Getting Over the Barrel- Achieving Independence from Foreign Oil in 2018
Bibliographic record
Abstract
The United States can achieve independence from foreign oil in 2018. Increasing production from current oil fields, developing untapped oil resources, converting coal to oil and oil shale extraction can produce an additional 4.1 million barrels per day. Increased ethanol production and proliferation of natural gas and hydrogen vehicles can produce the equivalent of 5.9 million barrels per day. Increased Corporate Average Fuel Economy standards, federal gasoline taxes and proliferation of hybrid vehicles can conserve 2.8 million barrels per day. Expanding the definition of domestic oil to include Canadian and Mexican oil reduces foreign oil imports by 3.3 million barrels per day. These initiatives eliminate the need for the United States to import oil in 2018 and beyond. Eliminating the nation's dependence on oil imports will improve security by guaranteeing an abundant, readily available domestic energy supply. Oil independence will free the Unites States from economic coercion by the world's oil producers via price fixing and production quotas. The leverage oil producing states wield will evaporate as the nation gains its independence thereby enabling the United States to reevaluate its foreign policy and diplomacy initiatives.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".