Analysis of 801 Work-Related Incidents in the Oil and Gas Industry That Occurred Between 2014 and 2016 in 6 Regions
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".