Minor shifts towards more natural conditions in captivity improve long-term survival among reintroduced Atlantic salmon
Bibliographic record
Abstract
Elevating winter water temperatures is a common practice when rearing salmonids for supplementation or reintroduction. Doing so elevates developmental rates, producing larger juveniles with greater smolt-to-adult survival, but does not guarantee improved adult returns to stocked tributaries. To test whether more natural developmental conditions improve adult returns to stocked tributaries, three consecutive cohorts of yearling (age 1+) landlocked Atlantic salmon (Salmo salar) were released into two tributaries of Lake Champlain. Cohorts were reared under two winter thermal conditions (seasonal surface water and above-seasonal groundwater) and (or) two release times (early and normal). Relative to standard hatchery practices, modelled returns to experimental tributaries increased over replicate cohorts by 286% on average following exposure to seasonal rearing temperatures, but decreased by 89% on average when release dates were advanced. By utilizing cost-effective shifts towards hatchery rearing techniques that more closely resemble natural growth conditions, we demonstrate how hatchery programs may improve long-term survival and returns for fish species with complex life histories involved in supplementation and reintroduction programs.
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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 | 0.000 | 0.000 |
| 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".