{"id":"W2397058931","doi":"10.1037/lhb0000196","title":"A Bayesian analysis on the (dis)utility of iterative-showup procedures: The moderating impact of prior probabilities.","year":2016,"lang":"en","type":"article","venue":"Law and Human Behavior","topic":"Memory Processes and Influences","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Suspect; Law enforcement; Identification (biology); Bayesian probability; Psychology; Culprit; Legal psychology; Computer science; Criminology; Social psychology; Law; Political science; Artificial intelligence","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":[0.0922651,0.001956128,0.002708995,0.003067135,0.001791891,0.004788438,0.003425594,0.003464597,0.0150375],"category_scores_gemma":[0.4846026,0.001379789,0.003377705,0.003689081,0.007362322,0.007766168,0.003855534,0.005700867,0.00100997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003984293,"about_ca_system_score_gemma":0.002971091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01433823,"about_ca_topic_score_gemma":0.01162372,"domain_scores_codex":[0.9429716,0.04820762,0.001076903,0.004057786,0.00262831,0.001057736],"domain_scores_gemma":[0.2612468,0.7094755,0.01451764,0.009585973,0.003969535,0.001204641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003976814,0.0009089332,0.09856635,0.001441006,0.004449554,0.001216871,0.005259334,0.1113594,0.001779729,0.5043049,0.01041492,0.2563223],"study_design_scores_gemma":[0.0002659389,0.001209968,0.04641545,0.0007933285,0.003370337,0.0007994302,0.001148806,0.3832809,0.001595957,0.5525398,0.008286709,0.000293323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3425736,0.009542493,0.5954032,0.009583182,0.0003614527,0.002165372,0.00190739,0.0006656133,0.03779768],"genre_scores_gemma":[0.8998369,0.003856716,0.08695158,0.001133534,0.0003135031,0.001164774,0.0005601888,0.0002092981,0.00597341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0922651,"threshold_uncertainty_score":0.4879503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0515653723083457,"score_gpt":0.3272503250775,"score_spread":0.2756849527691543,"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."}}