{"id":"W3006865146","doi":"10.1037/lhb0000364","title":"Using machine learning analyses to explore relations between eyewitness lineup looking behaviors and suspect guilt.","year":2020,"lang":"en","type":"article","venue":"Law and Human Behavior","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Regina","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Suspect; Psychology; Eyewitness identification; Witness; Recall; Social psychology; Identification (biology); Context (archaeology); Eyewitness memory; PsycINFO; Cognitive psychology; Computer science; MEDLINE; Criminology; Data mining","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":[0.007398327,0.0008685845,0.0005194517,0.002025407,0.0005210827,0.001477608,0.0006280168,0.0008979752,0.003162123],"category_scores_gemma":[0.05808409,0.0002948225,0.0009325462,0.00157903,0.0005523391,0.001476218,0.0006836226,0.00141009,0.000537939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007338868,"about_ca_system_score_gemma":0.0006836642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003229817,"about_ca_topic_score_gemma":0.00292092,"domain_scores_codex":[0.9964511,0.002066977,0.0002850314,0.0005649269,0.0004513738,0.000180456],"domain_scores_gemma":[0.9120314,0.07683026,0.007574497,0.001564578,0.001491657,0.0005075369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009791665,0.001315629,0.9036866,0.0002133609,0.0007266932,0.0002418303,0.0006439416,0.008856998,0.001954293,0.0006612265,0.001316932,0.07940339],"study_design_scores_gemma":[0.00007500492,0.001219708,0.7478915,0.0001398845,0.0002574762,0.0003372633,0.0008783866,0.2418123,0.002512076,0.003793843,0.001023979,0.00005845987],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623732,0.0003329933,0.03219329,0.0007911053,0.00004047746,0.0002465193,0.001351024,0.0004147484,0.002256666],"genre_scores_gemma":[0.9852021,0.00006893467,0.01314529,0.00007813638,0.00002597909,0.0002389678,0.0008044348,0.00001728308,0.0004189788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007398327,"threshold_uncertainty_score":0.03912652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3754186927736194,"score_gpt":0.4283467155230653,"score_spread":0.05292802274944591,"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."}}