Greenland shark (<i>Somniosus microcephalus</i>) behaviour at a Nordmøre grid
Bibliographic record
Abstract
Abstract Large Greenland sharks (Somniosus microcephalus) commonly enter bottom trawls, and in some cases this results in unintended bycatch when they become stuck in the Nordmøre grid system. Successful bycatch reduction strategies should focus on excluding species as early and quickly as possible in the capture process to prevent stress and mortality. Developing bycatch reduction devices (BRDs) that consider shark escape behaviours could reduce their bycatch while allowing for a quicker and earlier escape. This study describes Greenland shark behaviour in the Nordmøre grid system, offering insights for designing more effective BRDs. Video of Greenland sharks was collected in the offshore Northern shrimp factory-freezer trawl fishery in Eastern Canada. Greenland shark behaviours are described in qualitative and quantitative terms, including observed turns, rolls, contact and engagement with gear components, and time-to-escape. Given that the majority of Greenland sharks observed were large (∼2.5–>4.0 m), these behaviours often hindered their escape out of the escape opening of the grid system. Greenland sharks interacted with the grid itself in varying ways, 50% (n = 6) were observed sliding over the grid, and the grid quickly deflected immobile sharks out through the escape opening. Greenland sharks often became stuck in the grid system, one at the guiding panel and 6 (50%) at the escape opening and grid. Stuck individuals performed rolls and thrashes in apparent attempts to escape. Although all of the observed individuals eventually escaped in this study, that is not always the case in the fishery, and gear modifications should be considered to prevent prolonged interactions, including adding BRDs before the grid system, modifying the guiding panel, and/or increasing the dimensions of the escape opening.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".