{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001514964,0.00009308477,0.0001024933,0.0001112002,0.00007592516,0.00009933682,0.0004581606,0.00007463177,0.00001597726],"category_scores_gemma":[0.00002834395,0.00008027555,0.00004397078,0.000343859,0.00002666828,0.0002726747,0.00003212828,0.0001417789,0.000136504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001711178,"about_ca_system_score_gemma":0.00001540673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003515261,"about_ca_topic_score_gemma":4.40371e-7,"domain_scores_codex":[0.9991971,0.00002441047,0.0001239211,0.0003150001,0.0001188749,0.0002207311],"domain_scores_gemma":[0.9995233,0.00001924142,0.000038069,0.0003163289,0.00005854774,0.00004452258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001518725,0.0001506345,0.0000779628,0.000001863996,0.000003626658,0.000005638224,0.00007396255,0.000004446964,0.001652809,0.07587678,0.001076617,0.9210742],"study_design_scores_gemma":[0.003003503,0.00167982,0.3501768,0.0001342064,0.00003631224,0.000598047,0.0002511136,0.1156088,0.09737957,0.4142761,0.01465223,0.002203522],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02398309,0.00003193793,0.8924875,0.002527158,0.0000754808,0.00007595148,3.945633e-7,0.001180284,0.07963821],"genre_scores_gemma":[0.8384767,0.000003792108,0.1606022,0.0006524514,0.00002802953,0.000003890801,0.00000232605,0.000002681117,0.0002279743],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9188706,"threshold_uncertainty_score":0.3273542,"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."}}