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Record W2007301333 · doi:10.1080/02640414.2012.762598

A sit-ski design aimed at controlling centre of mass and inertia

2013· article· en· W2007301333 on OpenAlexaffabout
Ève Langelier, Stéphane Martel, Anne Millot, Jean-Luc Lessard, Cécile Smeesters, Denis Rancourt

Bibliographic record

VenueJournal of Sports Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMoment of inertiaInertiaRange (aeronautics)Suspension (topology)EngineeringComputer scienceSimulationMathematicsPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This article introduces a sit-ski developed for the Canadian Alpine Ski Team in view of the Vancouver 2010 Paralympic games. The design is predominantly based on controlling the mass distribution of the sit-ski, a critical factor in skiing performance and control. Both the antero-posterior location of the centre of mass and the sit-ski moment of inertia were addressed in our design. Our design provides means to adjust the antero-posterior centre of mass location of a sit-ski to compensate for masses that would tend to move the antero-posterior centre of mass location away from the midline of the binding area along the ski axis. The adjustment range provided is as large as 140 mm, thereby providing sufficient adaptability for most situations. The suspension mechanism selected is a four-bar linkage optimised to limit antero-posterior seat movement, due to suspension compression, to 7 mm maximum. This is about 5% of the maximum antero-posterior centre of mass control capacity (151 mm) of a human participant. Foot rest inclination was included in the design to modify the sit-ski inertia by as much as 11%. Together, these mass adjustment features were shown to drastically help athletes' skiing performance.

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 score0.036
Threshold uncertainty score0.566

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)0.0010.000

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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