Using Photosynthetic Rates to Estimate the Juvenile Sockeye Salmon Rearing Capacity of British Columbia Lakes
Bibliographic record
Abstract
We describe refinements to a simple sockeye salmon Oncorhynchus nerka rearing capacity model, the photosynthetic rate (PR) model, which was first described in an earlier paper. The model is based on a correlation between photosynthetic rate expressed as metric tons of carbon per year and sockeye salmon smolt biomass. Estimates of optimum escapements and spring fry recruitment required to produce maximum smolt numbers and biomass were taken from the Alaskan euphoric volume (EV) model. We define rearing capacity as the point at which the maximum number and biomass of smolts are produced and optimum escapement as the number of spawners that results in maximum smolt production. We compare model predictions to direct estimates of optimum escapements (developed from fry models—the relationship between numbers of spawners and numbers of fall fry) from two British Columbia (B.C.) lakes and discuss assumptions and limitations of the model. Although we currently have direct estimates of optimum escapement (e.g., fry models) for only two lakes that make up 16% of the total B.C. sockeye salmon nursery lake area, PR data are currently available for 57% of B.C.’s nursery lake area. We provide estimates of optimum escapements and smolt production from those lakes where suitable data are available. By making assumptions about productivity of lakes where PR is unknown, we also provide estimates of optimum sockeye salmon escapement to all major regions of B.C. Although more research and data are needed, the PR model is a promising tool to help managers make decisions regarding sockeye salmon escapement and enhancement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".