{"id":"W1967348488","doi":"10.14778/1920841.1920978","title":"Identifying, attributing and describing spatial bursts","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terabyte; Scalability; Computer science; Task (project management); Social media; Information retrieval; Scale (ratio); Data science; Data mining; World Wide Web; Database; Geography; Cartography","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":[0.0004011913,0.0001428234,0.000244185,0.00006074511,0.0001179142,0.00004691757,0.0002070226,0.00005162595,0.00004922239],"category_scores_gemma":[0.0005237689,0.0001027123,0.00008702921,0.0001436869,0.0001341901,0.0001277065,0.0003522183,0.0002631878,0.000007993111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003790813,"about_ca_system_score_gemma":0.00003321126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001334122,"about_ca_topic_score_gemma":0.00002235235,"domain_scores_codex":[0.9988023,0.000003946973,0.0002716986,0.0002729695,0.0003798025,0.0002692524],"domain_scores_gemma":[0.9992594,0.00002704565,0.0002135904,0.00016459,0.0001988991,0.0001365369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000712597,0.00008893235,0.4319229,0.0002449678,0.00006165592,0.000002125876,0.0003140573,1.487893e-7,0.5555875,0.001219028,0.001216316,0.009271135],"study_design_scores_gemma":[0.002216688,0.0001152183,0.538868,0.0006188931,0.0002190872,0.0001386382,0.0003549261,0.0002462291,0.4511668,0.0008454765,0.004951873,0.0002582025],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953951,0.0001337295,0.00002791994,0.000722863,0.0003469228,0.0004664829,0.00002654525,0.00007111351,0.002809289],"genre_scores_gemma":[0.9981066,0.00002243113,0.001350274,0.000126789,0.0001793749,0.00001942015,0.000005915622,0.00001940879,0.0001697944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.106945,"threshold_uncertainty_score":0.4188488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560125426761936,"score_gpt":0.2604515705919254,"score_spread":0.2348503163243061,"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."}}