{"id":"W2113152693","doi":"10.1109/icsmc.2009.5346616","title":"Gaze tracking: A sclera recognition approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Gaze; Sclera; Computer science; Computer vision; Artificial intelligence; Eye tracking; Tracking (education); Medicine; Psychology; Ophthalmology","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":[0.0003006568,0.000693774,0.0006941808,0.00258493,0.0005783653,0.0008213432,0.000601883,0.0009052223,0.003785717],"category_scores_gemma":[0.0007935895,0.0002910785,0.0006847649,0.001187211,0.0002705369,0.0007851925,0.0005954902,0.0005446798,0.002161539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798725,"about_ca_system_score_gemma":0.0004863133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007247107,"about_ca_topic_score_gemma":0.007276122,"domain_scores_codex":[0.999633,0.00006022187,0.00001765941,0.0001457476,0.0001078896,0.00003561833],"domain_scores_gemma":[0.9996639,0.00005962313,0.00004681783,0.0000356211,0.0001693095,0.00002468901],"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":[0.0003656969,0.0001211041,0.005105346,0.0003485323,0.0002242209,0.0003593059,0.0002949118,0.005861114,0.2504127,0.003144865,0.01112163,0.7226406],"study_design_scores_gemma":[0.0001746579,0.001062154,0.07356635,0.0002875021,0.0006370971,0.005221673,0.0005263302,0.5890537,0.2359243,0.00904876,0.08416819,0.0003292228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03518345,0.004586289,0.9396744,0.0005786979,0.0002634437,0.0002682633,0.0009923542,0.005334938,0.0131181],"genre_scores_gemma":[0.4117185,0.005742536,0.554141,0.0004492909,0.0004756055,0.0003307363,0.001716519,0.0004716605,0.02495424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007247107,"threshold_uncertainty_score":0.0144099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04493307491066278,"score_gpt":0.2466582309438085,"score_spread":0.2017251560331457,"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."}}