Financing Prospects in the Gas Industry: A Nigerian Perspective
Bibliographic record
Abstract
The objective of this paper is to investigate the financing prospects in gas industry. This paper classified gas financing into four (4) areas such as (a) associated gas (b) dry gas (c) condensated gas and (d) natural liquid gas. There are fourteen (14) job classifications in the gas industry which corporate bodies or individuals can be engaged. Variables are classified as y = gas production, x/ = gas utilization and xj = gas flared. The values of these variables were obtained from the 2004 statistical bulletin of central Bank of Nigeria (CBN) from 1983-2017. The SPSS/PC was used to subject these variables by the step wise regressional analysis. The result shows that 29.10% of gas produced was utilized while 70.90% were flared as financial waste. We tested hypotheses / by the use of coefficient of variation, we concluded that gas utilization has the highest risk investment potential. Hypothesis 2 accepted Ha2 that means v > Mean x/. While HA was accepted because skewness of x; > .v/. u e recommended a comprehensive investment policy in the gas industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".