{"id":"W2005973966","doi":"10.1021/ac701633m","title":"Characterizing and Compensating for Matrix Effects Using Atmospheric Pressure Chemical Ionization Liquid Chromatography−Tandem Mass Spectrometry:  Analysis of Neutral Pharmaceuticals in Municipal Wastewater","year":2008,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Chemistry; Atmospheric-pressure chemical ionization; Chromatography; Ion suppression in liquid chromatography–mass spectrometry; Mass spectrometry; Matrix (chemical analysis); Analyte; Liquid chromatography–mass spectrometry; Chemical ionization; Tandem mass spectrometry; Sample preparation; Electrospray ionization; Analytical Chemistry (journal); Ionization; Ion","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":[0.0001734776,0.0002312315,0.0005184833,0.0000279708,0.00008045749,0.00001578468,0.0001466999,0.0001379197,0.0003556983],"category_scores_gemma":[0.00005586764,0.0002080524,0.0002148432,0.0008534529,0.0004402947,0.0001519346,0.0001491178,0.0001966705,0.000002522176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008073454,"about_ca_system_score_gemma":0.000006241131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003471401,"about_ca_topic_score_gemma":6.067572e-7,"domain_scores_codex":[0.9984275,0.00003795113,0.0004147807,0.0004128382,0.0002415629,0.0004653806],"domain_scores_gemma":[0.999288,0.0001765269,0.0001072845,0.0001664811,0.00000579836,0.0002559411],"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":[0.00007479663,0.0001514112,0.1318411,0.0001418931,0.0002632673,0.00001534271,0.00007815301,0.001862884,0.8655445,0.000007647798,0.000002923197,0.00001607666],"study_design_scores_gemma":[0.0005658118,0.00003717986,0.02786713,0.00003795429,0.0008203675,0.00001509412,0.00002579373,0.4015139,0.5688747,0.00001342779,0.00002611728,0.000202602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988644,0.0001292563,0.0004508659,0.000049884,0.00001342835,0.0001855738,0.00001402751,0.00002487912,0.0002677474],"genre_scores_gemma":[0.9956461,0.00005906281,0.004090243,0.00008160754,0.0000329565,0.000003399588,0.00002738166,0.00001841948,0.00004086555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.399651,"threshold_uncertainty_score":0.8484129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333909704215519,"score_gpt":0.2977794282508994,"score_spread":0.2744403312087442,"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."}}