Exploring the Biological Basis of Age-Specific Return Variability of Chinook Salmon (Oncorhynchus tshawytscha) From the Robertson Creek Hatchery, British Columbia Using Biological or Physical Oceanographic Explanatory Variables
Bibliographic record
Abstract
We used information about hatchery rearing and release practices for 173 releases of age 0+ smolts between 1982 and 2012, as well as time series of early marine prey biomass and predator abundance/biomass, to investigate the biological basis of age-specific return variability of chinook salmon (Oncorhynchus tshawytscha) from the Robertson Creek Hatchery. We used survival rate as the response variable and considered the rate to be an apparent one because it is the product of the survival and maturation rates. Results of multiple regression analyses (adjusted R2 ranging between 0.43 and 0.59) showed that Pacific mackerel (Scomber japonicus) and Steller sea lion (Eumetopias jubatus) abundances accounted consistently for all of the explained variation in age-specific survival rate. We suggest that the persistence of the early marine (predation) effect with age shows that there is no effect of hatchery practice on age at maturity. Apparent survival rate variation was not explained when we used conventional physical oceanographic measurements (temperature, salinity, Pacific Decadal Oscillation Index, Northern Oscillation Index, Arctic Oscillation Index, Aleutian Low Pressure Index, Bakun Upwelling Index, timing of spring transition) in our analyses.
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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 | 0.001 | 0.001 |
| 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".