Temporal Trends of Legacy and Current-Use Halogenated Flame Retardants in Lake Ontario in Relation to Atmospheric Loadings, Sources, and Environmental Fate
Bibliographic record
Abstract
Since the phase-out of polybrominated diphenyl ethers (PBDEs), large amounts of alternative halogenated flame retardants (AHFRs) have been introduced to the market. Due to their persistence and toxicity, halogenated flame retardants (HFRs) have become a concern for the ecosystem and human health. However, there remains limited assessment of the atmospheric loadings, sources, and environmental fate of HFRs in Lake Ontario, which receives urban-related inputs and cumulative chemical inputs from the upstream Great Lakes from Canada and the United States. We combined long-term measurements with a modified multimedia model based on site-specific environmental parameters from Lake Ontario to understand the trends and fate of HFRs. All HFRs were detected in the air, precipitation, lake trout, and herring gull egg samples throughout the sampling periods. General decreasing trends were found for PBDEs, while the temporal trends for AHFRs were not clear. Physical-chemical properties and emissions significantly influence the levels, profiles, and trends. Using the probabilistic modeling, HFR concentrations in lake water and sediment were predicted to be close to the measurement, suggesting a good performance for the modified model. The loadings from tributaries and wastewater effluent were the primary input pathways. Transformations in the water and sedimentation were estimated to be the dominant output pathway for the three HFRs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".