{"id":"W3111654681","doi":"10.1016/j.visres.2020.11.007","title":"Binocular summation and efficient coding","year":2020,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Monocular; Binocular vision; Summation; Coding (social sciences); Computer science; Psychometric function; Psychophysics; Contrast (vision); Set (abstract data type); Artificial intelligence; Mathematics; Psychology; Perception; Neuroscience; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006401983,0.00005128521,0.0000611323,0.0001071161,0.0003560813,0.0002068302,0.0001219567,0.00003894119,0.0003044936],"category_scores_gemma":[0.0008937229,0.00004297393,0.00001482963,0.0005362811,0.00008681958,0.00008133264,0.0001332208,0.0002086203,0.0005278493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001341128,"about_ca_system_score_gemma":0.00002165839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003153911,"about_ca_topic_score_gemma":1.642485e-7,"domain_scores_codex":[0.9985368,0.000252358,0.00009876139,0.0003024889,0.0006112842,0.0001983241],"domain_scores_gemma":[0.9995335,0.0001450866,0.00001586657,0.00008428402,0.00006111315,0.0001601856],"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":[0.00002277788,0.00001818781,0.00001078607,0.00003180929,1.983848e-7,0.000005778651,0.0006370321,0.00003609617,0.9774451,0.003614407,0.0007139209,0.01746386],"study_design_scores_gemma":[0.0004090554,0.0004003384,0.0003498615,0.00004951315,0.000001120887,0.000005095069,0.0003804973,0.2919743,0.6910365,0.0007008239,0.01457481,0.0001180797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841851,0.00003289536,0.00607182,0.006434609,0.00005845788,0.0001992427,0.000002398755,0.0000937612,0.002921693],"genre_scores_gemma":[0.998799,0.00005262947,0.0001523743,0.000764055,0.00004324824,0.000004578087,8.313608e-7,0.000008255998,0.0001750197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2919382,"threshold_uncertainty_score":0.6784611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2990501521760211,"score_gpt":0.471449995773481,"score_spread":0.1723998435974599,"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."}}