00009445089,0.0002854445,0.0007835634,0.002275656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008925405,"about_ca_system_score_gemma":0.0002403428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001180352,"about_ca_topic_score_gemma":0.001236471,"domain_scores_codex":[0.9924707,0.0005656208,0.001164314,0.001546849,0.003596165,0.0006563935],"domain_scores_gemma":[0.9951077,0.001774304,0.0009361625,0.001192255,0.0005924616,0.0003970887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001078388,0.0003925737,0.0006701011,0.0001675936,0.00007513898,0.00004489989,0.00005681715,0.00004343169,0.00007411763,0.03829796,0.8876674,0.07240217],"study_design_scores_gemma":[0.0007756625,0.0007877319,0.001588316,0.000936644,0.00007608961,0.000007344966,0.0001297936,0.002740203,0.00003855474,0.04744902,0.9445784,0.0008922537],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004570795,0.01286955,0.2473367,0.00371092,0.006400554,0.005937757,0.1129844,0.0009963459,0.605193],"genre_scores_gemma":[0.01658486,0.01084318,0.07746396,0.001489056,0.001057718,0.00009329261,0.002206324,0.0005930181,0.8896686],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2844756,"threshold_uncertainty_score":0.9997288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1823082849611272,"score_gpt":0.4110886459547939,"score_spread":0.2287803609936667,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}