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Record W2174835294 · doi:10.1111/sms.12573

The Kenyan runners

2015· review· en· W2174835294 on OpenAlexaff
Henrik Larsen, A. William Sheel

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2015
Typereview
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKenyaVO2 maxRunning economyMedicineElitePhysical therapyCardiologyInternal medicineBiologyHeart rateEcologyBlood pressure

Abstract

fetched live from OpenAlex

Today the Kenyan dominance in middle- and long-distance running is so profound that it has no equivalence to any other sport in the world. Critical physiological factors for performance in running include maximal oxygen consumption (VO2max), fractional VO2max utilization and running economy (energetic cost of running). Kenyan and non-Kenyan elite runners seem to be able to reach very high, but similar maximal oxygen uptake levels just as there is some indication that untrained Kenyans and non-Kenyans have a similar VO2max. In addition, the fractional utilization of VO2max seems to be very high but similar in Kenyan and European runners. Similarly, no differences in the proportion of slow muscle fibers have been observed when comparing Kenyan elite runners with their Caucasian counterparts. In contrast, the oxygen cost of running at a given running velocity has been found to be lower in Kenyan elite runners relative to other elite runners and there is some indication that this is due to differences in body dimensions. Pulmonary system limitations have been observed in Kenyan runners in the form of exercise-induced arterial hypoxemia, expiratory flow limitation, and high levels of respiratory muscle work. It appears that Kenyan runners do not possess a pulmonary system that confers a physiological advantage. Additional studies on truly elite Kenyan runners are necessary to understand the underlying physiology which permits extraordinary running performances.

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)0.007
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.400
Teacher spread0.314 · 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 designOther design
Domainnot available
GenreReview

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

Citations51
Published2015
Admission routes1
Has abstractyes

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