{"id":"W4285685569","doi":"10.2139/ssrn.4152198","title":"Visual Decision Aids: Improving Laypeople’s Understanding of Forensic Science Evidence","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Ontario Museum; Queen's University; University of Toronto","funders":"","keywords":"Forensic science; Scientific evidence; Psychology; Decision aids; Medicine; Epistemology; Alternative medicine; 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":[0.006991685,0.001451403,0.0005434761,0.003798824,0.0009184788,0.007550481,0.00155221,0.002281545,0.02988438],"category_scores_gemma":[0.07170093,0.0004965033,0.0005537637,0.001282369,0.0008380949,0.005529921,0.004412074,0.001811623,0.003204627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008347171,"about_ca_system_score_gemma":0.001565704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844685,"about_ca_topic_score_gemma":0.002763422,"domain_scores_codex":[0.9974649,0.001690417,0.0001142834,0.0001974283,0.0004234306,0.0001095363],"domain_scores_gemma":[0.9573922,0.03438642,0.001505071,0.00224428,0.003564412,0.0009076711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003032039,0.000852568,0.009635125,0.002708058,0.0001634941,0.001587302,0.01952421,0.01116881,0.01347875,0.03439463,0.08516896,0.8182862],"study_design_scores_gemma":[0.001208611,0.001368017,0.02067969,0.006533193,0.000586158,0.00342054,0.03487077,0.2242789,0.03315546,0.2438379,0.429374,0.0006867575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.281081,0.004316233,0.5605924,0.02343788,0.001229753,0.001741581,0.004264024,0.01347124,0.1098659],"genre_scores_gemma":[0.6086496,0.002411834,0.3756769,0.001650191,0.0002947291,0.0005775124,0.001306028,0.0008464372,0.008586727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02988438,"threshold_uncertainty_score":0.09997326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235842495289115,"score_gpt":0.3179317606253595,"score_spread":0.2855733356724683,"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."}}