{"id":"W2049999174","doi":"10.1186/1471-2202-14-s1-p307","title":"How noise correlation impact population code in superior colliculus: an information theoretic approach","year":2013,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Correlation; Computer science; Code (set theory); Noise (video); Population; Inferior colliculus; Superior colliculus; Neuroscience; Theoretical computer science; Psychology; Artificial intelligence; Mathematics; Programming language; Sociology","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":[0.0002991487,0.0001809507,0.0001421705,0.0002627593,0.0002809236,0.001009469,0.0003581647,0.00008401387,0.00006865983],"category_scores_gemma":[0.000909585,0.000150067,0.00004629274,0.0009843603,0.0001506082,0.005937539,0.00004988112,0.0001737654,0.00008200129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009858009,"about_ca_system_score_gemma":0.00007648974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000978743,"about_ca_topic_score_gemma":0.00001279464,"domain_scores_codex":[0.998197,0.0002597333,0.0002769908,0.0004281157,0.0004878087,0.0003503943],"domain_scores_gemma":[0.9992986,0.00005566355,0.0001508714,0.0002767503,0.00005549602,0.0001625883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004684637,0.0002178718,0.02145969,0.00005302262,1.271084e-7,0.000001073607,0.001763715,0.01702765,0.9464514,0.006406699,0.00003963463,0.006532273],"study_design_scores_gemma":[0.0003580856,0.0002068289,0.2442373,0.00001244565,0.000002182633,0.00003404756,0.00028071,0.7451985,0.008208291,0.001184639,0.00005331737,0.0002235815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.943918,0.00000159859,0.05441958,0.0001025287,0.0002985212,0.0006235795,0.00001111297,0.0001380004,0.0004870029],"genre_scores_gemma":[0.9980687,0.000005517597,0.0007284228,0.0008742744,0.00002603192,0.00007248897,0.00001767268,0.00001298069,0.0001939042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9382431,"threshold_uncertainty_score":0.9734335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05263162324568698,"score_gpt":0.3079399109898501,"score_spread":0.2553082877441631,"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."}}