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Record W2284460091 · doi:10.14288/1.0104801

Some factors influencing the level of reducing sugar in the blood of black-tailed deer

2011· article· en· W2284460091 on OpenAlexaboutno aff
Philip E. Whitehead

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBlood sugarBiologyGeographyDiabetes mellitus

Abstract

fetched live from OpenAlex

Some of the factors that influence the blood reducing sugar level in the black-tailed deer Odeeoileus hemionus columbianus (Richardson) (Vancouver Island genotype), have been investigated. The distribution of reducing sugar in the blood of these animals was also examined. It was found that: feed intake during the hour preceeding blood letting, short periods of fast, nature of the feed, and sex of the animal apparently have no effect on the level of blood reducing sugar in deer. Blood samples taken in the evening generally had a higher reducing sugar level than those taken earlier in the day. The means used to restrain the animals during the blood letting procedure was also found to have a marked influence on the level of blood reducing sugar. Deer restrained by physical force exhibited significantly higher and more variable blood sugar levels than those immobilized with succinylcholine. The length of time required to draw a blood sample from an animal also influenced the blood sugar level. The longer the time to let a sample, the higher the blood sugar level in the sample. The results indicate that the degree of excitement, fear, and pain experienced by the animals preceeding and during the blood letting procedure was the principal cause of variability found in the level of blood reducing sugar. No reducing sugar could be detected in the erythrocytes of these deer.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.074
GPT teacher head0.197
Teacher spread0.123 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2011
Admission routes1
Has abstractyes

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