ANALYZING DOMINO EFFECTS OCCURRING ON GASOLINE STORAGE TANKS AT THE BULK OIL STORAGE AND TRANSPORTATION (BOST) DEPOT
Bibliographic record
Abstract
Since processed crude oil products are very vulnerable (susceptible) and highly flammable to cause massive catastrophes, such as fire and explosion, which are frequent and can create a chain reaction (Domino effects). This research was carried out at the Bulk Oil Storage and Transportation LTD depot on the Accra plain in Ghana where gasoline and Gasoil are stored. The research was conducted on a flammable gasoline area subjected to a vapor cloud explosion and the hazardous zone. Analyzing domino effects, propagation of a gasoline flammable vapor cloud caused by the explosion, ALOHA (Areal Location of Hazardous Atmospheres) software was used to find out how to apply effective safety measures to prevent future risks at any BOST facilities across the country. After the analysis, it was realized that 5.0 miles to the west-south-west the fuel concentration in the air was 2100 ppm lower than the explosive limit (LEL), and could not be as severe as that at 2.3 miles distance from the source point (12600 ppm LEL) with a greater fuel concentration in the atmosphere. The results made available would be useful in maximizing (improving) safety at the facility, residential area, and as well as minimizing future incidents.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".