{"id":"W2039488353","doi":"10.1109/vast.2010.5650854","title":"ALIDA: Using machine learning for intent discernment in visual analytics interfaces","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Visual analytics; Human–computer interaction; Visualization; Data visualization; Analytics; Rendering (computer graphics); Process (computing); User interface; Information visualization; User experience design; Data science; World Wide Web; Artificial intelligence","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":[0.004337958,0.0012047,0.0006496051,0.001756203,0.0005892091,0.002359871,0.002303235,0.001467885,0.005055713],"category_scores_gemma":[0.01599245,0.0005534437,0.0007838868,0.0006833672,0.0009801203,0.004432057,0.002454006,0.00221632,0.001312494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007390354,"about_ca_system_score_gemma":0.0008228096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002293561,"about_ca_topic_score_gemma":0.00272694,"domain_scores_codex":[0.9979851,0.0008772428,0.000152352,0.0003883755,0.0004889014,0.0001079877],"domain_scores_gemma":[0.9924186,0.005483853,0.0005280704,0.0005936573,0.0007368606,0.0002389429],"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":[0.0007587569,0.0005722637,0.004209653,0.0004910924,0.0001246316,0.0001894112,0.001310354,0.04366535,0.0172549,0.01398017,0.007127349,0.9103162],"study_design_scores_gemma":[0.00007019984,0.0002170965,0.0009843491,0.00008023781,0.0000370218,0.00008936658,0.0002056117,0.9421753,0.01747059,0.02808045,0.01052776,0.00006208386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007885889,0.0001929074,0.9821499,0.0002218592,0.0000432226,0.0001998561,0.0001362808,0.007932723,0.001237462],"genre_scores_gemma":[0.1713345,0.0001999172,0.825402,0.0002734981,0.00003831742,0.0004643284,0.0003187569,0.0004110591,0.001557522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005055713,"threshold_uncertainty_score":0.02294153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04021053278247524,"score_gpt":0.345161350865749,"score_spread":0.3049508180832737,"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."}}