Electric Fish Handling Gloves Provide Effective Immobilization and Do Not Impede Reflex Recovery of Adult Largemouth Bass
Bibliographic record
Abstract
Abstract Electric fish handling gloves (FHGs) have been developed to immobilize fish during handling, with the potential benefit of reducing the time needed for sedation and recovery of fish relative to chemical anaesthetics. We examined the secondary stress responses (i.e., hematocrit, blood glucose, lactate, and pH) and reflex responses of Largemouth Bass Micropterus salmoides that were immobilized in water using electric FHGs for multiple durations (0, 30, and 120 s) relative to fish that were handled using only bare hands in water. We also evaluated the efficacy of the immobilization by quantifying the number of volitional movements that were observed during handling. Our findings suggested that when FHGs were used, fish tended to remain still (i.e., to show full reflex impairment) during handling relative to controls. Fish that were held with FHGs showed negligible reflex impairment immediately after the electricity was terminated. After a 30-min posttreatment retention period, blood chemistry and ventilation rates were similar between fish held with FHGs and those held with bare hands. This study supports the notion that electric FHGs are a safe and effective tool for practitioners who need to temporarily immobilize fish for handling, enumeration, or performing various scientific procedures. Received November 15, 2016; accepted February 26, 2017 Published online May 4, 2017
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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 | 0.000 |
| 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) | 0.001 | 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".