{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005689803,0.0005894096,0.0006279465,0.0005515897,0.00035749,0.000635637,0.0004577359,0.0006190151,0.0006997592],"category_scores_gemma":[0.001307914,0.0002334535,0.0006566822,0.0006409282,0.0001769401,0.0005802786,0.0002638509,0.0005276036,0.0001798022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004493585,"about_ca_system_score_gemma":0.0005845376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02180239,"about_ca_topic_score_gemma":0.01215299,"domain_scores_codex":[0.9998057,0.00006375682,0.00001816357,0.00004865196,0.00003995145,0.00002385929],"domain_scores_gemma":[0.9995531,0.0003187418,0.000037851,0.00001339525,0.00006822094,0.000008502983],"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":[0.00002229376,0.00003358132,0.002385132,0.0000258153,0.00003024996,0.00003969784,0.00001574931,0.9848108,0.0004397089,0.0002955724,0.0001909147,0.0117104],"study_design_scores_gemma":[8.958954e-7,0.000005198197,0.0003208946,0.000001710428,0.000002018957,0.000002511492,0.00000258083,0.9994562,0.00009059072,0.00007659508,0.00003925065,0.000001480095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5676566,0.001363133,0.4216562,0.000425657,0.00009628446,0.00008931802,0.0003677513,0.0008239697,0.007521049],"genre_scores_gemma":[0.9832937,0.0003258583,0.01464461,0.0000272901,0.00001640095,0.00004972607,0.000208903,0.00001570031,0.001417843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02180239,"threshold_uncertainty_score":0.04335099,"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."}}