Influences of climate change on long-term time series of persistent organic pollutants (POPs) in Arctic and Antarctic biota
Bibliographic record
Abstract
Time series of contaminants in the Arctic are an important instrument to detect emerging issues and to monitor the effectiveness of chemicals regulation, based on the assumption of a direct reflection of changes in primary emissions. Climate change has the potential to influence these time trends, through direct physical and chemical processes and/or changes in ecosystems. This study was part of an assessment of the Arctic Monitoring and Assessment Programme (AMAP), analysing potential links between changes in climate-related physical and biological variables and time trends of persistent organic pollutants (POPs) in Arctic biota, with some additional information from the Antarctic. Several correlative relationships were identified between POP temporal trends in freshwater and marine biota and physical climate parameters such as oscillation indices, sea-ice coverage, temperature and precipitation, although the mechanisms behind these observations remain poorly understood. Biological data indicate changes in the diet and trophic level of some species, especially seabirds and polar bears, with consequences for their POP exposure. Studies from the Antarctic highlight increased POP availability after iceberg calving. Including physical and/or biological parameters in the POP time trend analysis has led to small deviations in some declining trends, but did generally not change the overall direction of the trend. In addition, regional and temporary perturbations occurred. Effects on POP time trends appear to have been more pronounced in recent years and to show time lags, suggesting that climate-related effects on the long time series might be gaining importance.
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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 | 0.001 |
| 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 | 0.001 | 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".