{"id":"W3161303689","doi":"10.31234/osf.io/jmr6u","title":"Eyewitness Identification and Distinctive Features: When Similarity Matters","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Suspect; Witness; Eyewitness identification; Psychology; Identification (biology); Similarity (geometry); Eyewitness memory; Social psychology; Cognitive psychology; Criminology; Artificial intelligence; Computer science; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01735832,0.000548991,0.0008500298,0.001199844,0.0008413458,0.002901935,0.001455732,0.002481267,0.006892703],"category_scores_gemma":[0.2265471,0.0005410716,0.0005540823,0.0005784034,0.003849023,0.00867011,0.004332152,0.00208676,0.0007004768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001343,"about_ca_system_score_gemma":0.0004954443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009811934,"about_ca_topic_score_gemma":0.0006863235,"domain_scores_codex":[0.9829337,0.006493629,0.001403695,0.003810944,0.004752823,0.0006051867],"domain_scores_gemma":[0.8088887,0.1209059,0.03463509,0.02577883,0.006708868,0.00308254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01526758,0.003583204,0.5816365,0.002056638,0.0009999003,0.001396781,0.01810623,0.006225248,0.1251873,0.02676219,0.001607932,0.2171705],"study_design_scores_gemma":[0.0004121953,0.01060005,0.8476792,0.0004277414,0.0005808839,0.003655323,0.004575693,0.01755644,0.04984134,0.06114863,0.003240303,0.0002821721],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776083,0.0005438856,0.01275447,0.0005560925,0.00006386607,0.0002361973,0.0001018846,0.00005896335,0.008076413],"genre_scores_gemma":[0.9975886,0.00005242508,0.0017716,0.0001461278,0.00001504745,0.00003838322,0.00005375492,0.00001643681,0.0003175649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01735832,"threshold_uncertainty_score":0.09180063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04135058425930772,"score_gpt":0.3406404967108294,"score_spread":0.2992899124515217,"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."}}