Bibliographic record
Abstract
U.S. and Canadian electric utility companies are in the process of integrating mobile computers into their fleet vehicle cabs. The placement of the mobile computer in the vehicle cab could have a significant effect on biomechanical loading, performance, and subjective assessment. The objective of this research is to determine the best location to place a mobile computer in a truck cab. In this experiment, four locations of mobile computers in a truck cab were selected and tested in a laboratory study to determine how location affected muscle activity of the lower back and shoulders; joint angles of the shoulders, elbows, and wrist; user performance; and subjective assessment. Along with location, subject size and type of computer task were also considered in the analysis. Twenty-two participants were tested in this study. Placing the mobile computer closer to the steering wheel reduced the low back and shoulder muscle activity required to use the mobile computer. Joint angles of the shoulders, elbows and wrists were also closer to neutral angle. In general there were no practical differences in performance between the locations. Subjective assessment indicated that users preferred the mobile computer to be as close as possible to the steering wheel. It was also found that using the touchscreen required more muscle force and less neutral joint angles than the keyboard. Locating the mobile computer close to the steering wheel reduces risk of injuries such as low back pain and shoulder tendonitis. Also, mobile computer users prefer the location to be close to the steering wheel. Results from this study can guide electric utility companies in the installation of mobile computers into vehicle cabs. Results may also be generalized to other industries that use truck-like vehicles, such as construction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".