{"id":"W2486457958","doi":"10.1016/j.scijus.2016.05.007","title":"What should a forensic practitioner's likelihood ratio be?","year":2016,"lang":"en","type":"article","venue":"Science & Justice","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council","keywords":"Normative; Statistics; Range (aeronautics); Forensic science; Population; Computer science; Econometrics; Value (mathematics); Confidence interval; Interval (graph theory); Likelihood ratios in diagnostic testing; Psychology; Mathematics; Law; Engineering; Medicine; Demography; Sociology","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":[0.06482705,0.001297066,0.002981614,0.003711549,0.002575333,0.01235759,0.004238626,0.01881908,0.006753982],"category_scores_gemma":[0.3479963,0.0007300586,0.0009349986,0.001251645,0.0156534,0.02302659,0.003940474,0.0177502,0.005184317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00254472,"about_ca_system_score_gemma":0.004542673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643395,"about_ca_topic_score_gemma":0.00137475,"domain_scores_codex":[0.9675437,0.01919424,0.002734934,0.002309428,0.007499627,0.0007179508],"domain_scores_gemma":[0.852094,0.100764,0.008227666,0.007813475,0.02579475,0.005306142],"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":[0.0003328434,0.000280528,0.006006281,0.001068995,0.0002606565,0.0006552032,0.0009998609,0.003243435,0.0008258499,0.4040746,0.2163428,0.3659089],"study_design_scores_gemma":[0.0001218622,0.0001698455,0.002032675,0.002743214,0.0001370695,0.002510714,0.001812995,0.009983107,0.001823004,0.8494896,0.1289246,0.0002513829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003337858,0.01771569,0.07296138,0.8843842,0.007084329,0.00005072627,0.0001555724,0.0003660459,0.01394415],"genre_scores_gemma":[0.5222079,0.03034857,0.155696,0.2384415,0.03738851,0.00042717,0.0002426712,0.0007672345,0.01448042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06482705,"threshold_uncertainty_score":0.3428423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03126367205876893,"score_gpt":0.3127678718706677,"score_spread":0.2815041998118988,"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."}}