{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003977885,0.0002706613,0.001619354,0.0008671629,0.0002342394,0.00001596517,0.0001753509,0.0001198383,0.001587526],"category_scores_gemma":[0.000314141,0.0002197037,0.003501677,0.00438879,0.00006477154,0.00005206264,0.00002660377,0.0001775646,0.000008853774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004973576,"about_ca_system_score_gemma":0.0001007298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005223361,"about_ca_topic_score_gemma":0.002613286,"domain_scores_codex":[0.9976392,0.0002843268,0.0006598234,0.0005470306,0.0004316806,0.0004378866],"domain_scores_gemma":[0.9973033,0.0006071996,0.0006085259,0.0008594428,0.0003140111,0.000307503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007156064,0.0002931717,0.7698252,0.0000226263,0.03125726,0.00001684562,0.00009974845,0.1832653,0.0001861556,0.00001206478,0.0006034258,0.01370255],"study_design_scores_gemma":[0.0009317613,0.0001034351,0.4077764,0.000002669256,0.09985256,3.873855e-7,0.00002442321,0.4906047,0.00009314503,0.000008902271,0.0004893154,0.0001122511],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7309172,0.001851956,0.2643755,0.0001090273,0.0000444557,0.0003946528,0.002175496,0.00005929176,0.00007245906],"genre_scores_gemma":[0.9888136,0.0004130969,0.009529837,0.0002653899,0.0002103545,0.00007735249,0.0004203259,0.00002730575,0.0002427354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3620488,"threshold_uncertainty_score":0.9993252,"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."}}