A holistic approach for assessing occupational health risk due to fugitive emissions in petrochemical processes: Inherent health hazard level index ( <scp>IHHLI</scp> )
Bibliographic record
Abstract
Abstract Fugitive emissions from petrochemical facilities have become a major concern due to their impact on plant productivity, the environment, and health. In regard to health, petrochemical workers are at higher occupational health (OH) risk due to their continuous exposure to these harmful emissions. Inherent OH and safety indexes are the most common methods used for assessing OH risk due to fugitive emissions. These methods usually focus on the sources of health hazards, such as chemical substances, process conditions, and process equipment. Therefore, these methods are considered good for measuring the severity of the OH risk. However, based on the source, pathway, receptor (SPR) model, the OH risk due to fugitive emissions is also dependent on the pathway and receptor, where leak and exposure hazards may take place, respectively. For a holistic OH risk assessment, these hazards need to be considered. This was achieved by developing an OH risk assessment methodology that provides an effective assessment that takes into consideration hazards at the source, pathway, and receptor. This paper focuses on the source part of the SPR model, while the pathway and receptor parts will be covered in future publications. This paper presents an index‐based method named the inherent health hazard level index (IHHLI) developed for evaluating the severity of the fugitive emission‐induced OH risk. The IHHLI is developed by an expert‐based selection of the most common and relevant health hazard indicators published in the literature. Based on industry testing, the IHHLI can provide a reliable OH hazard evaluation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".