{"id":"W4406620781","doi":"10.1016/j.scitotenv.2025.178464","title":"Real-time wastewater quality monitoring by fluorescence sensors: Validation for COD and CEC monitoring and implication for carbon footprint reduction","year":2025,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"European Commission","keywords":"Carbon footprint; Wastewater; Environmental science; Footprint; Reduction (mathematics); Environmental monitoring; Environmental engineering; Oceanography; Geology; Greenhouse gas; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001349587,0.0001207023,0.0001365837,0.00002840316,0.0004837395,0.0000566319,0.0002699157,0.00003642645,0.000001247535],"category_scores_gemma":[0.00005335652,0.00007944064,0.00005184128,0.000149208,0.0006938168,0.0001013281,0.000317195,0.00005673582,0.000001052149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002286465,"about_ca_system_score_gemma":0.000007832164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005600075,"about_ca_topic_score_gemma":5.647901e-9,"domain_scores_codex":[0.9987004,0.00007343735,0.0002694641,0.0003924589,0.0003328448,0.0002313897],"domain_scores_gemma":[0.9992963,0.0000836137,0.00015504,0.0004065354,0.000009543432,0.00004894778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001917216,0.00002277926,0.0001887485,0.00001777322,0.000009025289,5.621537e-9,0.0004414489,0.003357712,0.9955667,0.00002494605,0.000003697585,0.0003479692],"study_design_scores_gemma":[0.0001506024,0.00003540174,0.02092439,0.00003134236,0.00004723949,9.295157e-7,0.0003941371,0.0002470689,0.9774335,0.0006356832,0.000009909852,0.00008980119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998313,0.00001999154,0.000007249203,0.0009539442,0.0001673036,0.0004789823,0.000007983895,0.0000127543,0.00003885506],"genre_scores_gemma":[0.997645,0.0000563663,0.001301181,3.670122e-8,0.00005299985,0.00007483158,0.000001300703,0.00000622247,0.0008621246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02073564,"threshold_uncertainty_score":0.3720582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648796129222444,"score_gpt":0.2680746472573954,"score_spread":0.2515866859651709,"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."}}