{"id":"W2234779292","doi":"10.1145/2847557.2847560","title":"Machine learning meets visualization for extracting insights from text data","year":2016,"lang":"en","type":"article","venue":"AI Matters","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration; Boeing","keywords":"Computer science; Visual analytics; Visualization; Process (computing); Data science; Analytics; Text mining; Data visualization; Biomedical text mining; Path (computing); Natural language processing; Information retrieval; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002937688,0.001844815,0.0008456671,0.007045808,0.0007950497,0.004862798,0.001107903,0.001637213,0.008003464],"category_scores_gemma":[0.01528636,0.0005656836,0.001023875,0.006279535,0.0007964085,0.005477597,0.002432089,0.002067109,0.003458112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008853606,"about_ca_system_score_gemma":0.001396156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002246174,"about_ca_topic_score_gemma":0.002346165,"domain_scores_codex":[0.9977363,0.0008403916,0.0002283856,0.0003193681,0.000780508,0.00009504353],"domain_scores_gemma":[0.9940625,0.003474668,0.0006227636,0.0006198432,0.0009866364,0.0002336718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005337208,0.0001997172,0.005480079,0.002109044,0.0001927515,0.001284755,0.002237828,0.02923301,0.01999831,0.1261664,0.1070904,0.705474],"study_design_scores_gemma":[0.0001027022,0.000126674,0.003055713,0.0005499544,0.00009938346,0.001098357,0.0007829141,0.4152381,0.01863465,0.4066606,0.1535166,0.0001342421],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01312949,0.006242507,0.9336338,0.007669622,0.000537484,0.0003240257,0.006996432,0.02074709,0.01071955],"genre_scores_gemma":[0.1623137,0.004079496,0.8222643,0.0006165996,0.0003938877,0.0003860877,0.006182646,0.0008619708,0.002901322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008003464,"threshold_uncertainty_score":0.02677429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04219726391390411,"score_gpt":0.3290350505911965,"score_spread":0.2868377866772924,"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."}}