Using Time-Lapsed Video to Estimate Survey Life for Area-under-the-Curve Methods of Escapement Estimation
Bibliographic record
Abstract
Abstract Accurate estimation of spawner populations for Pacific salmon Oncorhynchus spp. is critically important in stock assessment for fisheries management and science. Survey life (SL) is one essential component for area-under-the-curve (AUC) estimation of spawner populations. However, AUC spawner estimates often rely on a constant or borrowed estimate of SL because reliable estimates require extensive and costly annual field programs. Using a constant SL estimate can introduce serious bias when estimating spawner populations for sockeye salmon O. nerka as well as other salmon species. In this study, inexpensive video observations of redd residency time (RRT) of female sockeye salmon were used to approximate SL. Data from past studies indicate that RRT and SL are often close in duration for sockeye salmon, Chinook salmon O. tshawytscha, and coho salmon O. kisutch. In addition, SL estimates for male and female sockeye salmon are not significantly different. As a result, the RRT of female sockeye salmon, as observed by time-lapsed video recordings, can be used to provide an inexpensive annual estimate of sockeye salmon SL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".