{"id":"W4245044940","doi":"10.22215/etd/2012-07011","title":"A non-intrusive and calibration-free gaze tracking system","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Gaze; Tracking (education); Computer graphics (images); Computer science; Computer vision; Calibration; Artificial intelligence; Tracking system; Art; Psychology; Mathematics; Kalman filter; Statistics","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":[0.0005164061,0.0005020028,0.0005833427,0.0007214556,0.000500688,0.0007412021,0.001192787,0.001131048,0.01319403],"category_scores_gemma":[0.001117024,0.0004566218,0.0003206539,0.0003713414,0.0001759543,0.001141312,0.001311362,0.0006414946,0.006357369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002827022,"about_ca_system_score_gemma":0.0006294696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002800314,"about_ca_topic_score_gemma":0.003431836,"domain_scores_codex":[0.9994751,0.00005033714,0.00003207579,0.0001808014,0.0002207953,0.00004093544],"domain_scores_gemma":[0.9994159,0.00009532954,0.00003535235,0.0001445476,0.0002529263,0.00005597036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000636792,0.0002337857,0.002432494,0.0002706188,0.00009266969,0.0002398016,0.000343948,0.0007374263,0.6424628,0.0009554361,0.01377039,0.3378238],"study_design_scores_gemma":[0.000549003,0.002983648,0.06718255,0.0002230729,0.0005284021,0.006537711,0.000394397,0.1115638,0.6464375,0.001139709,0.1620877,0.0003726235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1683078,0.001439251,0.7558538,0.0004918834,0.0005667583,0.001133235,0.002396069,0.04778701,0.02202425],"genre_scores_gemma":[0.4979317,0.0009890363,0.4145475,0.0005706247,0.0002127968,0.0009981327,0.004496305,0.001081046,0.0791729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01319403,"threshold_uncertainty_score":0.04413843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017862262176151,"score_gpt":0.2356623267863685,"score_spread":0.225483704164607,"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."}}