{"id":"W2115262138","doi":"10.1111/j.1539-6924.2008.01029.x","title":"A Neural Network‐Based Method for Risk Factor Analysis of West Nile Virus","year":2008,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; University of Guelph","funders":"Public Health Agency of Canada","keywords":"Artificial neural network; Machine learning; Generalization; Risk analysis (engineering); Forgetting; Computer science; Artificial intelligence; West Nile virus; Risk factor; Control (management); Data mining; Virus; Medicine; Virology; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000977823,0.0007114653,0.0006840074,0.001177931,0.0004304266,0.0005345081,0.0006680281,0.0005880864,0.001333823],"category_scores_gemma":[0.003360658,0.0003288701,0.0006332147,0.000809683,0.0002549119,0.0006278678,0.0005124093,0.0009133223,0.0002591486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006002211,"about_ca_system_score_gemma":0.001111603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0198057,"about_ca_topic_score_gemma":0.01196931,"domain_scores_codex":[0.9996191,0.000136557,0.000031916,0.00008192287,0.00009107782,0.00003937824],"domain_scores_gemma":[0.9994078,0.000361969,0.00004491852,0.00002678825,0.0001427693,0.00001582945],"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":[0.0001599602,0.00008842368,0.003325102,0.0000752356,0.0001719005,0.0001121045,0.00006319488,0.6938444,0.003404232,0.004407286,0.001125159,0.293223],"study_design_scores_gemma":[0.000004373932,0.00001013967,0.000291746,0.000003549047,0.000006414027,0.00001074892,0.000003733851,0.9984091,0.0002553736,0.0008328108,0.0001670458,0.000004999396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01597427,0.0002152116,0.982564,0.00008871646,0.00003783709,0.00005128285,0.00008909317,0.0004092027,0.0005703189],"genre_scores_gemma":[0.4789513,0.0005248855,0.5169516,0.00009434136,0.00008077751,0.0004042514,0.0004775371,0.00006492686,0.002450333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0198057,"threshold_uncertainty_score":0.03938085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877622016795279,"score_gpt":0.3127387677199748,"score_spread":0.2939625475520221,"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."}}