1636d Usability testing for ergonomic criteria matrix: case study of a deep mining cooling vest
Bibliographic record
Abstract
<h3>Introduction</h3> Deep mining and ultra-deep mining (UDM) push the boundaries of what is considered tolerable for workers in hot and humid environments. Since ventilation is costly for mining companies, a novel personal protective equipment (PPE), a cooling vest, is a possible means to safeguard the health and safety of miners. Such a PPE must meet both their needs and expectations. The objective of this study was to build a matrix of ergonomic criteria that would help define the base on which a cooling vest would be developed for deep and UDM. <h3>Methods</h3> First, a literature review was conducted on the constraints and requirements that miners are subjected to in deep mining conditions. Then, a field study was conducted in a mine in Abitibi Témiscamingue, Canada. A convenience sample of 20 participants was used to collect information such as height, weight, PPE worn as well as concerns as to the use of a cooling vest. The information collected was then interpreted to generate the matrix of ergonomic criteria suitable for an UDM environment. <h3>Results</h3> All participants agreed that a cooling vest would help alleviate the risk of a heat stroke, as well as improve their well-being during work. The main concerns of minors relate to the weight, the comfort and ease of movement. Additional criteria such as design aesthetics, maintenance and conformity to laws, regulations and standard have been added to the matrix. The resulting matrix contains 16 criteria, seven of which are centred on the user and nine on the design of the cooling vest. <h3>Discussion</h3> We are hopeful the matrix can be validated and that it will be possible to broaden its use to apply it, for instance, in the development of cooling vests for other hot and humid work environments such as foundries and certain construction projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".