Fisheries management applications of riverine hydroacoustics: 30 years’ experience with applied technology in the practical arena
Bibliographic record
Abstract
Some examples of the successes and challenges encountered by the Pacific Salmon Commission in the application of riverine hydroacoustics to fisheries management of Fraser River sockeye salmon are reviewed. Riverine hydroacoustics estimates have been an integral part of the fisheries data collected by the Pacific Salmon Commission for over 30 years. Real time estimates of fish passage provide intra-seasonal feedback on the progress toward escapement targets and information about changing total abundance levels. This information has allowed managers to adjust fisheries schedules and improved their ability to meet catch and escapement objectives. Despite these successes, application of technology has encountered a number of challenges including: (1) the interpretation of acoustics data in determining fish targets, (2) quantification of accuracy of hydroacoustic estimates in large rivers, (3) misperceptions about estimation methods by the public, and (4) inevitable comparisons with estimates from other sources and their effect on perceived accuracy of the hydroacoustic estimates. Lessons learned from the Pacific Salmon Commission experience are summarized with the objective of helping others engaged in the application of riverine acoustics technology to fisheries management problems.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".