{"id":"W2887351930","doi":"10.1002/gepi.22151","title":"The evidential statistical paradigm in genetics","year":2018,"lang":"en","type":"review","venue":"Genetic Epidemiology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Toronto; Cystic Fibrosis Canada; Cystic Fibrosis Foundation","keywords":"Frequentist inference; Statistical genetics; Statistical hypothesis testing; Bayesian probability; Statistical power; Approximate Bayesian computation; Sample size determination; Covariate; Statistical model; Econometrics; Statement (logic); Data science; Statistics; Computer science; Bayesian inference; Biology; Genetics; Machine learning; Artificial intelligence; Epistemology; Mathematics; Inference; Genomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02200709,0.001286444,0.002134524,0.005475976,0.001239482,0.005954599,0.003357472,0.006440998,0.003517241],"category_scores_gemma":[0.03747535,0.0005631442,0.001693972,0.004532382,0.03076508,0.008336819,0.002938596,0.01118821,0.001721934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004684712,"about_ca_system_score_gemma":0.005917828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002293099,"about_ca_topic_score_gemma":0.001558586,"domain_scores_codex":[0.9826103,0.01084177,0.001089379,0.001604183,0.003590098,0.0002642341],"domain_scores_gemma":[0.940675,0.05318787,0.001415388,0.001912382,0.002358499,0.0004509552],"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":[0.00002501977,0.00001533766,0.0002664911,0.002830012,0.00009634915,0.0001847945,0.0008152281,0.0008366964,0.0002182401,0.8365231,0.02006655,0.1381222],"study_design_scores_gemma":[0.00001200188,0.00002893315,0.0003533721,0.001732588,0.00003254574,0.0006143734,0.0002192979,0.0005437414,0.0001727942,0.7393555,0.2568969,0.00003800315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0007501607,0.7893611,0.1281348,0.05449324,0.005514934,0.00006636293,0.0001980889,0.0002410759,0.02124026],"genre_scores_gemma":[0.0565906,0.8011392,0.07619686,0.03780987,0.01934196,0.0005664526,0.0002573693,0.000330763,0.007766906],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02200709,"threshold_uncertainty_score":0.116386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1073806444096307,"score_gpt":0.42659462018519,"score_spread":0.3192139757755593,"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."}}