Size and timing of hatchery releases influence juvenile-to-adult survival rates of British Columbia Chinook (<i>Oncorhynchus tshawytscha</i>) and coho (<i>Oncorhynchus kisutch</i>) salmon
Bibliographic record
Abstract
Salmon hatcheries are management tools intended to stabilize declining abundance in salmon populations and sustain salmon fisheries. One key area of uncertainty is how hatchery release practices influence juvenile-to-adult survival. Data quality and quantity vary considerably among hatcheries making it difficult to assess the role of release practices. Using a Bayesian hierarchical approach, we analyzed releases and recoveries of Chinook ( Oncorhynchus tshawytscha; 21 hatcheries) and coho ( Oncorhynchus kisutch; 16 hatcheries) salmon in British Columbia from 1972 to 2017. Higher survival rates were associated with increasing weights-at-release, earlier releases of Chinook salmon, and later releases of coho salmon. The addition of environmental (sea surface temperature, Pacific Decadal Oscillation), and predator (harbour seals, killer whales) covariates did not improve model performance relative to models that used year effects to account for declines in survival and interannual variability in ocean conditions. Optimizing release practices could increase returns by 6%–245% for Chinook salmon and 5%–160% for coho salmon based on median posterior estimates for each hatchery. With these results, a large-scale adaptive management approach could be implemented to test our understanding of release practice–survival relationships and evaluate cost–benefit trade-offs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".