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Record W2559929911 · doi:10.1123/jab.2016-0071

Is There an Optimal Pole Length for Double Poling in Cross Country Skiing?

2016· article· en· W2559929911 on OpenAlexafffund
Franziska Onasch, Anthony Killick, Walter Herzog

Bibliographic record

VenueJournal of Applied Biomechanics · 2016
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Calgary
FundersKillam Trusts
KeywordsPolingCross countryControl theory (sociology)Physical medicine and rehabilitationMathematicsStructural engineeringComputer scienceEngineeringMedicineArtificial intelligenceEconomicsElectrical engineeringDemographic economics

Abstract

fetched live from OpenAlex

The aim of this study was to determine the effects of pole length on energy cost and kinematics in cross country double poling. Seven sub-elite male athletes were tested using pole sets of different lengths (ranging between 77% and 98% of participants' body height). Tests were conducted on a treadmill, set to a 2% incline and an approximate racing speed. Poling forces, contact times, and oxygen uptake were measured throughout the testing. Pole length was positively correlated with ground contact time (r = .57, p < .001) and negatively correlated with poling frequency (r = -.48, p = .003). Pole length was also positively correlated with pole recovery time and propulsive impulse produced per poling cycle (r = .36, p = .031; r = .35, p = .042, respectively). Oxygen uptake and pole length were negatively correlated (r = -.51, p = .004). This acute study shows that increasing pole length for double poling in sub-elite cross country skiers under the given conditions seems to change the poling mechanics in distinct ways, resulting in a more efficient poling action by decreasing an athlete's metabolic cost.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.347

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)0.0000.000

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 head0.019
GPT teacher head0.316
Teacher spread0.296 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

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