Comparing the size selectivity of a novel T90 mesh codend to two conventional codends in the northern shrimp (Pandalus borealis) trawl fishery
Bibliographic record
Abstract
The size selectivity and usability of three codends were quantified and compared for the first time in the inshore Northern shrimp (Pandalus borealis) trawl fishery of Iceland using the covered codend method: a conventional diamond-mesh codend (T0), conventional square-mesh codend (T45), and a 90° turned mesh codend (T90) constructed of four panels and with shortened lastridge ropes. Fishers, wanting to increase the average-individual size of captured shrimp, had requested the T90 codend to be compared with conventional codends for consideration in the fishery. Results showed that, on average, the T45 and T90 codends had better size selectivity than the T0 codend in terms of releasing individuals smaller than 13 mm carapace length (Minimum References Size; MRS). The T90 codend retained significantly less Northern shrimps between 9 and 19 mm than the T0 codend and between 15 and 19 mm than the T45 codend. No significant difference of size selectivity between T45 and T0 codends was observed. All three codends presented high retention ratios of Northern shrimps above MRS (>63%) for the population encountered. However, the T0 codend was not effective at sorting out small Northern shrimps; at least 86% of Northern shrimps smaller than 13 mm were retained in the T0 codend if encountered. Catches from T45 and T90 codends had a lower proportion of shrimp below MRS. Since discarding of undersized Northern shrimps is prohibited in Iceland and fishers wanted to catch on average larger shrimp, using the novel T90 codend would enable fishers to use their quotas more efficiently.
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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 | 0.001 |
| 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) | 0.000 | 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".