{"id":"W7117129848","doi":"10.3126/injet.v3i1.87014","title":"Comparative Analysis of Traditional and Ensemble Models for Water Quality Index Prediction with Explainable AI","year":2025,"lang":"","type":"article","venue":"International Journal on Engineering Technology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpretability; Ensemble forecasting; Water quality; Ensemble learning; Pipeline (software); Index (typography); Random forest; Feature (linguistics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005125061,0.0002217803,0.0004929306,0.001312382,0.0001361145,0.00006819799,0.0004015962,0.0002482088,0.0001123349],"category_scores_gemma":[0.00008378377,0.0001710298,0.0001290389,0.0006198711,0.000292079,0.0002842479,0.00009438323,0.0005280857,0.000002099526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004084842,"about_ca_system_score_gemma":0.00002930346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917327,"about_ca_topic_score_gemma":0.000009766372,"domain_scores_codex":[0.998255,0.00003034834,0.0005979561,0.0003627485,0.0004456599,0.0003083211],"domain_scores_gemma":[0.9992163,0.0001902104,0.0001911841,0.0001515357,0.0001830915,0.00006765896],"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":[0.0003589131,0.0002416634,0.00244829,0.0000134363,0.001920341,0.000009678576,0.000212332,0.9619102,0.00896838,0.02291811,0.0000703913,0.0009281998],"study_design_scores_gemma":[0.0009430858,0.000681208,0.006382042,0.0001464293,0.0003166423,0.00006584051,0.000049664,0.9569367,0.01394001,0.0197213,0.0006488497,0.0001682834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6343341,0.00003401584,0.3622402,0.002608626,0.0002949675,0.0001316267,0.00009477393,0.00004021502,0.0002214838],"genre_scores_gemma":[0.9958394,0.00002700686,0.003807114,0.0001305692,0.00004110382,0.00002612858,0.00001966651,0.000008397547,0.0001006485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3615052,"threshold_uncertainty_score":0.6974393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03006443010971537,"score_gpt":0.279891459769308,"score_spread":0.2498270296595926,"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."}}