Physical exertion and working efficiency of reforestation workers
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to quantify the physical exertion during tree planting work and to examine the relationships between exertion, task efficiency, and productivity. METHODS: Heart rate (HR) was monitored on 34 tree planters while they worked. HR data was collected for a complete working day on 19 subjects and for shorter periods of time on 15 subjects. Video of work tasks was recorded on 22 subjects (video was recorded on 7 of the subjects for whom HR was monitored through a full working day) and analyzed for working pace and proportion of time spent on each task. RESULTS: HR during a full day (9.0 ± 1.2 hours) of tree planting work was 115.2 ± 8.8 beats.min-1, and working HR was 128.2 ± 15.6 beats.min-1 for 82.5 ± 6.8% of the work day. Mean work pace was 452 ± 174 trees.h-1, and the proportion of time spent planting each tree was 53 ± 8% of the working time. Significant (P < 0.05) positive correlations were found between work pace and experience level, and between work pace and working HR, and a significant (P < 0.05) negative correlation was found between experience level and HR for a given work pace. No significant relationships were found between experience level or work pace and the proportion of time spent planting each tree. CONCLUSIONS: Tree planters work at approximately 65% of age-predicted HRmax, and maintain HR at approximately 59% of HRmax throughout the entire working day. Productivity in these workers appears to be related to effort rather than to experience or task efficiency per se.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".