{"id":"W4413875697","doi":"10.1007/s40899-025-01276-7","title":"From data to decision: leveraging machine learning and water quality index for groundwater quality evaluation","year":2025,"lang":"en","type":"article","venue":"Sustainable Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Newcastle University","keywords":"Index (typography); Groundwater; Water quality; Quality (philosophy); Hydrogeology; Computer science; Environmental science; Water resource management; Environmental economics; Engineering; Economics; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003310457,0.00137467,0.00107276,0.001878013,0.0002924094,0.001629065,0.00108766,0.001177529,0.0008843409],"category_scores_gemma":[0.008009167,0.0003068346,0.0009208829,0.001506883,0.0003972283,0.001994307,0.001067471,0.001429087,0.0002765228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007841,"about_ca_system_score_gemma":0.001158979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008419937,"about_ca_topic_score_gemma":0.008985879,"domain_scores_codex":[0.9986405,0.0005714139,0.0001286537,0.0002563875,0.0002770263,0.0001260636],"domain_scores_gemma":[0.9968386,0.002160319,0.0002561843,0.0001865448,0.0004468065,0.0001115368],"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":[0.0002900311,0.0006126373,0.03159694,0.0001933329,0.0003518731,0.0002305999,0.0001339569,0.6912708,0.002429302,0.001546754,0.002300038,0.2690437],"study_design_scores_gemma":[0.000004095934,0.0000597751,0.001012803,0.00001031421,0.00001665598,0.000008308069,0.00002009235,0.9966364,0.000672469,0.001328725,0.0002220264,0.000008324169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4358888,0.003121171,0.5483215,0.003035439,0.0002831285,0.0003821475,0.001158486,0.002695869,0.005113416],"genre_scores_gemma":[0.9401032,0.0003280101,0.05790051,0.0002657886,0.0000786157,0.0000920056,0.0005484365,0.00002813885,0.0006552374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008419937,"threshold_uncertainty_score":0.01750755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05355559996807747,"score_gpt":0.3419358829302127,"score_spread":0.2883802829621352,"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."}}