{"id":"W4381684732","doi":"10.3390/w15132318","title":"Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Philippine Council for Health Research and Development","keywords":"Mean squared error; Mean absolute percentage error; Artificial neural network; Mean absolute error; Manganese; Predictive modelling; Range (aeronautics); Machine learning; Soil science; Statistics; Environmental science; Hydrology (agriculture); Mathematics; Computer science; Engineering; Chemistry","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":[0.0002112206,0.00003288751,0.00005556232,0.0000148746,0.00002491552,0.000008413883,0.0000405403,0.00002697067,6.346281e-7],"category_scores_gemma":[0.000007851638,0.00002239609,0.000006373688,0.00003729924,0.00001482394,0.00008509899,0.00004775042,0.00002870653,5.521407e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003011946,"about_ca_system_score_gemma":0.000001312941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003020714,"about_ca_topic_score_gemma":0.000001244223,"domain_scores_codex":[0.9996622,0.00001198468,0.00009760932,0.0001054399,0.00004010534,0.0000827003],"domain_scores_gemma":[0.9998689,0.00001729085,0.00001940117,0.00005670507,0.00002764446,0.0000100281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007018877,0.000005333345,0.2297577,0.0001188113,0.000002559181,3.101325e-7,0.002103227,0.008142095,0.758311,0.00006195308,0.0000025625,0.001487499],"study_design_scores_gemma":[0.0001787629,0.00001966461,0.01999034,0.0000104373,0.000001371631,0.000001126804,0.00004078227,0.2578491,0.7209949,0.0006576497,0.0002302983,0.00002557569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906288,0.00001863594,0.008808102,0.0003732728,0.00002842396,0.00008408137,9.864305e-7,0.00002092067,0.00003675992],"genre_scores_gemma":[0.9993278,0.00001220675,0.0004336331,0.000001457895,0.00001120263,0.00000781279,0.00001936024,8.87314e-7,0.0001856041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.249707,"threshold_uncertainty_score":0.09132861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177979675015001,"score_gpt":0.2207531350159264,"score_spread":0.2089733382657764,"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."}}