{"id":"W4410542931","doi":"10.1007/s12665-025-12253-w","title":"Prediction of two groundwater sustainability indicators in semi-arid aquifers using machine learning","year":2025,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Aquifer; Groundwater; Biogeosciences; Arid; Environmental engineering science; Sustainability; Hydrology (agriculture); Water resource management; Geology; Environmental science; Earth science; Geotechnical engineering; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001060036,0.0001569506,0.0001877199,0.0002375701,0.0003199792,0.00002898244,0.0002944645,0.00006895109,0.0009775849],"category_scores_gemma":[0.00009002474,0.0001335316,0.0000547852,0.0009906958,0.00187948,0.0003250383,0.0003878972,0.0002535618,0.00002125425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002683483,"about_ca_system_score_gemma":0.00002411733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001569988,"about_ca_topic_score_gemma":0.0001223819,"domain_scores_codex":[0.9981044,0.0001826182,0.0003559717,0.000510268,0.0004449694,0.0004017745],"domain_scores_gemma":[0.9995843,0.00007010376,0.0001209222,0.0001560692,0.000001182222,0.00006737676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008808469,0.000103112,0.8080648,0.000005423956,0.000002510874,0.000002546262,0.0002210244,0.1753393,0.01490513,0.00002490162,0.000001420686,0.0013211],"study_design_scores_gemma":[0.0005029853,0.0002822643,0.7859317,0.0000329716,0.00001446059,0.000006665305,0.0002695442,0.1989792,0.01159539,0.001304133,0.0008929138,0.0001877761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971862,0.00004309327,0.0003349434,0.00007885151,0.0001028667,0.0002156905,0.000005833778,0.00003110974,0.002001452],"genre_scores_gemma":[0.9986117,0.000008049958,0.0009531242,0.00006346171,0.000008514561,0.000005249441,0.000005355346,0.000005526864,0.0003390514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02363991,"threshold_uncertainty_score":0.9999357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419509217344397,"score_gpt":0.2439488579366192,"score_spread":0.2297537657631752,"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."}}