An approach to modelling the influence of harp seal (Phoca groenlandica) predation on decline and recovery of the Northern Gulf of St Lawrence cod (Gadus morhua)
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The Canada-Greenland harp seal population is currently near an all time high while at the same time eastern Canadian cod stocks are at very low levels. Consequently, harp seal predation has been implicated as a possible reason for cod population decline and subsequent lack of recovery, though there is not strong evidence to support or refute this hypothesis. In order to evaluate the role of harp seal predation on cod populations, we developed a cod population cohort model for the Northern Gulf of St. Lawrence stock and partitioned mortality into a harp seal predation component and a residual component. We fitted this model to data from 1974-2005 allowing parameters related to harp seal predation, cod residual mortality and recruitment to vary. We subsequently came up with a series of parameters representing fits to different sub-periods reflecting productivity conditions for the cod. We performed stochastic projections for cod recovery by sampling historical per capita recruitment and under three scenarios of harp seal population size. It proved difficult to obtain reliable parameter sets from fitting the historical data and projections show that with some parameter sets, reductions in seal population numbers would make small improvement in recovery time for the cod population while with other parameter sets, there would be little effect; therefore, a major difficulty in obtaining useful results from this analysis lays in deciding which parameter set applies to the projection period. A full Bayesian implementation of this model might be a means to overcome this difficulty.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".