Fraser Sockeye run size determination
Bibliographic record
Abstract
Postseason run size estimates for Fraser Sockeye stocks are used to forecast future returns, report exploitation rates and en route mortality estimates, and conduct stock assessments. Run sizes for most Pacific salmon stocks are the sum of catch and spawning escapement estimates; Fraser Sockeye is unique in that it also includes estimates of stock-specific en route mortality that have accounted for adverse environmental and biological conditions. Stock-specific estimates of en route mortality for Fraser Sockeye have been increasing and can often exceed catch and occasionally spawning escapement in overall contribution to run size calculations. The current Fraser Sockeye postseason run size determination process, ongoing since 2009, is designed to improve postseason run size estimates by incorporating stock-specific en route mortality and scrutinizing the main components of the run size process—catch, spawning escapement, and en route mortality. An in-depth documentation and review of the process used to estimate postseason stock specific run sizes was provided with a focus on catch, escapement, and en route mortality, as well as uncertainty and input sensitivity in component error. Recommendations for improvements to the current stock-specific run size estimation process and guidance on use of the data sets generated by this process were provided. Major recommendations include the following: simplify the overall postseason run size structure; update the visual survey estimates to improve accuracy of spawning escapements; receive consistent and timely reporting of catch from all fisheries that have a reasonable potential to intercept Fraser Sockeye; update and extend en route mortality models to include all evaluated stocks; and work to quantify uncertainty in all inputs and outputs. A summary table of recommendations was developed along with evaluation criteria to assist with prioritization based on different end user objectives. Quantifying and reducing the uncertainty in run size components will support credibility for downstream uses, including exploitation rate and productivity analyses, and for estimating the impact of en route mortality. The postseason run size and its component data sets should be used for postseason evaluations of run size, exploitation rates, and productivity, as well as forecasting and assessments supporting long-term planning. At present, they should not be used for evaluations of in-season management or evaluations requiring underlying daily estimates. Large bias and imprecision of some stock-specific run size components and their derivatives require additional scrutiny for use (e.g., exploitation rates for small stocks). Better understanding and quantification of en route mortality will also support the Department’s recovery and rebuilding plans for Fraser Sockeye and other salmon populations, especially given the expected increased frequency of extreme migration conditions and reductions in catch and/or spawning escapements.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.019 | 0.010 |
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".