Comparison of Methods for Postural Assessment in the Operation of Agricultural Machinery
Bibliographic record
Abstract
The aim of this study was to perform a comparative analysis of the methods of ergonomics assessment RULA, REBA, OWAS and TOR-TOM, which the intent is to highlight similarities and differences in their use, during tillage operation with farm machinery. The study was conducted through an exploratory research in Boa Vista do Incra, RS, in August 2017, during soil preparation operation, with a tractor-subsoiler set. The operation was filmed over a period of 10 hours. After that, it was selected one hour of video, which was assumed to be representative, the video was analyzed by the methods RULA, REBA, OWAS and TOR-TOM through the software Ergolândia 5.0 and TOR-TOM, with the aid of information on noise, temperature and strength. For the comparative analyses, it was analyzed the ease of application, the importance of posture and complementary variables, and the scope of activity and postural factors. The OWAS method showed highlight in the ease of application, while the REBA and RULA methods stood out for the importance of the postural variables. For the importance of the complementary variables, RULA, followed by REBA, are worth mentioning as the most suitable. Similar results were obtained for the scope of posture factors, especially for these two methods. Regarding the scope of activity factors, the TOR-TOM obtained the most satisfactory results. Thus, the comparative analysis has highlighted the RULA method as the most suitable for postural analysis in agricultural machinery, besides the REBA method, for presenting very similar situations, contemplating full body analysis.
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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 | 0.002 | 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 | 0.001 | 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".