{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000672423,0.00006369253,0.0000974301,0.0000672418,0.00009345352,0.00002311025,0.0005838112,0.00004378578,0.00001937266],"category_scores_gemma":[0.0004376802,0.0000401931,0.0000647368,0.0004794194,0.0000709166,0.00005264115,0.00009375727,0.00007441542,0.0000272413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001282719,"about_ca_system_score_gemma":0.00002408634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023919,"about_ca_topic_score_gemma":0.000006305541,"domain_scores_codex":[0.9993044,0.00003251836,0.0001390658,0.0002376086,0.0001107171,0.0001757647],"domain_scores_gemma":[0.9987657,0.0006259947,0.00004965342,0.0004398258,0.0001041039,0.00001473038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004214869,0.0001683789,0.003311624,0.00004058396,0.00003897881,0.000002927927,0.0005488185,0.0003524011,0.004480164,0.7061411,0.02973863,0.2551343],"study_design_scores_gemma":[0.001290504,0.001974427,0.3676152,0.00009850085,0.00002148737,0.000002045485,0.001024837,0.1855308,0.1395228,0.2921687,0.01011863,0.0006321821],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518454,0.000004597306,0.1427172,0.002386463,0.0003404322,0.0003477337,0.000004523333,0.0005200161,0.001833612],"genre_scores_gemma":[0.9968075,9.062141e-7,0.002680966,0.0001083266,0.00001885498,0.00005634012,8.186977e-7,0.000003894898,0.0003224623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4139724,"threshold_uncertainty_score":0.1639027,"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."}}