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Record W2805351568 · doi:10.70252/shce2721

Pacing of an Untrained 17-Year-Old Teenager in a Marathon Attempt

2018· article· en· W2805351568 on OpenAlexaff
Beat Knechtle, Celina Knechtle, Thomas Rosemann, Pantelis Τ. Nikolaidis

Bibliographic record

VenueInternational journal of exercise science · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsMedicineBody mass indexPhysical therapyAnimal scienceDemographyInternal medicineBiology

Abstract

fetched live from OpenAlex

International Journal of Exercise Science 11(6): 856-866, 2018. Although there has been increased scientific interest for physiological responses to endurance running and pacing, limited information exists for adolescents participating in endurance events. We are reporting the case of an untrained 17-year-old female teenager (body mass 50.6 kg, height 167 cm and body mass index 18.1 kg/m2) who intended to run a marathon within 6 hours without preparation. The young woman missed her goal by just 2 km. When the average running speed per hour was analysed, there was a major effect of race hour on running speed (p = 0.013, h2 = 0.320), where the running speed in the fifth hour (6.3 ± 0.2 km/h) was lower than in the second hour (6.9 ± 0.1 km/h). Despite a progressive decrease in running speed, she was still able to put on a final spurt, indicated by a 4th degree non-linear regression (R2=0.55). Creatine-kinase reached the initial value again after 5 days and the fall of hemoglobin and hematocrit indicated expansion of plasma volume. Running a marathon as a teenager did not impair physical health, especially when a self-selected pace was adopted. Laboratory parameters during running showed similar changes as have been reported for teenagers and adults after running a marathon. Increased values returned to base line within a few days. In summary, a female teenager at the age of 17 years without specific running preparation is able to achieve nearly a marathon distance during 6 hours of continuous running without harmful effects on health.

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)0.001
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 score0.112
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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