Poly- and per-fluoroalkyl compounds in sediments of the Laurentian Great Lakes: Loadings, temporal trends, and sources determined by positive matrix factorization
Bibliographic record
Abstract
A recent data set for 22 poly- and per-fluorinated compounds (PFASs) in Ponar grab samples of surface sediments and cores from the Great Lakes of North America was examined for concentrations, loads, correlations with geographical coordinates and depth (time), and for sources. Correlations were determined by multivariate regression analyses. Source apportionment of PFASs was carried out by positive matrix factorization (PMF) for two cores from Lake Ontario. For the five lakes together, the total load of PFASs in sediments was estimated to be 245 ± 24 tonnes, which is about half the load for total PCBs. The recent annual loading was 1812 ± 320 kg/yr. Concentrations and inventories of PFASs were greatest in Lakes Erie and Ontario. Since 1947, concentrations of perfluorooctane sulfonic acid (PFOS) in ten cores have increased exponentially as a function of time with doubling times between 10 and 54 yr and have leveled off in three cores since 2000. PMF demonstrated an effective grouping of two particle-associated factors, characterized mainly by longer-chain PFASs (C ≥ 8) and two other factors of mainly shorter-chain compounds (C ≤ 6). Two factors feature only one dominant compound: factor 1, PFOS, and factor 3, perfluorobutane sulfonic acid (PFBS). Of all factors, factor 3 with PFBS has the largest contribution (47.8%). Significant scores for perfluorohexane sulfonic acid (PFHxS) and PFBS, along with flat or decreasing PFOS contributions since 2003, indicate that the replacement of PFOS with these compounds is beginning to take effect in the environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".