{"id":"W4410082984","doi":"10.1061/jhyeff.heeng-6440","title":"Discussion of “Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models”","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université Laval","funders":"","keywords":"Pan evaporation; Regression analysis; Tree (set theory); Support vector machine; Evaporation; Regression; Computer science; Kriging; Gaussian process; Econometrics; Statistics; Mathematics; Gaussian; Machine learning; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002206798,0.0001048665,0.0001840263,0.0001099602,0.00004666457,0.00001656362,0.00009532492,0.00006890588,0.00001159444],"category_scores_gemma":[0.00003510473,0.00006122511,0.00003354796,0.0001862408,0.00002069842,0.0003383861,0.00006098469,0.000148054,2.413433e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006502894,"about_ca_system_score_gemma":0.00002192799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003349336,"about_ca_topic_score_gemma":0.00001255506,"domain_scores_codex":[0.9992527,0.00001567615,0.0003434788,0.00010393,0.0001699222,0.0001143377],"domain_scores_gemma":[0.9996762,0.00001578219,0.0001774114,0.00007411453,0.00001585081,0.00004063748],"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":[0.000021181,0.00002308776,0.00394366,0.00005001762,0.00001088968,0.00001002422,0.0000721617,0.9727628,0.02228611,0.00007478353,0.000004284537,0.000741034],"study_design_scores_gemma":[0.0001933997,0.00007052311,0.0008352011,0.0001648614,0.00003727363,0.00005633898,0.00001022193,0.9966894,0.0007754695,0.001076253,0.00001887445,0.00007218888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008816,0.0002113998,0.09841333,0.0001755575,0.00008641576,0.00005339228,0.000003512817,0.00001025168,0.0001645375],"genre_scores_gemma":[0.995666,0.0001231722,0.004131147,0.000009103871,0.00001565826,7.283958e-7,0.000004840251,0.000006144478,0.00004319464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09478442,"threshold_uncertainty_score":0.2496687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00896224199464665,"score_gpt":0.2127838302110223,"score_spread":0.2038215882163756,"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."}}