Physical Properties of Agbabu and Yegbata Bitumen in Nigeria
Bibliographic record
Abstract
This experimental work evaluates the physical properties of Abagbu and Yegbata bitumen from Nigeria with comparison with that of Canada being the world largest producer of crude oil from bitumen. This study employed the American Society for Testing and Materials (ASTM) methods in conducting laboratory experiments in order to determine the viscosity, specific gravity, API gravity, pour point and flash point. A Gas Chromatograph (GC) was used to determine the hydrocarbon content of the bitumen samples. Test results show that the bitumen samples have specific gravity of 1.01 with a 0.9962 OAPI for Yegbata, while the Agbabu bitumen sample had 8.599 specific gravity and 10.54 OAPI. Kinematic viscosity ranges of 1.0×102 – 3.3×104 and 1.6×103 – 5.6×104, flash point of 288oC and 282oC and pour point of 44oC and 47oC. The gas chromatography analysis showed that the samples contained 46.35% and 7.59% saturates, 21.63% and 64.39% aromatics and 32.03% and 28.01% resins for Agbabu and Yegbata respectively. In comparison with Athabasca bitumen, the results were similar. The assessment and comparison of these properties with the properties of bitumen from Athabasca in Canada reveals that any surface or subsurface crude bitumen gotten from Agbabu and Yegbata in Ondo State, Nigeria can be exploited using similar technologies, if not the same as the technologies being used in Athabasca, Canada
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".