{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000922835,0.0002750081,0.0002508189,0.0004860384,0.0003376848,0.0009105238,0.0002839864,0.0001440882,0.0006254175],"category_scores_gemma":[0.0005383831,0.0002527556,0.00005829434,0.000256838,0.00007552464,0.005623788,0.00008983615,0.00014722,0.0001207857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008174588,"about_ca_system_score_gemma":0.00007028032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000313481,"about_ca_topic_score_gemma":0.00008903634,"domain_scores_codex":[0.9969513,0.0001410981,0.0009218352,0.0003398261,0.001390782,0.000255197],"domain_scores_gemma":[0.9973946,0.0001760621,0.0005916781,0.0001866696,0.001462966,0.0001880583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005749785,0.0002100414,0.379556,0.00003003791,0.0001803506,0.000001112258,0.08830308,0.004133807,0.0001904567,0.4579134,0.002175379,0.06673136],"study_design_scores_gemma":[0.0006453339,0.00005573997,0.05331417,0.00007669895,0.00004283648,0.000001717004,0.01372185,0.9217755,0.00001224107,0.001671944,0.00840125,0.0002807372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.469912,0.00007284008,0.5268909,0.0004573649,0.0006410319,0.0002843364,0.0002650511,0.00007225716,0.001404267],"genre_scores_gemma":[0.9952168,0.0002625163,0.0008936014,0.001174637,0.0002918002,0.00003921072,0.001322859,0.00001658254,0.0007819693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9176417,"threshold_uncertainty_score":0.9999925,"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."}}