Tail‐Beat Patterns in Dual‐Frequency Identification Sonar Echograms and their Potential Use for Species Identification and Bioenergetics Studies
Bibliographic record
Abstract
Abstract We observed patterns in echograms of data collected with a dual‐frequency identification sonar (DIDSON) that were related to the tail beats of fish. These patterns reflect the size, shape, and swimming motion of the fish and also depend on the fish's angle relative to the axis of the beam. When the tail is large enough to reflect sound of sufficient intensity and the body is angled such that the tail beat produces periodic changes in the range extent covered by the fish image, then the tail beat becomes clearly visible on echograms that plot the intensity maximum of all beams. The analysis of DIDSON echograms of a mix of upstream‐migrating Chinook salmon Oncorhynchus tshawytscha and sockeye salmon O. nerka resulted in the separation of two groups: (1) fish of sockeye salmon size that swam with a tail‐beat frequency (TBF) between 2.0 and 3.5 beats/s and (2) fish of Chinook salmon size with a TBF between 1.0 and 2.0 beats/s. There was no correlation between TBF and fish size within each group, which suggests that the observed difference in TBF between the two groups was species‐specific rather than an indirect effect of the groups' difference in size. The technique of extracting TBF from DIDSON echograms may also be useful for bioenergetics studies. Compared with electromyogram telemetry, it offers the advantages of being nonintrusive and faster to set up and analyze and therefore is suitable for analyzing larger sample sizes. The disadvantages are that the technique's potential is limited to relatively large fish, it can cover only relatively small areas, it cannot be used to follow individual fish over long distances, and some environments are too noisy to produce DIDSON images of sufficient quality.
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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) | 0.000 | 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 | 0.000 |
| 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".