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Record W1964652407 · doi:10.1249/mss.0b013e31817057a8

Energetic Costs of Incidental Visual Coupling during Treadmill Running

2008· article· en· W1964652407 on OpenAlexaff
Daniel Eaves, Nicola J. Hodges, Abigail Williams

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2008
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTreadmillKinematicsStability (learning theory)Coupling (piping)Physical medicine and rehabilitationPsychologySimulationComputer scienceComputer visionMathematicsPhysicsPhysical therapyMedicineEngineeringClassical mechanics

Abstract

fetched live from OpenAlex

PURPOSE: To determine the role of visual-spatial information in stabilizing movement during treadmill locomotion. METHODS: Physiological, biomechanical, and psychological indices of coordination stability were recorded while participants were visually coupled with a whole-body image of themselves during treadmill locomotion. Ten participants ran on a treadmill under three visual conditions: two dynamic images (symmetrical, mirror image; asymmetrical, reversed mirror image) and one static. Performance was examined at two speeds. RESULTS: Participants ran more economically (mL x kg x min(-1)) when they were visually coupled with a symmetrical rather than with an asymmetrical or static image. An asymmetrical coupling resulted in increased variability in footfall position at the faster speed, in comparison to the symmetrical and static conditions. However, at slower speeds, footfall variability and frequency were higher under both dynamic visual conditions in comparison to the static control. Changes in metabolic economy (mL x kg x min(-1)) were only partially mediated by movement kinematics. CONCLUSION: Visual information influences treadmill locomotion and associated measures of stability even when there is no intention to coordinate with external stimuli.

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 score0.005
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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
Published2008
Admission routes1
Has abstractyes

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