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Record W2020567666 · doi:10.1016/j.proeng.2010.04.171

Poster Session I, July 14th 2010 — Abstracts Design of an ergometer to train and evaluate elite crosscountry skiiers

2010· article· en· W2020567666 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProcedia Engineering · 2010
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsRowingKinematicsWork (physics)SimulationDisplacement (psychology)Computer scienceEngineeringMechanical engineeringPhysicsPsychology

Abstract

fetched live from OpenAlex

Sport ergometers offer a reasonable alternative for semi-specific training conditions as it provides a sheltered environment to practice. Their additional values from in situ performances are mainly due to real time feedback of mechanical variables as the external power generated by athlete at one (or more) contact with the ergometer (e.g. handle power while rowing an ergometer). These variables are mainly recorded using force and displacement sensors. As a result, in many sport (e;g. rowing, cycling, running), these machine are also used for performance assessment and both physiological and biomedical research program. However, the design of a specific ergometer has to reproduce the dynamics of the in situ movement for an accurate mechanical analysis. A first step in such a way is to analyse the three-dimensional kinematics in order that the ergometer design simulate accurately the kinematic performed in situ. In cross-country skiing, the kinematics observed while skiing the actually available ergometers is far from the one performed during in situ conditions. Thus, the mechanical parameters measured while skiing these ergometers are not pertinent to analyze and discriminate the performance produce by elite athletes. This work presents an approach based on a 3D kinematics analysis to design an innovative ergometer fully instrumented to acutely train and evaluate elite cross-country skiers. 3D kinematics analysis of in situ skating, performed using three video cameras showed characteristic 3D trajectories of the stick during the contact period with the snow. The ergometer was design to reproduce this specific kinematics (two specific phases) by adding one dof in translation of the contact point between the rope with the ergometer. This rope connects skier’s hand to an airbraked flywheel to reproduced the resistance. A selfrecoiling system allows to perform the following skating cycle. An instrumentation coupled with a specific interface allows real time feedback of the power generated by skier at each hand. During the last two years, this ergometer was skiing by the french national teams to prepare Vancouver 2010. Further investigations must be undertaken to support the accuracy of this ergometer with in situ conditions and to still improve his design.

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.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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