Extraction of oil from the tar sands of ofosu and environs, Edo State, Mid-Western Nigeria: A way to meet increasing energy demand
Bibliographic record
Abstract
Tar sands are primarily, aggregates of sands, clay that is rich in minerals, heavy oil and sometimes water. The Nigerian tar sand belt lies on the onshore areas of the Eastern Dahomey (Benin) Basin, and extends right up to parts of Edo State, Mid-Western Nigeria. Extraction techniques involved heating of the tar sand samples to a temperature of 140oF in order to reduce the apparent viscosity of the tar sand. This method of extraction makes use of a coupling agent known as sulphunated fatty acid, alkali metal salt. Results show that the volume of oil extracted from 50g of tar sand ranges between 1.8ml to 2.5ml; while the water residue is quite low, ranging from 2.1ml to about 3.86ml. The tar sands here possess a relatively large quantity of naphthenes, aromatics and asphaltenes that are similar to conventional oil. When compared with the quality of the Canadian tar, which produces 168l of oil per day from 2,000kg of tar sand, this makes the Nigerian tar sand useful alternative source of petroleum hydrocarbon and a potential feedstock for petrochemical industries.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".