{"id":"W2097774007","doi":"10.1039/c4ay02967d","title":"Analysis of nine N-nitrosamines using liquid chromatography-accurate mass high resolution-mass spectrometry on a Q-Exactive instrument","year":2015,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mass spectrometry; Chromatography; Chemistry; High resolution; Resolution (logic); Analytical Chemistry (journal); Liquid chromatography–mass spectrometry; Remote sensing; Computer science; Geology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006703797,0.0002183654,0.0004821343,0.0004810218,0.00007552286,0.0000293566,0.000132386,0.0000953629,0.0007232672],"category_scores_gemma":[0.0001835554,0.0001730919,0.0002720783,0.002866931,0.00021512,0.000216296,0.00007072152,0.000106447,0.00003127919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003745907,"about_ca_system_score_gemma":0.0000151756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349246,"about_ca_topic_score_gemma":0.00002318173,"domain_scores_codex":[0.9981158,0.0003758695,0.0003607346,0.0004197831,0.0004159577,0.0003117919],"domain_scores_gemma":[0.9990517,0.0001943445,0.0001694217,0.0003350242,0.00003135587,0.00021819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002131107,0.001965739,0.5196387,0.00002865275,0.007016211,0.00006158851,0.0004974974,0.1244095,0.3355819,0.003833854,0.0002713106,0.004563951],"study_design_scores_gemma":[0.001973874,0.00361651,0.3346689,0.00004362359,0.006860493,0.000006923663,0.0003875725,0.4558985,0.1899955,0.005399118,0.0002996775,0.0008493264],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8481308,0.00001655719,0.1470141,0.00009390184,0.00009438333,0.0001152539,0.00001252693,0.0000337102,0.004488723],"genre_scores_gemma":[0.8512962,0.000006420806,0.1484938,0.00003221129,0.00003495933,0.000006060917,0.00002183308,0.00001126079,0.00009723211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.331489,"threshold_uncertainty_score":0.7919269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05268803791494767,"score_gpt":0.3496143007109976,"score_spread":0.2969262627960499,"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."}}