Should potential for climate change refugia be mainstreamed into the criteria for describing EBSAs?
Bibliographic record
Abstract
Abstract The world's oceans are subject to the influence of climate change at all latitudes and depths. There is a growing body of literature on the responses of species to climate change, which has a strong deterministic component indicating that responses can be predicted. At the same time, advances in oceanographic data acquisition and modeling have facilitated the identification of potential climate change refugia. The Convention on Biological Diversity's “Voluntary Specific Workplan on Biodiversity in Cold‐Water Areas within the Jurisdictional Scope of the Convention” explicitly calls for the identification and protection of refugia in cold‐water areas. We propose adding “Climate Change Refugium” as an integral consideration for identification of Ecologically or Biologically Significant Marine Areas (EBSAs). We provide a description of this as a potential eighth criterion. We then briefly discuss the pros and cons of introducing this eighth criterion, or an alternative strategy to develop guidelines that explicitly link refugia to the rationale of existing EBSA criteria, in the hope that this opinion piece will launch further discussion on this notion.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".