{"id":"W2076525407","doi":"10.1016/j.visres.2014.10.032","title":"Visual scanning and recognition of Chinese, Caucasian, and racially ambiguous faces: Contributions from bottom-up facial physiognomic information and top-down knowledge of racial categories","year":2014,"lang":"en","type":"article","venue":"Vision Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Psychology; Face (sociological concept); Fixation (population genetics); Facial recognition system; Cognitive psychology; Linguistics; Medicine; Pattern recognition (psychology); Population","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":[0.0004675069,0.0002435752,0.0002725511,0.0004061946,0.0004051226,0.0008895564,0.0003384073,0.0004248826,0.003122317],"category_scores_gemma":[0.002669283,0.0002624134,0.0003336511,0.0002524769,0.0006118139,0.001015848,0.0004979159,0.0005220792,0.0002819357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004043587,"about_ca_system_score_gemma":0.0005915862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01095312,"about_ca_topic_score_gemma":0.01741293,"domain_scores_codex":[0.9998025,0.00002685573,0.000006351394,0.00006472642,0.00004353765,0.00005610924],"domain_scores_gemma":[0.9991205,0.000337952,0.0001858586,0.0001223079,0.0001397807,0.0000936239],"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":[0.001546206,0.0001495008,0.1090682,0.0001469683,0.0001316024,0.0002975608,0.002708816,0.0009939612,0.8129361,0.002200174,0.0007131382,0.06910776],"study_design_scores_gemma":[0.00001633027,0.00009891655,0.9567575,0.00001535436,0.00005754546,0.000244685,0.0007646562,0.004709365,0.03492656,0.002016867,0.0003633619,0.00002879377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935793,0.0001013207,0.001712692,0.0001035326,0.00001298342,0.00001770181,0.0001086728,0.00001641121,0.004347403],"genre_scores_gemma":[0.9966947,0.0001000027,0.001327987,0.000103845,0.0000121602,0.00001843949,0.0001726093,0.00002493662,0.001545338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01095312,"threshold_uncertainty_score":0.02177876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183060754880749,"score_gpt":0.390617868554486,"score_spread":0.3487872610056786,"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."}}