An Ecosystem Evaluation Framework for Global Seamount Conservation and Management
Bibliographic record
Abstract
In the last twenty years, several global targets for protection of marine biodiversity have been adopted but have failed. The Convention on Biological Diversity (CBD) aims at preserving 10% of all the marine biomes by 2020. For achieving this goal, ecologically or biologically significant areas (EBSA) have to be identified in all biogeographic regions. However, the methodologies for identifying the best suitable areas are still to be agreed. Here, we propose a framework for applying the CBD criteria to locate potential ecologically or biologically significant seamount areas based on the best information currently available. The framework combines the likelihood of a seamount constituting an EBSA and its level of human impact and can be used at global, regional and local scales. This methodology allows the classification of individual seamounts into four major portfolio conservation categories which can help optimize management efforts toward the protection of the most suitable areas. The framework was tested against 1000 dummy seamounts and satisfactorily assigned seamounts to proper EBSA and threats categories. Additionally, the framework was applied to eight case study seamounts that were included in three out of four portfolio categories: areas highly likely to be identified as EBSA with high degree of threat; areas highly likely to be EBSA with low degree of threat; and areas with a low likelihood of being EBSA with high degree of threat. This framework will allow managers to identify seamount EBSAs and to prioritize their policies in terms of protecting undisturbed areas, disturbed areas for recovery of habitats and species, or both based on their management objectives. It also identifies seamount EBSAs and threats considering different ecological groups in both pelagic and benthic communities. Therefore, this framework may represent an important tool to mitigate seamount biodiversity loss and to achieve the 2020 CBD goals.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".