{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000321412,0.0003667082,0.0003081847,0.0009041814,0.0006435329,0.0007821375,0.0002939967,0.0004451461,0.001115025],"category_scores_gemma":[0.0002400759,0.0001332601,0.0002232652,0.0009418502,0.0003678402,0.0002731325,0.0004536657,0.0003285868,0.0003321379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029165,"about_ca_system_score_gemma":0.001339219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0658933,"about_ca_topic_score_gemma":0.09816501,"domain_scores_codex":[0.9997899,0.00001645634,0.000006497159,0.0000367173,0.0001067331,0.00004358836],"domain_scores_gemma":[0.9998542,0.00002539103,0.00002246879,0.000006632773,0.00007569871,0.00001556816],"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":[0.0002469258,0.00007381512,0.01591545,0.0003343874,0.00003132327,0.0007193551,0.0004726189,0.001405168,0.9582809,0.0001919991,0.0006777269,0.02165022],"study_design_scores_gemma":[0.00003466576,0.000563761,0.1173707,0.0001056161,0.0001165826,0.0011227,0.003009175,0.01442064,0.8353179,0.0005347729,0.02731197,0.0000914462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854648,0.0007182872,0.006525374,0.0002454837,0.00001137458,0.0000727314,0.002365705,0.0002852608,0.004311008],"genre_scores_gemma":[0.972231,0.002042323,0.01835294,0.0002574172,0.00001562374,0.00003098487,0.001691552,0.00007359388,0.005304612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0658933,"threshold_uncertainty_score":0.1310195,"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."}}