Bibliographic record
Abstract
Status The 2023 southern Gulf of St. Lawrence snow crab commercial biomass index, estimated at 67,703 tonnes (t), is above the Upper Stock Reference (USR) with very high likelihood, placing the stock in the Healthy Zone of the Precautionary Approach (PA) Framework. Trends Following a period of high levels from 2018 to 2022, the commercial biomass index decreased by 21% in 2023. Pre-recruits to the fishery decreased to below the time series (1997-2023) average in 2023. Female spawning stock indices have increased since 2006 and have remained at high levels in 2023. The population recruitment index was at the highest recorded level in 2021, but has decreased in 2023 to the time series average. Ecosystem and Climate Change Considerations There is continued evidence of warming conditions in the southern Gulf of St. Lawrence that can impact snow crab population dynamics and distribution; and the mechanisms require further investigation. Stock Advice Based on the harvest decision rule, the 2023 commercial biomass index corresponds to a target exploitation rate of 38.59% and a catch option of 26,126 t for the 2024 southern Gulf of St. Lawrence fishery. A risk analysis indicates that this catch option would result in a very high likelihood that the commercial stock would remain in the Healthy Zone of the PA after the 2024 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".