Ergonomic Evaluation of Current Advancements in Blueberry Harvesting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Work-related musculoskeletal disorders (MSDs) accounted for 32% of days-away-from-work cases in private industry in 2016. Several factors have been associated with MSDs, such as repetitive motion, excessive force, awkward and/or sustained postures, and prolonged sitting and standing, all of which are required in farm workers’ labor. While numerous epidemiological studies on the prevention of MSDs in agriculture have been conducted, an ergonomics evaluation of blueberry harvesting has not yet been systematically performed. The purpose of this study was to investigate the risk factors of MSDs for several types of blueberry harvesting (hand harvesting, semi-mechanical harvesting with hand-held shakers, and over-the-row machines) in terms of workers’ postural loads and self-reported discomfort using ergonomics intervention techniques. Five field studies in the western region of the United States between 2017 and 2018 were conducted using the Borg CR10 scale, electromyography (EMG), Rapid Upper Limb Assessment (RULA), the Cumulative Trauma Disorders (CTD) index, and the NIOSH (National Institute for Occupational Safety and Health) lifting equation. In evaluating the workloads of picking and moving blueberries by hand, semi-mechanical harvesting with hand-held shakers, and completely mechanized harvesting, only EMG and the NIOSH lifting equation were used, as labor for this system is limited to loading empty lugs and unloading full lugs. Based on the results, we conclude that working on the fully mechanized harvester would be the best approach to minimizing worker loading and fatigue. This is because the total component ratio of postures in hand harvesting with a RULA score equal to or greater than 5 was 69%, indicating that more than half of the postures were high risk for shoulder pain. For the semi-mechanical harvesting, the biggest problem with the shakers is the vibration, which can cause fatigue and various risks to workers, especially in the upper limbs. However, it would be challenging for small- and medium-sized blueberry farms to purchase automated harvesters due to their high cost. Thus, collaborative efforts among health and safety professionals, engineers, social scientists, and ergonomists are needed to provide effective ergonomic interventions.
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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 it