{"id":"W2006656454","doi":"10.1167/7.9.605","title":"Orientation tuning of contour integration","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Advanced Theoretical and Applied Studies in Material Sciences and Geometry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Curvature; Orientation (vector space); Artificial intelligence; Ranging; Computer vision; Contour line; Bandwidth (computing); Geometry; Computer science; Optics; Mathematics; Physics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001686153,0.00003038376,0.00007616602,0.00003232539,0.00002140931,0.000007468696,0.0000499477,0.00002069912,0.00005787214],"category_scores_gemma":[0.00003796,0.00001968581,0.00002347267,0.00007909691,0.00003598763,0.00009177945,0.000007968028,0.00008855035,0.000001246376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004536829,"about_ca_system_score_gemma":0.000001997859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.44459e-7,"about_ca_topic_score_gemma":8.715092e-7,"domain_scores_codex":[0.9996646,0.000003194934,0.0001674407,0.00002225464,0.00009757273,0.00004494311],"domain_scores_gemma":[0.9998054,0.00002517989,0.00006276173,0.00002747536,0.00005609959,0.00002307272],"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":[0.00001570779,0.000009278619,0.00004735672,0.000007801337,0.00000371192,6.848186e-7,0.0001473354,0.0009838741,0.9104059,0.01724675,0.00008990578,0.07104163],"study_design_scores_gemma":[0.001868141,0.001461321,0.02659273,0.0004257383,0.0000590165,0.00005847087,0.003617031,0.0418314,0.6500958,0.2531444,0.02039578,0.0004502479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9311858,0.00003339748,0.06521638,0.00003225128,0.001046065,0.0000156299,6.526571e-7,0.000005532043,0.002464277],"genre_scores_gemma":[0.9947526,0.000044573,0.005065423,0.000004820533,0.0001235114,1.37689e-7,2.228127e-7,0.000001997936,0.000006697287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2603102,"threshold_uncertainty_score":0.0802764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004662600939858823,"score_gpt":0.2636921516303792,"score_spread":0.2590295506905204,"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."}}