Tilt angle and target strength: target tracking of Atlantic cod (Gadus morhua) during trawling
Bibliographic record
Abstract
Abstract Vertical orientation (tilt angle) is known to affect the target strength (TS) of ensonified fish and is a large component of the variability inherent in acoustic-biomass estimates. To measure the effects of changes in tilt angle on TS during diel vertical migrations, a concentration of migrating Atlantic cod (Gadus morhua) was observed acoustically from a research vessel over several days. Single-target data were collected from a split-beam echosounder and were subsequently tracked, corrected for vessel orientation and movement, and analysed for 3-dimensional displacement (speed and direction). The results revealed a large variability in TS and several patterns of swimming behaviour from random to directed orientation and movement, with changes in both vertical and horizontal displacements and inferred orientation. These behavioural patterns and their affects on TS were analysed as a function of “time-since-sunset”. Regular diel orientation patterns were observed as cod rose from the ocean bottom in the evening, increasing their tilt angle, and descended at sunrise to regain the ocean floor. Standardized TS (B20) was found to be highly correlated with tilt angle. This relationship can be used to correct for the diel changes in the TS of these migrating cod as a function of the in situ-measured tilt angle and thus to improve the accuracy of acoustic-biomass estimation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".