Systematic Conservation Planning: A Better Recipe for Managing the High Seas for Biodiversity Conservation and Sustainable Use
Bibliographic record
Abstract
Abstract At the UN Conference on Sustainable Development in Rio in June 2012, world leaders committed to the conservation and sustainable use of marine biological diversity in areas beyond national jurisdiction (the high seas). Our analysis of gaps in high seas management indicates that a paradigm shift to a more systematic approach will be needed to safeguard high seas biodiversity from mounting threats. Experience from terrestrial and coastal areas indicates that a systematic approach to conservation planning and management can help to maintain ecosystem health and productivity while enabling sustainable use. Our analysis further demonstrates that the current legal regime on the high seas is insufficient to realize these objectives: management institutions have neither an adequate mandate for integrated planning nor the ability to effectively coordinate across multiple management regimes. We identify key elements for future high seas management and posit that a two‐pronged approach is most promising: the development of an improved global legal regime that incorporates systematic planning as well as the expansion of existing and new regional agreements and mandates. This combined approach is most likely to achieve the required ecosystem‐based, integrated and science‐based management that world leaders at Rio acknowledged should underpin ocean management.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".