Climate change is likely to severely limit the effectiveness of deep-sea ABMTs in the North Atlantic
Bibliographic record
Abstract
In the North Atlantic, Area-Based Management Tools (ABMTs), including Marine Protected Areas (MPAs) and areas describing the inherent value of marine biodiversity, have been created in Areas Beyond National Jurisdiction (ABNJ). This deep-sea area (> 200 m) supports vitally important ecosystem services. Dealing with the multiple and increasing pressures placed on the deep sea requires adequate governance and management systems, and a thorough evaluation of cumulative impacts grounded on sound science. Notwithstanding the different objectives of various types of ABMTs, at an ocean scale it makes good sense to consider MPAs, Ecologically or Biologically Significant Areas (EBSAs) and other effective conservation measures, such as areas closed to protect Vulnerable Marine Ecosystems (VMEs), collectively to inform future systematic conservation planning. This paper focuses on climate change pressures likely to affect these areas and the need to evaluate implications for the state of biodiversity features for which they have been established. In a 20–50 year timeframe, virtually all North Atlantic deep-water and open ocean ABMTs will likely be affected. More precise and detailed oceanographic data are needed to determine possible refugia, and more research on adaptation and resilience in the deep sea is needed to predict ecosystem response times. Until such analyses can be made, a more precautionary approach is advocated, potentially setting aside more extensive areas and strictly limiting human uses and/or adopting high protection thresholds before any additional human use impacts are allowed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".