An examination of harvest rates and brood-take rates as management strategies to assist recovery of Cowichan River Chinook salmon.
Bibliographic record
Abstract
The Cowichan River fall-run Chinook salmon (Oncorhynchus tshawytscha) population has been a serious conservation concern since 1997.I developed a stochastic lifehistory simulation model of both hatchery-origin and naturally-spawning Chinook in this system to evaluate management options involving specific harvest and hatchery broodtake rates.Ocean harvest was more influential than supplementation on the stock's abundance.My results suggest that this stock's recovery is unlikely to occur under current poor marine survival conditions and either the status-quo management strategy or lower harvest rates that managers are likely to find realistic.Model outputs also allowed analysis of trade-offs among management objectives involving conservation, Food, Social and Ceremonial and ocean harvests, and hatchery operations.Analyses of these performance indicators showed that under poor or intermediate marine survival conditions, the best management strategies (according to most indicators, including ocean harvest) involved the lowest ocean harvest rates examined here, i.e., 30% or 40% annually.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".