Developing a semi-pelagic trawl to capture redfish in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
spp.) and potentially reduce the capture of bycatch species and seabed impacts in the Gulf of St. Lawrence, Canada. The new trawl used an innovative technique connecting the upper bridles of the trawl to the warps, anterior of the trawl doors, leading to the trawl system being fished off the seabed. Such a technique can be used to match the heights of redfish as they move above the seabed during their diurnal cycle while allowing bycatch species related to the seabed to escape under the trawl. A 1:10 scale model of the trawl was constructed and evaluated in a flume tank to optimize the rigging and then a full-scale trawl was constructed for sea trials. Two field experiments subsequently evaluated the trawl at sea. The first field experiment concentrated on the experimental trawl's operation and video observations of redfish behaviour in the trawl mouth and its effect on trawl entry. The second field experiment concentrated on a small-scale preliminary test on the catch of redfish and bycatch species when the trawl was on or off the seabed. Capture results, though preliminary, indicate that redfish can be targeted commercially with a semi-pelagic trawl, though some redfish will escape under the trawl. Additionally, results suggest that the catches of bycatch species may be reduced. In conclusion, this study suggests that a semi-pelagic trawl could be considered an effective technique to harvest redfish sustainably.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".