Underwater observations of seal–fishery interactions and the effectiveness of an exclusion device in reducing bycatch in a midwater trawl fishery
Bibliographic record
Abstract
Interactions between seals and midwater trawl operations in the Australian Small Pelagic Fishery are common and can be lethal. The nature of interactions and effectiveness of a seal exclusion device (SED) in mitigating lethal interactions was assessed using underwater video. Recent fishing activity and the phase of the trawl operation significantly influenced interaction rates; interactions increased with the amount of recent trawl activity and were highest while the trawl was being set. Most seals accessed the trawl via the net entrance and exited via an escape opening located at the base of the SED. The size of the escape opening was the only operational factor that influenced mortality rates — simply enlarging the escape hole reduced lethal interactions by 79%. However, since all deceased seals dropped out of the net before they were brought on board, they would have gone unobserved without video monitoring. Limiting the concentration of fishing activity in space and time and refinement of the SED design, in particular to address dropouts, is recommended if mortality rates are to be reduced.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".