Detection of population trends in threatened coho salmon (<i>Oncorhynchus kisutch</i>)
Bibliographic record
Abstract
Populations of coho salmon (Oncorhynchus kisutch) in California are listed as threatened under the U.S. Endangered Species Act. Such listings refer to adult populations, but often, juvenile life history stages are censused, so it is important to understand what affects the relationship between true adult and observed juvenile numbers. We present models to address how observational uncertainty, census length, and autocorrelation in vital rates affect our ability to observe trends. We ask two questions about our ability to detect declines in one life history stage from censuses of another. First, given an observed decline in parr numbers, what is the chance that this reflects a decline in adults? Second, given that adult numbers are declining, what is the chance that we see that decline in parr? Our results indicate that statistical power decreases with increasing observational uncertainty and decreasing census lengths and demonstrate how these two parameters interact. Power increases as the level of autocorrelation in mortality rates increases. Management recommendations include obtaining more accurate estimates of autocorrelation in mortality and of observational uncertainty.
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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 | 0.005 | 0.027 |
| 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 | 0.001 |
| 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".