Effect of Using GPS Autosteer Guidance Systems on the Eye-Glance Behavior and Posture of Tractor Operators
Bibliographic record
Abstract
Tractor operators are prone to neck and back discomfort and disorders. It is well known that awkward posture is a major contributor to this problem. Previous studies have investigated the prevalence of awkward posture and the resulting discomfort and disorders among tractor operators. They have also suggested various ways to mitigate this problem. With the introduction of new autosteer guidance systems, the tractor operator is relieved from the task of steering the tractor for most of the time during field work. Therefore, it is expected that the operator's posture will change. The goal of this study was to investigate the changes in the eye-glance behavior and posture of tractor operators as a result of using autosteer guidance systems. An eye-tracking system and a camcorder were used to record the eye-glance behavior and posture of 13 tractor operators as they performed seeding operations. The experiment with each operator consisted of two sessions. In one session, the operator used an autosteer system, while in the other session the operator steered the tractor manually. Analysis of the data showed that the eye-glance behavior and posture of the operators were significantly different between the autosteer and manual steering sessions. With the autosteer system, the operators spent less time in an awkward posture, and they changed their posture less frequently. However, even with the autosteer system, the operators spent 1/3 of their time in an awkward posture. Subjective feedback from the operators indicated that more than half of them experienced back or neck/shoulder discomfort during or after seeding. It is essential that the recommendations of the previous studies, such as using large rear-view mirrors or a rotating tractor seat, be evaluated when the operator is using an autosteer system. Other tools, such as video cameras that show the attached equipment, should also be tested to evaluate their effectiveness in reducing the operator's exposure to awkward postures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".