{"id":"W4412994843","doi":"10.1021/acsenvironau.5c00062","title":"Machine Learning-Assisted Recognition of Environmental Sulfur-Containing Chemicals in Nontargeted Mass Spectrometry Analysis of Inadequate Mass Resolution","year":2025,"lang":"en","type":"article","venue":"ACS Environmental Au","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Canada Research Chairs; Genome British Columbia; Michael Smith Health Research BC; Canada Foundation for Innovation; Genome Canada","keywords":"Mass spectrometry; Sulfur; Resolution (logic); Chemistry; Gas chromatography–mass spectrometry; Chromatography; Environmental chemistry; Computer science; Artificial intelligence; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001780685,0.001147933,0.0007977609,0.001534155,0.0002799383,0.000706782,0.0008829008,0.0007655111,0.0008041273],"category_scores_gemma":[0.002763151,0.0002309348,0.0007852659,0.0006490243,0.0003666978,0.00073204,0.0006108769,0.0006935261,0.0007189791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004804484,"about_ca_system_score_gemma":0.0008032056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374104,"about_ca_topic_score_gemma":0.003312253,"domain_scores_codex":[0.9992031,0.0001483558,0.00007685362,0.0003280456,0.0001692565,0.00007432474],"domain_scores_gemma":[0.9984804,0.0006641282,0.0002640561,0.0001274995,0.0004086654,0.00005533443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001105958,0.0007352093,0.03792615,0.0004707355,0.0003416267,0.0006068593,0.0001728042,0.1183279,0.3197913,0.0008657771,0.005717149,0.5139385],"study_design_scores_gemma":[0.00002452601,0.0002153081,0.005554968,0.00001410113,0.00004432962,0.0001168844,0.00004145593,0.8882908,0.1036786,0.0007462989,0.001242195,0.00003045414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5679467,0.001143811,0.415051,0.0004102431,0.0001249116,0.0001582204,0.001556887,0.01132611,0.002282091],"genre_scores_gemma":[0.7860493,0.0002773445,0.2089446,0.0003647532,0.0000490712,0.0001202954,0.002533574,0.0001217598,0.001539273],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002374104,"threshold_uncertainty_score":0.009417295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007556660804609349,"score_gpt":0.2004643776019908,"score_spread":0.1929077167973815,"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."}}