Prioritization of hazards by means of a QFD- based procedure
Bibliographic record
Abstract
Despite the evolution of regulations in the field of occupational health and safety promoted in EU countries, the number of accidents and victims has not significantly decreased in recent years, especially in constructions and agriculture sectors, as underlined by official reports of the Italian Workers' Compensation Authority.Main reasons of such a situation are due to the characteristics of working activities in these sectors.The variety of operations, the frequent exchange of tasks among workers within the same company, the continuous change of workplaces, the frequent exchange of workers for the same activity (e.g.seasonal workers), and the workers' stress caused by seasonal jobs.For these reasons both risk assessment and safety management activities result in being more difficult than in other working sectors.Thus, it is important to provide methodologies and tools that allow companies to carry out these tasks more effectively.In such a context, the study proposed by Esra Bas in 2014 certainly represents an attempt to provide a supporting methodology for engineers engaged in risk assessment activities.This approach consists in the use of the Quality Function Deployment (QFD) method, and it is aimed at evaluating how specific tasks can be in relationship with specific hazards, which in turn are related to specific events, and finally at defining what preventive/protective measures can be introduced against those events.Based on this, we tried to further investigate such an approach, with the goal of providing an easier-to-use tool, which can be used in risk assessment activities of critical contexts as the agriculture one.With this aim in mind, a case study concerning the risk assessment of an agricultural machinery was carried out.
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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".