{"id":"W4400542653","doi":"10.1139/cjc-2024-0013","title":"Profiling organic pollutants in environmental water by dansylation-based non-targeted liquid chromatography-high resolution mass spectrometry analysis","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Chemistry; Mass spectrometry; Chromatography; Pollutant; Profiling (computer programming); Resolution (logic); Environmental analysis; Environmental chemistry; Organic chemistry; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004399669,0.0005784059,0.0003972134,0.0007267555,0.0002708859,0.0004277129,0.0003384508,0.0004690623,0.001010137],"category_scores_gemma":[0.0005399068,0.0001979953,0.0004407224,0.0005993396,0.0003365022,0.0006040832,0.0004188516,0.0004114525,0.0004949046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003276518,"about_ca_system_score_gemma":0.0003225672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001207577,"about_ca_topic_score_gemma":0.002348082,"domain_scores_codex":[0.999459,0.0000566639,0.00003843414,0.0001724109,0.0002161225,0.00005732516],"domain_scores_gemma":[0.9997624,0.00005894896,0.00006814028,0.00001887174,0.00007194571,0.00001961],"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":[0.00006633753,0.00001457046,0.0009023633,0.00007140331,0.00001039107,0.00003861548,0.00002478651,0.0000931829,0.9948772,0.00004387374,0.00004377561,0.003813468],"study_design_scores_gemma":[0.000006367071,0.0000896717,0.005077037,0.000005946394,0.00002577334,0.00008971738,0.00003621475,0.002564952,0.9899135,0.00007538997,0.00209978,0.00001567143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9291183,0.001931584,0.06280936,0.0001465794,0.00005009941,0.0001696151,0.003176798,0.0005224593,0.002075352],"genre_scores_gemma":[0.8523881,0.003652658,0.1310386,0.0003045293,0.00002740978,0.0002763233,0.005612397,0.0002049931,0.006494934],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001207577,"threshold_uncertainty_score":0.003379285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00527587389282481,"score_gpt":0.2080090125424395,"score_spread":0.2027331386496147,"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."}}