{"id":"W2419036010","doi":"10.1152/jn.01071.2015","title":"Decisions in motion: vestibular contributions to saccadic target selection","year":2016,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Saccadic masking; Saccade; Vestibular system; Acceleration; Computer science; Fixation (population genetics); Eye movement; Motion (physics); Computer vision; Biological motion; Artificial intelligence; Physics; Communication; Psychology; Neuroscience; Population","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":[0.0001856981,0.0003269734,0.0001952406,0.0002391906,0.0001698988,0.0004635655,0.000107788,0.0002764311,0.002402322],"category_scores_gemma":[0.001642469,0.0001932305,0.0001545003,0.0001735392,0.0001985347,0.0002724178,0.0003505673,0.0001714209,0.0001901102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002269981,"about_ca_system_score_gemma":0.0002952365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002429046,"about_ca_topic_score_gemma":0.002858834,"domain_scores_codex":[0.999907,0.00002376981,0.000006522021,0.00002310097,0.00001947948,0.00002017364],"domain_scores_gemma":[0.9995822,0.0002104122,0.00007940413,0.00002806975,0.00004501037,0.00005494753],"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":[0.0007734634,0.00004457157,0.01252475,0.0001216069,0.00004676052,0.0001552797,0.0001690432,0.001370351,0.9549078,0.0005746825,0.0001593155,0.02915235],"study_design_scores_gemma":[0.0001837421,0.001087623,0.8755885,0.00003927029,0.0002163381,0.0004511212,0.0001859172,0.02475123,0.09280077,0.002887761,0.001752714,0.00005508401],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908386,0.0008101565,0.005505112,0.0001083895,0.00002473442,0.00001932381,0.00009723254,0.00006938321,0.002527085],"genre_scores_gemma":[0.9981395,0.0002604047,0.0009936811,0.00002674089,0.00001457898,0.000007980885,0.00004186297,0.0000133127,0.0005018469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002429046,"threshold_uncertainty_score":0.008036613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04319908349193605,"score_gpt":0.3431452629944841,"score_spread":0.299946179502548,"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."}}