Hydrodynamic Performance of Full-Scale T0 and T90 Codends with and without a Codend Cover
Bibliographic record
Abstract
The hydrodynamic performance of one full-scale T0 (mesh size 90 mm) and three T90 (mesh size 90, 100, and 110 mm) codends was investigated and compared using flume tank testing, with and without a small-mesh cover. We evaluated how flow velocity, mesh circularity, and drag changed in each codend at five different towing speeds (0.5–0.9 m/s). The results demonstrated that flow velocity decreased along the length of a codend, and this effect was pronounced in the T0 codend. Increasing the mesh size of T90 codends from 90 to 110 mm did not significantly affect flow velocity. A novel parameter, termed mesh circularity, was developed and introduced to describe mesh opening. Mesh circularity in the T0 codend decreased along the length of the codend, which contrasted with the T90 codends. Results showed that the T90 codends maintained relatively open meshes (circularity ranged from ~0.8 to 1.0 along the length of the codend) compared to the T0 (circularity ranged from ~0.6 to 0.4). Each T90 codend had a significantly (p < 0.05) higher drag than the T0 codend when using the same simulated catch. For the covered codend comparisons, the flow velocity in the area between codend and cover did not change for the T0 codend (p > 0.05), but was significantly different for the T90 codend (p < 0.05). The results of this research provide fundamental knowledge useful for understanding and improving selectivity of trawls in marine fisheries, especially for revealing the masking effects of the cover net on the codend.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".