Office of Naval Petroleum and Oil Shale Reserves
Bibliographic record
Abstract
Worldwide supplies of conventional oil will soon reach a peak production rate and begin an irreversible long-term decline. Options to augment liquid fuel supplies in the United States have once again begun to focus on oil shale as long-term source of reliable, affordable, and secure oil. The United States government has long recognized the strategic potential of the nation’s vast oil shale resources to support national security. President Taft in 1912 established an Office of Naval Petroleum and Oil Shale Reserves and charged that office with ensuring oil for naval military operations. This office continues to oversee the United States strategic interest in oil shale. America’s 2 trillion barrel oil shale resource is recognized as having the same production potential as Canada’s tar sands. Tar sand production, initiated in the 1960s, has increased steadily to more than 1 million per barrels/day and is moving toward a near-term goal of 2.5 million barrels per day by 2017. This amount of oil is equivalent to the volume of oil currently imported by the United States from Middle East countries. Tar sands production has enabled Canada to add 174 billion barrels to its recoverable oil reserves, making Canada’s proved reserves second only to those of Saudi Arabia.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.085 |
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".