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Record W2092969079 · doi:10.4141/a99-041

Effect of water restriction and environmental temperatures on metabolic rate and physiological parameters in sheep

2000· article· en· W2092969079 on OpenAlexafffundvenue
B. T. Li, R. J. Christopherson, S. Cosgrove

Bibliographic record

VenueCanadian Journal of Animal Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnimal scienceCreatinineInternal medicineChemistryPlasma osmolalityAldosteroneEndocrinologyBiologyVasopressinMedicine

Abstract

fetched live from OpenAlex

The hypothesis that water restriction reduces metabolic rate and contributes to energy conservation of sheep, and induces changes in blood parameters was tested. Four of eight adult sheep were housed in either a warm (24.8 ± 1.5 °C) or cold (0.4 ± 1.2 °C) environment and fed a diet of alfalfa pellets at 1.2 × maintenance. Each sheep was fasted with or without water according to a crossover design. Average heat production (HP) and rectal temperature (Tr) were higher (P < 0.05) in the cold than in the warm. Fasting decreased HP and Tr (P < 0.05). Water restriction had no additional effect on HP and Tr. Fasting and fasting plus water restriction influenced plasma osmolality and creatinine concentration. Plasma creatinine concentration was lower (P < 0.01) and haemoglobin (Hb) concentration higher in the cold than in the warm environment. Hb concentration was increased with water restriction (P < 0.01) in the warm environment. Plasma cortisol concentration was altered by fasting. Packed cell volume (PCV) in blood, plasma volume and plasma aldosterone were not affected by treatments. The results suggest that water restriction, per se, for 3 d does not suppress metabolic rate in sheep below that resulting from fasting alone. Key words: Heat production, sheep, temperature, water restriction

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.220

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 studies0.0000.001
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 head0.008
GPT teacher head0.195
Teacher spread0.188 · 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 designBench or experimental
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

Citations35
Published2000
Admission routes3
Has abstractyes

Explore more

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