Nordic Seals: Seal populations in the North-Atlantic, Arctic Ocean and adjacent waters
Bibliographic record
Abstract
This report is a part of the Nordic networking project Nordic Seals, which is supported by the Nordic Council of Ministers Working Group for Fisheries (AG Fisk). The project’s objectives are to gather, analyse and disseminate information on the populations of seals in the North-Atlantic, Arctic, and adjacent waters, and their environmental, social, and economic impacts. As several seal populations have grown in the North Atlantic, Arctic, and adjacent waters, they have become a controversial topic with fishermen and other stakeholders within seafood value chains who claim that they negatively affect commercial fish stocks, catch, product quality and economic viability of the fisheries. Many scientists and conservationists have on the other hand pointed out the lack of understanding of the functioning of seals in the ecosystem. Although seals are known to feed on commercial fish species, research on their effect on fish size and age distribution of prey populations, as well as stock size, is incomplete. More knowledge on the role and effects of seals in the ecosystem is needed. As some seal populations still suffer from hunting that took place in the past, decisions on seal management must be well founded. Bycatch of seals is today the main threat to seal populations in many areas, which must be taken seriously. Depredations and damage to fishing gear and fish farms caused by some species of seals is well documented. The exact ecological and economic impact of these is however largely unknown. There are ongoing initiatives that aim to fill in these knowledge gaps, but results are largely lacking. The issue of nematode roundworms that are parasites causing quality defects in commercial fisheries, which seals play a major role in distributing as hosts, has been a major concern for fishermen. Controlling seal populations was in the past believed to be important to limit nematode distribution and therefore considered vital to safeguard the economic viability of the seafood industries in the North Atlantic. Seals have a long history as an important food source. Seal meat is nutritious and full of important amino acids, vitamins, and minerals. But they also contain food safety threats, such as nematode ringworm parasites, and bioaccumulated trace elements. The import bans on seal products imposed by the US and EU have made any kind of trade in seal products difficult. But as some seal populations grow in certain areas, the question on potential utilisation becomes more pressing. To answer that question there is a need for more research to better understand the role of seals in the ecosystem, and on how to produce sustainable, safe and stable food or feed ingredients from seals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".