Analysis of Human Factors for Enhancing Safety and Security Management System in Fossil and Renewable Power Plants
Bibliographic record
Abstract
While Safety and Security Management System (SSMS) in the energy industry has recently improved in the last decades, it remains a hazardous working environment where fatalities and serious accidents still reoccur.Human Factors (HFs) and worker Safety-Related Behavior (S-RB) have been identified as the underlying causes of the majority of occurred accidents.Hence, this work aimed to identify those HFs which affect the SSMS from the most to the least significant, through the impact rate of these factors on workers S-RB at workplaces of twenty-one power plants (PPs), including Fossil Fuel Power Plants (FFPPs) and Onshore Wind Farms (OWFs) that are located across nine countries.Likewise, to give adequate countermeasures for the SSMS enhancement and accidents prevention.To do so, the data were collected through the survey questionnaire of a fivepoint Likert scale.The study has led us to conclude that, all the analyzed HFs have influenced the SSMS of all the evaluated PPs with an impact rate of 3.3/5 on Likert scale.Whereas the enhancement of the SSMS can be achieved if the job factors are improved and if the employees' "Workload" is well managed at the workplace, as well as if the management clearly demonstrates their H&S commitment and leadership.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".