{"id":"W1967456285","doi":"10.3758/bf03194835","title":"Configural face encoding and spatial frequency information","year":2003,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Spatial frequency; Psychology; Face (sociological concept); Encoding (memory); Pattern recognition (psychology); Facial recognition system; Face perception; Communication; Cognitive psychology; Artificial intelligence; Perception; Computer science; Neuroscience; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001018742,0.0001506808,0.0001117961,0.00008122131,0.0002189919,0.0001336596,0.00006783075,0.00008230702,0.001332129],"category_scores_gemma":[0.0001073451,0.0001530657,0.00005297,0.000171666,0.00009150222,0.001020681,0.000008137353,0.0001731521,0.001370638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004271468,"about_ca_system_score_gemma":0.00001927491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004758647,"about_ca_topic_score_gemma":0.000005621936,"domain_scores_codex":[0.998974,0.0001214614,0.0002255359,0.0002378956,0.0002371702,0.0002039395],"domain_scores_gemma":[0.9995757,0.00004130102,0.000090023,0.000147173,0.00004946024,0.00009636648],"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":[0.0000151028,0.00004208386,0.0002795348,0.00001983643,0.000001600493,3.868947e-7,0.002539996,0.00001365858,0.8968772,0.003289873,0.00038416,0.09653659],"study_design_scores_gemma":[0.01875008,0.002026635,0.2476905,0.0005129191,0.0002357899,0.0005129993,0.0260695,0.03158946,0.3748243,0.1258799,0.1647666,0.007141327],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9206008,0.000004053165,0.0303629,0.0002703985,0.000824735,0.00028356,0.00003436976,0.0001464598,0.04747273],"genre_scores_gemma":[0.9976656,0.0002026379,0.0004243944,0.001376313,0.00009946756,0.00002039822,0.00002557379,0.00001058329,0.0001750073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5220529,"threshold_uncertainty_score":0.9995808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03333792773900014,"score_gpt":0.2802351013872765,"score_spread":0.2468971736482763,"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."}}