Assessing the role of juvenile life history diversity and adult translocation on cohort productivity in a Chinook Salmon population
Bibliographic record
Abstract
Abstract Translocation of adult salmon is an expanding management tactic, especially in rivers with impassable dams. Understanding the productivity of translocated adults compared to donor populations and contribution to subsequent adult returns (i.e., via cohort replacement rate [CRR]) is critical to evaluating the efficacy of translocation programs. However, empirical CRR estimates are difficult to generate due to life history (LH) complexity, including spatiotemporal diversity created by translocation protocols and diverse juvenile rearing habitats. We built a CRR model for spring-run Chinook Salmon Oncorhynchus tshawytscha in Oregon’s North Santiam River, where two large dams lack fish passage facilities. The model estimates relative replacement metrics for two adult groups: below-dam spawners and above-dam spawners translocated into historical upstream habitat. It also tracks the relative contributions of 13 distinct juvenile phenotypes (i.e., “LH pathways”): 3 produced by the below-dams group and 10 from the above-dams group that includes reservoir-rearing pathways. The fractional CRR of LH pathways varied by >60-fold, with higher CRR for less common spring yearling smolt LHs. Simulated management scenarios indicated low likelihood of replacement of the original adult cohort. The approach provides a framework for evaluating trade-offs and feedbacks among intraspecific diversity, management actions, and LH pathway composition in spatially structured migratory populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".