Applying MFC knowledge to meet global conservation targets for the deep and high seas
Bibliographic record
Abstract
The importance of ecological connectivity is being recognized in most major international fora dealing with biodiversity conservation, such as the Global Biodiversity Framework (GBF), the agreement on Biodiversity Beyond National Jurisdiction, and the regional environmental management plans for deep-sea mining by the International Seabed Authority. However, this recognition is currently not well-coupled with implementation in the oceans. For example, connectivity metrics included in the monitoring framework of the GBF have not been tested or applied in the ocean and in many instances are not even appropriate for estimating MFC specifically. I will present the current state of the incorporation of MFC in global conservation targets. I will then address some of the gaps and limitations for assessing MFC in the high seas and the deep ocean, particularly given the limited information on species distributions and life histories of deep sea species. I will provide examples of studies on connectivity that have direct implications for the design of networks of marine protected areas in the deep ocean and some of the tools and approaches that are possible to use. I will provide ideas on future directions to address the gap between the science of connectivity and its implementation in the management and conservation of the high seas and the deep ocean.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".