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Record W4323908085 · doi:10.5751/jfo-00219-940110

Refinements in adipose tissue biopsy collection in shorebirds: effect on pain, wound healing, and mass gain

2023· article· en· W4323908085 on OpenAlexfundno aff
Christy A. Morrissey, Kurtis J. Swekla

Bibliographic record

VenueJournal of Field Ornithology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdipose tissueMedicineBiopsyPopulationWound healingSurgeryPhysiologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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 (<em>Charadrius vociferus</em>) 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.284
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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