Beyond static spatial management: Scientific and legal considerations for dynamic management in the high seas
Bibliographic record
Abstract
Natural and human stressors in the high seas act across a wide range of spatial and temporal scales. These include direct interaction such as fisheries bycatch or indirect interaction like warming oceans and plastic ingestion. Area-based management tools (ABMTs), such as marine protected areas and time-area closures, are a widely accepted and a broadly successful form of management used to mitigate localized human impacts on marine species and ecosystems. Protection provides an opportunity for population recovery, which can then propagate outside of the closure. As the United Nations negotiates a new treaty on the conservation and sustainable use of biodiversity beyond national jurisdiction, efforts to design and implement high seas ABMTs at appropriate scales are critical to ensure that these spatial protection measures are most effective and climate-ready in the face of changing oceans. Here we identify the four most important temporal scales – contemporary, intra-annual, multi-annual and multidecadal – for aligning high seas ABMTs to relevant ecological, oceanographic and atmospheric processes. From this, we explore how managers and decision-makers can integrate this knowledge when implementing a new treaty.
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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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".