Refinements in adipose tissue biopsy collection in shorebirds: effect on pain, wound healing, and mass gain
Bibliographic record
Abstract
Non-lethal methods to sample adipose tissues from fat depots in small birds are highly valuable as a time integrated sample matrix for ecotoxicology and ecophysiology research. However, for investigators to have confidence to use adipose tissue biopsy methods, welfare concerns remain regarding minimizing pain and ensuring there are no lasting effects on health and survival, particularly for small shorebird species actively undergoing refueling for seasonal migration. We tested refinements in adipose tissue biopsies in a captive Killdeer (Charadrius vociferus) population using either injectable or topical analgesics for pain control and monitored effects on mass, fat, and wound healing time over 21 days to critically evaluate the technique. Injectable analgesics provided rapid and superior short-term pain control compared to topical treatments, and there were no lasting effects of the analgesic or biopsy treatment on healing time, mass, or fat gain over the experiment. Average time for complete healing was 17 ± 3.5 days and all Killdeer continued to gain body mass and fat post procedure. The results suggest that adipose biopsies, with some recommended refinements, should be safe and effective and are not anticipated to cause significant impacts on fueling in migratory shorebirds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".