Effect of receiving environment on the transport and fate of polybrominated diphenyl ethers near two submarine municipal outfalls
Bibliographic record
Abstract
The fate of contaminants entering the marine environment through wastewater outfalls depends on the contaminant's persistence and affinity for particles. However, the physical characteristics of the receiving environment, for example, current velocity and sedimentary processes, may be even more important. Because of the complexity of natural settings and the lack of appropriate comparative settings, this is not frequently evaluated quantitatively. The authors investigated the near-field accumulation of particle-reactive polybrominated diphenyl ethers (PBDEs) entering coastal waters by way of two municipal outfalls: one discharging into a high-energy, low-sedimentation environment near Victoria, BC, Canada; the other into a low-energy, high-sedimentation environment, near Vancouver, BC. The authors used ²¹⁰Pb profiles in box cores together with an advection-diffusion model to determine surface mixing and sedimentation rates, and to model the depositional history of PBDEs at these sites. Surprisingly, 88 to 99% of PBDEs were dispersed beyond the near-field at both sites, but a greater proportion of PBDEs was captured in the sediment near the Vancouver outfall where rapid burial was facilitated by inorganic sediment supplied from the nearby Fraser River. Although the discharge of PBDEs was much lower from the Victoria outfall than from Vancouver, some sediment PBDE concentrations were higher near Victoria.
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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 | 0.000 | 0.000 |
| 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".