The sampling volume of trawl and acoustics: estimating availability probabilities from observations of tracked individual fish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effective sampling volume of trawl and acoustics is an important parameter in fish abundance estimation surveys. This paper presents a method to compute the probability of a fish being available to the bottom trawl and the probability of it being seen on the echo sounder, given its initial position relative to the vessel path. These probabilities are then related to the calculation of the effective observational volume for trawl and acoustics, the two main tools of measuring abundance of Atlantic cod ( Gadus morhua ) and haddock ( Melanogrammus aeglefinus ). As an example, the computation is carried out for a typical vertical distribution in the Barents Sea. Our model is based on an Ornstein–Uhlenbeck model for the fish swimming trajectories, and its parameters are estimated using observations of swimming trajectories for individual fish, recorded by a split-beam echo sounder. The model itself constitutes a general method to translate observations on behaviour of individual fish to probability maps. The results indicate a typical fishing height of 20 m for the bottom trawl, but it is also shown that there is a relatively low probability of catching by the trawl what you see on the echo sounder, even for fish positioned directly in the trawl path. This is because of strong lateral movements of the fish.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it