Long life spans can mitigate the genetic effects of strays from temporary conservation hatchery programs
Bibliographic record
Abstract
While conservation fish hatchery programs can be a valuable tool providing demographic support, they can also cause domestication, risking unintended fitness consequences to both target and connected populations. When conservation hatcheries are used over a fixed amount of time, the interaction between program duration and species life history might determine the scale of fitness effects and the effectiveness of mitigation measures. We develop a mathematical model to quantify the effectiveness of approaches to mitigate unintended fitness consequences in such temporary conservation hatchery programs. We parameterize our model to represent a conservation aquaculture-based recovery program for white sturgeon ( Acipenser transmontatus) in the Nechako River (British Columbia), which might impose genetic risks on the adjacent populations within the Fraser River. We find that over a period spanning 50–200 years of hatchery operation, the life history characteristics of white sturgeon, particularly late age of maturity and longevity, reduce the genetic risks of conservation hatcheries when compared to shorter-lived species. The genetic impacts of the hatchery accumulate slowly increasing the potential for adaptive management in this system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".