Bibliographic record
Abstract
One of the hottest commodities today is a barrel of oil. While the price has fluctuated dramatically over the last several years, it has remained substantially higher than the December 2001 price of $15.95. 1 The reasons for the increase are multi-faceted. First, world demand is increasing, particularly in newly developed countries such as China and India. Indeed, demand rose more in 2004 than in any other year since 1976, mainly because of China, which is now the second biggest user of oil after the United States. On the supply side, geopolitical conflicts have destabilized oil supplies, leading to increased prices. Also, much of the oil is now more difficult to extract—wells are deeper, drilling occurs offshore, special technology is needed for the oil sands. This translates to higher production costs and higher prices for consumers (see The downside). Canada is currently the eighth-largest producer of crude oil at about 2.5 million barrels per day. Current world demand is approximately 84 million barrels per day (CAPP n.d. a), while production stands at about 86 million barrels (Government of Alberta, DOE n.d. a).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".