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Record W2148403071 · doi:10.2192/08gr012.1

Recovery of grizzly and American black bears from xylazine, zolazepam, and tiletamine

2009· article· en· W2148403071 on OpenAlexaboutno aff
Thomas G. Radandt

Bibliographic record

VenueUrsus · 2009
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsnot available
Fundersnot available
KeywordsXylazineUrsusAnesthesiaMedicineYohimbineKetamineInternal medicineAntagonistPopulation

Abstract

fetched live from OpenAlex

Field workers handling bears continually strive to improve their field methods and reduce risks to animals during capture. Zolazepam–tiletamine (ZT) is the standard anesthesia currently used in bear captures, but has a prolonged recovery because there is no antagonist. Researchers are increasingly using xylazine, zolazepam, and tiletamine (XZT) in combination as an improvement to ZT alone. Because xylazine provides excellent analgesic qualities and can be antagonized, XZT has the potential for effective anesthesia and faster recovery time for bears. I assessed recovery times and considered physiological parameters to asses the quality of anesthesia of grizzly (Ursus arctos) and American black (U. americanus) bears anesthetized with XZT, for which the xylazine portion was antagonized by yohimbine (XZT/Y). I compared these recovery times with unpublished recovery time data on bears anesthetized with ZT only. My XZT/Y samples came from research projects in western Montana, northern Idaho, and southeast British Columbia; bears anesthetized with ZT only came from Alberta, Canada, and the Greater Yellowstone Project of Montana, Wyoming, and Idaho, USA. Bears administered the XZT/Y protocol recovered from anesthesia 1.61 (95% CI = 1.28–2.01) times faster than bears anesthetized with ZT combinations. Bears administered XZT/Y at dosage rates presented here received adequate anesthesia for humane handling as indicated by the physiological parameters monitored.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.555

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 communication0.0000.000
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 head0.028
GPT teacher head0.303
Teacher spread0.275 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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