{"id":"W3197104079","doi":"10.1029/2020gl091919","title":"Sensitivity Analysis of the Maximum Entropy Production Method to Model Evaporation in Boreal and Temperate Forests","year":2021,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec en Outaouais; National Aeronautics and Space Administration; National Science Foundation","keywords":"Environmental science; Temperate climate; Climate change; Atmospheric sciences; Boreal; Evaporation; Latent heat; Entropy production; Temperate forest; Sensitivity (control systems); Transpiration; Climatology; Meteorology; Ecology; Physics; Thermodynamics; Geology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004570965,0.000762642,0.0005760616,0.0007468897,0.0003976653,0.0006743525,0.000675592,0.0008277927,0.000492904],"category_scores_gemma":[0.01019442,0.0004095528,0.0009253483,0.0004450565,0.0005236284,0.0007979688,0.0008420768,0.0007542339,0.00004441238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007994313,"about_ca_system_score_gemma":0.0003738698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142234,"about_ca_topic_score_gemma":0.00367755,"domain_scores_codex":[0.9988465,0.0008605648,0.00004589891,0.00009105726,0.00008576173,0.00007027896],"domain_scores_gemma":[0.9884421,0.01057167,0.0002925868,0.0002916708,0.0002946466,0.0001072488],"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":[0.00006906501,0.00001986591,0.007163772,0.00001996678,0.00007659096,0.00003018171,0.00001423346,0.9905306,0.0005582743,0.0002274426,0.000043042,0.00124704],"study_design_scores_gemma":[0.000007310505,0.00005365106,0.003134151,0.00000552077,0.00001379246,0.00001080599,0.00001275188,0.9954666,0.0008432174,0.0003856014,0.00005708736,0.000009449975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967122,0.0002553797,0.03077548,0.0001458867,0.0000197646,0.00004755709,0.0002554808,0.000152424,0.0012261],"genre_scores_gemma":[0.9979311,0.000020709,0.001874888,0.00001237996,0.000003552907,0.00001517876,0.0000697807,0.00001025858,0.00006211505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01142234,"threshold_uncertainty_score":0.02417386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02070062416882866,"score_gpt":0.2996049289422551,"score_spread":0.2789043047734264,"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."}}