{"id":"W2957426165","doi":"10.1109/globalsip45357.2019.8969561","title":"A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Gaze; Computer science; Eye tracking; Desk; Computer vision; Artificial intelligence; Reading (process); Track (disk drive); Eye movement; Tracking (education); Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007007713,0.000349035,0.000470762,0.0001508893,0.0001016507,0.0001292594,0.002978716,0.0003182793,0.00002008479],"category_scores_gemma":[0.000111357,0.0002199875,0.000180329,0.0003314595,0.00009373469,0.0001504465,0.002573311,0.0006674991,0.000106134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006291618,"about_ca_system_score_gemma":0.0001202786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001048592,"about_ca_topic_score_gemma":0.00001239688,"domain_scores_codex":[0.9974007,0.00008166908,0.0004868036,0.0009391401,0.0006649703,0.0004267261],"domain_scores_gemma":[0.9972287,0.00008254534,0.000375765,0.00198862,0.0002682426,0.00005608252],"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":[0.0001790599,0.00341277,0.07345665,0.001825506,0.001326086,0.00003875104,0.01670143,0.006204717,0.2596684,0.1511903,0.04803663,0.4379597],"study_design_scores_gemma":[0.003381009,0.001810693,0.2438838,0.009141457,0.0002946281,0.00008112796,0.0005488273,0.03280276,0.6515658,0.02152471,0.03088499,0.004080083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04601869,0.0001102661,0.9305136,0.005747422,0.001459347,0.001172327,0.00001117824,0.0005679988,0.01439913],"genre_scores_gemma":[0.8394581,0.000006181524,0.1591043,0.0003803231,0.00005598172,0.00007960811,0.000004101018,0.00002068753,0.0008906847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7934394,"threshold_uncertainty_score":0.8970832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09313811808775489,"score_gpt":0.338396840597478,"score_spread":0.2452587225097231,"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."}}