{"id":"W2026841397","doi":"10.1016/j.visres.2012.11.003","title":"Discrimination of rotated-in-depth curves is facilitated by stereoscopic cues, but curvature is not tuned for stereoscopic rotation-in-depth","year":2012,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Australian Research Council","keywords":"Stereoscopy; Curvature; Computer vision; Artificial intelligence; Depth perception; Coding (social sciences); Rotation (mathematics); Stereopsis; Adaptation (eye); Computer science; Mathematics; Optics; Physics; Geometry; Psychology; Perception; Neuroscience","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":[0.0003472957,0.0004154827,0.0005934463,0.0004018337,0.0002743654,0.0009293261,0.0007548281,0.0005378805,0.00329878],"category_scores_gemma":[0.003007889,0.000437555,0.0004052184,0.0002164642,0.0007105183,0.001570422,0.0009295864,0.0008840713,0.0006460626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000544714,"about_ca_system_score_gemma":0.000448891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00199424,"about_ca_topic_score_gemma":0.001572242,"domain_scores_codex":[0.9995146,0.00003870332,0.00001963758,0.0001373678,0.0001737839,0.0001158968],"domain_scores_gemma":[0.9978086,0.0003628676,0.0006869708,0.0002761988,0.0003021105,0.0005632231],"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":[0.0001598608,0.000008419282,0.0003839797,0.00001842841,0.000003129302,0.00001403473,0.00001768726,0.00004339386,0.9965121,0.0001927983,0.00005069334,0.002595425],"study_design_scores_gemma":[0.000123258,0.0005290299,0.2257577,0.00003515655,0.00004440865,0.0008269232,0.0001464169,0.008992059,0.7595909,0.002064763,0.001810196,0.00007919032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855543,0.0003259475,0.009592944,0.0001577984,0.00005683826,0.00002320319,0.0001424796,0.000149523,0.00399685],"genre_scores_gemma":[0.9951152,0.000237114,0.002728297,0.00009711495,0.0000180004,0.00001270106,0.0002287426,0.0001626543,0.001400124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00329878,"threshold_uncertainty_score":0.01103556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922209901698135,"score_gpt":0.4616089738908752,"score_spread":0.2693879837210618,"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."}}