Genetic and demographic implications of aquaculture in white sturgeon (<i>Acipenser transmontanus</i>) conservation
Bibliographic record
Abstract
This study uses a genetic individual-based model of white sturgeon (Acipenser transmontanus) populations in a river to examine the genetic and demographic trade-offs associated with operating a conservation hatchery. Simulation experiments evaluated three management practices: (i) setting quotas to equalize family contributions in an effort to prevent genetic swamping, (ii) an adaptive management scheme that interrupts stocking when introgression exceeds a specified threshold, and (iii) alternative broodstock selection strategies that influence domestication. The first set of simulations, designed to evaluate equalizing the genetic contribution of families, did not show the genetic benefits expected. The second set of simulations showed that simulated adaptive management was not successful in controlling introgression over the long term, especially with uncertain feedback. The third set of simulations compared the effects of three alternative broodstock selection strategies on domestication for hypothetical traits controlling early density-dependent survival. Simulated aquaculture selected for a density-tolerant phenotype when broodstock were taken from a genetically connected population. Using broodstock from an isolated population (i.e., above an upstream barrier or in a different watershed) was more effective at preventing domestication than using wild broodstock from a connected population.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".