Helmet Stick Design for BC3 Paramlympic Bocia Games
Bibliographic record
Abstract
Boccia is one of the fastest-growing Paralympic sports and one of the few sports to have an Olympic rival. The BC3 boccia class is intended for athletes with locomotor dysfunction so severe in all four extremities that they require assistance from assistants and aids in the form of ramps and helmets equipped with pointer sticks. Therefore, the design of the BC3 boccia stick helmet was carried out to meet the needs of the athletes. The design was carried out by action research methodology with the concept of design, static simulation, manufacturing, and the house of quality. The design is divided into three main parts: the helmet bracket, shaft, and lock with bearings using M4 and M8 bolts. Static and design simulations used Fusion 360 and Ansys 2021 R2 software to determine design strength value. Stainless steel material is used in the design, considering the material selection using the Ashby diagram method. The maximum displacement value produced by design is 0.006 mm, and the minimum safety factor is 9.15. The manufacturing results produced helmet products with adequate accuracy and safety standards. A house of quality (HOQ) of 39% indicates that it can meet consumer needs better than other products.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".