{"id":"W4407523552","doi":"10.2166/aqua.2025.001","title":"Cutting-edge innovations in drinking water management","year":2025,"lang":"en","type":"article","venue":"AQUA - Water Infrastructure Ecosystems and Society","topic":"Water resources management and optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Business; Environmental science; Computer science; Artificial intelligence","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":[0.004532319,0.0006771819,0.0005652186,0.001763129,0.0006282329,0.003713307,0.00182549,0.002045892,0.00381465],"category_scores_gemma":[0.007945414,0.0004218213,0.0008174786,0.002394247,0.002579251,0.005489431,0.002934283,0.002389677,0.0006403591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003036706,"about_ca_system_score_gemma":0.00162196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581288,"about_ca_topic_score_gemma":0.00152379,"domain_scores_codex":[0.995625,0.001617181,0.0002071689,0.0005328667,0.001732473,0.0002854784],"domain_scores_gemma":[0.9941573,0.003678941,0.0003065562,0.0006105989,0.001042956,0.0002036853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004549203,0.0001016326,0.001261241,0.001404962,0.00005842595,0.00009534714,0.0008712485,0.01723544,0.0027299,0.4409518,0.01183062,0.5234139],"study_design_scores_gemma":[0.00002153193,0.0002793925,0.002357777,0.001050281,0.0000531709,0.0001399226,0.001305837,0.06924558,0.005854897,0.5287136,0.3908928,0.00008524102],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05171799,0.1076521,0.6634933,0.03840681,0.005417516,0.000407507,0.000247432,0.0006342636,0.1320231],"genre_scores_gemma":[0.6437468,0.074452,0.2498318,0.004027684,0.0025911,0.0002776964,0.0002705257,0.0002029143,0.02459948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004532319,"threshold_uncertainty_score":0.02396947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003310828610809557,"score_gpt":0.1812880353463294,"score_spread":0.1779772067355199,"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."}}