A mechanistic model for industrial workplace design based on inherent safety and health concepts
Bibliographic record
Abstract
Abstract This study aims to establish a mechanistic model of using inherent safety and health concepts to conduct industrial workplace risk reduction strategies during the project planning stage. Accordingly, a dedicated implementation tool termed inherently safer and healthier design model for industrial workplaces (ISHDM‐IW) is developed. First, the safety and health by design checklist is adapted to derive industrial workplace safety and health risk indicators. Then, the risk indicators are rated and weighted using the methods of risk matrix and precedence chart. Subsequently, the risk indicators are aggregated via fuzzy risk representation with the indicators' risk values and weights. Last, the inherent safety and health principles are used to do risk‐based baseline design optimization. The case study demonstrates the capabilities of the proposed ISHDM‐IW to upgrade the workplace design scheme from risk level II to risk level I, thus generating a more satisfactory workplace design alternative. The proposed ISHDM‐IW offers a novel design mechanism of inherent safety and health for industrial workplaces, which can be expected to ensure employees' safety, health, and well‐being with fundamentally better ergonomics of industrial work environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".