Potential benefits of a conservation hatchery program for supplementing Oregon coast coho salmon (<i>Oncorhynchus kisutch</i>) populations: a stochastic model investigation
Bibliographic record
Abstract
This study developed a stochastic life cycle model to simulate idealized supplementation strategies to investigate the following question: under what circumstances could hatchery fish stocking contribute to the recovery of Oregon coast coho salmon (Oncorhynchus kisutch)? Simulations were used to find a solution space, defined by the attributes of wild and hatchery-bred salmon, their offspring, and their environments, where hatchery fish could supplement natural production without further depressing it until natural or human factors restricting production were relieved. These simulations suggest that short-duration, tightly controlled, low-intensity conservation hatchery programs designed to minimize genetic and ecological risks may yield minor short-term increases in adult coho salmon abundance while posing significant ecological and genetic risks. No solution space was found that indicated clear long-term benefits from such a supplementation program. Of all the management actions modeled, habitat restoration offered by far the largest and only permanent gains in coho salmon abundance while posing no genetic or ecological risk to the fish. The modeled benefits of habitat restoration were significant regardless of assumptions made about the fitness of hatchery fish and their offspring.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".