Carbon Dioxide Emission Reduction Potentials in West Virginia’s Power Generation Sector
Bibliographic record
Abstract
In this paper, research will be discussed on how to scientifically, systematically, and economically reduce greenhouse gas emissions within the state of West Virginia, USA. While fossil fuels such as coal and natural gas remain the top resources within this particular state, there are new technologies, different approaches and modifications to current power generation cycles, and different fuels that can be presented to gain further reduction of these harmful emissions. To achieve this objective, eight different scenarios were introduced. In the first scenario, existing power stations’ fuel was switched to natural gas. Existing power plants were replaced by natural gas combined cycle (NGCC), integrated gasification combined cycle (IGCC), solid oxide fuel cell (SOFC), hybrid SOFC, and SOFC-IGCC hybrid power stations in scenarios number 2 to 6, respectively. The last two scenarios involved carbon capture systems. It has been found that the CO2 emissions can be significantly reduced by introducing changes and alternatives to the current cycles and methods that are in place today.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".