Bibliographic record
Abstract
In recent years, with the increasing awareness of global environmental protection, the development of new energy has gradually become a focus of national policies and research. Ethanol fuel, as a renewable energy source, has been widely used worldwide due to its environmental, economic, and safety advantages, and has become an effective way to address national energy security and environmental protection issues. This review aims to explore the application of ethanol fuel in the United States, as well as its related policies, environmental and social impacts, and future development trends. Firstly, an overview of ethanol fuel is introduced, including its raw materials, production process, and common types of ethanol fuels. Next, the application of ethanol fuel in the United States is analyzed, including its historical and current development trends. Policies related to ethanol fuel are also discussed, including legislation and regulation, tariffs, and tax incentives. In addition, the environmental and social impacts of ethanol fuel are analyzed, including its demand for land and water resources and its impact on food prices. Finally, this review looks at the future development trends of ethanol fuel, including its future prospects and development trends, as well as competition and integration with other renewable energy sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".