Incidental seabird bycatch in the Greenland Halibut fishery in northern Canada reported via at-sea observer programs
Bibliographic record
Abstract
ABSTRACT Objective In the eastern Canadian Arctic, the Greenland Halibut Reinhardtius hippoglossoides fishery has been active since the 1990s, but to date, no comprehensive examination of seabird bycatch patterns has been carried out. Incidental catch of seabirds by fisheries can be detrimental to seabird populations and can also result in the loss of time, bait, and targeted fish species for the fishery. Thus, bycatch of seabirds can have negative impacts on both conservation efforts and fisheries. We aimed to understand seabird bycatch by the Greenland Halibut fishery across seasons, across years, and associated with different types of gear, which is critical to informing potential mitigation solutions that may reduce seabird bycatch in fishing gear. Methods We compiled all available information on seabird bycatch in the eastern Canadian Arctic region (Northwest Atlantic Fisheries Organization Subarea 0, Divisions 0A and 0B) to assess spatial and temporal patterns of bycatch of all seabirds reported by at-sea fisheries observers from 2010 to 2019. Results We found that northern fulmars Fulmarus glacialis were the most frequently reported seabird bycatch species in the fishery, with high levels of monthly and annual variation in the number of birds reported as bycatch. Gulls and shearwaters were also reported at lower levels in the bycatch database. Conclusions Seabird bycatch by the Greenland Halibut fishery in Divisions 0A and 0B is highly variable from year to year but does not appear to be related to the level of total allowable catch. August, September, and October had the highest levels of northern fulmars reported in the bycatch records, suggesting that fulmars may be more vulnerable to being caught in the fishery during these months, although future examinations of effort data are needed to confirm this.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".