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Record W2103313233 · doi:10.1139/p01-031

A realistic quasi-physical model of the 100 m dash

2001· article· en· W2103313233 on OpenAlexfundvenueno aff
Jonas Mureika

Bibliographic record

VenueCanadian Journal of Physics · 2001
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAltitude (triangle)DragSprintRange (aeronautics)DashWind speedWorld classMeteorologyMechanicsAerospace engineeringMathematicsGeometryComputer science

Abstract

fetched live from OpenAlex

A quasi-physical model (having both physical and mathematical roots) of sprint performances is presented, accounting for the influence of drag modification via wind and altitude variations.The race-time corrections for world class male sprinters are discussed, and theoretical estimates for the associated drag areas are presented. The corrections are consistent with constant-wind estimates of previous authors. At sea level, world class men's race times are adjusted by about 0.10 s for a wind speed w = 2 m s -1 , while every 1000 m of altitude provides an advantage of roughly 0.03–0.04 s. Corrections are provided for a wide range of wind speeds (-5 to +5 m s -1 ) and altitudes (0–2500 m), as well as for variable winds whose time-averaged value does not realistically reflect the ambient conditions. A simplified algebraic expression is also presented to correct 100 m sprint times for ambient wind and altitude effects. The primary aim is to demonstrate the utility and robustness of the full model in making such predictions, after which accurate measurement of each parameter can help to fine-tune these results. As a practical example of its utility, the nullified World Record and 1988 Olympic 100 m race of Ben Johnson is studied, and compared with the present World Record of 9.79 s. PACS Nos.: 01.80, 02.60L

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.226

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 head0.046
GPT teacher head0.278
Teacher spread0.232 · 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

Citations36
Published2001
Admission routes2
Has abstractyes

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