A brief history of lumpfishing, assessment, and management across the North Atlantic
Bibliographic record
Abstract
Abstract This report documents the fishery, assessment, and management of lumpfish (Cyclopterus lumpus) across its distribution range. Targeting lumpfish for their roe on a large scale began in the 1950s in Iceland and Norway and then in Canada in the 1970s and Greenland in the 1990s. When the fishery began, there were few regulations, but limits on vessel size, mesh size, number of nets, and length of the fishing season were gradually implemented over time. Worldwide landings have varied from ca. 2000 to 8000 tonnes of roe between 1977 and 2016. Iceland and Canada accounted for >80% of the landings until 2000. After 2013, Greenland and Iceland accounted for >94%. All countries except Iceland show a decreasing trend in the number of boats participating in the fishery, which is related to several factors: the monetary value of the roe, changes in the abundance of lumpfish, and increasing age of artisanal fishers. Each country has a different combination of data available for assessment from basic landings and fishing effort data to more detailed fishery independent survey indices of abundance. The management of total catch also differs, with an effort-controlled fishery in Iceland and Canada, a total allowable catch (TAC) per boat in Norway, and TAC per area in Greenland. Population abundance is above management targets in Iceland and Norway, but the status is less clear in Greenland and around Denmark/Sweden and appears to be depleted around Canada. Certification by the Marine Stewardship Council was instrumental in the adoption of a management plan in Greenland; however, benefits to the fishers remain unclear. Aspects surrounding the biology of lumpfish, which is poorly understood and requires investigation, include growth rate, natural mortality, and population differentiation. In addition, there is concern about the potential impacts that the recent escalation in production of lumpfish for use as cleaner fish in the aquaculture industry could have on the wild population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".