{"id":"W2293495121","doi":"10.1109/hicss.2016.180","title":"Introduction to the Minitrack on Interactive Visual Decision Analytics","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Visual analytics; Computer science; Data science; Big data; Analytics; Visualization; Interactive visual analysis; Context (archaeology); Data visualization; Information visualization; Cultural analytics; Business analytics; Semantic analytics; World Wide Web; Artificial intelligence; Data mining","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002390893,0.00008242793,0.00007278864,0.0001242621,0.00007849329,0.0001465479,0.000542596,0.00002230858,0.0002154905],"category_scores_gemma":[0.0003901764,0.00003795558,0.00003546431,0.0004809101,0.00001751557,0.0003997037,0.0001852518,0.00004435493,0.002082092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004651915,"about_ca_system_score_gemma":0.00002138426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002863319,"about_ca_topic_score_gemma":0.00001662258,"domain_scores_codex":[0.9991,0.00004112366,0.0001624039,0.0002982215,0.0002661142,0.0001321112],"domain_scores_gemma":[0.9990334,0.0002366507,0.00004567488,0.0004872226,0.0001226684,0.00007441385],"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":[0.00002987513,0.0001389531,0.00007303723,8.615241e-7,0.00001896634,0.000002142121,0.0002196943,0.000184588,0.0007695142,0.1384443,0.4514159,0.4087021],"study_design_scores_gemma":[0.0003738326,0.0004570576,0.001422319,0.00004107818,0.00001002238,0.000008679172,0.0001343118,0.08637495,0.01437534,0.002316176,0.8942339,0.0002523322],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003290627,0.000001138932,0.9626015,0.03178079,0.0005577182,0.00007163885,0.000002785061,0.00006789897,0.001625926],"genre_scores_gemma":[0.958725,0.00002352905,0.01678392,0.009131972,0.00137275,0.000006469106,0.000006282271,0.00001325695,0.01393677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9554344,"threshold_uncertainty_score":0.9986949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019648181242597,"score_gpt":0.3318664301350676,"score_spread":0.3122182488924706,"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."}}