Climate change is likely to severely limit the effectiveness of deep-sea ABMTs in the North Atlantic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it