{"id":"W3097805027","doi":"10.1167/jov.20.11.1647","title":"Configural processing of 2D shape","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Curvature; Pooling; Pattern recognition (psychology); Artificial intelligence; Mathematics; Stimulus (psychology); Geometry; Computer science; Psychology","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":[0.0003259385,0.0006510532,0.0003250805,0.0003952297,0.0001460482,0.0009062768,0.0006330793,0.0005039789,0.004594967],"category_scores_gemma":[0.00224192,0.0002562213,0.0006730638,0.000466528,0.0005533625,0.001054973,0.0006133422,0.0004231215,0.0007481289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000518235,"about_ca_system_score_gemma":0.0002388701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001536301,"about_ca_topic_score_gemma":0.001538874,"domain_scores_codex":[0.9997236,0.0000435259,0.00001248042,0.0001357315,0.00005994438,0.00002461647],"domain_scores_gemma":[0.9990544,0.0003493208,0.0002244537,0.000238022,0.00006775034,0.00006602349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001087275,0.0002175835,0.03897025,0.0009269659,0.0002989272,0.0006569638,0.0003887492,0.1226784,0.3982194,0.007636235,0.004465579,0.4244536],"study_design_scores_gemma":[0.00007130273,0.0004423745,0.15232,0.0001428125,0.00009630345,0.001716586,0.0001576967,0.7358389,0.06459858,0.03836659,0.006130052,0.0001188309],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8310844,0.001226662,0.1574355,0.0004202772,0.00009169536,0.00007336853,0.00195822,0.001150131,0.006559718],"genre_scores_gemma":[0.9785136,0.0003565024,0.01907576,0.00006179634,0.00002330601,0.0000257779,0.001241535,0.00005984323,0.0006418821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004594967,"threshold_uncertainty_score":0.01537162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307097416774502,"score_gpt":0.2419956362813992,"score_spread":0.2289246621136542,"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."}}