{"id":"W2973445564","doi":"10.1016/j.envpol.2019.113166","title":"Poly- and per-fluoroalkyl compounds in sediments of the Laurentian Great Lakes: Loadings, temporal trends, and sources determined by positive matrix factorization","year":2019,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Per- and polyfluoroalkyl substances research","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Masarykova Univerzita; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Saskatchewan; U.S. Environmental Protection Agency","keywords":"Sulfonic acid; Apportionment; Environmental chemistry; Chemistry; Environmental science; Factorization; Matrix (chemical analysis); Mathematics; Chromatography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000119395,0.0001754559,0.0001681864,0.0000625844,0.000123347,0.00003073223,0.0001466064,0.00009079561,0.0009507835],"category_scores_gemma":[0.000002503265,0.0001418652,0.00004113409,0.0001490194,0.0003253213,0.0003429046,0.000180002,0.0001311987,0.00003556507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002294321,"about_ca_system_score_gemma":0.000003934315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007827814,"about_ca_topic_score_gemma":0.000315201,"domain_scores_codex":[0.9986826,0.0001028042,0.0002169543,0.0003289119,0.0003958859,0.0002729035],"domain_scores_gemma":[0.9996241,0.00001646494,0.0001088571,0.0001599227,8.64241e-7,0.0000898043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004349495,0.00005932691,0.7507394,0.000005618641,0.000007532612,5.106984e-7,0.0007217151,0.000009812959,0.2281994,0.000003139286,0.0002010778,0.02000894],"study_design_scores_gemma":[0.0008952737,0.0001360854,0.9708382,0.00002361273,0.00001197081,0.000008000477,0.0004035473,0.0005952216,0.02454269,0.00002866307,0.002341196,0.0001755625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948402,0.003913079,0.00002052594,0.0002044215,0.00007076795,0.0002378335,0.0002018607,0.000008987659,0.0005023312],"genre_scores_gemma":[0.9975381,0.000148925,0.00004124238,0.0000437205,0.00001330087,0.000006201094,0.0001071851,0.0000136416,0.002087629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2200988,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004563756952839134,"score_gpt":0.2168610555371324,"score_spread":0.2122972985842932,"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."}}