{"id":"W2340249237","doi":"10.1177/1473871615609787","title":"Eye tracking evaluation of visual analytics","year":2015,"lang":"en","type":"article","venue":"Information Visualization","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Visual analytics; Computer science; Eye tracking; Visualization; Cultural analytics; Analytics; Human–computer interaction; Tracking (education); Data science; Cognition; Field (mathematics); Process (computing); Data visualization; Artificial intelligence; Semantic analytics; Psychology","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":[0.01077066,0.0008264746,0.0005673676,0.003892515,0.0006542946,0.002318904,0.000605027,0.001051127,0.004779012],"category_scores_gemma":[0.06191874,0.0001783,0.0006167405,0.002040768,0.0004801152,0.00215433,0.001309897,0.0006455139,0.0008420524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037559,"about_ca_system_score_gemma":0.0008971278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00274202,"about_ca_topic_score_gemma":0.002304144,"domain_scores_codex":[0.9889665,0.006258632,0.0007873351,0.0008456944,0.002847263,0.0002945769],"domain_scores_gemma":[0.9437712,0.0299483,0.005434293,0.002749096,0.01707016,0.001026982],"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":[0.005170766,0.000853902,0.1157853,0.004504997,0.0007582705,0.0003786819,0.008909157,0.009246766,0.08138155,0.01381831,0.01578543,0.7434069],"study_design_scores_gemma":[0.0005017815,0.008913,0.660966,0.002378926,0.001363234,0.001732149,0.008385839,0.1071539,0.1282432,0.02546962,0.05404136,0.0008510163],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5992164,0.005135604,0.3278844,0.001075255,0.0005984153,0.002316553,0.003824281,0.002435578,0.05751345],"genre_scores_gemma":[0.945097,0.001113095,0.04725288,0.0002581912,0.0001042261,0.00103163,0.001190458,0.0001942689,0.003758332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01077066,"threshold_uncertainty_score":0.05696142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06827886543160647,"score_gpt":0.3785115235552849,"score_spread":0.3102326581236784,"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."}}