{"id":"W3027569513","doi":"10.1109/ssiai49293.2020.9094591","title":"Improving mosquito population predictions in the Greater Toronto Area using remote sensing imagery","year":2020,"lang":"en","type":"article","venue":"","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Response Biomedical (Canada); Public Health Ontario","funders":"Los Alamos National Laboratory; Laboratory Directed Research and Development; National Nuclear Security Administration; U.S. Department of Energy","keywords":"Multispectral image; Abundance (ecology); Population; Geography; Vegetation (pathology); Precipitation; Outbreak; Habitat; Environmental science; Cartography; Ecology; Remote sensing; Physical geography; Meteorology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001576508,0.0005847848,0.0001591961,0.0006050391,0.0002860154,0.0004587714,0.0003606165,0.0002270048,0.0007759861],"category_scores_gemma":[0.0006947722,0.0002010377,0.0003422417,0.0005768085,0.0001479997,0.000321246,0.0002258268,0.0002012573,0.0001813123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002339305,"about_ca_system_score_gemma":0.001640399,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.76046,"about_ca_topic_score_gemma":0.7951514,"domain_scores_codex":[0.9999239,0.000007709584,0.000003419718,0.00002482114,0.00001879235,0.00002120446],"domain_scores_gemma":[0.9998395,0.00003359701,0.00003174725,0.00001206595,0.00005789364,0.00002516742],"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":[0.0001929957,0.0001331733,0.3214579,0.0001397736,0.0001712576,0.0005081236,0.0003586473,0.5864388,0.00759386,0.000590416,0.007891848,0.07452322],"study_design_scores_gemma":[0.00001033537,0.0000195396,0.1329395,0.00001520881,0.00002542548,0.00002272491,0.0001931125,0.8648999,0.0007809074,0.00007511828,0.001004542,0.00001362779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912394,0.0003439654,0.003397226,0.0002002318,0.00002023338,0.0000264296,0.002206892,0.0002452131,0.002320471],"genre_scores_gemma":[0.9931619,0.0001803058,0.003290009,0.0000167244,0.00001027854,0.000006855012,0.002562392,0.00001104902,0.0007604142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.23954,"threshold_uncertainty_score":0.4819015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086996830098994,"score_gpt":0.272127947560222,"score_spread":0.2412579792592321,"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."}}