{"id":"W4405097993","doi":"10.1016/j.ecoinf.2024.102933","title":"Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models","year":2024,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Ministry of Education and Science of the Russian Federation","keywords":"Computer science; Evaporation; Machine learning; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002302816,0.0006733529,0.0006235167,0.0006386486,0.0002502459,0.0009786861,0.0006613933,0.0006133918,0.0002692064],"category_scores_gemma":[0.00379263,0.0002738297,0.0004875593,0.0005575662,0.0003270769,0.001124081,0.0006949155,0.0005266676,0.00005676044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005118084,"about_ca_system_score_gemma":0.0004400679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006768514,"about_ca_topic_score_gemma":0.005066229,"domain_scores_codex":[0.9995161,0.0002108474,0.0000407785,0.00009511796,0.00009906241,0.00003811868],"domain_scores_gemma":[0.9984542,0.001105312,0.0001420205,0.00009240409,0.0001850725,0.00002095042],"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":[0.00005832587,0.00002237867,0.004489931,0.00001740408,0.00004824725,0.00002044227,0.0000193643,0.9755477,0.0004884802,0.0003075717,0.00007480795,0.01890535],"study_design_scores_gemma":[0.000001749317,0.00001336936,0.0006101038,0.000002077451,0.000004635126,0.000003242558,0.00000431415,0.9989085,0.0002052988,0.0002132153,0.00003102907,0.000002461089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7889941,0.0009200636,0.2071162,0.0003568721,0.00002907287,0.00003548246,0.0001961218,0.0004522348,0.001899686],"genre_scores_gemma":[0.9915643,0.00008108554,0.008095307,0.00002100348,0.000009316903,0.0000111309,0.00006907421,0.000005774535,0.0001430363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006768514,"threshold_uncertainty_score":0.01345825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03802932901188066,"score_gpt":0.249889703471029,"score_spread":0.2118603744591483,"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."}}