{"id":"W1987548787","doi":"10.1167/12.9.496","title":"Congruency effects in the identification of upright versus inverted faces","year":2012,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Identification (biology); Psychology; Biology","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":[0.0008580928,0.0002216817,0.0003097638,0.0005978904,0.0002669697,0.0008640997,0.0002683795,0.000592862,0.008194964],"category_scores_gemma":[0.01619073,0.0002890884,0.0001677311,0.0002881468,0.0003894566,0.0008951677,0.000801894,0.0005427017,0.001000727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201707,"about_ca_system_score_gemma":0.0002990174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048565,"about_ca_topic_score_gemma":0.001542262,"domain_scores_codex":[0.9996598,0.00007402629,0.0000441077,0.00007881096,0.00008502427,0.00005838386],"domain_scores_gemma":[0.9949102,0.003933788,0.0003433227,0.0002136141,0.0003816325,0.0002173974],"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":[0.01213442,0.0001484541,0.01047323,0.0002514495,0.00003463623,0.0002193656,0.000605897,0.0002246813,0.9503031,0.00152981,0.0005024962,0.02357241],"study_design_scores_gemma":[0.0006760866,0.001647006,0.8529295,0.0001290493,0.000309107,0.001833644,0.0009501458,0.003905146,0.1283633,0.005737457,0.003427937,0.00009153686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880672,0.0004361288,0.001601135,0.0001295188,0.0001326755,0.00005454106,0.0002532217,0.00003553457,0.009290204],"genre_scores_gemma":[0.9956617,0.0002152375,0.001472929,0.0002293174,0.00005916582,0.00004717704,0.0002686003,0.00007994576,0.001965973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008194964,"threshold_uncertainty_score":0.02741486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177375140988017,"score_gpt":0.2983112947358492,"score_spread":0.2805737806370475,"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."}}