{"id":"W4399222353","doi":"10.1016/j.scitotenv.2024.173682","title":"Fast analysis of short-chain and ultra-short-chain fluorinated organics in water by on-line extraction coupled to HPLC-HRMS","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Per- and polyfluoroalkyl substances research","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Ministère des Forêts, de la Faune et des Parcs; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Strategic Environmental Research and Development Program; Transport Canada","keywords":"Chromatography; Extraction (chemistry); High-performance liquid chromatography; Chemistry; Chain (unit); Environmental chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005766354,0.001259187,0.0006706499,0.0008087762,0.0004763783,0.0004578302,0.0006712424,0.0007857846,0.001768192],"category_scores_gemma":[0.000631946,0.0003668619,0.0004068278,0.0004911689,0.0004087042,0.0005688366,0.000533685,0.0005444275,0.0009875806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651778,"about_ca_system_score_gemma":0.00168669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005693952,"about_ca_topic_score_gemma":0.0129087,"domain_scores_codex":[0.9990314,0.0001015268,0.0000389461,0.000216316,0.0005091879,0.0001025628],"domain_scores_gemma":[0.9996231,0.00006521764,0.00005655037,0.00001896733,0.0002159101,0.00002023642],"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":[0.0001129954,0.00004720441,0.002910656,0.0001544874,0.00004059786,0.00007937646,0.00004538739,0.0002361698,0.9632311,0.0001001883,0.0002835238,0.03275822],"study_design_scores_gemma":[0.00004596289,0.0004634086,0.01181783,0.00002736517,0.00005983074,0.0006012024,0.00009814098,0.005949731,0.9734421,0.0002184199,0.007221922,0.0000541427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6757847,0.00514217,0.3051981,0.000285813,0.0001688997,0.001064592,0.004050392,0.002623841,0.005681589],"genre_scores_gemma":[0.6366968,0.004352537,0.3417511,0.0009986303,0.00008641534,0.0009402147,0.0034137,0.0002437579,0.01151684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005693952,"threshold_uncertainty_score":0.0113216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470230317321098,"score_gpt":0.2675312685194438,"score_spread":0.2528289653462328,"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."}}