{"id":"W4221092059","doi":"10.5194/egusphere-egu22-5580","title":"Assessing the generalization power of three machine learning models and three evapotranspiration formulas using 143 FLUXNET towers data","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Sherbrooke","funders":"","keywords":"Overfitting; Evapotranspiration; Eddy covariance; FluxNet; Random forest; Artificial intelligence; Machine learning; Mathematics; Computer science; Statistics; Remote sensing; Algorithm; Artificial neural network; 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":[0.007761304,0.001802055,0.0006516047,0.001421884,0.0005030317,0.0009465002,0.001150689,0.001621907,0.0008000017],"category_scores_gemma":[0.01210596,0.0003984582,0.001752363,0.0008225396,0.0005180321,0.001518976,0.0007630349,0.00138819,0.000548375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009817221,"about_ca_system_score_gemma":0.0008071878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02672387,"about_ca_topic_score_gemma":0.02272859,"domain_scores_codex":[0.998596,0.0004993136,0.0001587019,0.0004396196,0.0001950146,0.0001113675],"domain_scores_gemma":[0.9938068,0.003985797,0.000359296,0.000857316,0.0007899253,0.0002007604],"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":[0.0009523213,0.0005279494,0.09128851,0.0002595823,0.0008962653,0.0002005637,0.0002301473,0.7365173,0.004439698,0.00063816,0.004531397,0.1595182],"study_design_scores_gemma":[0.00003710651,0.0002395367,0.02856105,0.00003539458,0.00006354496,0.00005014845,0.00008109509,0.9674411,0.002640728,0.0003749194,0.0004379863,0.00003733133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776449,0.0008377848,0.01629925,0.0003490748,0.0001253643,0.00006621841,0.001717485,0.00135479,0.001605037],"genre_scores_gemma":[0.9744702,0.0002336772,0.01953981,0.00008482279,0.00003344675,0.00005444108,0.004964162,0.00006515726,0.0005543228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02672387,"threshold_uncertainty_score":0.05313665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08895210065100456,"score_gpt":0.2898494588432659,"score_spread":0.2008973581922613,"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."}}