Garment Specifications and Mock-ups for Protection from Steam and Hot Water
Bibliographic record
Abstract
This work is part of a larger project to develop improved, innovative textiles and garments for workers in the oil industry. Use of steam and hot water in extracting bitumen from oil sands, in oilfields, and in plants has become extensive in recent years. Personal protective equipment (PPE) is currently well designed to protect against hazards such as flash fire and radiant thermal exposures; however, due to an increase in workplace injuries reported in the last five years, including incidents of steam and hot water burns, further protection for workers is considered a priority. Steam used at sites is up to 375°C and under extreme pressures of up to 13 500 kPa; hot water is under significantly less pressure but is 80°C–90°C, which is well above temperatures that result in partial thickness burns. This research presents several stages of the design process: (1) identifying specific tasks which expose workers to steam and hot water; (2) setting the criteria for determining the needs addressed in specific types of PPE; (3) developing specifications for PPE garment design; and (4) presenting a preliminary mock-up garment. A multi-method research approach was taken that included observing, photographing, interviewing, and analyzing the movements of workers in western Canada. Results indicate extreme workplace conditions both indoors (up to 40°C in summer) and outdoors (down to −30°C in winter). Hazardous activities include steam quality sampling, cleaning filters and sludge traps, loading and unloading hot water, opening traps and high pressure steam valves, working very close to hot valves and pipes, and spraying steam onto wellheads. Specifications for improved garment design and mock-up garments were developed based on analysis of interviews and observations. This work also contributes insights into the process for understanding the complexities of a workplace environment with specific hazards and worker needs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".