Evaluation of visual survey methods for monitoring Pacific salmon (<i>Oncorhynchus</i> spp.) escapement in relation to conservation guidelines
Bibliographic record
Abstract
Canada’s Wild Salmon Policy requires that biological status of conservation units of Pacific salmon (Oncorhynchus spp.) be assessed regularly in relation to abundance-based benchmarks. Visual survey methods, in which periodic counts of spawning fish are made throughout a season, will likely be used for this purpose because they provide a cost-effective means of monitoring interannual trends in escapement. Trend detection performance for visual survey methods depends mainly upon consistency in (i) the ability of observers to detect fish and (ii) the annual timing of fish presence in the survey area. We developed a Monte Carlo simulation procedure to evaluate the ability of four visual survey methods (peak count, mean count, trapezoidal area-under-the-curve (AUC), and likelihood AUC) to detect 30% declines in coho salmon (Oncorhynchus kisutch) escapement over 10 years (i.e., the magnitude of trend that would warrant listing a coho population as threatened using the listing criteria of the Committee on the Status of Endangered Wildlife in Canada (COSEWIC)) given realistic levels of variability in these two factors. The mean count outperformed all other approaches across a wide range of scenarios about true population dynamics and survey designs, suggesting that a simple mean count method is suitable for monitoring coho escapements in relation to COSEWIC 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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".