Stock Assessment Southern Gulf of St. Lawrence Snow Crab 2024
Bibliographic record
Abstract
Status The 2024 sGSL snow crab commercial stock biomass, estimated at 51,786 tonnes (t), is above the Upper Stock Reference (USR), placing the stock in the Healthy Zone of the Precautionary Approach (PA) Framework. Trends Snow crab are known to go through cycles of abundance. After a period of high biomass from 2018 to 2022, the commercial biomass index decreased by 21% in 2023 and a further 24% in 2024. Pre-recruits (> 56 mm carapace width) have declined from high levels in 2019 to 2021 and are currently below average levels. Female spawning stock abundance remains high, although abundance of new mature females is low. A strong cohort resulted in the highest population recruitment index in 2021 and it has decreased below average levels in 2024. Ecosystem and Climate Change Considerations The area of suitable snow crab habitat rose in 2024 from 2023 and is above the long-term average. However, water temperature in the core snow crab habitat is warmer than average in the sGSL, which may impact the snow crab population dynamics and distribution. Stock Advice Based on the harvest decision rule, the 2024 commercial biomass index corresponds to a target exploitation rate of 35.73% and a catch option of 18,503 t for the 2025 fishery. A risk analysis indicates that for this catch option, there is a moderate likelihood (40%) that the commercial stock would be below the upper stock reference and in the cautious zone of the PA after the 2025 fishery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".