The ecosystem approach to marine management in the Arctic: Opportunities and challenges for integration
Bibliographic record
Abstract
Climate change is strongly impacting Arctic marine ecosystems, and the Arctic coastal communities whose identities, traditions and livelihoods are closely interconnected with the marine environment. The Ecosystem Approach (EA) is a promising approach for understanding and managing the occurring shifts in the Arctic marine ecosystems. Based on our analysis, we find that assessments conducted by international and regional instruments and institutions, most notably the Arctic Council, as well as the wealth of Indigenous knowledge present in the region, provide valuable starting points for the implementation of EA in the Arctic. Yet, mechanisms for translating knowledge into joint coordinated and integrated action in accordance with EA are currently lacking. Our analysis suggests that incremental steps can be taken now to promote the implementation of EA, while working to establish a more comprehensive governance framework. In our view, bottom-up initiatives may provide the most promising avenue for promoting the application of EA in the region under the current geopolitical circumstances. Support by civil society, Indigenous and conservation organizations, as well as global momentum will be necessary to coordinate, finance and elevate community-driven initiatives. Other opportunities we identify for advancing EA is to engage with sectoral management bodies and to advance EA through climate change adaptation measures.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".