{"id":"W4411171967","doi":"10.1109/iccit64611.2024.11022078","title":"Low Latency Single-Cycle EOG Classification Using Cascaded ANN &amp; CNN","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Latency (audio); Electrooculography; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Telecommunications; Eye movement","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":[0.0001666194,0.0001259928,0.0001167552,0.0001969307,0.0001104873,0.0002532486,0.0004783715,0.0001210939,0.00003816237],"category_scores_gemma":[0.00003598219,0.000108452,0.00005895671,0.0006474564,0.00006851731,0.0003618045,0.0001186329,0.0001930477,0.0004279601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009802975,"about_ca_system_score_gemma":0.0000571918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004671258,"about_ca_topic_score_gemma":0.00002082419,"domain_scores_codex":[0.9988715,0.0000348618,0.0001939697,0.0004712017,0.0001574011,0.0002710613],"domain_scores_gemma":[0.9993182,0.000061565,0.00003812144,0.000472547,0.00005784105,0.00005171701],"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":[0.000003624451,0.0002991835,0.001296978,0.00009633388,0.0000587375,0.00009026864,0.0006677826,0.0002490834,0.3761687,0.2888228,0.003877404,0.3283691],"study_design_scores_gemma":[0.0002702546,0.0001163933,0.01277704,0.0002344057,0.00003046945,0.0002029656,0.00007017154,0.8867831,0.0455922,0.03138642,0.02184479,0.0006918258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2356109,0.0001669756,0.7540639,0.002654154,0.0005740094,0.00007461951,0.000001208345,0.001673193,0.005181074],"genre_scores_gemma":[0.9477965,0.000005337608,0.05080848,0.0001008051,0.00005773581,0.000004302578,0.000002735479,0.00001149268,0.001212636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.886534,"threshold_uncertainty_score":0.5500704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07332535835734232,"score_gpt":0.2957951005062063,"score_spread":0.2224697421488639,"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."}}