{"id":"W2080435016","doi":"10.1145/1056808.1057041","title":"Media eyepliances","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Focus (optics); Computer science; Modality (human–computer interaction); Selection (genetic algorithm); Point (geometry); Human–computer interaction; Multimedia; Artificial intelligence","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":[0.0004119097,0.000790662,0.0002788024,0.0009000933,0.0006589483,0.001169354,0.001279135,0.0009296082,0.03113341],"category_scores_gemma":[0.001439851,0.000307182,0.0004332247,0.0004692208,0.0004011151,0.00217563,0.001614407,0.0004977823,0.005992886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004957079,"about_ca_system_score_gemma":0.0002283978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008032795,"about_ca_topic_score_gemma":0.001204873,"domain_scores_codex":[0.9995977,0.00005301506,0.00002073405,0.0001096357,0.0001687512,0.00005011913],"domain_scores_gemma":[0.9992816,0.0002335288,0.00008062307,0.0001763903,0.0001650236,0.00006290277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001126338,0.0001667836,0.002946926,0.0007699273,0.00004871233,0.001467843,0.001262656,0.001485916,0.2928697,0.01992931,0.01955407,0.6583717],"study_design_scores_gemma":[0.00009352186,0.001168711,0.007118477,0.0002254281,0.0001274673,0.007428739,0.0003982687,0.01419757,0.3391255,0.004595274,0.6253771,0.0001439638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1739417,0.009909242,0.5238276,0.00153053,0.0008164141,0.0006110538,0.001385121,0.01229765,0.2756808],"genre_scores_gemma":[0.6358651,0.003496962,0.1667184,0.001440472,0.0002804883,0.0004311598,0.001008948,0.0009130684,0.1898454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03113341,"threshold_uncertainty_score":0.1041517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162116643240569,"score_gpt":0.2428542504012054,"score_spread":0.2266425860771485,"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."}}