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Record W2066110785 · doi:10.1080/02640414.2012.718091

Physiological demands of downhill mountain biking

2012· article· en· W2066110785 on OpenAlexafffund
Jamie F. Burr, C. Taylor Drury, Adam Ivey, Darren E. R. Warburton

Bibliographic record

VenueJournal of Sports Sciences · 2012
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsHeart rateCyclingPerceived exertionPhysical medicine and rehabilitationCardiovascular fitnessPhysical fitnessPsychologyMedicinePhysical therapyBlood pressureGeographyInternal medicine

Abstract

fetched live from OpenAlex

Mountain biking is a popular recreational pursuit and the physiological demands of cross-country style riding have been well documented. However, little is known regarding the growing discipline of gravity-assisted downhill cycling. We characterised the physiological demands of downhill mountain biking under typical riding conditions. Riding oxygen consumption (VO(2)) and heart rate (HR) were measured on 11 male and eight female experienced downhill cyclists and compared with data during a standardised incremental to maximum (VO(2max)) exercise test. The mean VO(2) while riding was 23.1 ± 6.9 ml · kg(-1) · min(-1) or 52 ± 14% of VO(2max) with corresponding heart rates of 146 ± 11 bpm (80 ± 6% HRmax). Over 65% of the ride was in a zone at or above an intensity level associated with improvements in health-related fitness. However, the participants' heart rates and ratings of perceived exertion were artificially inflated in comparison with the actual metabolic demands of the downhill ride. Substantial muscular fatigue was evident in grip strength, which decreased 5.4 ± 9.4 kg (5.5 ± 11.2%, P = 0.03) post-ride. Participation in downhill mountain biking is associated with significant physiological demands, which are in a range associated with beneficial effects on health-related fitness.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.202

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 head0.059
GPT teacher head0.334
Teacher spread0.275 · 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 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

Citations28
Published2012
Admission routes2
Has abstractyes

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