{"id":"W2775672741","doi":"10.1016/j.jarmac.2017.10.005","title":"Improving identity matching of newly encountered faces: Effects of multi-image training.","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Research in Memory and Cognition","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Matching (statistics); Identity (music); Image (mathematics); Training (meteorology); Social psychology; Cognitive psychology; Artificial intelligence; Computer science; Aesthetics; Statistics; Mathematics","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":[0.0007539328,0.0007604912,0.0005789599,0.000276828,0.0002088046,0.0004398685,0.000958305,0.0006862947,0.002342426],"category_scores_gemma":[0.006768825,0.0002740496,0.0003237953,0.0001656242,0.000504542,0.001346439,0.0009367467,0.001052426,0.000413874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002931545,"about_ca_system_score_gemma":0.0003699792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376521,"about_ca_topic_score_gemma":0.001780735,"domain_scores_codex":[0.9996344,0.00007899783,0.00002660285,0.0001326136,0.0000655416,0.0000618726],"domain_scores_gemma":[0.9980667,0.0009600116,0.0003239943,0.0003116022,0.0001395045,0.0001980974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005305947,0.01035077,0.00598996,0.0007710579,0.0001956491,0.0001761256,0.0005114537,0.008619608,0.5717737,0.0003373215,0.001181612,0.3947867],"study_design_scores_gemma":[0.0009578797,0.03128278,0.1332576,0.0002942066,0.0007143946,0.001755951,0.0006309349,0.1487602,0.6685227,0.005606179,0.008019039,0.0001981481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822446,0.001028707,0.01268249,0.0002244423,0.0001737427,0.0001936131,0.00008484499,0.0002985416,0.003069151],"genre_scores_gemma":[0.980669,0.0005167665,0.0167815,0.0001751315,0.00003514166,0.00009188261,0.0001125209,0.00006246995,0.00155549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002342426,"threshold_uncertainty_score":0.007836223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1527027811645529,"score_gpt":0.4035534402583393,"score_spread":0.2508506590937865,"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."}}