{"id":"W1995752765","doi":"10.1167/14.10.1287","title":"The role of spatial frequencies in expert object recognition","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Object (grammar); Sparrow; Identification (biology); Computer science; Feature (linguistics); Cognitive neuroscience of visual object recognition; Artificial intelligence; Range (aeronautics); Pattern recognition (psychology); Computer vision; Ecology; Biology; Engineering","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":[0.0008931308,0.0003867187,0.0002810779,0.0007302416,0.000257702,0.001000813,0.0005027492,0.0008369153,0.0023314],"category_scores_gemma":[0.008038589,0.0004389704,0.0002171219,0.0003150819,0.0005455147,0.002125129,0.0006523806,0.0005109393,0.0005508347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003591728,"about_ca_system_score_gemma":0.0003168798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002510874,"about_ca_topic_score_gemma":0.00229417,"domain_scores_codex":[0.9993798,0.00008898513,0.0000450201,0.0002199181,0.000161763,0.0001045173],"domain_scores_gemma":[0.9949647,0.002974601,0.000765696,0.0004949047,0.0003703047,0.0004298052],"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":[0.001223145,0.0002064551,0.03435973,0.0002932621,0.00005509343,0.0002142799,0.0007079736,0.002445799,0.8204908,0.0009866797,0.0003087744,0.138708],"study_design_scores_gemma":[0.00006531786,0.001416074,0.8406881,0.00008812115,0.00008416858,0.001291453,0.000635921,0.02345954,0.1265432,0.003988723,0.001644565,0.00009484073],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893711,0.0002891132,0.007228593,0.00005646692,0.00001399227,0.00001655042,0.00003584422,0.00003958837,0.002948816],"genre_scores_gemma":[0.9919291,0.0002009577,0.006636646,0.00004603431,0.00001441477,0.00001425561,0.00007103729,0.00002837406,0.001059274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002510874,"threshold_uncertainty_score":0.007799327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672217524334079,"score_gpt":0.2982263813467936,"score_spread":0.2815042061034528,"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."}}