RESTORATION OF NATIVE BIODIVERSITY IN ALTERED ENVIRONMENTS: REINTRODUCTION OF ATLANTIC SALMON INTO LAKE ONTARIO
Bibliographic record
Abstract
ii Less than a quarter of reintroduction programs have succeeded in re-establishing a self-sustaining population of an extirpated species. Optimal source population selection, based on an evolutionary and ecological perspective, could increase the fitness of translocated individuals, thereby improving the success rate of restoring extirpated populations. Here, using three source populations of Atlantic salmon, Salmo salar (LaHave River, Sebago Lake, and Lac Saint-Jean), that are being used for reintroduction efforts into Lake Ontario, I examined two optimal source population selection approaches: environment matching and adaptive potential. For environment matching, source populations from locations containing similar key environment features as the reintroduction location should contain adaptations to these features. For adaptive potential, source populations with high heritable genetic variation should have the potential to adapt to new selection pressures, such as the key environment features in the reintroduction location. I tested environment matching using experimental settings by exposing the three source populations to two key environment
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".