{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007486659,0.0005975256,0.0003171454,0.0005036711,0.0002072958,0.0004317404,0.0004663406,0.0005139396,0.0003052252],"category_scores_gemma":[0.00186452,0.0002152253,0.0004599316,0.000471217,0.0001515339,0.0005828381,0.0002922066,0.0004416546,0.0001038188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005404961,"about_ca_system_score_gemma":0.0005341924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067988,"about_ca_topic_score_gemma":0.009936874,"domain_scores_codex":[0.9996612,0.0000923854,0.00002497001,0.0000866945,0.0001083334,0.0000263274],"domain_scores_gemma":[0.9995695,0.0002437378,0.00004722009,0.00002742293,0.0001029181,0.000009164287],"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":[0.00009630642,0.000203932,0.05015439,0.0001502181,0.0001671539,0.000154438,0.0000895344,0.7613223,0.009072286,0.0007765327,0.0005556882,0.1772571],"study_design_scores_gemma":[0.000002499309,0.00004130385,0.003538088,0.000006567419,0.00001120551,0.00001465253,0.00001493415,0.9934527,0.002303393,0.0003157387,0.0002924899,0.00000647493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.69134,0.001827617,0.2998556,0.000668188,0.000146395,0.00009618755,0.0003503881,0.000715268,0.005000416],"genre_scores_gemma":[0.9724381,0.0004322962,0.02623317,0.00004104725,0.00001783177,0.00002770245,0.0001373885,0.0000100618,0.000662367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01067988,"threshold_uncertainty_score":0.02123541,"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."}}