Understanding the mechanics and balance control of the carrying pole through modeling and simulation
Bibliographic record
Abstract
The carrying pole has existed as a load carrying tool for thousands of years and is still popular in many parts of Asia. Previous studies attempted to determine whether the elasticity of the carrying pole is energetically beneficial compared with other load carrying methods. However, conflicting results indicate that the effects of the carrying pole stiffness on the carrier are still unclear. The carrying pole exhibits more complex characteristics beyond stiffness, which invites further investigation. As the first step towards the goal, this paper explores the underlying mechanics of the carrying pole, including the structural and dynamic properties, to determine its impact on the carrier. The structure of the carrying pole is modeled and characterized by pole length, pole stiffness, and length of suspension rope. We argue that maintaining the pole's balance should be a major prerequisite during load carriage and that active feedback control from the carrier is required. Simulations reveal that the structural parameters of the pole have significant influences on the pole's balance and the interaction between the pole and the carrier. This work suggests mechanical characteristics of the carrying pole can potentially have an extensive impact on gait mechanics and energetics of the carrier.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".