Assessment of Lobster on the North Shore, Quebec, in 2022
Bibliographic record
Abstract
Lobster landings on the North Shore increased sharply to 1,468 t in 2022, up 36.3% from 2018. In LFA 15, landings totalled 204 t in 2022, up 194.2% from 2018 and up 408.6% from the average of the past 25 years (1997–2021). In LFA 16, landings totalled 194 t in 2022, up 121% from 2018 and up 473% from the average of the past 25 years. In LFA 18, landings totalled 167 t in 2022, up 30.5% from 2018 and up 386.3% from the average of the past 25 years. The 2022 values are among the highest in the historical series. In LFA 17B, landings totalled 902 t in 2022, following an all-time high in 2021 (1,120 t). The 2022 landings were up 14.0% from 2018 and up 158.1% from the average of the past 25 years. The catch per unit effort (CPUE) by weight from logbooks has increased by 79% in LFA 15 and 16 since 2018, reaching 1.11 kg/trap in 2022. This value is 246% higher than the 1993– 2021 average. In LFA 18D, the 2022 CPUE (6.04 kg/trap) was up 43.8% from 2018 and up 88.8% from the 2012–2021 average. In LFA 17B, the 2022 CPUE (4.1 kg/trap) was up 19.2% from 2018 and up 86.4% from the 2006–2021 average. Overall, fishing effort has been increasing since 2011 in the North Shore and Anticosti Island fishing areas. Very little sampling is done on the North Shore and Anticosti Island for the assessment of demographic indicators, particularly for LFAs 15 and 16 where data are missing for 2020, 2021 and 2022. Given the significant rise in fishing effort in these areas, scientific sampling effort should be increased. Size structures in LFA 17B are wide ranging, and the average size is stable for commercial-sized lobsters. Temperature indicators were examined in keeping with the ecosystem approach, but further work is required to incorporate them into the assessment of resource status. Small rock crab is a key prey source for lobster. However, over the past two years, no data has been available for the North Shore and Anticosti Island. Abundance indicators (landings and CPUE) have risen sharply on the North Shore and Anticosti Island. Lobster populations in these areas appear to be in good condition. Nevertheless, these populations may be vulnerable to overexploitation, given that the legal size is smaller than their size at sexual maturity, and they are slow-growing. It is not possible to provide comments from an ecosystem perspective because of the lack of data and/or their interpretation.
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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 | 0.003 |
| 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) | 0.006 | 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".