{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004257399,0.0002194277,0.0004650596,0.00008028543,0.0001828471,0.00004405741,0.0001088758,0.00005150494,0.00001918951],"category_scores_gemma":[0.0003687187,0.0002001692,0.00008116335,0.0002227302,0.00009669856,0.0003168128,0.0001052876,0.0002037887,4.282793e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003918847,"about_ca_system_score_gemma":0.002044753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08328369,"about_ca_topic_score_gemma":0.1505431,"domain_scores_codex":[0.9982432,0.0001428937,0.0004497538,0.0003560705,0.0004825598,0.0003255581],"domain_scores_gemma":[0.996994,0.000159132,0.0005457877,0.001215231,0.0005076964,0.0005781577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008056788,0.0002071503,0.9968954,0.0004460106,0.0002756242,0.0002165131,0.00002938593,0.00007641276,0.0000616911,0.00004913403,0.0001180268,0.0008189381],"study_design_scores_gemma":[0.002011708,0.000273759,0.9910058,0.0003728461,0.0006881726,0.00002090657,0.0001830648,0.004150105,0.000008383445,0.00009221269,0.00101518,0.0001778216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922265,0.000503909,0.0001329705,0.00009311522,0.0001518298,0.001720732,0.003819938,0.0001244006,0.001226585],"genre_scores_gemma":[0.9975929,0.000222861,0.00005029057,0.0000904742,0.00003472919,0.0003486185,0.001550309,0.00004362566,0.00006619682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06725939,"threshold_uncertainty_score":0.9228208,"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."}}