Estimating Sockeye Salmon Smolt Flux and Abundance with Side-Looking Sonar
Bibliographic record
Abstract
Abstract Abundance estimates of out-migrating sockeye salmon Oncorhynchus nerka smolts are used to prepare preseason forecasts of adult returns and to set escapement goals. Here we describe a method for estimating smolt flux and abundance that uses side-looking sonar. This method more efficiently covers the river cross section and is logistically easier to deploy than up-looking sonar systems. To account for the skewed vertical distribution of smolts, we used a recently developed model for correcting echo integration bias associated with nonuniform target distribution. The correction is based on adjusting the integrated beam pattern for a given distribution of targets relative to the transverse cross section of the beam. We compared the results with estimates derived from video data and modeled the effect of three vertical distributions of smolts and three transducer pitch angles. The model estimated correction factors that ranged from 0.6 to 2.6. Correction factors were greater than 1 (which indicates negative bias in conventional echo integration) for smolt distributions skewed toward the edge of the beam and less than 1 (which indicates positive bias in conventional echo integration) for distributions skewed towards the center of the beam. For the scenarios modeled, the effect of the transducer pitch angle was small between the horizontal and −0.6° but increased nonlinearly as the angle increased. We conclude that, in the given application and at shallow transducer pitch angles, the bias in conventional echo integration is small and predictable enough to be corrected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".