{"id":"W2888274461","doi":"10.1167/18.8.9","title":"Frequency tuning of shape perception revealed by classification image analysis","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Artificial intelligence; Weighting; Pattern recognition (psychology); Computer science; Projection (relational algebra); Shape analysis (program analysis); Mathematics; Nonlinear system; Active shape model; Spatial frequency; Image processing; Computer vision; Image (mathematics); Algorithm; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003480047,0.0001392626,0.000181818,0.0003657657,0.0001154737,0.0003939891,0.0002365098,0.0002347064,0.001152166],"category_scores_gemma":[0.002460164,0.0001626398,0.0002736945,0.000217096,0.0004337553,0.0003312884,0.0003120188,0.0003107337,0.0002090064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003525794,"about_ca_system_score_gemma":0.000228161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008962456,"about_ca_topic_score_gemma":0.0007116008,"domain_scores_codex":[0.9998043,0.00003738975,0.000007553264,0.00005809513,0.00006531076,0.00002734861],"domain_scores_gemma":[0.9993209,0.0003374486,0.00008639991,0.0001360127,0.00007902058,0.00004031856],"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":[0.0004153606,0.00006120862,0.009853488,0.00007653005,0.00003821833,0.0001026776,0.0002384858,0.02458515,0.8394297,0.01170488,0.000616661,0.1128775],"study_design_scores_gemma":[0.00002807324,0.00013029,0.06821885,0.00001319134,0.00002225489,0.0004040399,0.00006049945,0.7883306,0.1279218,0.01337468,0.001447171,0.00004863831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6942595,0.00009256522,0.3007214,0.00009605508,0.00002644043,0.00003906338,0.000113978,0.0003931578,0.004257838],"genre_scores_gemma":[0.9680098,0.0000471059,0.03142561,0.00002104874,0.000007002,0.00001723317,0.00006377245,0.00004419694,0.0003641338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001152166,"threshold_uncertainty_score":0.003854334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05534539710212753,"score_gpt":0.3714976730130717,"score_spread":0.3161522759109441,"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."}}