{"id":"W4206300744","doi":"10.1109/smc52423.2021.9658688","title":"Algorithms for Reading Line Classification","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Gaze; Eye tracking; Computer vision; Kalman filter; Line (geometry); Noise (video); Pattern recognition (psychology); Eye movement; Support vector machine; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002736763,0.0001797148,0.0002327736,0.0001431695,0.0001117674,0.0004385165,0.0005512932,0.0001397849,0.00002575342],"category_scores_gemma":[0.00009359975,0.0001774952,0.00005922608,0.0001515904,0.00006654368,0.0001390311,0.00009031272,0.000178956,0.00005270907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006984896,"about_ca_system_score_gemma":0.0001068981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003435061,"about_ca_topic_score_gemma":0.00002005848,"domain_scores_codex":[0.9984254,0.00006511022,0.0003648091,0.0005899411,0.0003278125,0.0002269703],"domain_scores_gemma":[0.9984592,0.0001499822,0.0001904531,0.0003843872,0.0007418332,0.00007415615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008628963,0.00008252262,0.0003093629,0.00003028096,0.00006084179,0.00002380859,0.0001103203,0.00004416251,0.006348603,0.9605031,0.001271074,0.0312073],"study_design_scores_gemma":[0.001340448,0.0003595006,0.004717746,0.0005457134,0.00004132756,0.0001815084,0.0008703135,0.8795365,0.01341902,0.01887789,0.07939492,0.0007151055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02558003,0.0003305515,0.9274716,0.008308031,0.004664908,0.0003984168,0.00009422242,0.000237642,0.03291458],"genre_scores_gemma":[0.9829518,0.0002502704,0.007186963,0.0001709674,0.0003313806,0.00007490718,0.00005285806,0.00001354591,0.0089673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573718,"threshold_uncertainty_score":0.7238042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.128026174090953,"score_gpt":0.33867211704886,"score_spread":0.210645942957907,"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."}}