{"id":"W4416135232","doi":"10.9734/ijecc/2025/v15i115118","title":"A Combined Statistical and Machine Learning Approach for Predicting Surface Water Quality in Burkina Faso","year":2025,"lang":"","type":"article","venue":"International Journal of Environment and Climate Change","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Water quality; Multilayer perceptron; Surface water; Artificial neural network; Multivariate statistics; Salinity; Total dissolved solids; Hydrology (agriculture); Nutrient","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":[0.002131372,0.0002308497,0.0003783656,0.0001136018,0.0001642289,0.0001310899,0.0002278,0.0001124999,0.0004817481],"category_scores_gemma":[0.00003739553,0.0001875785,0.00007679441,0.00004014412,0.0002717369,0.0003507703,0.0004434831,0.0003601187,0.000004897458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000326444,"about_ca_system_score_gemma":0.00000770385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001927599,"about_ca_topic_score_gemma":0.00001990268,"domain_scores_codex":[0.9976494,0.0002697859,0.0009056865,0.0003277341,0.0004816318,0.0003657374],"domain_scores_gemma":[0.9992303,0.0001940231,0.0003479185,0.00008305171,0.00001707511,0.0001276412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001653487,0.0009573458,0.9598091,0.0003022412,0.0002470012,0.00002809606,0.007126987,0.002771903,0.006569955,0.003037425,0.00002992334,0.0174665],"study_design_scores_gemma":[0.00921724,0.001057289,0.9025784,0.0004397085,0.0001993157,0.00003909013,0.003070397,0.07245647,0.00221438,0.00180573,0.006344673,0.000577308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815687,0.0007208966,0.0116673,0.004379485,0.0004266035,0.0004816404,0.0003305711,0.000005556337,0.0004192374],"genre_scores_gemma":[0.9910772,0.005738597,0.002526033,0.0002901418,0.00009291133,0.00001534658,0.00006145636,0.00001167013,0.00018668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06968457,"threshold_uncertainty_score":0.764923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05815587607230301,"score_gpt":0.3206115021137155,"score_spread":0.2624556260414125,"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."}}