{"id":"W2624398640","doi":"","title":"Exploring Eye Tracking to Increase Bandwidth in User Modeling","year":2005,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Eye tracking; User modeling; Bandwidth (computing); User interface; User interface design; Cognition; User experience design; Artificial intelligence; Telecommunications","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":[0.003967545,0.0006383936,0.0004831241,0.0009083581,0.0004395705,0.002153363,0.0007524918,0.000948459,0.001838913],"category_scores_gemma":[0.05022708,0.000340821,0.0002741278,0.0006379216,0.0004031102,0.002814976,0.001215245,0.0009090707,0.0002680242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331497,"about_ca_system_score_gemma":0.0005544548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005068152,"about_ca_topic_score_gemma":0.00405831,"domain_scores_codex":[0.9965732,0.002438628,0.00008558205,0.000418458,0.0003578186,0.0001263928],"domain_scores_gemma":[0.9580663,0.03617001,0.001793309,0.002009619,0.001623973,0.0003367973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005483395,0.00257793,0.1307304,0.001358499,0.0005304149,0.0005224591,0.03728755,0.05299537,0.1721243,0.009984253,0.003867142,0.5825383],"study_design_scores_gemma":[0.0003452862,0.002268712,0.1357555,0.0003396911,0.0004600724,0.0004944987,0.005681621,0.7668679,0.06403883,0.01554294,0.007957658,0.00024742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8708076,0.0002752683,0.1196337,0.0003410354,0.00002721994,0.0002511395,0.0002086,0.0009220407,0.007533382],"genre_scores_gemma":[0.9781744,0.00005523223,0.02129195,0.00003736216,0.000005349512,0.00006330099,0.00005404084,0.00003845901,0.0002798458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005068152,"threshold_uncertainty_score":0.02098262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1715340051628593,"score_gpt":0.2879757338273465,"score_spread":0.1164417286644872,"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."}}