Organophosphate Esters in Sediment of the Great Lakes
Bibliographic record
Abstract
This is the first study on organophosphate ester (OPEs) flame retardants and plasticizers in the sediment of the Great Lakes. Concentrations of 14 OPEs were measured in three sediment cores and 88 Ponar surface grabs collected from Lakes Ontario, Michigan, and Superior of North America. The sum of these OPEs (Σ 14 OPEs) in Ponar grabs averaged 2.2, 4.7, and 16.6 ng g –1 dw in Lakes Superior, Michigan, and Ontario, respectively. Multiple linear regression analyses demonstrated statistically significant associations between logarithm concentrations of Σ 14 OPEs as well as selected congeners in surface grab samples and sediment organic carbon content as well as a newly developed urban distance factor. Temporal trends observed in dated sediment cores from Lake Michigan demonstrated that the recent increase in depositional flux to sediment is dominated by chlorinated OPEs, particularly tris(2-chloroisopropyl) phosphate (TCPP), which has a doubling time of about 20 years. Downward diffusion within sediment may have caused vertical fractionation of OPEs over time. Two relatively hydrophilic OPEs including TCPP had much higher concentrations in sediment than estimated based on equilibria between water and sediment organic carbon. Approximately a quarter (17 tonnes) of the estimated total OPE burden (63 tonnes) in Lake Michigan resides in sediment, which may act as a secondary source releasing OPEs to the water column for years to come.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".