Seals, Cod, Ecology and Mythology
Bibliographic record
Abstract
"Canadian elected officials and Department of Fisheries and Oceans (DFO) staff have stated that the culling of seals will benefit the recovery of Northwest Atlantic cod stocks. In contrast, published reports in scientific journals, including those authored by DFO biologists, unequivocally conclude that seals are having no demonstrable impact on cod recovery. 'Common sense' arguments that culling seals will 'obviously' benefit the fishery are premised on a mythological view of predators that is unsubstantiated by most scientific evidence. Research conducted in other fisheries has indicated that the complexity of marine food webs, and the diversity of seal diets mean increased seal numbers can sometimes lead to positive effects on commercial fish stocks. Consistently, recent research in terrestrial systems indicates that top predators can have a significant positive impact on numbers of herbivores by reducing numbers of smaller predators. The Canadian political agenda for dealing with the collapse of the cod stocks has evolved to include a subsidized seal cull, and suppression of internal reports contradicting the 'common sense' position adopted by the political leadership."
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".