The sampling volume of trawl and acoustics: estimating availability probabilities from observations of tracked individual fish
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.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".