{"id":"W2077959290","doi":"10.7490/f1000research.1093245.1","title":"Stereoscopy benefits processing of dynamic visual scenes by disambiguating object occlusions","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Open peer review; Plant biology; Stereoscopy; Object (grammar); Computer vision; Computer science; Neuroscience; Artificial intelligence; Communication; Computer graphics (images); Biology; 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":[0.0003207542,0.0009601168,0.0008324026,0.001281776,0.0004530036,0.001057363,0.0005960532,0.0007905214,0.004593393],"category_scores_gemma":[0.001824474,0.0005708503,0.0005841522,0.001439902,0.0005883637,0.00141013,0.001521967,0.0008038362,0.001207473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005108209,"about_ca_system_score_gemma":0.0009922255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002189782,"about_ca_topic_score_gemma":0.004372812,"domain_scores_codex":[0.9996812,0.00003085835,0.00001199614,0.00006629851,0.0001479984,0.00006157142],"domain_scores_gemma":[0.999186,0.0002369481,0.0001004658,0.000236163,0.0001710378,0.00006945305],"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":[0.0007761926,0.0001920389,0.002574733,0.000259873,0.0000902616,0.0002791586,0.0001806726,0.01253336,0.6777247,0.008415465,0.003502186,0.2934714],"study_design_scores_gemma":[0.0001957166,0.0005160313,0.041318,0.0001156679,0.0003590377,0.002950437,0.0003800798,0.3201756,0.5586799,0.04167928,0.03346403,0.0001662155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1744665,0.0009159124,0.8081498,0.0005153833,0.0002535401,0.00009226665,0.0006535467,0.003016044,0.01193711],"genre_scores_gemma":[0.6125547,0.002202261,0.3754345,0.0006959597,0.0003607411,0.00006520242,0.001320383,0.001307946,0.006058312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004593393,"threshold_uncertainty_score":0.01536644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008050681525157689,"score_gpt":0.2931901310653509,"score_spread":0.2851394495401932,"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."}}