FLUME TANK TESTING OF AN INNOVATIVE FOOTGEAR TECHNOLOGYUSING SIMULATED SEABEDS
Bibliographic record
Abstract
There have been many advancements in bottom trawls to reduce physical and biological impacts on benthic habitats. In this study, an innovative aligned-rolling footgear was designed and evaluated for use in the Northern shrimp (Pandalus borealis) fishery in Eastern Canada. We document a novel technique for comparing traditional and experimental footgears using engineering models and simulated seabed conditions in a flume tank. Footgears were compared using direct observation and by measuring warp load during simulated smooth, semi-rough, and rough seabed conditions in contact with bosom or wing footgear sections. Results revealed that the traditional footgear bottom trawl experienced significantly higher warp loads for smooth (0.26 t higher), semi-rough (0.68 t higher), and rough seabed conditions (0.74 t higher) in the bosom section. In the wing section, traditional bottom trawl produced significantly higher warp loads for smooth (0.38 t higher) and rough seabed conditions (0.30 t higher). Bottom trawl with aligned-rolling footgear reduces seabed contact up to 71.5% depending upon depth of penetration modelled. To our knowledge, this study represents the first attempt at using simulated seabed conditions in a flume tank testing footgear technology.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".