3D peak and cumulative low back and shoulder loads and postures during greenhouse pepper harvesting using a video-based approach
Bibliographic record
Abstract
BACKGROUND: In agricultural field work many tasks have been cited as high priority risk factors for the development of work-related musculoskeletal disorders (WRMDs). Although video-based biomechanical approaches have been effective in documenting the physical demands and risks associated with various occupational and non-occupational tasks, to date, this method has yet to be used to document jobs such as crop harvesting in a greenhouse environment. OBJECTIVE: To document and assess the postural characteristics and 3D peak and cumulative low back and shoulder loads associated with greenhouse pepper harvesting using a video-based posture sampling approach. METHODS: Nine male (28.2 (4.1) years) pepper harvesters from a greenhouse in Southwestern Ontario, Canada were videotaped during a normal shift. 3DMatch was used to document working trunk and shoulder postures, from which 3D peak and cumulative forces and moments were quantified. RESULTS: On average, workers spent the majority of their time in neutral trunk postures (lateral bend: 99.1%; axial twist: 59.9%; flexion: 89.8%). Consistent results were found for the left and right shoulder, with the arms held in a neutral flexion posture 50% of the time or more. Four participants experienced peak L4/L5 compression forces (between 4116.3 N and 5937.0 N) which exceeded the NIOSH Action Limit (3400 N) during the cart pushing/pulling task, but remained below the threshold during picking. Mean cumulative L4/L5 extension and shoulder flexion moments ranged in magnitude from 18.5 Nm to 28.2 Nm, and between 19.4 Nm and 23.2 Nm, respectively, across all tasks. CONCLUSIONS: The postural characteristics and biomechanical loads associated with greenhouse pepper harvesting were quantified with a video-based biomechanical approach. Further investigations of the physical risk factors for low back and shoulder musculoskeletal disorders is warranted in pepper harvesting, given the postures and loads documented in this study.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".