Exploring ecosystem‐based management in the North Atlantic
Bibliographic record
Abstract
The United States, the EU and Canada established a trilateral working group on the ecosystem approach to ocean health and stressors under the Atlantic Ocean Research Alliance. Recognizing the Atlantic Ocean as a shared resource and responsibility, the working group sought to advance understanding of the Atlantic Ocean and its dynamic systems to improve ocean health, enhance ocean stewardship and promote the sustainable use and management of its resources. This included consideration of multiple ocean-use sectors such as fishing, shipping, tourism and offshore energy. The working group met for 4 years and worked through eight steps that covered the development of common language as a basis for collaboration, challenges of stakeholder engagement, review of the governance mandates, exploring the links between sectors and ecosystems effects, identifying gaps in knowledge and uptake of science, identification of tools for ecosystem-based management, customary best practice for tool development and communication of key research priorities. The key findings were that ecosystem-based management enables new benefits and opportunities, and that we need to make the business case. Further findings were that adequate mandates and effective tools exist for ecosystem-based management, and that ecosystem-based management urgently requires integration of human dimensions, so we must diversify the conversation. In addition, it was found that stakeholders do not see their stake in ecosystem-based management, so greater engagement with stakeholders and targeting of ocean literacy is required and a sustainable future requires a sustained investment in ecosystem-based management, so long-term commitment is key.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".