{"id":"W7160611923","doi":"10.1109/icbiti65527.2025.11500855","title":"Predictive Analytics for Smart City Water Quality Monitoring","year":2025,"lang":"","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"Smart city; Predictive analytics; Quality (philosophy); Analytics; Water quality; Big data","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002070471,0.0004972902,0.0005956109,0.0001783778,0.0006334217,0.0002342937,0.001196919,0.0005194042,0.0003162996],"category_scores_gemma":[0.0004408776,0.0003925868,0.0003038382,0.0004650719,0.0008515624,0.000472901,0.001832934,0.0004599816,0.0001796827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214291,"about_ca_system_score_gemma":0.00003810286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00154116,"about_ca_topic_score_gemma":0.00003126476,"domain_scores_codex":[0.9959099,0.0001505618,0.001036603,0.001146373,0.0005875895,0.001168921],"domain_scores_gemma":[0.9979967,0.0003727534,0.0001407172,0.001295835,0.00005540269,0.0001385514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002640876,0.0004442505,0.9458179,0.0002578303,0.0003906993,0.000002867393,0.0009742851,0.0007333637,0.03513203,0.001503042,0.00202566,0.01245398],"study_design_scores_gemma":[0.0005714119,0.0001747363,0.2440266,0.00009568473,0.000136441,3.36631e-7,0.00122726,0.0009537684,0.7256299,0.021501,0.005243471,0.0004394354],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.918909,0.00009412467,0.06173556,0.005424741,0.004144694,0.001475262,0.0001057105,0.0007581092,0.007352846],"genre_scores_gemma":[0.9702179,0.00007280019,0.01053126,0.00005423759,0.0001946441,0.0001408281,0.000005811589,0.00002382324,0.0187587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7017913,"threshold_uncertainty_score":0.9998526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07524448169276714,"score_gpt":0.3389344104012694,"score_spread":0.2636899287085023,"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."}}