Current research and guidelines for euthanasia in laboratory fish with a focus on fathead minnows
Bibliographic record
Abstract
This paper reviews the methods and approaches used to humanely anesthetize (render unconscious) and or euthanize (kill) laboratory fish (in research settings), with a specific focus on the fathead minnow. We surveyed the literature (333 scientific studies published 2004-2021) to examine euthanasia methods used for various life stages. Our findings showed that many published scientific papers do not provide an adequate description of anesthesia or euthanasia methods, particularly for larval fathead minnows. Over the two decades there was a 20% increase in the number of papers that described their euthanasia method(s). In addition, the review shows evidence that younger minnows require higher concentrations of anesthetic (compared with adults) for effective euthanasia. Recommendations from the review include the use of a two-step euthanasia method (immersion in anesthetic followed by spinal severance and/or exsanguination). As well, it is recommended that details of anesthesia and euthanasia are more fully captured in published scientific manuscripts to allow for comparison among studies and for progress in animal welfare methods. Specific research questions remain on whether rapid cooling is a humane first-step euthanasia method, better investigations into understanding when anesthesia has occurred in fish, and research into methods of euthanasia in larval and juvenile fish.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".