{"id":"W4231924584","doi":"10.31234/osf.io/6atw5","title":"On measuring agreement with numerically bounded linguistic probability schemes: A re-analysis of data from Wintle, Fraser, Wills, Nicholson, and Fidler (2019)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Range (aeronautics); Probabilistic logic; Ambiguity; Measure (data warehouse); Set (abstract data type); Term (time); Mathematics; Statistics; Computer science; Equivalence (formal languages); Linguistics; Discrete mathematics; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1207377,0.00111722,0.001575786,0.007879726,0.00292898,0.003877387,0.003377713,0.002325354,0.006446863],"category_scores_gemma":[0.4417797,0.0008894345,0.00323105,0.007278114,0.006155284,0.009104155,0.006444782,0.007901503,0.002099592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002687933,"about_ca_system_score_gemma":0.002226428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009419342,"about_ca_topic_score_gemma":0.01197813,"domain_scores_codex":[0.9350619,0.03390316,0.006060558,0.006020835,0.01837227,0.0005812969],"domain_scores_gemma":[0.2604984,0.6063877,0.02565851,0.04905041,0.0572555,0.001149474],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005441107,0.001616378,0.2216648,0.01454836,0.005560955,0.0003600997,0.2667731,0.00274925,0.008313886,0.02752461,0.03070418,0.4147434],"study_design_scores_gemma":[0.0006874777,0.004237033,0.6568514,0.0109894,0.005123097,0.001089857,0.1037516,0.01189373,0.01364874,0.03965387,0.1505887,0.001484991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8165407,0.01457988,0.1084061,0.005855189,0.001394128,0.004280683,0.009307382,0.0005065154,0.03912941],"genre_scores_gemma":[0.9303023,0.002689365,0.04702826,0.004336197,0.0003390309,0.006296147,0.005022721,0.000699749,0.00328619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8792623,"threshold_uncertainty_score":0.6385295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1647482846357095,"score_gpt":0.3585717618529267,"score_spread":0.1938234772172172,"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."}}