{"id":"W4289702914","doi":"","title":"Sharpen statistical significance: Evidence thresholds and Bayes factors sharpened into Occam's razors","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"occam; Occam's razor; Bayes' theorem; Computer science; Psychology; Artificial intelligence; Statistics; Mathematics; Bayesian probability; Programming language","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":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01473038,0.0005456894,0.0007231276,0.000376632,0.0008316024,0.001844731,0.00373487,0.0004717099,0.0007326487],"category_scores_gemma":[0.01985497,0.0004708713,0.0002210425,0.0008153862,0.001250069,0.0003983747,0.004161901,0.0008334017,0.0001203133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432981,"about_ca_system_score_gemma":0.0004534122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002735772,"about_ca_topic_score_gemma":0.002635045,"domain_scores_codex":[0.9909208,0.003715572,0.00128696,0.002020315,0.00152961,0.0005267627],"domain_scores_gemma":[0.980795,0.01009239,0.001030568,0.003868536,0.003759339,0.0004541164],"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":[0.0001353564,0.001324357,0.1180749,0.0005374863,0.0002411962,0.00002243657,0.03843423,0.00009898951,0.007395694,0.4602837,0.139458,0.2339936],"study_design_scores_gemma":[0.0006486282,0.000007776336,0.06168318,0.005599828,0.0001786067,0.00002635027,0.0006912503,0.05785177,0.04827941,0.7585091,0.06424495,0.002279128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4210695,0.0009455948,0.5526143,0.01220106,0.0002719063,0.001283291,0.0004169676,0.000526123,0.01067125],"genre_scores_gemma":[0.8368406,0.0004198918,0.1578882,0.0001060964,0.00004665296,0.000195562,0.0002556444,0.00005446902,0.004192854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4157711,"threshold_uncertainty_score":0.9997743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08542580921466954,"score_gpt":0.3498155986085293,"score_spread":0.2643897893938598,"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."}}