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Assessment of two methods to determine the relative contributions of the aerobic and anaerobic energy systems in racehorses

2019· article· en· W2914141475 on OpenAlexaff
Stephanie L. Bond, Persephone Greco‐Otto, R. H. SIDES, Grace P. S. Kwong, Renaud Léguillette, Warwick M. Bayly

Bibliographic record

VenueJournal of Applied Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsAnaerobic exerciseVO2 maxTreadmillAerobic exerciseBlood lactateOxygenExercise physiologyAnimal scienceChemistryIncremental exerciseExercise intensityMathematicsHeart rateInternal medicinePhysical therapyMedicineBlood pressureBiology

Abstract

fetched live from OpenAlex

A prospective, randomized, controlled study was designed to determine relative aerobic and anaerobic (lactic and alactic) contributions at supramaximal exercise intensities using two different methods. Thoroughbred racehorses ( n = 5) performed a maximal rate of oxygen consumption (V̇o 2max ) test and three supramaximal treadmill runs (105, 115, and 125% V̇o 2max ). Blood lactate concentration (BL) was measured at rest, every 15 s during runs, and 2, 5, 10, 20, 30, 40, 50, and 60 min postexercise. In method 1, oxygen demand was calculated for each supramaximal intensity based on the V̇o 2max test, and relative aerobic and anaerobic contributions were calculated from measured V̇o 2 and the accumulated oxygen deficit. In method 2, aerobic contribution was calculated using the trapezoidal method to determine V̇o 2 during exercise. A monoexponential model was fitted to the postexercise V̇o 2 curve. Alactic contribution was calculated using the coefficients of this model. Lactate anaerobic contribution was calculated by multiplying the peak to resting change in BL by 3. Linear mixed-effects models were used to examine the effects of exercise intensity and method (as fixed effects) on measured outcomes ( P ≤ 0.05). Relative aerobic and anaerobic contributions were not different between methods ( P = 0.20). Horses’ mean contributions were 81.4, 77.6, and 72.5% (aerobic), and 18.5, 22.3, and 27.4% (anaerobic) at 105, 115, and 125% V̇o 2max , respectively. Individual alactic anaerobic energy was not different between supramaximal exercise intensities ( P = 0.43) and was negligible, contributing a mean of 0.11% of the total energy. Relative energy contributions can be calculated using measured V̇o 2 and BL in situations where the exercise intensity is unknown. Understanding relative metabolic demands could help develop tailored training programs. NEW & NOTEWORTHY Relative energy contributions of horses can be calculated using measured V̇o 2 and BL in situations where the exercise intensity is unknown. Horses’ mean contributions were 81.4, 77.6, and 72.5% (aerobic), and 18.5, 22.3, and 27.4% (anaerobic) at 105, 115, and 125% of V̇o 2max , respectively. Individual alactic capacity was unaltered between supramaximal exercise intensities and accounted for a mean contribution of 0.11% of energy use.

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)0.001
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 score0.947
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.348
Teacher spread0.331 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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