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Record W2087797293 · doi:10.7589/0090-3558-48.1.122

CHEMICAL IMMOBILIZATION OF RACCOONS (PROCYON LOTOR) WITH KETAMINE-MEDETOMIDINE MIXTURE AND REVERSAL WITH ATIPAMEZOLE

2012· article· en· W2087797293 on OpenAlexafffundabout
Karine Robert, Dany Garant, Fanie Pelletier

Bibliographic record

VenueJournal of Wildlife Diseases · 2012
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAtipamezoleMedetomidineKetamineBiologyAnesthesiaAnimal scienceAdult maleMedicineEndocrinology

Abstract

fetched live from OpenAlex

Safe and reliable capture techniques for wild animals are important for ecologic studies and management operations. We assessed the efficiency of ketamine-medetomidine (K:M) injection and reversal with atipamezole. We anesthetized 67 raccoons (Procyon lotor; 34 males, 33 females) 103 times (individuals captured between one and five times) from April 2009-October 2010 in Mont-Orford Provincial Park, Quebec, Canada. We administered a 1:1 mixture by volume of ketamine and medetomidine by intramuscular injection. Mean (±SD) induction times for males and females were 6.1±2.8 and 6.6±3.7 min, respectively. Mean induction time was 2 min longer for juveniles than for adults (7.8±3.9 and 5.8±2.9 min, respectively) and longer in autumn than in spring for adults (7.7±3.8 and 5.4±2.9 min, respectively). Recovery time after administration of atipamezole was 9.6±3.8 and 8.4±4.4 min for males and females, respectively. Recovery time was longer in spring than in autumn (10.2±4 and 7.4±3.8 min, respectively) for adults. Induction time increased by 166% after five captures of the same individual. Immobilization did not affect body mass, adult survival, or female reproductive success. We suggest the K:M mixture used is a safe and reliable method for anesthetizing raccoons in field conditions.

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)0.000
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 score0.066
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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

Citations17
Published2012
Admission routes3
Has abstractyes

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