{"id":"W4391823480","doi":"10.32920/25219208","title":"The Importance of Internal and External Features in Face Recognition","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Facial recognition system; Face (sociological concept); Psychology; Task (project management); Artificial intelligence; Cognitive psychology; Sorting; Pattern recognition (psychology); Computer science; Communication; Linguistics; Engineering","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":[0.0006181264,0.0002729807,0.0002788146,0.0003339667,0.0002494382,0.001146802,0.0002556891,0.0003673843,0.003262867],"category_scores_gemma":[0.003326209,0.0001799578,0.000214685,0.0001638442,0.0005475471,0.00180244,0.000940747,0.0004591962,0.0006923124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002159922,"about_ca_system_score_gemma":0.0001808947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008042989,"about_ca_topic_score_gemma":0.001014003,"domain_scores_codex":[0.9995253,0.00007227191,0.00003703022,0.000173231,0.000135442,0.00005662001],"domain_scores_gemma":[0.9987051,0.0004059823,0.0002999109,0.0002335214,0.0002169485,0.0001385705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000619632,0.000132581,0.05069314,0.0002177159,0.00007330467,0.0002527553,0.001015323,0.000367665,0.6967571,0.003188931,0.0009508778,0.245731],"study_design_scores_gemma":[0.00003457785,0.0009361095,0.8143092,0.00008942642,0.00009901143,0.001938924,0.0006261535,0.00551721,0.1656743,0.006894393,0.003793705,0.00008693044],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714952,0.0006218501,0.0104935,0.0001420262,0.00005889892,0.00002659252,0.0001933176,0.00006853937,0.01690006],"genre_scores_gemma":[0.9927834,0.0001819827,0.005254994,0.00006780073,0.00002784488,0.00001225413,0.0002133446,0.00003687003,0.001421518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003262867,"threshold_uncertainty_score":0.0109154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05081781671792229,"score_gpt":0.3197286880568799,"score_spread":0.2689108713389576,"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."}}