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Record W2189012089 · doi:10.17161/ch.vi1.11963

USE OF INHALANT ANESTHETICS IN THREE SNAKE SPECIES

2000· article· en· W2189012089 on OpenAlexafffund
Gabriel Blouin‐Demers, Patrick J. Weatherhead, Catherine M. Shilton, Christopher E. Parent, Gregory P. Brown

Bibliographic record

VenueContemporary Herpetology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaParks CanadaMinistry of Natural ResourcesWorld Wildlife Fund
KeywordsIntoxicative inhalantAnestheticIsofluraneHalothaneVaporizerAnesthesiaOphidiaMedicineBiologyToxicologyEcologyEndocrinologyVenom

Abstract

fetched live from OpenAlex

Different snake species respond differently to various anesthetic agents. Hence, an anesthetic procedure developed for one species cannot necessarily be safely transferred to another species. The goal of this paper is to summarize our experience using inhalant anesthetics on three snake species, including both procedures that were successful and those we found to be less satisfactory. We found isoflurane delivered with a precision vaporizer to be the best agent to anesthetize black rat snakes (Elaphe o. obsoleta). Sex and mass did not seem to affect induction times in black rat snakes, but larger female rat snakes recovered faster from anesthesia than smaller females. Halothane delivered in the open method provided consistent anesthesia in northern water snakes (Nerodia s. sipedon), although it caused some mortality and should not be used on debilitated patients. Halothane delivered with a precision vaporizer may be used to anesthetize eastern massasauga rattlesnakes (Sistrurus c. catenatus). However, care must be taken to prevent mortality resulting from anesthetic overdose. Sex and mass had no effect on induction and recovery times in the rattlesnakes, but stressed animals require longer induction and recovery times.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.998

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)0.0030.000

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 head0.054
GPT teacher head0.223
Teacher spread0.168 · 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.

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

Citations29
Published2000
Admission routes2
Has abstractyes

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