Evaluating the physical demands of three tarping systems for flatbed transport trailers
Bibliographic record
Abstract
OBJECTIVE: Tarping and untarping loads on flatbed trailers creates concerns related to falls as well as high musculoskeletal demands. The purpose of this study is to compare the demands and risks present when using three different tarping systems and to determine which system is preferred to reduce demand and injury risks. PARTICIPANTS: Nine male volunteers from a flatbed trucking company participated in the study. METHODS: The truck drivers covered the load on the flatbed trailer using three different tarping systems: manual tarps, sliders, and rack and tarp kits. Multiple measures were used to characterize the three tarping systems, including required forces, identifying injury risk by assessing peak, average and cumulative forces, moments and electromyography, heart rate, and exposure to fall hazards. RESULTS: Manual tarping resulted in greater physical demands and safety risks than the two alternate systems, both of which all participants preferred. The slider method was preferred overall as it has numerous advantages. CONCLUSIONS: The slider and rack and tarp kit methods offered a wide range of benefits including reduced physical demands, reduced exposure to fall hazards as well as improved productivity due to the shorter execution times, but had the disadvantage of being less versatile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".