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Record W4298093052 · doi:10.20338/bjmb.v16i1.261

Power output and energy cost: crucial measures to understand motor skill learning in handrim wheelchair propulsion

2022· article· en· W4298093052 on OpenAlexaff
L.H.V. van der Woude, Rachel E. Cowan, Félix Chénier, Ursina Arnet, Riemer J. K. Vegter

Bibliographic record

VenueBrazilian Journal of Motor Behavior · 2022
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsWheelchairWheelingMotor learningDreyfus model of skill acquisitionPropulsionStandardizationComputer sciencePhysical medicine and rehabilitationPsychologyEngineeringMedicineNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: This current opinion is a call for standardization of measurements of manual wheeling ability among larger and diverse populations to support our understanding of motor control and learning. VIEW OF THE PAST: Handrim wheelchair propulsion remains the most common mode of wheeled ambulation and has stood the test of time as a practical upper-body alternative to walking. CURRENT STATE: Two theoretical models appeared useful in understanding the demands on the wheelchair-user combination and the role of motor skill acquisition: Power Balance Model and Constraint-based Approach. FUTURE PERSPECTIVE: Power output and energy cost measures are crucial mediators in the development of a motor control theory of cyclic motions in rehabilitation, adapted sports and beyond.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.043
GPT teacher head0.334
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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