{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000322736,0.00008362524,0.00009930589,0.00009356933,0.00008592298,0.0002217178,0.0003968238,0.00003719604,0.00002198689],"category_scores_gemma":[0.0001822917,0.00007341011,0.00004660339,0.000269703,0.00002904854,0.0003775923,0.00009601717,0.00003226289,0.00004342334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001630233,"about_ca_system_score_gemma":0.00006064018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000101939,"about_ca_topic_score_gemma":0.000007499248,"domain_scores_codex":[0.999216,0.00002865017,0.0001835538,0.0002246386,0.0001892298,0.0001579329],"domain_scores_gemma":[0.9993849,0.00005231156,0.00005795664,0.0002506785,0.0001469681,0.0001071276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001538218,0.00003134764,0.00027173,0.00002667515,0.00000903279,2.258807e-7,0.0001830114,0.0001197676,0.0002047145,0.9524032,0.01084789,0.03590087],"study_design_scores_gemma":[0.0002078675,0.0001014214,0.0005556376,0.000004608533,0.00001424871,0.000001385551,0.0000261795,0.9618578,0.003464306,0.009319847,0.02432763,0.0001190517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00210621,0.000002608627,0.9937431,0.0009342247,0.000146107,0.00006919722,0.000002657475,0.0001363962,0.002859537],"genre_scores_gemma":[0.7593164,0.000005324073,0.2302127,0.006617168,0.0002430195,0.00001063222,0.0001006739,0.00001364285,0.003480425],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.961738,"threshold_uncertainty_score":0.2993577,"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."}}