Stooped Postures Are Modified by Pretask Walking in a Simulated Weed-Pulling Task
Bibliographic record
Abstract
Seasonal agricultural workers are hired in some sectors for intermittent manual weed removal, a stoop and grasp harvesting task likely similar to those associated with the high prevalence of musculoskeletal disorders in agriculture. Evaluation of this task in an experimental situation would be useful for identifying and controlling musculoskeletal injury risks, presuming a valid experimental model of the task can be created. The purpose of the present study was to examine how a relevant work-related task, namely prolonged walking, altered the biomechanics of manual weed removal in a laboratory setting. Preliminary field assessments informed the development and analysis of a simulated manual weed removal with two separate conditions: not primed, where 11 participants (4 female, mean age 21.6 years) manually removed a simulated weed six times, and primed, where 23 participants (13 female, mean age 22.1 years) walked 1600 m prior to manually removing the same simulated weed six successive times. Segment end point markers and experimental motion capture were used to determine hip, knee, and ankle angles, as well as toe-target proximity, during weed removal. Significant differences between primed and not primed participants were found for angular displacement at the ankle (t(32) = 5.08, P < .001) and toe-target proximity (t(32) = 2.78, P = .008), where primed participants had increased ankle flexion and a greater distance to the weed, leading to decreased trunk flexion during the harvesting task. These findings suggest that priming can positively influence whole-body postures for manual weed removal.
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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) | 0.000 | 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".