Hook disgorgers remove deep hooks but kill fish: A plea for cutting the line
Bibliographic record
Abstract
Abstract Recreational fishing can result in deep hooking (e.g. in the gullet) of fish that are intended to be released, leading to the development of various tools intended to assist with hook removal. So‐called “hook disgorgers” are typically marketed as being a mechanism to retrieve the hook while doing so in a way that reduces harm to the fish, despite there being many studies that demonstrate that it is best to cut the line for deeply hooked fish. A study was designed to test the effectiveness of six different hook disgorgers for deeply hooked smallmouth bass, Micropterus dolomieu Lacépède, captured using baitholder hooks relative to shallow hooked controls and fish for which the line was cut. Reflex impairment and survival at 10 min, 1 hr and 24 hr were assessed. The study was terminated after early results revealed that all but one of the fish that had the hook removed died (n = 17), while all fish that were hooked in the jaw (n = 4) or had the line cut (n = 5) survived. The ethical conundrum faced by the research team is discussed here, recognising that an incomplete study would have less statistical rigour even though it was very clear that disgorgers used when hooks were in the gullet killed the fish. Stopping rules are common in pharmaceutical trials and can also be used to inform catch‐and‐release research to maintain fish welfare. Best practices for anglers include cutting the line when fish are hooked in the gullet, and changing fishing strategies and gear type when deep hooking is encountered on a routine basis, otherwise mortality can be unnecessarily high.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".