Physical Demand Comparisons between Cleco’s Low Torque Reaction and Bosch’s ERGOSTOP Right-Angle Power-Tool Fastening Strategies
Bibliographic record
Abstract
The purpose of this work was to compare the physical demands associated with the right-angle power-tool (RAPT) operation when using Cleco’s Low Torque Reaction (LTR) and Bosch’s ERGOSTOP fastening strategies during simulated tightening of various joints. Twenty healthy participants (N=10 M, N=10 F, 21.1 ± 0.3 years) having no RAPT experience and no injuries to the arms or trunk participated in the study, however, 8 participants data were not analyzed due to equipment malfunction. Participants completed a five-minute bout of 5 joint fastening per minute for every experimental condition. Participant’s completed twelve total conditions including: 3 target torques (30, 55 and 75 Nm); 2 tightening strategies (Cleco’s Low Torque Reaction and Bosch’s ERGOSTOP); 2 joint hardness (Hard, which target torque was achieved at 30° of spindle rotation; Soft, which target torque was achieved at 520° of spindle rotation). All fastenings were completed with a common joint location-orientation of horizontal (53 cm), vertical (103 cm), lateral (35 cm) using a downward shot direction. Our data revealed that for all conditions (Target Torque, Joint Type & Tightening Strategy), Cleco’s LTR strategy resulted in lower physical demands. This is based on LTR fastenings resulting in lower push/pull magnitudes force impulse, peak forces and pulse-width times. From an ergonomics perspective, the reduction of physical force demand is a primary element of reducing the risk of work-related-musculoskeletal-disorders, the outcomes from this work was designed to aid ergonomists/engineers in understanding the physical demand magnitudes of RAPT operations in an effort to optimize manufacturing operations.
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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 | 0.000 | 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) | 0.003 | 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".