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Record W4389915107 · doi:10.1038/s41598-023-49683-0

Investigation of individual strategies in the aerial phase in ski jumping

2023· article· en· W4389915107 on OpenAlexaff
Petter Andre Husevåg Jølstad, Matthias Gilgien, Ola Elfmark

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsJumpingJumpAthletesLift (data mining)DragLift-to-drag ratioMechanicsVertical jumpMathematicsGeodesyPosition (finance)TrajectoryAerodynamicsSimulationPhysicsGeologyComputer scienceMedicinePhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this investigation was to examine the performance strategy of three ski jumpers during the steady glide phase and explain how different strategical solutions can lead to jumps of roughly the same length. In this study, a total of 24 jumps performed by two World Cup (WC) athletes and one Continental Cup (COC) athlete were measured with a differential Global Navigation Satellite System (dGNSS) on a large ski jumping hill. For each athlete, the continuous position data, velocity, aerodynamic forces and lift-to-drag ratio (LD-ratio) were averaged and compared for the steady glide phase to examine individual jump strategies. The dGNSS yielded accurate measurements of trajectory, velocity and aerodynamic forces, revealing clear differences between the athletes. The largest differences were found between the WC athletes and the COC athlete. The WC athletes focused on maximizing horizontal velocity while the COC athlete minimized vertical velocity. This difference may be explained by the different hill sizes the athletes normally compete on. One of the WC athletes consistently increased their horizontal velocity during the end of the steady glide phase by maintaining a high LD-ratio, which highlights the effect of aerodynamics on the resulting velocity, trajectory and jump length.

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)0.003
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 score0.157
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.331
Teacher spread0.284 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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