{"id":"W4414113751","doi":"10.1016/j.scitotenv.2025.180429","title":"A province-wide mapping of per- and polyfluoroalkyl substances (PFAS) in surface waters of the St. Lawrence River watershed, Québec, Canada","year":2025,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Per- and polyfluoroalkyl substances research","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Strategic Environmental Research and Development Program; Université de Montréal; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Coordinating Committee","keywords":"Tributary; Surface water; Perfluorooctanoic acid; Hydrology (agriculture); Flux (metallurgy); Perfluorooctane; Water quality","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":[0.0002846002,0.0003218751,0.0002386693,0.001281731,0.001262942,0.0008197415,0.0005423189,0.0002697951,0.001138152],"category_scores_gemma":[0.0005960424,0.0001692106,0.0002745405,0.002972835,0.0004131111,0.0001927767,0.0003929729,0.0002455656,0.0001929175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01345637,"about_ca_system_score_gemma":0.02119266,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952993,"about_ca_topic_score_gemma":0.9979672,"domain_scores_codex":[0.9996443,0.00002055553,0.00001542495,0.0001043329,0.0001186205,0.00009686766],"domain_scores_gemma":[0.9989269,0.00004296986,0.0001112207,0.00002356405,0.0007742593,0.0001209612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000781635,0.00002678346,0.9687909,0.0001003013,0.00008025394,0.0001380708,0.00110606,0.0005359082,0.005245181,0.0001499834,0.00292645,0.02082197],"study_design_scores_gemma":[0.000003881478,0.00001082143,0.9954679,0.00002403643,0.00001634536,0.00003272545,0.0009424684,0.0006877334,0.0002189011,0.00001137959,0.002575757,0.000008110831],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984923,0.000916034,0.0007080949,0.0002462294,0.000008423539,0.0000784909,0.009077654,0.0000473105,0.00399485],"genre_scores_gemma":[0.9924513,0.0003775949,0.0009113424,0.00008586622,0.000002659594,0.00003352467,0.003341478,0.000008104905,0.002788157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01345637,"threshold_uncertainty_score":0.09763324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00871091174697638,"score_gpt":0.1993561852275276,"score_spread":0.1906452734805512,"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."}}