Multifrequency backscattering properties of Atlantic herring (<i>Clupea harengus</i>) and Norway pout (<i>Trisopterus esmarkii</i>)
Bibliographic record
Abstract
The multifrequency backscattering characteristics of echotraces of Atlantic herring (Clupea harengus) and Norway pout (Trisopterus esmarkii) are described. These fish cohabit similar areas of the North Sea in summer and echotraces of their schools are difficult to distinguish. Mean volume backscattering strengths at 18, 38, 120, and 200 kHz were taken from the International North Sea Herring Acoustic Surveys along with coincident pelagic trawl samples. The results indicate that echotraces of these fish species cannot be distinguished on the basis of differences in backscattering at discrete frequencies typically used in fish surveys and on fishing vessels. However, some discrimination between herring size-classes was evident. The empirical data for herring were then compared with a backscattering model for herring combining fish flesh, the swimbladder, and the effect of increased pressure at depth. Both the empirical data and model data indicate that, compared with large herring, progressively smaller herring generally have higher backscattering at the lowest frequency (18 kHz), although variability was high. According to the model, this frequency-specific signature is due to the progressively more significant contribution made by the fish body compared with the swimbladder, as the latter diminishes owing to an increase in ambient pressure in deeper water.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".