Development of a Simple Morphometric Model to Identify Sex in Chinook Salmon Returning to Spawn in the Yukon River
Bibliographic record
Abstract
Abstract Chinook Salmon Oncorhynchus tshawytscha support major fisheries in the Yukon River in Canada and the United States. Demographic data, including length, age, and sex composition of annual runs underpin management programs tasked with meeting escapement goals and providing for various fisheries within the drainage. Numerous sampling projects along the river collect these data annually, most without sacrificing fish. However, substantial error can occur when assigning sex based on observations of secondary sexual characteristics, particularly early in the spawning migration. We sought to develop a quantitative method of analyzing morphology of Chinook Salmon in the Yukon River that would achieve ≥95% accuracy of sex assignment with live fish. We collected snout lengths (SL) and mid-eye-to-fork lengths (MEF) from 2,342 known-sex fish from six regions of the drainage ranging from 40 to 2,000 km upstream from the sea, including a collection from postspawning fish. We fit three logistic regression models to the data using MEF, the ratio of SL to MEF, and regional group as covariates, including interactions and quadratic terms. Model selection was made using Akaike information criterion and cross-validation of prediction performance. Accuracy rates of the final quantitative model ranged from 79% for a lower Yukon River group to 92% for the postspawning group. Though short of our accuracy goal, the quantitative method may provide an improvement over existing methods of sex assignments in some regions of the drainage. Lower assignment rates in the lower river may reflect the mixed-stock composition of the fishery, where samples include fish with both short and long migrations ahead of them. However, we speculate that Chinook Salmon migrating to upper Yukon River spawning areas minimize the expression of secondary sexual characteristics, such as SL in males, and instead allocate their energy reserves to accomplishing the long migrations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".