Energy Expenditure According to the Tasks in Physical Education Teachers
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to quantify energy expenditure of physical education teachers according to the tasks they perform at work. METHODS: Sixty four physical educators aged 35.5 ± 9.1 years (49 males and 15 females, VO2max=45.3 ± 7.0 ml·kg-1·min-1) had their oxygen consumption/heart rate (VO2/HR) relationship measured in the laboratory. On another day while working, PE teachers wore a monitor to record heart rate. Heart rate was later used to estimate energy expenditure using interpolation of the (VO2/HR) relationship. According to their daily work logbook, their tasks were regrouped in one of the four following categories: office work, supervision tasks, mixed participation and active participation tasks. RESULTS: The average energy expenditure (156 and 276 kcal·h-1) varied according to the tasks performed. The most demanding task is active participation, followed by mixed participation, supervision tasks. Office work was indeed the less demanding. CONCLUSIONS: Energy expenditure can be considered as light during office tasks (120-168 kcal·h-1) or during supervision tasks (150-198 kcal·h-1). Workload can be estimated as average during mixed participation (204-234 kcal·h-1) or during active participation (246-318 kcal·h-1). However during some period, the workload can be considered as very heavy reaching values as high as 528-774 kcal·h-1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".