Local environment affecting northern shrimp recruitment: a comparative study of Gulf of St. Lawrence stocks
Bibliographic record
Abstract
Abstract Climate and density-dependent effects are important drivers of recruitment (R). In the Gulf of St. Lawrence (GSL), recent years indicated an exceptional warming of water associated with variations in plankton phenology and fish abundance. At the same time, northern shrimp (Pandalus borealis) recruitment and stock dynamics fluctuated greatly, but the underlying mechanisms remain poorly understood. We estimated recruitment from yearly fisheries independent abundance estimates for three different northern shrimp stocks in the GSL (Sept-Iles, Anticosti, and Esquiman). For 2001–2016, we quantified how northern shrimp R changed in relation to physical variables, phytoplankton bloom characteristics, zooplankton abundance and phenology, and predator biomass. Results indicated that northern shrimp R seemed related to phytoplankton bloom characteristics and resulting zooplankton phenology in addition to northern shrimp adult abundance, rather than to fish predator biomass. Importantly, the significant variables explaining the R were stock specific, implying that environmental variability and stock abundance effects depend on the area considered. In future, Esquiman area might show increasing northern shrimp R under moderate warming but northern shrimp Sept-Iles R might be impaired. These results improve our understanding of stock-specific northern shrimp recruitment dynamics in a changing environment and can ultimately improve its management in the GSL.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".