{"id":"W2967330611","doi":"10.1109/uemcon.2018.8796799","title":"SmartEye: An Accurate Infrared Eye Tracking System for Smartphones","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Eye tracking; Computer vision; Tracking (education); Gaze; Tracking system; Calibration; Phone; Mathematics","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":[0.0004949442,0.0006636278,0.0007831249,0.0008350115,0.0002186664,0.0004597996,0.0007906247,0.0007654115,0.009780319],"category_scores_gemma":[0.001249459,0.0003331441,0.0003707349,0.0003789183,0.000125932,0.0007913347,0.0007718593,0.0003832865,0.005256509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256917,"about_ca_system_score_gemma":0.0002652488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605061,"about_ca_topic_score_gemma":0.002252016,"domain_scores_codex":[0.9994956,0.00005148652,0.00004344217,0.0001115916,0.0002559182,0.00004187589],"domain_scores_gemma":[0.9993142,0.00009594173,0.00008743153,0.0001066097,0.0003574153,0.00003843744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001681367,0.0001522858,0.008838995,0.001040942,0.0001689814,0.0006764718,0.0004390524,0.001296365,0.4199787,0.001085818,0.07575381,0.4888873],"study_design_scores_gemma":[0.000831208,0.003272315,0.1156639,0.0006003336,0.0006822607,0.01013264,0.0003506934,0.1446475,0.4266378,0.001707598,0.2947673,0.0007064918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1978615,0.006448426,0.6532564,0.0006938988,0.0008198035,0.001488865,0.0127885,0.09112593,0.03551668],"genre_scores_gemma":[0.6184842,0.002402934,0.3166352,0.001343579,0.0003088427,0.00122538,0.01114576,0.001742123,0.04671211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009780319,"threshold_uncertainty_score":0.03271842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03170176207312449,"score_gpt":0.3012886270537058,"score_spread":0.2695868649805813,"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."}}