{"id":"W4303183418","doi":"10.1093/jme/tjac127","title":"A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily <i>Culex pipiens/restuans</i> Mosquito Density","year":2022,"lang":"en","type":"article","venue":"Journal of Medical Entomology","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Natural Resources Canada","funders":"Los Alamos National Laboratory; Laboratory Directed Research and Development; Research and Development; National Nuclear Security Administration","keywords":"Biology; Culex pipiens; Culex; Process (computing); Vector (molecular biology); Ecology; Larva; Computer science","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":[0.0004975261,0.0006102916,0.000513753,0.0002723205,0.0002864709,0.0007700458,0.0008314664,0.0007550256,0.000655632],"category_scores_gemma":[0.001441916,0.0004495219,0.0008609011,0.0002699451,0.0003672029,0.0008479317,0.0005612976,0.0008253897,0.0001599508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007757938,"about_ca_system_score_gemma":0.001218015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03622214,"about_ca_topic_score_gemma":0.02045892,"domain_scores_codex":[0.9997955,0.00004297983,0.00001276474,0.00008349365,0.00003385095,0.00003145101],"domain_scores_gemma":[0.9994356,0.0002835856,0.0001203886,0.00003750922,0.00008776321,0.00003509633],"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":[0.00001375611,0.00002658464,0.003147308,0.000007295662,0.00001604727,0.00002224412,0.0000113661,0.9939505,0.0004209325,0.0004373965,0.00009611671,0.001850446],"study_design_scores_gemma":[0.000002857239,0.00000715362,0.0005739148,0.000001004109,0.000004716293,0.000003342943,0.000001952817,0.9991009,0.00007413748,0.0001723122,0.00005529278,0.000002494373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8257108,0.0001858228,0.166892,0.0005278829,0.00005022614,0.000059987,0.0008830271,0.000573702,0.005116487],"genre_scores_gemma":[0.9922598,0.00006912089,0.006216978,0.00003684922,0.00001184669,0.000038207,0.0003748813,0.00001728853,0.0009749926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03622214,"threshold_uncertainty_score":0.07202268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645713036200889,"score_gpt":0.2888104187723001,"score_spread":0.2723532884102912,"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."}}