Pilot Study of Manual Sugarcane Harvesting Using Biomechanical Analysis
Bibliographic record
Abstract
In many countries, sugar cane harvesting is a very labor-intensive activity in which workers usually become fatigued after manually cutting the cane for a few hours. They need frequent pauses for rest, and they experience sustained injuries from excessive stress on the joints and muscles of the body. The cutting tool and motion involved directly influence the stresses created. A cutting tool that has not been designed by taking into consideration occupational biomechanics can lead to unnecessary strains in the body's muscle system, resulting in injuries. The purpose of this research was to carry out a pilot study of the impact of two common manual sugarcane cutting tools and the cutting posture they induce on the body with the aid of biomechanics. The machete and the cutlass from South Africa and Guyana, respectively, were examined to determine the cutting forces. Using static strength prediction modeling, the body stress levels at the point of cut in the cutting motion were determined. The cutting postures of three subjects were contrasted, their extreme postures were identified, and suggestions were made to improve the ergonomics of the cutting activity. The results of this pilot study showed that the cutlass required less cutting force than the machete because of the slicing cut provided by the curved blade edge of the cutlass. However, the biomechanical analysis indicated that the bent blade of the machete required less flexion of the back and therefore was likely to cause less back fatigue and injury. An improved design of the sugarcane manual harvesting tool should incorporate the bend of the machete to reduce flexion and a curved cutting edge that provides a slicing cut.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".