{"id":"W4389720695","doi":"10.1016/j.jhydrol.2023.130605","title":"Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Shahid Chamran University of Ahvaz","keywords":"Evapotranspiration; Unavailability; Decision tree; Computer science; Random forest; Machine learning; Gradient boosting; Remote sensing; Synthetic aperture radar; Regression; Irrigation scheduling; Estimation; Data mining; Artificial intelligence; Environmental science; Statistics; Mathematics; 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":[0.0004280307,0.00006411109,0.000174737,0.0000891973,0.00005681086,0.000008172487,0.00004908523,0.00008738508,0.00003147712],"category_scores_gemma":[0.00004637206,0.00005257408,0.00003151999,0.0001498063,0.00006507691,0.0001496696,0.00003510078,0.0001724691,0.000001916851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002988102,"about_ca_system_score_gemma":0.000006336733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005663093,"about_ca_topic_score_gemma":0.00006619053,"domain_scores_codex":[0.9992368,0.0001276327,0.0003001225,0.00009050893,0.000138402,0.0001064845],"domain_scores_gemma":[0.9995454,0.0001214447,0.0002386987,0.00004790879,0.00001008929,0.0000364612],"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":[0.0000422753,0.000004925859,0.002870977,0.000001843051,0.00001649998,0.00002200346,0.0001497459,0.9282603,0.06158645,0.00001553968,7.196572e-7,0.007028765],"study_design_scores_gemma":[0.0002449501,0.0001213608,0.00171992,0.00001109628,0.00005521403,0.0001488009,0.000003968612,0.9851092,0.0004838468,0.01203892,0.00001670439,0.00004604146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8871251,0.00004174706,0.1125469,0.0001308202,0.00004148909,0.00002912955,0.000004339666,0.000009240326,0.00007125214],"genre_scores_gemma":[0.9808608,0.00008181405,0.01898289,0.0000297085,0.0000137106,2.547872e-8,0.00001750734,0.000005177259,0.00000842408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09373567,"threshold_uncertainty_score":0.2143909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781720840884288,"score_gpt":0.2313176160410705,"score_spread":0.2035004076322276,"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."}}