{"id":"W4377996913","doi":"10.1145/3588015.3588413","title":"On The Visibility Of Fiducial Markers For Mobile Eye Tracking","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"CMC Microsystems (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Computer vision; Computer science; Gaze; Artificial intelligence; Eye tracking; Visibility; Distraction; Fixation (population genetics); Tracking (education); Medicine; Psychology; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009833481,0.0004809725,0.0002919963,0.0006143645,0.0003384112,0.001168403,0.0006549911,0.0006737143,0.001437806],"category_scores_gemma":[0.01344079,0.0003210277,0.0003393477,0.0003538722,0.0007475537,0.001467493,0.001281207,0.0006120481,0.0003729213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003886255,"about_ca_system_score_gemma":0.000410772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953013,"about_ca_topic_score_gemma":0.001716355,"domain_scores_codex":[0.9985819,0.0005030708,0.00008805413,0.0002220002,0.0004799413,0.0001249963],"domain_scores_gemma":[0.9887024,0.007176774,0.001209294,0.00100679,0.001605791,0.0002990327],"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":[0.002120245,0.0001702418,0.01148877,0.001093838,0.00008286467,0.0009635062,0.002554325,0.007159914,0.6092812,0.01116233,0.001879588,0.3520431],"study_design_scores_gemma":[0.0002434522,0.008382213,0.1163055,0.002091158,0.0007072789,0.01070797,0.002273876,0.1504489,0.6263173,0.01178619,0.07031154,0.0004245194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5065531,0.01111666,0.4685148,0.0006179594,0.0003845757,0.0001809608,0.0001464416,0.0007840301,0.01170144],"genre_scores_gemma":[0.9496596,0.001859908,0.04714391,0.00007619696,0.00008240961,0.00003752629,0.00008373445,0.00007542179,0.0009812926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001953013,"threshold_uncertainty_score":0.005200446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806218061318273,"score_gpt":0.3167476342097588,"score_spread":0.288685453596576,"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."}}