{"id":"W2064564816","doi":"10.1016/j.chroma.2006.07.033","title":"Stereoisomer quantification of the -blocker drugs atenolol, metoprolol, and propranolol in wastewaters by chiral high-performance liquid chromatography–tandem mass spectrometry","year":2006,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":155,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; American Chemistry Council","keywords":"Chemistry; Chromatography; Atenolol; Effluent; Enantiomer; Wastewater; Liquid chromatography–mass spectrometry; Tandem mass spectrometry; Metoprolol; Analyte; High-performance liquid chromatography; Mass spectrometry; Organic chemistry; Waste management","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":[0.0004497566,0.0004569534,0.0004468494,0.0006712842,0.0004931533,0.0007531354,0.0004013611,0.0009317433,0.0008764831],"category_scores_gemma":[0.0008596142,0.0002909043,0.0002937666,0.0003939102,0.0004016827,0.0003783842,0.0003003448,0.0007049238,0.0005467166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007104953,"about_ca_system_score_gemma":0.001264989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002449792,"about_ca_topic_score_gemma":0.004238873,"domain_scores_codex":[0.9992986,0.0001201264,0.00005322135,0.0001339391,0.000300173,0.00009389106],"domain_scores_gemma":[0.9996,0.00007757218,0.00007714163,0.00002868004,0.0001605825,0.00005608771],"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":[0.0003991665,0.00005152317,0.001543122,0.00003509389,0.00001816838,0.000034857,0.00003110496,0.0001662231,0.991604,0.0001033627,0.00009263104,0.005920652],"study_design_scores_gemma":[0.00003914256,0.0002255795,0.004199384,0.000008804041,0.00002840993,0.0001491184,0.00006362851,0.002458563,0.9916458,0.0001578046,0.001006792,0.00001706666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758636,0.001600427,0.01754338,0.0002655048,0.0001201498,0.00008711784,0.00130356,0.0002319467,0.002984398],"genre_scores_gemma":[0.9747704,0.001329959,0.01900476,0.0004858256,0.00003609537,0.0001029914,0.001171626,0.0000599345,0.003038474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002449792,"threshold_uncertainty_score":0.005155087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006907631787512427,"score_gpt":0.2139658665861886,"score_spread":0.2070582347986762,"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."}}