{"id":"W2124079061","doi":"10.1145/1719970.1720062","title":"Workshop on intelligent visual interfaces for text analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visual analytics; Computer science; Visualization; Cultural analytics; Analytics; Data science; Point (geometry); Information visualization; Human–computer interaction; Interactive visual analysis; Face (sociological concept); Quality (philosophy); Data visualization; World Wide Web; Semantic analytics; Artificial intelligence; The Internet","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":[0.01722652,0.002484902,0.00188272,0.002815626,0.0016687,0.01116917,0.00533786,0.004490047,0.02594902],"category_scores_gemma":[0.0326353,0.001369331,0.004309752,0.002015478,0.002376756,0.01146231,0.005549022,0.007302439,0.008733178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751869,"about_ca_system_score_gemma":0.002100402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004125322,"about_ca_topic_score_gemma":0.003157276,"domain_scores_codex":[0.99381,0.002946178,0.0006245007,0.001087495,0.001188453,0.0003433806],"domain_scores_gemma":[0.9784502,0.01120194,0.0003376341,0.002573794,0.005668881,0.001767488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009485158,0.0005044634,0.0008510622,0.001098716,0.0003625221,0.000860877,0.004573422,0.006611696,0.01720325,0.08262514,0.543923,0.3404374],"study_design_scores_gemma":[0.0002235953,0.0003196018,0.001033429,0.001087835,0.0001838498,0.0006999758,0.0009158513,0.04968521,0.01146945,0.07873693,0.8554657,0.0001784678],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.006620821,0.01062946,0.9247336,0.0159085,0.01180393,0.001044984,0.001813788,0.006678513,0.02076631],"genre_scores_gemma":[0.0485789,0.01059344,0.8521062,0.004662339,0.005024663,0.002074846,0.006583643,0.004030665,0.06634533],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02594902,"threshold_uncertainty_score":0.09110361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221064116656758,"score_gpt":0.3597855726852556,"score_spread":0.3275749315186881,"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."}}