{"id":"W2060816813","doi":"10.1002/hyp.7771","title":"Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models","year":2010,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Evapotranspiration; Environmental science; Genetic programming; Hydrological modelling; Wind speed; Regression analysis; Eddy covariance; Statistical model; Meteorology; Mathematics; Statistics; Computer science; Machine learning; Climatology; Geography; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000855728,0.00009079998,0.0001001339,0.00001529554,0.00007633198,0.00002527647,0.00007173725,0.000113048,0.0000831442],"category_scores_gemma":[0.00002656536,0.00006903207,0.00001281591,0.0001105694,0.0002588425,0.0001862443,0.00003553786,0.0001595721,0.000001095407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008043683,"about_ca_system_score_gemma":0.000006671157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005806378,"about_ca_topic_score_gemma":0.00007494968,"domain_scores_codex":[0.9992846,0.00002159694,0.0001782343,0.0002034522,0.0001512146,0.0001608862],"domain_scores_gemma":[0.9997508,0.00004363724,0.00005836114,0.00006864461,0.00001196744,0.00006659176],"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":[0.00002684704,0.00008015484,0.06050812,0.00002138116,0.000004774576,0.000004407433,0.00009455276,0.9282286,0.003420531,0.0001663965,0.000002778076,0.007441507],"study_design_scores_gemma":[0.0001130497,0.00015225,0.01477979,0.000002667203,0.00002483685,0.00003384659,0.000001926654,0.9824478,0.00004735623,0.00228831,0.00004062326,0.00006753622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8883402,0.00003345905,0.1112694,0.00001601577,0.00003393052,0.0001315857,0.00002757659,0.00003111292,0.0001166982],"genre_scores_gemma":[0.9926471,0.00002130516,0.007230483,0.00002513835,0.00002523607,0.000009074397,0.00003152358,0.000005634683,0.000004531871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1043069,"threshold_uncertainty_score":0.2815046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458803110390008,"score_gpt":0.2208923176499176,"score_spread":0.1963042865460176,"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."}}