A preliminary investigation of upper limb muscle activity during simulated Canadian forest harvesting operations
Bibliographic record
Abstract
OBJECTIVE: The forest industry is a major economic sector of Canada. While mechanized machines have reduced injuries workers suffered during manual operations, these machines have also created other musculoskeletal concerns. The purpose of this study was to obtain data regarding upper limb musculoskeletal stress during typical harvesting operations using surface electromyography (EMG). PARTICIPANTS: Students currently training in a forest machine operations course were recruited for this study. Four operators (1 female and 3 males, mean age = 24.6 ± 13.4 years, mean height = 172.7 ± 4.6 cm, mean weight = 75.4 ± 27.4 kg) participated in this study. METHODS: Surface electrodes were placed over the muscles of the upper arm and shoulder to monitor muscular activity during Harvester Simulator operation. Operators were provided specific instructions and visual feedback. Data were collected over a two hours of operation. RESULTS: Preliminary data suggests that while the movements used in the simulator do not require large force, they are repetitive and constant and can result in muscle fatigue. CONCLUSIONS: The EMG data indicated signs of fatigue in several muscles of the upper arms. This preliminary data suggests that while operation of these machines does not require large force contractions, the continuous and repetitive nature of the work can result in muscular fatigue. This suggests that long term operation of mobile machines may result in fatigue and future studies should examine job design.
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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 | 0.001 |
| 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) | 0.001 | 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".