{"id":"W2013616540","doi":"10.1016/j.visres.2005.01.012","title":"The nature of synthetic face adaptation","year":2005,"lang":"en","type":"article","venue":"Vision Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Eye Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Adaptation (eye); Psychology; Face (sociological concept); Perception; Identity (music); Viewpoints; Face perception; Cognitive psychology; Communication; Artificial intelligence; Computer vision; Computer science; Neuroscience; Physics; Acoustics","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":[0.0005602682,0.0001595901,0.000220716,0.000228373,0.000232903,0.001070812,0.0004636213,0.0006753111,0.003017018],"category_scores_gemma":[0.004380498,0.0002741539,0.000214211,0.0001762603,0.000960449,0.001663174,0.0005044823,0.0008671482,0.0005404364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002900715,"about_ca_system_score_gemma":0.0002001268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003054595,"about_ca_topic_score_gemma":0.0001815539,"domain_scores_codex":[0.9997379,0.00007669584,0.00000750168,0.00005725155,0.00009787102,0.00002274438],"domain_scores_gemma":[0.9988335,0.0005964775,0.0000607852,0.0002672384,0.0001777762,0.00006431644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000359577,0.0001177645,0.003871122,0.0001753712,0.0001001543,0.0003064689,0.0003027208,0.05607913,0.2147131,0.5976057,0.004590136,0.1217787],"study_design_scores_gemma":[0.00003013429,0.00009889108,0.01588909,0.00003011659,0.00002637077,0.00156524,0.0002288196,0.5313521,0.03692378,0.40211,0.01169143,0.0000540734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.356253,0.002757957,0.5334705,0.003871215,0.0007242418,0.00005740181,0.0003612024,0.0006378353,0.1018667],"genre_scores_gemma":[0.9802513,0.0006601596,0.01301535,0.0002919654,0.0001381772,0.00002784241,0.0001730171,0.0001155326,0.005326682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003017018,"threshold_uncertainty_score":0.01009291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1404634443344923,"score_gpt":0.4508259626763814,"score_spread":0.310362518341889,"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."}}