Comparison of five approaches to keeping power line maintainers’ hands warm during work in the cold
Bibliographic record
Abstract
Electric utility workers in Canada must frequently work in the cold and must wear thick rubber gloves which can result in rapid fatigue and reduced performance. The purpose of the study was therefore to document the challenge of working in the cold wearing the standard five-finger rubber gloves and covers and compare them to two equipment options, mitten style gloves or a prototype wool liner, and two heating options, glove or torso heating. The dependent measures were grip force, temperature, dexterity (modified Purdue pegboard test and a simulated occupational task), finger sensitivity (Von Frey hair test), perceived effort and thermal sensation. The study population consisted of 10 experienced male utility workers. They worked in a controlled temperature walk-in chamber (-20 degrees Celsius) and performed simulated utility work for 45 minutes with interspersed test batteries. The mitten style glove and woolen liner in a standard glove reduced the effects of working in the cold compared to the standard five-fingered variety with a thin cotton liner. We found that the mitten style glove showed lesser drops in skin temperature for the 3rd and 5th digits than the other conditions (p < 0.05).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".