Bibliographic record
Abstract
Prosthetic limbs present a highly unique research challenge, requiring intense customization for optimal use cases. These challenges are heightened for athletes, who engage with their prosthetics in highly competitive environments, placing them under tremendous stress. Thomas Normandeau, a champion Para Athlete, approached Camosun Innovates to develop such a prosthetic, able to aid him in his training regimens and reduce the risk of injury. Camosun Innovates undertook a human-centered design approach that foregrounded Normandeau’s needs and observations. Through a mixture of digital scanning and 3D-printed parts constructed using Nylon 12, a unique carbon-reinforced nylon traditionally used in the aerospace and automotive industries, the Camosun team was able to rapidly iterate a customized, properly-fitted prosthetic with unique, lightweight joint pieces precisely calibrated to Normandeau’s arm length, and without sacrificing the prosthetic’s ability to hold up under tension. The success of this prototype holds potential for other prosthetics both within and without the athletic sphere, with the potential to help users with exercise routines and common lifting and carrying tasks, from grocery shopping to household chores.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".