MétaCan
Menu
Back to cohort
Record W1989036050 · doi:10.1139/p03-062

Modeling wind and altitude effects in the 200 m sprint

2003· article· en· W1989036050 on OpenAlexfundvenueno aff
Jonas Mureika

Bibliographic record

VenueCanadian Journal of Physics · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSprintPhysicsCrosswindAltitude (triangle)DragMeteorologyAtmospheric sciencesMechanicsGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

A quasi-realistic mathematical model of 100 m sprint performances is modified to simulate the 200 m race, a portion of which is run around a curve. The calculated effects of wind are complex functions of the wind direction and the lane in which the athlete is running. It is shown that wind and altitude-assisted marks for the 200 m are in some cases significantly higher than the corresponding adjustments for the 100 m sprint under similar conditions. The estimated advantage of a 2 m s–1 tail wind is between 0.09–0.14 s, with the greater advantage going to the runner in the outside lane. At higher altitudes (>2000 m), these corrections can rise to over 0.3 s. Crosswinds can further enhance the performance by over 0.5 s due to decreased drag forces around the curve. A consequence of these results suggests that record ratification procedures for such performances be reconsidered. The model is also used to study Michael Johnson's world record race of 19.32 s from the 1996 Olympic Games in Atlanta, Georgia. PACS No.: 01.80.L

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.194
Teacher spread0.171 · 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 designSimulation or modeling
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

Citations16
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicSports Analytics and PerformanceFrench-language works237,207