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Record W3128176596 · doi:10.1051/sm/2020016

Les dispositifs d’évaluation des parasportifs pratiquant des sports de petits terrains en fauteuil roulant manuel

2021· article· fr· W3128176596 on OpenAlexaff
Sadate Bakatchina, Thierry Weissland, Arnaud Faupin

Bibliographic record

VenueMovement & Sport Sciences - Science & Motricité · 2021
Typearticle
Languagefr
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesArtPhysics

Abstract

fetched live from OpenAlex

Le but de cette revue de littérature narrative est d’identifier à travers les logiques internes de deux disciplines collectives paralympiques en fauteuil roulant manuel (FRM), l’apport et les limites des principaux dispositifs accessibles aux parasportifs en FRM. Dans le cadre de l’optimisation des performances, les parasportifs sont habituellement testés au laboratoire et/ou sur le terrain. Au laboratoire, les ergomètres à manivelles (EM), les ergomètres à rouleaux pour fauteuil (ERF) et le tapis roulant (TR) sont les plus utilisés. Les EMs ne permettent pas de simuler la gestuelle mécanique de la propulsion du FRM. Les ERFs permettent l’utilisation du FRM personnel mais, neutralisent les forces de résistance des roulettes du FRM. Le TR est plus réaliste mais neutralise les mouvements latéraux du FRM. La technologie embarquée est une évolution des outils de laboratoire. Ainsi, les roues instrumentées (RI) et des centrales inertielles (CI) sont adaptées pour les mesures en situations de terrain. Cependant, la masse des RI limite le comportement du FRM et les CI ne quantifient pas les forces développées sur les mains courantes. La simulation des forces exercées sur les mains courantes à partir des données des CI permettra en perspective le développement de capteurs de force miniaturisés.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0030.010
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.405
Teacher spread0.330 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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Same venueMovement & Sport Sciences - Science & MotricitéSame topicSpinal Cord Injury ResearchFrench-language works237,207