MétaCan
Menu
Back to cohort
Record W2901571729 · doi:10.1242/jeb.190983

Determinants of optimal leg use strategy: horizontal to vertical transition in the parkour wall climb

2018· article· en· W2901571729 on OpenAlexafffund
James L. Croft, Ryan T. Schroeder, John E. A. Bertram

Bibliographic record

VenueJournal of Experimental Biology · 2018
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClimbWork (physics)Task (project management)Transition (genetics)SimulationGround reaction forceClimbingControl theory (sociology)TrajectorySensitivity (control systems)AthletesMechanicsComputer scienceControl (management)Artificial intelligenceStructural engineeringEngineeringMechanical engineeringAerospace engineeringClassical mechanicsPhysicsPhysical therapyChemistryKinematics

Abstract

fetched live from OpenAlex

This study examined the mechanics of the horizontal to vertical transition used by parkour athletes in wall climbing. We used this task as an alternative to normal running - where the functional options differ substantially - exposing the movement control priorities required to successfully complete the task. Ground reaction forces were measured in several expert parkour athletes and centre of mass trajectory was calculated from force plates embedded in the ground and the wall. Empirical measures were compared with movements predicted by a work-based control optimization model. The model captured the fundamental dynamics of the transition and therefore allowed an exploration of parameter sensitivity for success at the manoeuvre (run-up speed, foot placement, etc.). The optimal transition of both the model and the parkour athletes used a common intermediate run-up speed and appears determined largely by a trade-off between positive and negative leg work that accomplishes the task with minimum overall work.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.266

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 head0.035
GPT teacher head0.301
Teacher spread0.266 · 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 designBench or experimental
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

Citations12
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicLower Extremity Biomechanics and PathologiesFrench-language works237,207