Actions taken by fishing Nations towards identification and protection of vulnerable marine ecosystems in the high seas: the Spanish case (Atlantic Ocean)
Bibliographic record
Abstract
In reply to the United Nations General Assembly Resolutions on sustainable fisheries, Spain, either by itself or in collaboration with other Nations, has been carrying out studies on vulnerable marine ecosystems (VMEs) in the high seas of the Atlantic Ocean (areas beyond national jurisdictions) since 2005. Such studies provide advice to the Spanish Government, the Regional Fisheries Management Organizations and the European Union. This paper presents the multidisciplinary methodology used and summarises the following management results: (i) contribution to identification of cold-water corals and provision of evidence to close part (∼16, 000km2) of the Hatton Bank (NE Atlantic) to bottom fishing; (ii) compilation of an international data base to identify VMEs on the slopes of the Grand Banks of Newfoundland, Flemish Pass, and Flemish Cap (NW Atlantic) and to redefine areas currently closed to fishing; (iii) improvement of knowledge about deepwater ecosystems on Walvis Ridge and adjacent seamounts (SE Atlantic) as a pilot project for implementation in this region; and (iv) identification of VMEs and closure of an area (∼41, 300km2) on the high seas of the SW Atlantic. Also discussed are progress and challenges related to identifying and protecting VMEs.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".