Wrist postures and forces in tree planters during three tree unloading conditions
Bibliographic record
Abstract
The aims of this study were to investigate wrist postures and forces while operating the shovel during tree planting and to determine if different tree unloading techniques result in variations in wrist postures and forces. Experienced tree planters performed the planting task in a laboratory environment for three conditions: (1) symmetric tree unloading, and asymmetric unloading resulting in (2) right-loaded planting bags and (3) left-loaded planting bags. An optoelectric system and a shovel instrumented with strain gauges captured wrist posture and forces at the wrist, respectively. Wrist extension of up to 45° was observed, and this posture, in combination with varying degrees of wrist deviation, may be a primary risk factor for musculoskeletal pain. Average resultant forces at the wrist were moderately high (>30 N) for each unloading condition, indicating increased risk for the development of repetitive strain injuries such as carpal tunnel syndrome. No significant differences in wrist posture or forces existed between unloading conditions. PRACTITIONER SUMMARY: Wrist pain is a major musculoskeletal complaint among tree planters. This study measured wrist postures and forces at the wrist while operating the shovel during tree planting. The wrist extension observed, in combination with deviation, may be a key risk factor for musculoskeletal pain. Forces at the wrist indicate increased risk for repetitive strain injuries.
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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) | 0.002 | 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".