Bibliographic record
Abstract
Abstract There has been considerable price volatility in the crude oil market since 1861. At times the yearly variations, as reported by the BP Statistical Review, have been minor but on occasion they have been quite significant. The small and large variations have been matched successfully with a Variable Shape Distribution (VSD) model. Remarkably, the comparison between actual price variations and those calculated by the VSD leads to a coefficient of determination (R2) larger than 0.98. Given this validation, we integrate the results with a Global Energy Market (GEM) model developed in 2007 that presented an oil consumption forecast to 2030. Thus far, the forecast has been in line with actual oil consumption to 2012. Contrary to statements made by various oil commentators, the integration of results suggests that the possibilities of seeing sudden, large and permanent increases in future oil prices are very low. Given the huge quantities of conventional and unconventional oil available at or below current market prices, society should be able to substitute between alternative sources long before depletion causes oil to become unduly expensive. Further, a case can be made that with the vast global oil resource base and the significant technological advances being implemented by the industry, oil prices could decrease in the future.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".