Enhanced data collection in the Canadian Arctic for seabird bycatch information yields highly variable results
Bibliographic record
Abstract
Abstract Incidental catch of seabirds (bycatch) in fisheries has been identified as a major threat to the conservation of seabird populations globally. Acquiring accurate, detailed data on seabird bycatch is an ongoing challenge to effective integrated ecosystem management of commercial fisheries. This is especially true in the Arctic region where different countries have highly variable reporting and data systems to track and understand seabird bycatch in fisheries. To collect detailed data on seabird bycatch in the Greenland halibut ( Reinhardtius hippoglossoides ) fishery in northern Canada, we applied two methods that asked for more information than standardly reported in the fishery as a voluntary effort with industry partners. We found that the amount of bird bycatch reported in both enhanced datasheets completed by at-sea observers (ASO) and carcass collections yielded different results when compared to the typical seabird bycatch reporting in the fisheries ASO database. Across three years of data collection (2016, 2018, and 2019), the number of seabirds reported using the enhanced data collection methods were 0.5-11-fold the number from typical ASO database values. In a fourth year of observations (2023), enhanced datasheets from the ASO reported no bycaught fulmars (gulls and terns were reported), but the accompanying photographs showed northern fulmars as bycatch, further supporting that seabird identification is hampering accurate reporting. We then used these data to model how the differences between data sources may fluctuate across years. These large discrepancies between the methods highlight the challenges with obtaining accurate and precise seabird bycatch data needed to implement a meaningful ecosystem approach to the management of the fisheries. Future modelling efforts need to take these differing data sources and variability into account to fully understand the potential population level impacts of fisheries on seabird populations.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".