{"id":"W2544119733","doi":"10.1109/tic-sth.2009.5444523","title":"Auto-calibration of Support Vector Machines for detecting disease outbreaks","year":2009,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Outbreak; Support vector machine; Hyperplane; Computer science; Kernel (algebra); Telehealth; Data set; Data mining; Set (abstract data type); Calibration; Relation (database); Artificial intelligence; Machine learning; Statistics; Mathematics; Medicine; Health care; Telemedicine","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":[0.004817071,0.0006954822,0.0006626724,0.001246808,0.0002817038,0.0007196612,0.001056831,0.0008833072,0.0005683117],"category_scores_gemma":[0.02216812,0.0005061759,0.0005259913,0.0007481232,0.0004784108,0.001175946,0.0007183908,0.00123217,0.0003676651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006262449,"about_ca_system_score_gemma":0.000420815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001282426,"about_ca_topic_score_gemma":0.0006867975,"domain_scores_codex":[0.9977465,0.001075377,0.0001349092,0.0003872758,0.0005579139,0.00009798114],"domain_scores_gemma":[0.9911832,0.004983672,0.000919295,0.001031211,0.001788387,0.00009419417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001442507,0.0001362692,0.009874372,0.00006409211,0.0001099003,0.00006030895,0.0001603929,0.7451838,0.009289575,0.002022558,0.0006040467,0.2323504],"study_design_scores_gemma":[0.00000506066,0.00003502816,0.001635505,0.000007355722,0.000006523455,0.0000382191,0.00001245694,0.9921926,0.004548273,0.001242226,0.0002643294,0.00001243491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1416417,0.0002610703,0.8553938,0.0001093366,0.00003480526,0.0000594885,0.00003780306,0.001642774,0.0008192364],"genre_scores_gemma":[0.8632191,0.00009175205,0.1358519,0.00004826226,0.00001657208,0.00007786287,0.000136288,0.0001237925,0.0004344565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004817071,"threshold_uncertainty_score":0.02547538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416445692260355,"score_gpt":0.2711292363519767,"score_spread":0.2569647794293731,"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."}}