Recovery of grizzly and American black bears from xylazine, zolazepam, and tiletamine
Bibliographic record
Abstract
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 I compared these recovery times with unpublished recovery time data on bears anesthetized with ZT only.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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".