Assessment of Scotian Shelf ecosystem Snow Crab in 2024
Bibliographic record
Abstract
In the Maritimes Region, Snow Crab (Chionocetes opilio) are managed as three fishing areas: north-eastern Nova Scotia (N-ENS; formerly crab fishing areas [CFA] 20-22), south-eastern Nova Scotia (S-ENS; formerly CFAs 23, 24), and Northwest Atlantic Fisheries Organization (NAFO) Division 4X (CFA 24W). Results from an annual fishery independent survey suggest that recruitment into N-ENS and 4X continues to decline while that of S-ENS builds in strength. Poor recruitment in N-ENS and 4X may be due to elevated predation by groundfish and habitat reduction. Reproductive potential continues to be comparable to 2023, though it is expected to decline in both N-ENS and 4X. Environmental conditions (bottom temperature) have reverted to a cold-water phase in all areas; however, snow crab habitat viability still declined, especially in N-ENS and 4X. Elevated mortality and poor recruitment of immature male crab will continue to be an issue in these two areas for at least the next three years. Fishable biomass densities also declined in 2024 in both N-ENS and 4X while they increased in S-ENS. The fishable biomass indices are near historical lows in each area. In N-ENS, the modelled biomass (pre-fishery) of Snow Crab in 2024 was 2.7 kt, relative to 3.4 kt in the previous year. In S-ENS, the 2024 modelled biomass (pre-fishery) was 41.5 kt, relative to 40.6 kt in the previous year. In 4X, the modelled biomass (pre-fishery) for the 2024-2025 season was 0.18 kt, relative to 0.14 kt in the previous season. There is a continuing decrease in modelled biomass in N-ENS since 2021 and 2019 in S-ENS and 4X. Fishing mortality in N-ENS in 2024 was estimated to have been 0.32 (annual exploitation rate of 37%), up from 0.26 (annual exploitation rate of 30%) in the previous year. In S-ENS, the 2024 fishing mortality is estimated to have been 0.17 (annual exploitation rate of 18.6%), while in the previous year it was 0.18 (annual exploitation rate of 19%). Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished (e.g., continental slope areas and western, inshore areas of CFA 24). In 4X, the 2024–2025 season (ongoing) fishing mortality is currently estimated to be 0.052 (annual exploitation rate of 5%), while in the previous season it was 0.27 (annual exploitation rate of 31%). Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".