Best practices for non‐lethal blood sampling of fish <i>via</i> the caudal vasculature
Bibliographic record
Abstract
Blood sampling through the caudal vasculature is a widely used technique in fish biology for investigating organismal health and physiology. In live fishes, it can provide a quick, easy and relatively non-invasive method for obtaining a blood sample (cf. cannulation and cardiac puncture). Here, a general set of recommendations are provided for optimizing the blood sampling protocol that reflects best practices in animal welfare and sample integrity. This includes selecting appropriate use of anaesthetics for blood sampling as well as restraint techniques for situations where sedation is not used. In addition, ideal sampling environments where the fish can freely ventilate and strategies for minimizing handling time are discussed. This study summarizes the techniques used for extracting blood from the caudal vasculature in live fishes, highlighting the phlebotomy itself, the timing of sampling events and acceptable blood sample volumes. This study further discuss considerations for selecting appropriate physiological metrics when sampling in the caudal region and the potential benefits that this technique provides with respect to long-term biological assessments. Although general guidelines for blood sampling are provided here, it should be recognized that contextual considerations (e.g., taxonomic diversity, legal matters, environmental constraints) may influence the approach to blood sampling. Overall, it can be concluded that when done properly, blood sampling live fishes through the caudal vasculature is quick, efficient and minimally invasive, thus promoting conditions where live release of focal animals is possible.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".