{"id":"W1859906963","doi":"10.1167/15.12.522","title":"Psychophysical evaluation of planar shape representations for object recognition","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Mathematics; Fourier transform; Representation (politics); Ellipse; Boundary (topology); Shape analysis (program analysis); Fourier series; Cognitive neuroscience of visual object recognition; Wavelet; Computer science; Computer vision; Object (grammar); Algorithm; Geometry; Mathematical analysis","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":[0.00276092,0.0004140735,0.0002813559,0.00102897,0.0001828566,0.0009636937,0.0003413775,0.0005873249,0.001624283],"category_scores_gemma":[0.01996678,0.0001927585,0.0004245101,0.0004501544,0.0005838714,0.00131516,0.001189652,0.0004917973,0.000290323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828481,"about_ca_system_score_gemma":0.0003146281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079244,"about_ca_topic_score_gemma":0.0006987426,"domain_scores_codex":[0.9987532,0.0003334919,0.0001203527,0.0001964904,0.0004988629,0.00009756727],"domain_scores_gemma":[0.9911535,0.005767595,0.0007945877,0.00115952,0.0008029724,0.0003217456],"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":[0.003952611,0.0005906204,0.02212615,0.0004346301,0.00032733,0.0002261253,0.0005450952,0.04732875,0.6763947,0.00369769,0.0007212127,0.243655],"study_design_scores_gemma":[0.0002107232,0.006724196,0.2191109,0.00008554453,0.0002824121,0.001240281,0.0005530684,0.5701263,0.1936264,0.005948138,0.00193162,0.0001603945],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.951491,0.000242794,0.04596515,0.00007962629,0.00002468053,0.00009540877,0.0001619983,0.000154928,0.001784342],"genre_scores_gemma":[0.9763424,0.0001219325,0.02271626,0.00002349209,0.000008886997,0.00003022243,0.0002397153,0.00003997587,0.0004771365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00276092,"threshold_uncertainty_score":0.01460129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1249295900318534,"score_gpt":0.374412046979004,"score_spread":0.2494824569471507,"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."}}