Polar lessons learned: long‐term management based on shared threats in Arctic and Antarctic environments
Bibliographic record
Abstract
The Arctic and Antarctic polar regions are subject to multiple environmental threats, arising from both local and ex‐situ human activities. We review the major threats to polar ecosystems including the principal stressor, climate change, which interacts with and exacerbates other threats such as pollution, fisheries overexploitation, and the establishment and spread of invasive species. Given the lack of progress in reducing global atmospheric greenhouse‐gas emissions, we suggest that managing the threats that interact synergistically with climate change, and that are potentially more tractable, is all the more important in the short to medium term for polar conservation. We show how evidence‐based lessons learned from scientific research can be shared between the poles on topics such as contaminant mitigation, biosecurity protocols to reduce species invasions, and the regulation of fisheries and marine environments. Applying these trans‐polar lessons in tandem with expansion of international cooperation could substantially improve environmental management in both the Arctic and Antarctic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".