Evaluation of visual survey programs for monitoring coho salmon escapement in relation to conservation guidelines
Bibliographic record
Abstract
Canada's Wild Salmon Policy (WSP) requires that quantitative survey designs be used to monitor annual trends in Pacific salmon escapement.Visual survey methods, in which periodic counts of spawning fish are made throughout a season, are often employed for this purpose.Coho salmon populations are difficult to monitor using visual survey methods due low probability of fish detection and high variability in the annual timing of fish presence in the survey area.I developed a Monte Carlo simulation procedure to evaluate the power of peak-count, mean-count, trapezoidal area-under-thecurve (AUC), and likelihood AUC methods to detect 30% declines in coho salmon escapement over 10 years, which is the magnitude of trend that would warrant listing a population as threatened under the Canadian Species at Risk Act (SARA).My results suggest that a simple mean-count method would be best suited for monitoring coho salmon abundance in relation to SARA and WSP guidelines.
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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 | 0.034 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".