{"id":"W4408010915","doi":"10.18280/isi.300206","title":"A Deep Learning Approach for Water Quality Assessment: Leveraging Gated Linear Networks for Contamination Classification","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Contamination; Water quality; Computer science; Quality (philosophy); Quality assessment; Artificial intelligence; Deep learning; Environmental science; Evaluation methods; Reliability engineering; Engineering; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001650266,0.0001740452,0.0002088401,0.0001432044,0.0005807695,0.0001953257,0.0002518792,0.0002013374,0.000009131306],"category_scores_gemma":[0.0003890694,0.000152613,0.0000781336,0.0002572291,0.0001477648,0.001626651,0.0001309518,0.0001747997,0.000009671578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008730997,"about_ca_system_score_gemma":0.00001347589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008789766,"about_ca_topic_score_gemma":0.000004055551,"domain_scores_codex":[0.9984472,0.0001106889,0.0006606262,0.0002216516,0.0001961872,0.0003636562],"domain_scores_gemma":[0.9991987,0.0001530079,0.0002655193,0.0002375836,0.0001153722,0.00002979604],"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":[0.0002470968,0.0001395847,0.06908393,0.001376053,0.0001319746,1.457986e-7,0.01007879,0.5339816,0.008273736,0.012277,0.000538039,0.363872],"study_design_scores_gemma":[0.0007187433,0.00006853245,0.04019875,0.00004673158,0.00002713812,8.404385e-7,0.002311509,0.940166,0.008685036,0.003216235,0.004326868,0.0002335943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1751027,0.00000771961,0.8212782,0.0001092612,0.0001794962,0.0009887887,0.0000036712,0.0003554114,0.001974798],"genre_scores_gemma":[0.9452842,0.000005899992,0.05307476,0.00005904118,0.00003485252,0.000591606,0.0007497428,0.00001107949,0.0001887741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7701815,"threshold_uncertainty_score":0.6223376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04299181304612694,"score_gpt":0.2945877812874589,"score_spread":0.251595968241332,"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."}}