Parentage-based tagging improves escapement estimates for ESA-listed adult Chinook salmon and steelhead in the Snake River basin
Bibliographic record
Abstract
Parentage-based tagging (PBT) is a nonlethal, genetic tagging method that has been successfully applied in hatchery-supplemented populations to manage hatchery brood stock and monitor hatchery harvest and straying rates. We show that PBT can also improve the accuracy of escapement estimates by significantly reducing the number of hatchery-origin fish falsely classified as natural-origin. Unlike conventional abundance estimates, which use physical marks and tags to distinguish hatchery individuals from their wild counterparts, PBT identifies origin independent of physical form. We applied PBT to populations of Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss), which are classified as Threatened under the Endangered Species Act and subject to extensive hatchery supplementation efforts. For spawn years 2014–2018, 16 511 adipose-intact Chinook salmon and 21 953 adipose-intact steelhead were sampled, and PBT identified 19.6% of returning Chinook salmon and 8.3% of steelhead were of hatchery-origin, despite having no physical or mechanical marks. The 90% confidence intervals for escapement estimates of natural-origin Chinook salmon and steelhead made with and without corrections using PBT were nonoverlapping for nine of ten comparisons, indicating that failing to account for unmarked, untagged hatchery-origin fish would result in a significant overestimation of natural abundance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".