Assessment of Scotian Shelf Snow Crab in 2023
Bibliographic record
Abstract
In the Scotian Shelf Ecosystem, Snow Crab (Chionoecetes opilio) have been a dominant macro-invertebrate since the decline of the groundfish fishery in the 1990s. They are generally observed in deep, soft-bottom substrates ranging from 60-300 m and at temperatures usually less than 6 degrees Celsius. The Scotian Shelf Snow Crab are in the southern-most extreme of their spatial distribution in the Northwest Atlantic Ocean and vulnerable to climate variability. The fishery is made up of 3 fishing areas: north-eastern Nova Scotia (N-ENS), south-eastern Nova Scotia (S-ENS) and 4X. The Snow Crab assessment is based on a fishery independent survey, the Maritimes Region Snow Crab Trawl Survey, and focuses on indicators of abundance, reproductive potential, recruitment, and exploitation rates. Spatiotemporal models incorporate habitat viability based on ecosystem variables such as depth, species composition and bottom temperatures. At-sea observer coverage aims to quantify bycatch, and carapace conditions for the crabs in the fishery but has been unreliable since 2020 with minimal observations per year. Fishery data provides catch rates, landings, effort, and monitors historic trends. Total allowable catches (TACs) are generally caught every year (except 4X as the fishery is always ongoing at the time of assessment). Survey indices indicate recruitment in N-ENS will be minimal with a potential gap for the next 1 to -3 years. In S-ENS, recruitment is likely to continue at a moderate rate in the upcoming season. 4X is expected to see low to moderate levels of recruitment for two years. Fishing mortality was higher than desirable in N-ENS. Bottom temperatures have been continually rising since the early 2000s until a decrease observed during the 2023 survey. This is positive news for the Snow Crab fishery and will be monitored with future surveys in hopes this decreasing trend continues. The model suggests that N-ENS and S-ENS are in the healthy zone and 4X is in the critical zone.
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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 | 0.001 | 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".