{"id":"W4410068707","doi":"10.3390/w17091384","title":"An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Water","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evapotranspiration; Estimation; Environmental science; Computer science; Artificial intelligence; Remote sensing; Geography; Engineering; Ecology; Systems engineering; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001282022,0.00005223017,0.00006055216,0.00003075035,0.00004325568,0.00001697857,0.00008448173,0.00003788742,0.000172986],"category_scores_gemma":[0.000001354224,0.00003898701,0.0000218193,0.00008398787,0.00003553374,0.0003196727,0.00002060802,0.00003681303,0.00004618264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002872999,"about_ca_system_score_gemma":0.000002902521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001352083,"about_ca_topic_score_gemma":0.00006013951,"domain_scores_codex":[0.9994997,0.00002217653,0.0001742216,0.000117459,0.00009677945,0.00008969297],"domain_scores_gemma":[0.9998291,0.000004962324,0.00001846065,0.0001261421,0.000005128658,0.00001626488],"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":[0.000007148653,0.00003685886,0.0004601323,0.00001166531,0.00000314753,3.853773e-7,0.0003872348,0.9119683,0.03466662,0.01944157,0.000003049841,0.03301388],"study_design_scores_gemma":[0.00001348195,0.00001310324,0.0002633065,0.00001389783,0.000009533546,3.860009e-7,0.000007303192,0.8661197,0.04504799,0.08841391,0.00005358899,0.000043785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.753039,0.00002289613,0.2423762,0.0001326924,0.00005947736,0.0001024047,0.00000506006,0.00002162092,0.004240626],"genre_scores_gemma":[0.9976968,0.00001551845,0.002103156,0.00006391803,0.000003141254,0.000005065444,0.0000383713,0.000002805246,0.00007127909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2446577,"threshold_uncertainty_score":0.1894076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06219388677361695,"score_gpt":0.2981716875217041,"score_spread":0.2359778007480871,"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."}}