{"id":"W2063171660","doi":"10.1167/5.8.821","title":"Upright & inverted face recognition relies on the same, narrow band of spatial frequencies","year":2005,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Spatial frequency; Facial recognition system; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Frequency domain; Masking (illustration); Computer science; Orientation (vector space); Psychology; Computer vision; Speech recognition; Communication; Mathematics; Physics; Optics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005893965,0.00009586997,0.0001954448,0.0002188202,0.0001058862,0.00007984421,0.0003731734,0.00005551746,0.0002318808],"category_scores_gemma":[0.000215064,0.0000559864,0.0001762688,0.0002848066,0.0000525756,0.0004670355,0.00003052571,0.0002109749,0.0001036216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004082299,"about_ca_system_score_gemma":0.00004866956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002646576,"about_ca_topic_score_gemma":0.00007010301,"domain_scores_codex":[0.9986993,0.0001430825,0.0004367215,0.0001135177,0.0004957209,0.0001116664],"domain_scores_gemma":[0.9987386,0.0001893152,0.0004709547,0.0001988406,0.0003393565,0.00006295418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001800714,0.0007356419,0.0004200384,0.00004144321,0.0002019294,0.00001809142,0.004696589,0.001971881,0.2370723,0.0008121728,0.05257466,0.7012751],"study_design_scores_gemma":[0.004357798,0.003217402,0.02532542,0.00247204,0.0003012813,0.0003923282,0.001198748,0.1043676,0.74066,0.03171303,0.08484749,0.001146815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144711,0.0003872544,0.06107886,0.02178451,0.0002946412,0.00009307687,0.000006726661,0.00002551129,0.001858286],"genre_scores_gemma":[0.9919812,0.000238216,0.0066843,0.0007447538,0.0001234765,5.96145e-7,0.000002163363,0.000004696745,0.000220615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7001283,"threshold_uncertainty_score":0.2538932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951843520879922,"score_gpt":0.2654165508023322,"score_spread":0.235898115593533,"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."}}