Ocean Ecosystem-Based Management Mandates and Implementation in the North Atlantic
Bibliographic record
Abstract
Ecosystem-based management (EBM) necessarily requires a degree of coordination across countries that share ocean ecosystems, and among national agencies and departments that have responsibilities relating to ocean health and marine resource utilization. This requires political direction, legal input, stakeholder consultation and engagement, and complex negotiations. Currently there is a common perception that within and across national jurisdictions there is excessive legislative complexity, a relatively low level of policy coherence or alignment with regards to ocean and coastal EBM, and that more aligned legislation is needed to accelerate EBM adoption. Our Atlantic Ocean Research Alliance (AORA) task group was comprised of a small, focused and interdisciplinary mix of lawyers, social scientists, and natural scientists from Canada, the USA, and the EU. During a four-day workshop, we characterized, compared, and synthesized the mandates that govern marine activities and ocean stressors relative to facilitating EBM in national and international waters of the North Atlantic. We identified formal mandates across jurisdictions and, where possible, policy and other non-regulatory mandates. We found that irrespective of the detailed requirements of legislation or policy across AORA jurisdictions, or the efficacy of their actual implementation, most of the major ocean pressures and uses posing threats to ocean sustainability have some form of coverage by national or regional legislation. The coverage is, in fact, rather comprehensive. Still, numerous impediments to effective EBM implementation arise, potentially relating to the lack of integration between agencies and departments, a lack of adequate policy alignment, and a variety of other socio-political factors. We note with concern that if challenges regarding EBM implementation exist in the North Atlantic, we can expect that in less developed regions where financial and governance capacity may be lower, that implementation of EBM could be even more challenging.
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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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".