{"id":"W2408389737","doi":"","title":"When to Adapt: Detecting User's Confusion During Visualization Processing.","year":2013,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of British Columbia","funders":"","keywords":"Confusion; Computer science; Visualization; Human–computer interaction; Data visualization; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001894695,0.0001220709,0.0001147768,0.0001253542,0.0002990172,0.0005254531,0.0003663394,0.00004473038,0.0001019824],"category_scores_gemma":[0.00006694419,0.0001050631,0.00003053297,0.0002378125,0.000005737318,0.0008013793,0.0002213388,0.00008710268,0.0004699063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005625014,"about_ca_system_score_gemma":0.0000255796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004459809,"about_ca_topic_score_gemma":0.0000133394,"domain_scores_codex":[0.9988434,0.00004619098,0.0002332473,0.0003413313,0.0002521458,0.0002836282],"domain_scores_gemma":[0.9993404,0.00002508276,0.00008749971,0.0002175062,0.0002242177,0.0001052616],"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":[0.00001274515,0.0001177332,0.01034108,0.0002915032,0.00003649266,0.00002240468,0.02602883,0.006821137,0.3407636,0.3639882,0.00186698,0.2497093],"study_design_scores_gemma":[0.000742018,0.000436775,0.0365673,0.001193912,0.000009832364,0.00008551333,0.002369812,0.4817367,0.3163315,0.001268015,0.1575878,0.001670889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1694748,0.00002215046,0.8271628,0.0002856085,0.0002092064,0.0002489413,4.167548e-8,0.0003510116,0.002245421],"genre_scores_gemma":[0.9556653,6.025509e-7,0.0229599,0.0002003424,0.0001360262,0.00003084656,2.82119e-7,0.0000137977,0.02099293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8042029,"threshold_uncertainty_score":0.6039851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568163846183473,"score_gpt":0.2465419027506995,"score_spread":0.2308602642888648,"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."}}