Bycatch analyses from inshore lobster fisheries in LFAs 27, 31A, 31B, 33, 34, 35
Bibliographic record
Abstract
In 2013, the Government of Canada released the Policy on Managing Bycatch as part of the Sustainable Fisheries Framework. This policy identified Canada’s need to systematically address bycatch in all fisheries and included the objective of accounting for total catch, including retained and non-retained bycatch. In 2018, standardized protocols were introduced to an at-sea data collection program in Lobster Fishing Areas (LFAs) 33, 34, and 35 with sampling by two groups: an industry association and at-sea observer companies. Pre-existing voluntary industry-led programs in LFAs 27, 31A, 31B, and 32 were aligned with these protocols in 2018. The at-sea data collection program sought to sample a sufficient number of trap hauls to be representative of the entirety of the fishery. This program set a preliminary target to collect data from 1% of commercial fishing trips within each LFA in the inshore Lobster fishery. Combining all available years (2018–2021) and sampling sources (i.e., industry-led association and at-sea observer companies), more than 60,000 traps were sampled for bycatch with a total of 46 species or species groupings. These efforts represent between 0.09% and 0.59% of total commercial fishing trips and between 0.03% and 0.4% of total commercial trap hauls. Although these sampling targets were not met, analyses suggested sampling was representative of the fishery in most LFAs. Species richness varied between LFAs, but also between sampling sources within the same LFA. Results indicate the diversity of bycatch species in Lobster traps is well described in these sampling data. A generalized modelling framework that explicitly incorporates spatial-temporal dependence structure was applied to the at-sea sampled data to predict incidental capture of Atlantic Cod, Cusk, Jonah Crab, and Cunner. Sampling source and depth were evaluated as covariates in these models. The predicted incidental capture (bycatch) of Atlantic Cod in the Lobster fishery ranged from 1.02 tons (t)/fishing season in LFA 27 to 243 t/fishing season in LFA 34. Cusk were only present in at-sea samples from LFAs 33–35. The predicted incidental capture (bycatch) of Cusk in the Lobster fishery was 33.3 t/fishing season in LFA 33, 220.4 t/fishing season in LFA 34, and 1.8 t/fishing season in LFA 35. The predicted incidental capture (bycatch) of Jonah Crab ranged from less than 0.2 t/fishing season in LFAs 31A and 31B to 3,098 t/fishing season in LFA 34. While Cunner were present in all LFAs, bycatch analyses focused on LFA 27 where a Cunner retention pilot project is in place, and neighbouring LFAs 31A and 31B. The predicted incidental capture (bycatch) of Cunner was 9.8 t/fishing season in LFA 27, 1.05 t/fishing season in LFA 31A, and 0.57 t/fishing season in LFA 31B. To track annual changes in estimates of bycatch, increased spatial and temporal coverage of sampling would be required. Expanding the analyses to include data from sampling in western LFA 31B would improve spatial representativity of the LFA. The explicit incorporation of spatial and temporal effects should be considered in future investigations of bycatch.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".