{"id":"W3136662993","doi":"10.1016/j.chemosphere.2021.130216","title":"A data-independent acquisition approach based on HRMS to explore the biodegradation process of organic micropollutants involved in a biological ion-exchange drinking water filter","year":2021,"lang":"en","type":"article","venue":"Chemosphere","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; National Research Council Canada; Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Biodegradation; Raw water; Chromatography; Filter (signal processing); Water treatment; Environmental chemistry; Fragmentation (computing); Degradation (telecommunications); Environmental science; Environmental engineering; Computer science; 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":[0.0006110424,0.0007500298,0.0003259252,0.0006879475,0.0004274764,0.0003681538,0.0005149033,0.0005688594,0.001365511],"category_scores_gemma":[0.0006185669,0.0002860212,0.0004091443,0.0003906158,0.0002604165,0.0005555594,0.000613755,0.0007041181,0.0004204261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002308393,"about_ca_system_score_gemma":0.0008738193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129343,"about_ca_topic_score_gemma":0.00213156,"domain_scores_codex":[0.9997026,0.00003330215,0.00001883907,0.0000827443,0.0001321389,0.0000303118],"domain_scores_gemma":[0.999674,0.00007006482,0.00004153833,0.00002826623,0.0001607144,0.00002536723],"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":[0.000173286,0.00006617684,0.001039291,0.0001016333,0.00003304227,0.000058363,0.00003862521,0.0002687472,0.9847711,0.0001677679,0.0001766898,0.01310543],"study_design_scores_gemma":[0.00003471396,0.0003428682,0.004123502,0.00001124986,0.00007225874,0.0003706953,0.00007143176,0.02045652,0.9719345,0.0001687321,0.002373835,0.00003967285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7078457,0.001233642,0.28262,0.0005035393,0.0001989662,0.000398906,0.001630164,0.002201935,0.003367185],"genre_scores_gemma":[0.7869521,0.0007932428,0.2073209,0.0004940022,0.00005433545,0.0002702289,0.0009346413,0.0001548094,0.003025824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001365511,"threshold_uncertainty_score":0.0045681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08316275468009275,"score_gpt":0.2950901325052423,"score_spread":0.2119273778251495,"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."}}