{"id":"W4403966144","doi":"10.1016/b978-0-323-85601-0.00032-1","title":"HRMS-based suspect and non-target screening for contaminants of emerging concern: Current analytical strategies, compounds identification workflows, general research trends, and future perspectives","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Identification (biology); Suspect; Workflow; Data science; Biochemical engineering; Computer science; Management science; Risk analysis (engineering); Environmental science; Engineering; Psychology; Business; Biology; Ecology; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002501063,0.001398602,0.001144469,0.001867315,0.0004247718,0.002645591,0.002000917,0.001622656,0.01290582],"category_scores_gemma":[0.00162764,0.0007025292,0.000844432,0.001379078,0.0007823756,0.002678615,0.001516077,0.0015571,0.01444163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581474,"about_ca_system_score_gemma":0.001338712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017974,"about_ca_topic_score_gemma":0.003629783,"domain_scores_codex":[0.9987531,0.0001446231,0.00004760766,0.0002755389,0.0007111983,0.0000680033],"domain_scores_gemma":[0.9990432,0.0004415861,0.00007017169,0.00009033575,0.0003006853,0.00005397035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000345085,0.0002537136,0.001418954,0.002772487,0.0001382683,0.0002188326,0.0001377887,0.0007689424,0.4456562,0.004408628,0.03823259,0.5056484],"study_design_scores_gemma":[0.00004937253,0.0009477572,0.004694973,0.0007991488,0.0002165503,0.003451134,0.0005720714,0.01096551,0.6354328,0.01229539,0.3303448,0.0002304134],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.05164085,0.251017,0.5739375,0.006470199,0.004877809,0.001012341,0.0116441,0.01888238,0.08051778],"genre_scores_gemma":[0.07794261,0.1736006,0.5358213,0.00966384,0.00160153,0.0005271412,0.01631917,0.002300389,0.1822235],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01290582,"threshold_uncertainty_score":0.04317421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04119579830911096,"score_gpt":0.3431081737731149,"score_spread":0.301912375464004,"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."}}