{"id":"W2120842651","doi":"10.1016/j.visres.2004.06.019","title":"Curvature population coding for complex shapes in human vision","year":2004,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Curvature; Coding (social sciences); Optics; Population; Physics; Optometry; Computer science; Computer vision; Artificial intelligence; Psychology; Mathematics; Geometry; Medicine; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.001260626,0.00009841628,0.0001280818,0.0004543959,0.0007017092,0.0002207825,0.0002190598,0.0001182635,0.0002667404],"category_scores_gemma":[0.000563217,0.00008669148,0.00004285832,0.0007558612,0.00007138321,0.0002800227,0.0001037447,0.0003617412,0.00008455709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258907,"about_ca_system_score_gemma":0.00003125057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008520261,"about_ca_topic_score_gemma":0.00006657814,"domain_scores_codex":[0.9980093,0.0002109655,0.0002304329,0.0004526835,0.0007043622,0.0003922363],"domain_scores_gemma":[0.9994256,0.0001949667,0.00004095514,0.0001433082,0.0001045554,0.00009065256],"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":[0.00005466546,0.0001208503,0.00004753614,0.00004525016,2.527937e-7,0.000004417019,0.0002579263,0.0002276294,0.9727528,0.01886101,0.0005093648,0.007118308],"study_design_scores_gemma":[0.007264955,0.003769291,0.09381572,0.001282418,0.000005505862,0.00002623779,0.000668596,0.06965661,0.500585,0.3030342,0.01898296,0.0009084622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892074,0.00001425142,0.006152758,0.002280673,0.0001068017,0.0007134031,0.00001184317,0.0001073843,0.001405458],"genre_scores_gemma":[0.9981958,0.00001394045,0.000914561,0.0003528526,0.00007672326,0.00002876935,0.0000233604,0.00002054494,0.0003734128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4721678,"threshold_uncertainty_score":0.5397051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3439111689719971,"score_gpt":0.5425824973026883,"score_spread":0.1986713283306912,"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."}}