Survey of dual frequency identification sonar (DIDSON) applications in fisheries assessment and behavioral studies
Bibliographic record
Abstract
The Dual Frequency Identification Sonar (DIDSON) is a forward-looking sonar that operates in shallow riverine environments with rocky, uneven substrates and near concrete structures such as dams. This allows a number of fisheries applications in environments previously too hostile for reliable sonar operation. Currently 35 DIDSONs have been obtained by 16 groups to accomplish a variety of fish assessment and behavioral studies. This paper surveys the work of these groups and highlights novel assessments allowed by this new acoustic tool. The groups include Alaska Department of Fish and Game, U.S. Fish and Wildlife Service, NOAA, USGS, Bureau of Reclamation, California Department of Water Resources, Pacific Northwest National Laboratories, Nez Perce Tribal Fisheries, Puyallup Tribal Fisheries, Department of Fisheries and Oceans Canada, and Fisheries Engineering Japan. Assessments include: (1) Counting fish migrating up rivers of various sizes, bottom substrates, turbidity, and velocity; (2) Analysis of fish behavior at (A) prototype fish protection devices on dams, (B) irrigation intakes along muddy rivers, (C) intakes of trawl nets; and (3) Detection and measurement of redds in alluvial riverbeds. [Work for the survey supported by Sound Metrics Corp.]
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".