Evaluating hook removal techniques on jaw-hooked Smallmouth Bass captured with soft plastic jigs
Bibliographic record
Abstract
ABSTRACT Objective Products that are intended to facilitate the release of angled fish continue to be developed by the fishing industry without systematic and objective evaluation to test their effectiveness for releasing fish without causing undue harm. Here, we evaluated the efficacy of dehooking methods (i.e., removing the hook with bare hands, pliers, or a mechanical dehooking device) while either holding the fish in air by the lower mandible or without touching the fish. Methods We captured 131 Smallmouth Bass Micropterus dolomieu by using barbed Ned rigs (single-hook, soft plastic jig-style lures) and assessed the duration of time needed to remove the hook, the extent of physical injury caused by the hook removal method, and the extent of reflex impairment of the fish. Results Unhooking time was influenced by hook removal method and fish length. Physical injury was also influenced by the unhooking method; the use of pliers while holding the fish by the lower mandible resulted in no observed injuries compared to all other dehooking methods, which resulted in some proportion of fish being injured. Longer unhooking time increased reflex impairment. The traditional method of holding the lower mandible of black bass yielded faster dehooking times and fewer injuries irrespective of hook removal method, but the use of hands proved to be the fastest method. Conclusions Our research suggested that an alternative method of touchless dehooking and the use of a mechanical dehooking tool were not effective when releasing shallow-hooked Smallmouth Bass. Our findings also suggested that gripping the fish by the lower mandible and using hands constituted the most effective hook removal approach for Smallmouth Bass in the context studied here.
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 | 0.001 |
| 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) | 0.001 | 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".