{"id":"W2081749267","doi":"10.1109/vast.2014.7042543","title":"VACI: Towards visual analytics for criminal investigation","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Visual analytics; Computer science; Visualization; Analytics; Data visualization; Cultural analytics; Interactive visual analysis; Data science; Data analysis; Criminal investigation; Human–computer interaction; Artificial intelligence; Data mining; Semantic analytics","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":[0.005294193,0.001739662,0.001181785,0.005558234,0.001362553,0.008067496,0.003345383,0.001759746,0.007462854],"category_scores_gemma":[0.01739737,0.00095292,0.001383796,0.003632435,0.002144791,0.006928975,0.008754938,0.005241424,0.004345036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045988,"about_ca_system_score_gemma":0.001978811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002539825,"about_ca_topic_score_gemma":0.002953284,"domain_scores_codex":[0.9954153,0.001533394,0.0002539246,0.0005628737,0.00193382,0.000300567],"domain_scores_gemma":[0.9906483,0.002537647,0.0005061157,0.002257562,0.003208153,0.0008421553],"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":[0.0005592995,0.0003344372,0.003268757,0.001504362,0.0002133218,0.0003569663,0.003689302,0.01546639,0.02960325,0.1472306,0.1557134,0.6420598],"study_design_scores_gemma":[0.0001008872,0.0003445273,0.002014239,0.0007075706,0.00009205154,0.0007407241,0.001300862,0.3142372,0.03735793,0.2242806,0.4186315,0.0001918854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005314664,0.001088957,0.9510865,0.003455413,0.0003878514,0.0005894236,0.002094637,0.02547666,0.01050582],"genre_scores_gemma":[0.051918,0.001323533,0.9334335,0.0009256408,0.0003516169,0.0005030352,0.005306323,0.002718091,0.003520222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008067496,"threshold_uncertainty_score":0.02799869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05187364616846072,"score_gpt":0.3342446115827653,"score_spread":0.2823709654143046,"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."}}