Vulnerable Marine Ecosystems: a concept to be applied for different anthropogenic activities
Bibliographic record
Abstract
The vulnerability of deep-sea biodiversity has been recognized by the United Nations General Assembly (UNGA). The Food and Agricultural Organization (FAO) of the United Nations were then asked to develop guidelines for the management of deep-sea fisheries in the high seas (FAO, 2009). Those guidelines included criteria for defining what constitutes a VME: (1) uniqueness or rarity; (2) functional significance of the habitat; (3) fragility; (4) life history traits of component species that make recovery difficult; and (5) structural complexity. The VMEs concept was developed in the framework of fisheries. However, with the crescent use of deep-sea environments, we advocate that the concept should also be framed to other industries such as deep-sea mining, oil and gas, and use in future regulations. The concept shall also be used for the identification of areas to be protected under the Kuming -Montreal agreement and BBNJ treaty.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".