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Record W2582617159 · doi:10.1111/sms.12821

Performance trends in age‐group runners from 100 m to marathon—The World Championships from 1975 to 2015

2017· article· en· W2582617159 on OpenAlexaff
Pantelis Τ. Nikolaidis, Matthias Alexander Zingg, Beat Knechtle

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2017
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsDemographyAthletesTrack and field athleticsNationalityAge groupsRace (biology)MedicineGeographyPhysical therapyImmigrationGender studiesSociology

Abstract

fetched live from OpenAlex

This study examined changes in performance in age-group track runners across years from 1975 to 2015 for 100, 200, 400, 800, 1500, 5000, 10 000 m, and marathon and the corresponding sex differences. Athletes were ranked in 5-year age-group intervals from 35-39 to 95-99 years. For all races and all years, the eight female and male finalists for each age-group were included. Men were faster than women and this observation was more pronounced in the shorter distances. The younger age-groups were faster than the older age-groups and age exerted the largest effect on speed in 800 m and the smallest in marathon. There was a small variation of speed by calendar years. The competition density varied by sex and race distance. Half of participants were from USA, Germany, Australia, and Great Britain, but the participants' nationality varied by sex and race distance. In summary, the variation of competitiveness by sex in short race distances might be important for athletes and coaches. Considering the event's competitiveness and that athletes are participating in both 100 and 200 m or in 200 and 400 m, master women should be oriented to 200 m and master men should be oriented to 100 and 400 m.

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 score0.063
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.355
Teacher spread0.295 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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