The Changing Face of Global Oil and Gas Production and its Implication on Nigeria
Bibliographic record
Abstract
Abstract The media are awash with discussions of the US becoming the largest oil producer in less than a decade and to be self-sufficient in energy in about two-decade's time. That means a lot for Nigeria since the US has been the largest buyer of Nigeria's crude oil. Losing one's largest customer only to see the erstwhile customer becoming a major competitor is a dreaded nightmare for any business outfit. India and China may not necessarily have the capacity to buy off what the US is predicted to leave off as there are potentially new players like the countries of East Africa, Brazil and Canada coming on the scene or strengthening their hold on the market. It is also important to note that the shale business may also thrive in China, a country that is estimated to have the largest shale gas reserve. This paper investigates the far-reaching implication of these strategic developments and prospects on the future of oil and gas production in Nigeria and on the Nigerian economy which is currently heavily dependent on petroleum revenue. It concludes that Nigeria can only have a chance if it aggressively increases it local energy utilization and become more competitive in the oil and gas business by improving community integration and environmental responsiveness, transparency, professionalism and efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".