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Record W1932701889 · doi:10.1155/2015/240153

Effects of Surface Inclination on the Vertical Loading Rates and Landing Pattern during the First Attempt of Barefoot Running in Habitual Shod Runners

2015· article· en· W1932701889 on OpenAlexaff
Winko W. An, Michael J. Rainbow, Roy T.H. Cheung

Bibliographic record

VenueBioMed Research International · 2015
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBarefootHeelTreadmillOrthodonticsMedicineGeologyPhysical medicine and rehabilitationMathematicsPhysical therapyAnatomy

Abstract

fetched live from OpenAlex

Barefoot running has been proposed to reduce vertical loading rates, which is a risk factor of running injuries. Most of the previous studies evaluated runners on level surfaces. This study examined the effect of surface inclination on vertical loading rates and landing pattern during the first attempt of barefoot running among habitual shod runners. Twenty habitual shod runners were asked to run on treadmill at 8.0 km/h at three inclination angles (0°; +10°; -10°) with and without their usual running shoes. Vertical average rate (VALR) and instantaneous loading rate (VILR) were obtained by established methods. Landing pattern was decided using high-speed camera. VALR and VILR in shod condition were significantly higher (p < 0.001) in declined than in level or inclined treadmill running, but not in barefoot condition (p > 0.382). There was no difference (p > 0.413) in the landing pattern among all surface inclinations. Only one runner demonstrated complete transition to non-heel strike landing in all slope conditions. Reducing heel strike ratio in barefoot running did not ensure a decrease in loading rates (p > 0.15). Conversely, non-heel strike landing, regardless of footwear condition, would result in a softer landing (p < 0.011).

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.055
GPT teacher head0.319
Teacher spread0.265 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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