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Record W2111929469 · doi:10.1136/bjsm.2007.043125

Workload demands in professional multi-stage cycling races of varying duration

2007· article· en· W2111929469 on OpenAlexaff
José Antonio Rodríguez Marroyo, Juan García-López, Gerardo Villa

Bibliographic record

VenueBritish Journal of Sports Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWorkloadCyclingDuration (music)Stage (stratigraphy)Computer sciencePhysical medicine and rehabilitationMedicineBiologyGeographyOperating system

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyse and compare the workload exerted by professional cyclists in 5-day, 8-day and 21-day stage races (5-SR, 8-SR, 21-SR). METHODS: The study subjects were 30 professional cyclists competing in 10 5-SR, 5 8-SR and 5 21-SR. Heart rate (HR) was measured during the races and categorised into three intensity zones: Z1 (below the ventilatory threshold (VT)), Z2 (between VT and the respiratory compensation threshold (RCT)) and Z3 (above RCT). The training impulse (TRIMP) was calculated by multiplying the sum of the time spent in each zone by 1, 2 and 3, respectively. Monotony (average TRIMP/SD) and strain (total TRIMPxmonotony) were also calculated for each race type. RESULTS: The average time spent in Z3 during each stage was significantly (p<0.05) higher for 5-SR ( approximately 31 min) and 8-SR ( approximately 28 min) than for 21-SR ( approximately 14 min). Daily TRIMP values in 5-SR ( approximately 400) and 8-SR ( approximately 395) were also higher than in 21-SR ( approximately 370). Monotony was similar across races ( approximately 3) but strain was about three times higher for 21-SR than for 5-SR and 8-SR. CONCLUSIONS: The cyclists' effort by stage was less for 21-SR than for 5-SR and 8-SR. Competition strain and monotony accumulated during longer races influence the choice of strategies adopted by cyclists. It is likely that the intensity of each stage is modulated by total race duration, with longer races averaging the lowest daily workload.

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 score0.261
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.344
Teacher spread0.307 · 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

Citations56
Published2007
Admission routes1
Has abstractyes

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