Greenhouse gas (GHG) emissions in the Sultanate of Oman
Bibliographic record
Abstract
Abstract Worldwide, many countries are being affected by greenhouse gas (GHG) emissions. The Sultanate of Oman is no exception. In Oman, both oil‐ and natural‐gas‐related activities have the most important shares of the nation's Gross Domestic Product (GDP). Hence, they are expected to be the primary cause of GHG emissions within the country. In this study, the greenhouse carbon dioxide emissions (CO2) released from the fossil fuels (i.e., oil and natural gas) used in the country for energy production purposes was computed by using the Intergovernmental Panel on Climate Change (IPCC) reference approach for National Greenhouse Gas Inventories. The objective was to develop the CO2 emissions for Oman over the last 40 years starting from year 1972. The obtained results indicated that Oman has a growth in its CO2 GHG emissions. This study is very important and essential, as it will assist Oman to monitor its progress in reducing CO2 emissions. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.002 |
| 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".