Climate Change Is Likely To Severely Limit The Effectiveness Of Deep-Sea Abmts In The North Atlantic
Bibliographic record
Abstract
ATLAS work package 7 presentation at ATLAS 3rd General Assembly. 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. In the North Atlantic, Area-Based Management Tools (ABMTs), including Marine Protected Areas (MPAs), Ecologically or Biologically Significant Areas (EBSAs) and other effective conservation measures, such as areas closed to protect Vulnerable Marine Ecosystems (VMEs), have been created in Areas Beyond National Jurisdiction (ABNJ). Notwithstanding the different objectives of various types of ABMTs, at an ocean scale it makes good sense to consider them collectively to inform future systematic conservation planning. This presentation 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. It draws on the discussions held at ATLAS GA2, and peer review by ATLAS colleagues that contributed to a recently published paper (Johnson et al., 2018) produced in the framework of the ATLAS project, based on published data and on expert judgement. Results suggest that in a 20–50 year timeframe, virtually all North Atlantic deep-water and open ocean ABMTs will likely be affected by the effects of rapidly changing ocean variables such as temperature, pH, dissolved oxygen, fluxes of particulate organic carbon, and by changes in ocean circulation patterns. Results further suggest that resilience of populations, habitats and deep-sea ecosystems to changes in one or more of these variables is likely to be low, in which case the effectiveness of deep-sea ABMTs in the North Atlantic is likely to be severely limited by the effects of climate change. 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. The presentation will invite colleagues to suggest ways in which emerging results from ATLAS Work Packages can now inform the issue.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".