{"id":"W2943804616","doi":"10.14745/ccdr.v45i05a02","title":"Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change","year":2019,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Natural Resources Canada; Canadian Forest Service; Université du Québec à Montréal; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Infectious disease (medical specialty); Warning system; Outbreak; Public health; Disease; Climate change; The Internet; Computer science; Data science; Social media; Early warning system; Disease surveillance; Environmental health; Risk analysis (engineering); Business; Medicine; Biology; Ecology; World Wide Web; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.007273609,0.002213908,0.001108473,0.005858953,0.0007599319,0.003221453,0.001889982,0.001461478,0.004824371],"category_scores_gemma":[0.02090651,0.0007029424,0.001387272,0.001702838,0.0006274135,0.002575511,0.001826361,0.001445048,0.0009462608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00221412,"about_ca_system_score_gemma":0.002795671,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01785486,"about_ca_topic_score_gemma":0.01271949,"domain_scores_codex":[0.9970757,0.001260316,0.0002476898,0.0003833838,0.0008723315,0.0001605763],"domain_scores_gemma":[0.9875635,0.007318563,0.001667087,0.0004055511,0.002770345,0.0002749352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002693753,0.0003590296,0.05398092,0.000944891,0.0007539178,0.0003990405,0.0006090028,0.4954692,0.002514284,0.04366664,0.01804326,0.3829904],"study_design_scores_gemma":[0.00004062926,0.0002311391,0.01100145,0.0003117903,0.0002130818,0.0002010114,0.0005585069,0.9211702,0.001362209,0.05104038,0.01374028,0.0001293095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02976541,0.005387263,0.9388887,0.004362645,0.0002163996,0.0009616775,0.001864409,0.002128569,0.01642493],"genre_scores_gemma":[0.5439232,0.004606736,0.4414609,0.0005944584,0.0002532753,0.0008086067,0.002162792,0.0001291701,0.006060822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9821451,"threshold_uncertainty_score":0.03846699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392362393867303,"score_gpt":0.2545924932677637,"score_spread":0.2406688693290907,"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."}}