Influence of Landing Net Mesh Type on Handling Time and Tissue Damage of Angled Brook Trout
Bibliographic record
Abstract
Abstract Recreational catch-and-release angling is a popular activity. Anglers often use landing nets to shorten fight times, reduce stress on the line and rod, restrict fish movement to facilitate dehooking of the fish, and protect fish from undue harm caused by handling or dropping. Landing nets are constructed using a variety of netting materials that could have varied consequences when coming in contact with fish. Salmonids are among the most targeted fishes in the world, but little is known about how landing nets contribute to postcapture tissue damage. We compared handling time and instances of fin fraying, scale loss, and mucus loss sustained by Brook Trout Salvelinus fontinalis landed by four net mesh types (i.e., large, knotless rubber mesh; knotless nylon micromesh; large, knotted polypropylene mesh; and small, knotless rubber-coated nylon mesh) or by using bare wet hands in a recreational fishery. The knotted polypropylene mesh resulted in the greatest extent of fin fraying, whereas the bare wet hands method, knotless nylon micromesh, and rubber-coated nylon mesh resulted in the most scale loss. Interestingly, extended handling times were noted for several mesh types (i.e., knotless nylon micromesh and rubber-coated nylon mesh) relative to bare wet hands because of hook entanglement in the netting material. However, using bare wet hands to land Brook Trout resulted in higher odds of the fish being dropped into the bottom of the boat. We concluded that the large, knotless rubber mesh was the least damaging to Brook Trout. Changes to angler practices, such as using appropriate landing tools, can benefit fish welfare in catch-and-release fisheries.
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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 | 0.001 | 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".