Bycatch in the West Greenland lumpfish fishery, with particular focus on the common eider population
Bibliographic record
Abstract
Incidental bycatch is a well-known challenge in gillnet fisheries throughout the world, and the fishery for North Atlantic lumpfish Cyclopterus lumpus roe is no exception. In Greenland, the fishery was Marine Stewardship Council-certified in 2015 but has pending conditions related to bycatch quantification, enforcement and mitigation strategies. To improve this situation and to assess the potential impact of bycatch, we collected independent on-board observer data on non-target fish and seabirds over 2 seasons (2019 and 2021). We recorded 6 fish species, but the only species constituting >1% of the lumpfish landings was the spotted wolffish Anarhichas minor. The bycatch of fish likely had little impact on the involved fish stocks. We recorded 4 seabird species, of which common eider Somateria mollissima was most common. When extrapolated to the entire West Greenland lumpfish fishery, the estimated bycatch of common eider was considerably higher in 2019 (19938; 95% CI: 3486-59661) than in 2021 (9802: 1260-29940) due to a longer fishing season in 2019. On average, for 2019 and 2021, the bycatch was modelled to reduce the growth potential for the West Greenland winter population by 51%. In comparison, the current hunting level (16538 birds yr-1) reduced the growth potential by 30%. The larger impact of bycatch was mainly due to a larger proportion of adults and females being targeted. The common eider bycatch impacts mainly the breeding population in Canada and Southwest Greenland and less so in Northwest Greenland. As mitigation, we recommend temporal closures of the fishery unless modified gillnets, which markedly reduce bycatch, become available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".