Ecosystem Based Management (EBM) in a Rapidly Warming Arctic: Sharing experiences and challenges
Bibliographic record
Abstract
The Arctic continues to warm and becomes more accessible with activities expanding into new, and often vulnerable areas. It has therefore never been more important to assess the ecosystem and support a holistic ecosystem-based management to point out solution options for ecological, social and economic sustainability across and beyond National jurisdictions.Strengthening cooperation on Ecosystem Based Management across the marine Arctic is among the main priorities for the Norwegian Chair ship of the Arctic Council 2023-2025 and facilitating the implementation in the 18 Arctic Large Marine Ecosystems is the main goal across working groups, Indigenous knowledge and Observers in the Arctic Council.In 2024, the Norwegian Chair ship of the Arctic Council and the Institute of Marine Research hosted a International Conference on the Ecosystem Approach to Management in the Arctic Large Marine Ecosystems in Tromsø, Norway.The conference spanned across three main components of the EA framework –“Governance/Policy” and “Knowledge/Science” and the “communication links” which includes the goals, advice and value, making the link between “Governance/Policy” and “Knowledge/Science” operative and brought together 246 science, industry, policy and management experts from the Arctic Council states and from Europe and Asia (total 27 countries) as well as three of the six Permanent Participants, including the Saami Council, the Aleut International Association, and the Inuit Circumpolar Council, academics, non-profit organizations, and students to explore implementation of the ecosystem approach in the arctic.The suggestions and solutions from this conference on how to proceed in develop and implement the EBM in the Arctic Large Marine Ecosystems are presented in this talk and includes an iterative ecosystem based and holistic managment around common values.
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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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".