Influence of climate change on persistent organic pollutants and chemicals of emerging concern in the Arctic: state of knowledge and recommendations for future research
Bibliographic record
Abstract
Persistent organic pollutants (POPs) have accumulated in polar environments as a result of long-range transport from urban/industrial and agricultural source regions in the mid-latitudes. Climate change has been recognized as a factor capable of influencing POP levels and trends in the Arctic, but little empirical data have been available previously. A growing number of recent studies have now addressed the consequences of climate change for the fate of Arctic contaminants, as reviewed and assessed by the Arctic Monitoring and Assessment Programme (AMAP). For example, correlations between POP temporal trends in air or biota and climate indices, such as the North Atlantic Oscillation Index, have been found. Besides the climate indices, temperature, precipitation and sea-ice were identified as important climate parameters influencing POP levels in the Arctic environment. However, the physical changes are interlinked with complex ecological changes, including new species habitats and predator/prey relationships, resulting in a vast diversity of processes directly or indirectly affecting levels and trends of POPs. The reviews in this themed issue illustrate that the complexity of physical, chemical, and biological processes, and the rapid developments with regard to both climate change and chemical contamination, require greater interdisciplinary scientific exchange and collaboration. While some climate and biological parameters have been linked to POP levels in the Arctic, mechanisms underlying these correlations are usually not understood and need more work. Going forward there is a need for a stronger collaborative approach to understanding these processes due to high uncertainties and the incremental process of increasing knowledge of these chemicals. There is also a need to support and encourage community-based studies and the co-production of knowledge, including the utilization of Indigenous Knowledge, for interpreting trends of POPs in light of climate change.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".