{"id":"W2058381280","doi":"10.1167/11.11.591","title":"Implicit face prototype learning from geometric information","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Face (sociological concept); Artificial intelligence; Space (punctuation); Psychology; Computer science; Computer vision; Cognitive psychology","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":[0.001071459,0.0002401123,0.000394916,0.0002159966,0.0001473803,0.0007663922,0.0005564147,0.0004151569,0.001307767],"category_scores_gemma":[0.008760837,0.0003483051,0.0003427612,0.0001142655,0.0006082353,0.00207263,0.001298824,0.0008860463,0.0001559065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002508663,"about_ca_system_score_gemma":0.0002850971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000360892,"about_ca_topic_score_gemma":0.000488758,"domain_scores_codex":[0.9993061,0.0001417415,0.00004645848,0.0001905624,0.0002581195,0.00005695459],"domain_scores_gemma":[0.9949164,0.001910551,0.0007239588,0.001915846,0.0003419806,0.000191302],"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":[0.001617705,0.0003782292,0.04494512,0.0002777747,0.0001001661,0.0002458583,0.001093385,0.004215561,0.7861673,0.002681995,0.0003153862,0.1579615],"study_design_scores_gemma":[0.0001800832,0.005393469,0.3315544,0.00007167909,0.000159494,0.00229994,0.0008028079,0.08736424,0.5551936,0.0142414,0.00260462,0.0001342743],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916592,0.00005142687,0.007299362,0.00002767185,0.000007493917,0.0000155649,0.00001468379,0.0000300832,0.0008945668],"genre_scores_gemma":[0.9956799,0.00004594394,0.003798614,0.00001552941,0.000004736904,0.00001040972,0.00005854549,0.000009873448,0.000376405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001307767,"threshold_uncertainty_score":0.005666435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0649991429908567,"score_gpt":0.3051415235475459,"score_spread":0.2401423805566892,"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."}}