Analysis of 801 Work-Related Incidents in the Oil and Gas Industry That Occurred Between 2014 and 2016 in 6 Regions
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
Oil and Gas Industry (OGI) faces a number of evolving and various types of risks and hazards that give rise to serious incidents. To conduct this study 801 incidents reports have been numerically analyzed, evaluated and interpreted. These incidents reports covered various severity levels, which have been occurred in 6 regions across the world, from 2014 to 2016. The analysis focused on global oil and gas industry. The study has shown that Occupational Work-related Incidents (OWRIs) occur mostly in October and especially in spring time. In addition, it has pointed out that the region of North America is the most affected area; almost one-third of OWRIs occurs in turbine hall area. Surprisingly, the study also revealed that three-quarters of the OWRIs did not occur in High-Risk Activity (HRA) and half of the incidents took place with no tooling involved. Noticeably, the main recurrent and frequent event was struck against or by (SAoB) that resulted dominantly in slight injuries that required only first aid care, and the most affected body part is the finger by “Cut (Laceration)/ Pinch”. The study has confirmed that the hazardous working environment in OGI was the direct cause for half of the OWRIs. However, based on the revealed results, it will be the assumptions that human factors have a crucial impact on the workplace safety and a contributing factor in the incidents. Some control measures were suggested accordingly.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it