Bibliographic record
Abstract
NEW SOURCES OF OIL AND GAS We have seen in the previous chapter that there will be considerable pressure on conventional fossil fuel reserves over the next few decades. Demand for oil in particular will experience substantial annual growth, and it will be difficult to maintain the recent historical reserves-to-production ratio of around 40. There is a need, therefore, to develop new or “non-conventional” sources of fossil fuels to supplement the traditional crude oil supplies. These will likely be needed until at least the end of the twenty-first century, when extensive supplies of truly renewable, or sustainable, primary energy should be available in sufficient quantities to satisfy most global energy demand. In the near-term these “new” sources of fossil fuels include the unlocking of “synthetic oil” from the extensive oil sands and oil shale deposits found in many parts of the world, and the extraction of natural gas from unused coal seams, known as “coal-bed methane.” In the longer term the use of fossil fuels in a much more environmentally benign way may be prolonged by accessing the extensive global coal supplies using so-called “clean coal” technologies, or even by accessing the extensive methane hydrate resources to be found in the deep ocean. If carbon mitigation, in the form of CO 2 capture and storage, also known as “carbon sequestration,” is proven to be technically and economically viable, then we may still be using fossil fuels well into the twenty-second century.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.021 |
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".