Absence of predator control increases cod extirpation risk in a Northwest Atlantic ecosystem: inference from multispecies modelling
Bibliographic record
Abstract
Atlantic cod Gadus morhua in the southern Gulf of St. Lawrence (sGSL) declined to low abundance in the early 1990s and have since failed to recover due to high natural mortality, which has been linked to predation by grey seals Halichoerus grypus. Increased grey seal harvests have been suggested to improve cod survival; however, predicting the response of cod to changes in seal abundance in the sGSL is complicated by a hypothesized triangular food web involving seals, cod, and small pelagic fishes, wherein the pelagic fishes are prey for cod and grey seals, but may also prey on young cod. Grey seals may therefore have an indirect positive effect on pre-recruit cod survival via predation on pelagic fish. Using a multispecies model of intermediate complexity fitted to various scientific and fisheries data, we found that seal predation accounted for the majority of recent cod mortality and that cod will likely be extirpated without a strong and rapid reduction in grey seal abundance. We did not find evidence that reducing grey seal abundance will impair cod recovery by causing large increases in pelagic biomass so long as pelagic fishing mortality continues at historical levels.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".