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Record W2613947001 · doi:10.1139/cjas-2016-0189

The effect of exercise intensity and use of an electrolyte supplement on plasma electrolyte concentrations in the Standardbred horse

2017· article· en· W2613947001 on OpenAlexafffundvenue
Emily Walker, Stephanie A. Collins

Bibliographic record

VenueCanadian Journal of Animal Science · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsHorseElectrolyteHeart rateMedicineAnimal scienceSodiumLatin squarePotassiumInternal medicineChemistryBlood pressureBiologyBiochemistry

Abstract

fetched live from OpenAlex

Eleven Standardbred horses were tested to assess plasma electrolyte fluctuations during different intensities of exercise (at rest, immediately following 20 min of jogging, and immediately following a 2 min race), with and without an orally administered electrolyte supplement 2 h prior to sample collection. Exercise treatments were repeated after horses were given an oral dosage of an electrolyte supplement 2 h prior to sample collection. Data were analyzed using a repeated incomplete 6 × 6 Latin-square design with a 3 × 2 factorial arrangement of treatments (exercise × supplement). Jogging horses had an elevated heart rate as compared with resting horses and plasma potassium concentrations that were higher than those of the other two treatments (P < 0.05). Racing horses had the highest heart rate of the three treatments and plasma sodium concentrations that were higher than those of resting and jogging horses (P < 0.05). Provision of an electrolyte supplement significantly increased heart rate, as well as blood potassium and sodium concentrations. Further studies on the dietary electrolyte supplementation would benefit from investigating additional performance and recovery parameters, with focus on the hydration status of the horse before, during, and after exercises.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.867
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.058
GPT teacher head0.365
Teacher spread0.307 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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