Emerging pollutants in the North Sea in comparison to Lake Ontario, Canada, data
Bibliographic record
Abstract
In the present study, the concentrations and fate of contaminants such as organophosphate flame retardants and plasticizers, musk compounds such as galaxolide (HHCB), tonalide (AHTN), musk ketone and musk xylene, the bactericide triclosan, as well as the metabolites HHCB-lactone and triclosan-methyl were compared in the aqueous phase of the German Bight (North Sea). The concentrations of these compounds were around 1 to 10 ng/L in nearshore areas, and the concentrations were lower in the more pristine areas. The highest concentrations were determined for tris-(2-chloro-isopropyl) phosphate in the North Sea with concentration exceeding 10 ng/L even for the offshore samples. The samples contained 1 to 20 ng/L chlorinated organophosphates, approximately 1 ng/L nonchlorinated organophosphates, and 0.3 to 3 ng/L fragrance compounds. Some samples from Lake Ontario (Canada) were analyzed in comparison. Per capita emissions were calculated for both regions. These emissions were compared and turned out to be very similar for the Canadian and German locations. For the North Sea, some observations concerning stability, dilution, and degradation, as well as sources of the respective substances, were performed. These data indicate that the chlorinated organophosphates and some musk fragrances exhibit half lives exceeding the residence times and thus can be considered to be persistent in this ecosystem. In the German Bight, the river Elbe is the dominating source for the more hydrophilic compounds, such as chlorinated organophosphate flame retardants, which are diluted only into the North Sea. However, for the more lipophilic compounds such as the musk fragrances, different input patterns as well as distribution patterns are relevant, though the river Elbe is still a major source of pollution to the German Bight of the North Sea. The data seem to indicate either relevant inputs further west of the sampling area or mobilization from the sediments.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".