{"id":"W1556854658","doi":"10.1016/j.csbj.2015.12.001","title":"Measuring statistical evidence using relative belief","year":2016,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistical inference; A priori and a posteriori; Objectivity (philosophy); Measure (data warehouse); Statistical hypothesis testing; Statistical evidence; Statistical theory; Statistical model; Falsifiability; Econometrics; Frequentist inference; Inference; Statistical power; Statistical analysis; Computer science; Psychology; Epistemology; Statistics; Mathematics; Artificial intelligence; Data mining; Bayesian probability; Bayesian inference; Null hypothesis; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.05385871,0.001593996,0.002577618,0.00968248,0.001783078,0.009200937,0.005116697,0.004076258,0.004600632],"category_scores_gemma":[0.2681063,0.00138848,0.002172434,0.005625019,0.01314612,0.02123349,0.008466882,0.007316954,0.0007530169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004037652,"about_ca_system_score_gemma":0.002490548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001920643,"about_ca_topic_score_gemma":0.001074383,"domain_scores_codex":[0.9517721,0.0261879,0.003215867,0.006007257,0.01194754,0.0008692138],"domain_scores_gemma":[0.770555,0.1798442,0.01580997,0.0197725,0.01195595,0.002062384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001024491,0.00005863713,0.002681449,0.0004612185,0.0002371947,0.00007392096,0.0008869737,0.01711706,0.001148986,0.9349167,0.0007321125,0.04158335],"study_design_scores_gemma":[0.00002025569,0.00007733143,0.001027343,0.0001678455,0.00008133338,0.00007732347,0.0001271531,0.03436261,0.001120081,0.960916,0.001961947,0.00006073103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01444277,0.001389711,0.97307,0.002709328,0.0001337356,0.0001057641,0.0001587217,0.0001829065,0.007807072],"genre_scores_gemma":[0.5109808,0.00168518,0.4834555,0.001063488,0.0004775569,0.0005494204,0.0002662563,0.0001389575,0.00138288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05385871,"threshold_uncertainty_score":0.2848355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07381097496316864,"score_gpt":0.2903501682461396,"score_spread":0.2165391932829709,"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."}}