Accuracy of Using Scales to Age Mixed‐Stock Chinook Salmon of Hatchery Origin
Bibliographic record
Abstract
Abstract Despite a long history of using scales to age Pacific salmon, there have been few attempts to validate scale‐derived ages. This is particularly true for Chinook salmon Oncorhynchus tshawytscha, a species exhibiting a wide range of life histories across stocks. This has led to continuing questions regarding the accuracy of scale‐based age determination for Chinook salmon. This study assessed the accuracy of Chinook salmon scale age data produced by multiple readers from multiple agencies, who aged hatchery fish of known age from mixed‐stock, nonterminal fisheries conducted along the Pacific coast of Canada from 1991 to 2003. The test sample consisted of scales from 434 fish from both stream‐ and ocean‐type stocks marked with coded wire tags (i.e., fish origin and total age were known). Sample stocks originated from Oregon, Washington, and British Columbia. Five readers from three federal or state Pacific Northwest fisheries agencies participated in the study. The readers possessed various levels of experience, which was classified as (1) deep or shallow (depth) depending on the number of years the reader was involved in aging Chinook salmon scales and (2) broad or narrow (breadth) depending on the variety of stocks the reader had previously encountered. Accuracy ranged from 84% to 94%, although readers with both deep and broad experience consistently achieved accuracies greater than 90%, while those with a narrower breadth of experience tended to show age bias. Overall, the results suggest that aging Chinook salmon scales from ocean‐caught hatchery fish can be accurate and that readers' previous exposure to stocks comprising a wide range of life history types may be at least as important as the number of years of experience in achieving a high level of aging accuracy.
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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 | 0.004 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".