{"id":"W2794615166","doi":"10.71781/3504","title":"Adapting to vector-borne diseases under climate change : an evidence-informed approach","year":2017,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Viral Infections and Vectors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre; Public Health Agency; Public Health Agency of Canada","keywords":"Climate change; Data science; Vector (molecular biology); Political science; Computer science; Environmental planning; Environmental resource management; Geography; Environmental science; Biology; Ecology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07938942,0.001750526,0.003465725,0.005055547,0.001352379,0.008297968,0.004810882,0.006247446,0.007253227],"category_scores_gemma":[0.1664028,0.0008610783,0.003818054,0.003207962,0.003055047,0.00538095,0.004033844,0.005937364,0.0009967012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005999056,"about_ca_system_score_gemma":0.02068315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007028908,"about_ca_topic_score_gemma":0.01407921,"domain_scores_codex":[0.9283919,0.05954957,0.00402008,0.002115175,0.005117769,0.0008055921],"domain_scores_gemma":[0.7512938,0.2185434,0.01051269,0.005838645,0.01215558,0.001655828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001288322,0.0008571823,0.01185041,0.08453052,0.0081381,0.0008150923,0.001888428,0.03270222,0.001044798,0.09872171,0.01906261,0.7391006],"study_design_scores_gemma":[0.001444721,0.003417189,0.014302,0.1873618,0.01688498,0.001032064,0.004795202,0.03364359,0.003595419,0.467718,0.2653242,0.000480786],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.02218591,0.4841526,0.2023124,0.2128661,0.005121205,0.007514558,0.00350594,0.0004644071,0.06187691],"genre_scores_gemma":[0.3850645,0.3488576,0.2280686,0.0251045,0.002391147,0.00580475,0.00128308,0.00009307834,0.003332739],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07938942,"threshold_uncertainty_score":0.4198564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568788292160389,"score_gpt":0.269012403130709,"score_spread":0.2333245202091051,"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."}}