Incorporating Uncertainty into Area-under-the-Curve and Peak Count Salmon Escapement Estimation
Bibliographic record
Abstract
Uncertainty can be incorporated into area-under-the-curve (AUC) and peak count estimates of salmon escapement by conducting replicate fish counts and developing independent escapement estimates over several years. We describe a bootstrap procedure that follows the trapezoidal AUC method and incorporates the uncertainty associated with fish counts, the shape of the spawner curve, observer efficiency, and residence time. However, the procedure does not incorporate all sources of uncertainty or address the problems posed by sparse surveys or nonzero first or last counts. For the peak count method, the procedure was modified to include the uncertainty from fish counts, observer efficiency, the expansion factor, and the timing of scheduled flights with respect to peak spawning activity. Data from spring-run, stream-type chinook salmon Oncorhynchus tshawytscha in the Nicola River, British Columbia, were used to demonstrate the procedures' applications. Replicate aerial spawner counts were similar and repeatable, and annual residence times were consistent over 4 years. The AUC escapement estimates were precise, reliable, and accurate when compared with independent mark–recapture escapement estimates, whereas the peak count escapement estimates were precise but less reliable and accurate. We expect that AUC escapement estimates calculated from the mean residence time of 4 years will be less biased (−8% to +5%) than peak count estimates from the mean expansion factor of 4 years (−14% to +21%). The procedures that we describe for incorporating uncertainty into AUC and peak count escapement estimation should enable fisheries biologists to more adequately assess changes in abundance and stock status.
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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 | 0.001 | 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 | 0.001 |
| 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".