{"id":"W2398227848","doi":"","title":"Adaptive Information Visualization - Predicting user characteristics and task context from eye gaze.","year":2012,"lang":"en","type":"article","venue":"International Conference on User Modeling, Adaptation, and Personalization","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Eye tracking; Visualization; Task (project management); Gaze; Context (archaeology); Information visualization; User interface; Data visualization; Task analysis; Visual analytics; User modeling; Artificial intelligence; Engineering","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":[0.001136853,0.0005525239,0.0003630316,0.0008285483,0.0001906895,0.0007240291,0.000309752,0.0005356582,0.0008687116],"category_scores_gemma":[0.009130269,0.0002757561,0.0003082046,0.0005002059,0.0001469592,0.001243966,0.0004154421,0.0004499206,0.0002815499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145105,"about_ca_system_score_gemma":0.0003086272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0040104,"about_ca_topic_score_gemma":0.005095433,"domain_scores_codex":[0.9994701,0.0002365047,0.00002413993,0.0001283346,0.0001062036,0.00003483235],"domain_scores_gemma":[0.9965196,0.002200151,0.0005366189,0.0002968567,0.0003184407,0.000128402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002169774,0.0008566917,0.3052667,0.0008308683,0.000442237,0.0002812996,0.002567625,0.03209794,0.1850612,0.001282925,0.003479157,0.4656636],"study_design_scores_gemma":[0.00008398995,0.001664911,0.6484785,0.0001233243,0.0002146993,0.0006875583,0.0007224808,0.3157997,0.02590486,0.003386616,0.002783172,0.0001500473],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8634178,0.001289306,0.1293651,0.0003607164,0.0000407565,0.0002005711,0.0007511472,0.001544933,0.003029638],"genre_scores_gemma":[0.974682,0.0002210175,0.02441138,0.00002926012,0.00001026342,0.00006231553,0.0002079149,0.00002620532,0.0003497364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0040104,"threshold_uncertainty_score":0.007974148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2694014997513866,"score_gpt":0.3953963308317035,"score_spread":0.1259948310803169,"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."}}