{"id":"W4410332348","doi":"10.1002/mrc.5527","title":"NMR as a Discovery Tool: Exploration of Industrial Effluents Discharged Into the Environment","year":2025,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Ministry of the Environment, Conservation and Parks; The Scarborough Hospital; Carleton University; Environment and Climate Change Canada; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Krembil Foundation; Canada Foundation for Innovation; Health Canada; Government of Ontario","keywords":"Chemistry; Effluent; Proton NMR; Carbon-13 NMR; Nuclear magnetic resonance spectroscopy; Industrial effluent; Biochemical engineering; Environmental chemistry; Environmental science; Organic chemistry; Environmental engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001126853,0.0001548643,0.0002074112,0.00001652155,0.00006747313,0.00003783662,0.0003755465,0.0001479356,0.0004041165],"category_scores_gemma":[0.0001158376,0.0001209666,0.00009641669,0.000267398,0.0001443367,0.00009760226,0.0001206626,0.0003049298,0.00001234284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009670114,"about_ca_system_score_gemma":0.00006639689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007966423,"about_ca_topic_score_gemma":0.000005061435,"domain_scores_codex":[0.9987504,0.00001303322,0.0004493672,0.0003391747,0.0002485645,0.0001994437],"domain_scores_gemma":[0.9992095,0.0001146036,0.0001114087,0.0005236734,0.00001445196,0.00002633679],"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":[0.00004583349,0.0001919538,0.006068709,0.00006850695,0.00001202141,9.472133e-7,0.00015165,0.00004201451,0.9644554,0.0002823965,0.0003312896,0.02834932],"study_design_scores_gemma":[0.0006137007,0.000007522102,0.0002835371,0.0001567131,0.00003493291,3.870785e-7,0.0002067231,0.0003094023,0.959592,0.004697474,0.0339564,0.0001411678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895766,0.00222445,0.00006255628,0.001195434,0.000008708792,0.00009682162,0.00000912603,0.00001289468,0.006813451],"genre_scores_gemma":[0.9892188,0.0004353051,0.00006873041,0.00005590913,0.00006263553,0.0002931738,0.00004890925,0.000009219079,0.009807305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03362511,"threshold_uncertainty_score":0.4932874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154914040795516,"score_gpt":0.2362893211568387,"score_spread":0.2247401807488836,"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."}}