Experimental management for Snake River springsummer chinook (<i>Oncorhynchus tshawytscha</i>): trade-offs between conservation and learning for a threatened species
Bibliographic record
Abstract
Using Snake River springsummer chinook (Oncorhynchus tshawytscha) as an example, we explore trade-offs between conservation (restoring population abundance to self-sustaining levels) and learning (reliably estimating how management strategies affect productivity). The population has been studied extensively, especially since 1992, when the evolutionarily significant unit (ESU) was listed under the U.S. Endangered Species Act. Understanding both the conservation and learning dimensions is crucial in evaluating management actions. Using a Bayesian simulation model calibrated with 40+ years of spawnerrecruit estimates, we performed population viability analyses to examine the biological risks of an array of management strategies. We also performed power analyses to estimate the precision of estimates of the actions' effects. The results suggest that if one can take actions that increase productivity and manage those actions as experiments, one can simultaneously increase fish numbers and reduce the uncertainty about the effects of those actions. However, because more powerful experiments will utilize controls where no action is taken, an experimental approach may increase risks to the ESU when compared to a strategy that tries to maximize productivity as soon as possible.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".