Bibliographic record
Abstract
The worldwide global demand for oil has grown by 150 % since 1965 and 20 % in the past 20 years to the current 80 million barrels per day, and is projected to grow by 50 % more in the next 20 years [1]. The growth in global demand for oil comes at a time when the supply from relatively cheap conventional sources is declining, and reserves are not being replaced with new discoveries [2]. However, the world has over twice as much supply of heavy oil and bitumen than it does conventional oil. Not including hydrocarbons in oil shale, it is estimated that there are 8-9 trillion barrels of heavy oil and bitumen in place worldwide, of which potentially 900 billion barrels of oil are commercially exploitable with today’s technology [3]. Canada alone has, by some estimates, 175 billion barrels of bitumen reserves that can be processed with today’s technology, making it second only to Saudi Arabia in proven oil reserves in the world [4]. This figure remains controversial; a more cautious estimate has been of the order of 17 billion barrels as recoverable [5]. Regardless of the ‘true ’ number, it is most important to assess what impact unconventional oil will have on the world oil supply and in what time frame, given the financial, economic, environmental, engineering and technological constraints. In this regard, the Western Canadian Sedimentary Basin, with its declining conventional oil and gas resources and its replacement requiring large investments in higher-risk but vast oil sands resources, provides a vital case study. II. “TECHNOLOGY OIL” It is important to consider that the definition of “conventional oil ” is not constant. As has been pointed out by Jaccard [6], offshore oil was not considered conventional 40 years ago, and technological development shifts using enhanced recovery techniques, including thermal production, have moved unconventional sources to the conventional category. For example, in the Faja del Orinoco of Venezuela, 10 0 API crude can be produced at economically attractive rates using long horizontal well technology, because of the high native reservoir
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".