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Record W3000268126 · doi:10.1080/17461391.2020.1712480

Changes in spatio‐temporal gait parameters and vertical speed during an extreme mountain ultra‐marathon

2020· article· en· W3000268126 on OpenAlexaff
David Jeker, Mathieu Falbriard, Gianluca Vernillo, Frédéric Meyer, Aldo Savoldelli, Francis Degache, Federico Schena, Kamiar Aminian, Grégoire P. Millet

Bibliographic record

VenueEuropean Journal of Sport Science · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAltitude (triangle)STRIDEGaitEffects of high altitude on humansGeodesyAtmospheric sciencesMathematicsPhysicsMedicinePhysical medicine and rehabilitationGeologyMeteorologyGeometry

Abstract

fetched live from OpenAlex

Abstract The aim of the present study was to investigate the effects of altitude and distance on uphill vertical speed (VS) and the main spatio‐temporal gait parameters during an extreme mountain ultra‐marathon. The VS, stride height (SH) and stride frequency (SF) of 27 runners were measured with an inertial sensor at the shank for two different altitude ranges (low 1300–2000 m vs high 2400–3200 m) of 10 mountains passes distributed over a 220 km course. There was a significant interaction ( F (4,52) = 4.04, p < 0.01) for the effect of altitude and distance on VS. During the first passes, the mean VS was faster at lower altitudes, but this difference disappeared at a quarter of the race length, suggesting that neuromuscular fatigue influenced the uphill velocity to a larger extent than the oxygen delivery. The average VS, SH and SF were 547 ± 135 m/h, 0.23 ± 0.05 m and 0.66 ± 0.09 Hz. The individual VS change for each uphill portions was more strongly correlated with the changes in SH ( r = 0.80, P < 0.001, n = 321) than SF ( r = 0.43, P < 0.001, n = 321). This suggests a large effect of the knee extensors strength loss on the diminution of VS.

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 score0.019
Threshold uncertainty score0.348

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 communication0.0000.001
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 head0.062
GPT teacher head0.272
Teacher spread0.210 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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