Bibliographic record
Abstract
This paper first analyses price and income elasticity of oil demand in the United States, Canada and Mexico for the period 1980‐99. Economic activity is the main driving force that influences oil consumption in each country. Changes in oil consumption generally lagged by a few years before the full impact of changes in oil prices was realized. Consumers in the short run are constrained by technological and other barriers and, therefore, less sensitive to changes in oil prices; however, they are more responsive in the long run — though response is still inelastic. The use of advanced technology facilitated these countries to use less oil over time. The paper then looks at demand over the next 20 years. The best‐fitting model predicts that, by the end of 2020 (reference case), the USA, Canada and Mexico will respectively consume 24,900, 2,596 and 2,321 thousand barrels daily, compared with 19,519, 1,943 and 1,970 thousand b/d in 1999. The model forecasts economic slowdown during 2000/2002. The USA and Canada are expected to recover quickly, while Mexico will take longer.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".