Fish behaviour and orientation-dependent backscatter in acoustic Doppler profiler data
Bibliographic record
Abstract
Abstract It is well known that fish target strength can be affected by fish behaviour. In particular, the specific orientation between a target (fish) and an echosounder system is critical in interpreting the apparent target strength. This situation is further complicated for Doppler current-profiler systems that have an array of diverging beams and where each beam will interact with targets with a different orientation. When targets themselves have random orientations, each beam will see the same range of target strengths and the average values would be the same. However, if targets such as schooling fish have a preferred orientation, then an asymmetry is present and different target strengths can be expected. This hypothesis is explored using observations of Norwegian spring-spawning herring (Clupea harengus) under a variety of schooling conditions. When schooling fish are migrating and have well-defined, coherent orientations, differences in backscatter strength of about 5 dB can be seen depending on beam orientation. This difference disappears when fish are not actively migrating and so do not have coherent orientation. These differences can be used to infer behaviour of schooling fish but they also indicate that caution must be exercised when averaging backscatter data from separate Acoustic Doppler Current Profiler (ADCP) beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".