Bibliographic record
Abstract
Energy is critical to the vitality and endurance of a nation's economy, fueling all industries and supporting military, diplomatic, informational and economic instruments of power. Energy is the only industry sector addressed in the U.S. National Security Strategy. Ensuring the security of America's energy resources is challenging due to our dependency on foreign fuel sources, especially oil and gas. This dependency is caused by a predominate use of fossil fuels, the large reserves of fossil fuels located outside the United States, a lack of adequate infrastructure to move natural gas and oil from pump to consumer, environmental and political restrictions within the United States, and a relatively minor U.S. surge capability to counteract geopolitical realities. As our domestic supply production decreases, projected increases in economic growth will drive increases in demand, which must be increasingly met through imported sources. This reality makes policies that promote international trade and diversity in fuel types and sources the cornerstone for energy security. Our study incorporates issues and findings from the countries we visited: Canada, Britain, and Spain. We found that the energy industry is not in crisis; however, U.S. government policies, laws, dollars, and even public awareness campaigns must be used to increase energy security.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.284 | 0.153 |
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".