Occupational Risk Assessment of Wind Turbines in Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wind energy is among the foremost vital renewable energy sources in the world. With the increase in its popularity and use, the requirement for safety measures regarding this type of energy is becoming more prevalent. The development and operation requirements that come with installing and running wind turbines have many risks that need managing and mitigation. This study implemented a risk evaluation method for the transportation, construction, operation, and maintenance of wind turbines, employing the fuzzy method. Fuzzy Analytical Hierarchy Process (FAHP), a multi-criteria higher cognitive process technique, was used to determine the weights of the risk parameters evaluated with the Fine–Kinney method. After that, the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) was employed for ranking the hazard’s source. Using Occupational Health and Safety (OHS) consultants, this study was conducted in Bangladesh regarding its onshore turbines. Findings have revealed that the most prevalent hazards during transportation, construction, operation, and maintenance, respectively, are “Driving vehicles at night in dark weather conditions”, “Work in hot and humid conditions”, “Inclement weather”, and “Entering of unauthorized persons”. The results of this study can help the OHS department to track these risks and to control and minimize them.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it