{"id":"W4225677208","doi":"10.3390/mca27020032","title":"On the Prediction of Evaporation in Arid Climate Using Machine Learning Model","year":2022,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adaptive neuro fuzzy inference system; Pan evaporation; Particle swarm optimization; Mean squared error; Inference system; Wind speed; Meteorology; Environmental science; Computer science; Evaporation; Statistics; Mathematics; Fuzzy logic; Machine learning; Artificial intelligence; Geography; Fuzzy control system","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":[0.0002981253,0.00005033319,0.00006665914,0.00002249589,0.0003116103,0.000008437434,0.00006988455,0.00001348578,0.0003544526],"category_scores_gemma":[0.00002949928,0.00003749525,0.00001633863,0.0001917674,0.00008486359,0.00002719268,0.0001262168,0.0001357441,0.00001396468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000492789,"about_ca_system_score_gemma":0.000005056882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009467568,"about_ca_topic_score_gemma":0.000001014197,"domain_scores_codex":[0.9993346,0.00005222735,0.000185022,0.0001288036,0.0002212638,0.00007811324],"domain_scores_gemma":[0.9996228,0.0002222248,0.00007046542,0.00005968764,0.000005354972,0.00001948758],"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":[0.000004793968,0.0001003102,0.0007866445,0.000004553528,0.000001187637,8.452835e-8,0.0001035078,0.8902262,0.0003319348,0.108136,0.000006424919,0.0002983226],"study_design_scores_gemma":[0.00004760628,0.00002317996,0.0004591639,0.000002539737,0.000002943915,0.000003792891,0.000009564618,0.6697299,0.000008776005,0.3296684,0.00002244994,0.00002165347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8525018,0.000003333289,0.1455291,0.000458879,0.000003264009,0.0002347706,0.00002679547,0.00001912803,0.001222893],"genre_scores_gemma":[0.9909753,5.593863e-7,0.008741678,0.0001076457,0.000003440886,0.0001232291,0.00002557513,0.000004272079,0.00001831279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2215324,"threshold_uncertainty_score":0.3881007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03542537804968154,"score_gpt":0.2478875092802272,"score_spread":0.2124621312305456,"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."}}