{"id":"W3197738895","doi":"10.1167/jov.21.9.1898","title":"Using electrooculography to track closed-eye movements.","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Saccadic masking; Electrooculography; Eye movement; Saccade; Computer vision; Computer science; Artificial intelligence; Calibration; Eye tracking; Saccadic suppression of image displacement; SIGNAL (programming language); Vergence (optics); Noise (video); Artifact (error); Mathematics; Statistics","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":[0.0004519202,0.0005353625,0.0003061602,0.001082709,0.0001554699,0.0007216473,0.0003151658,0.0005878082,0.0006527528],"category_scores_gemma":[0.002472682,0.0001574972,0.0002625918,0.0008496224,0.0002353072,0.000867754,0.0004354813,0.0005035434,0.0003915346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001740599,"about_ca_system_score_gemma":0.0002297552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001988327,"about_ca_topic_score_gemma":0.00397367,"domain_scores_codex":[0.9995671,0.00006738325,0.0000307271,0.0001420722,0.0001723539,0.00002043527],"domain_scores_gemma":[0.9993112,0.0002428735,0.0001465917,0.00007985078,0.0001909278,0.00002843661],"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":[0.0002130961,0.0001119509,0.02487368,0.0007587381,0.0003135758,0.0003482407,0.0003455148,0.003133542,0.5843964,0.000715317,0.002092163,0.3826977],"study_design_scores_gemma":[0.0001718449,0.001105667,0.5005835,0.0003768089,0.0004293389,0.004566233,0.0004436008,0.123179,0.3330986,0.005317738,0.03046831,0.0002591776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3811406,0.008431192,0.5990351,0.0003322535,0.0003761125,0.0004288185,0.001884562,0.002620688,0.005750644],"genre_scores_gemma":[0.7138889,0.004271978,0.2775657,0.0003711953,0.0001409229,0.000206933,0.001396723,0.0002168852,0.001940756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001988327,"threshold_uncertainty_score":0.003953576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208637364169977,"score_gpt":0.3224395378025046,"score_spread":0.3003531641608049,"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."}}