CANADA’S FUTURE OIL PRODUCTION: Projected 2000-2020
Bibliographic record
Abstract
Canada, in addition to its own substantial petroleum needs for its vast cold areas (1,775,000 B/D(4/), is a most important source of oil and gas for the U.S. (imports = 1,576,000 B/D(5/). The U.S. is extremely interested in the probable size of Canada’s near-term “Synthetic Oil ” production. The “Hubbert Production Peak ” for Western Canada’s conventional oil was reached in 1973, soon after the US/48 Hubbert Peak in 1970. Since then, Western Canada’s conventional oil production from wells has declined steadily and today is less than half that in 1973. The new Atlantic Ocean production will offset Western Canada’s future oil decline. By 2020, Newfoundland’s offshore conventional crude is expected to equal Western Canada’s production of some 360,000 B/D (barrels per day). The huge (+300 recoverable billion barrels in place) Alberta Tar Sands will be the backbone of Canada’s long-term oil production. Tar Sands production began in earnest after 1982, and “Synthetic Oil ” is expected to grow from 400,000 B/D (2000) to 1,000,000 B/D (2020). Tar sands production is more like coal mining than production from oilwells and is not cheap; it is a deep-pocket game for major oil companies and/or the government. An investment of more than $1,000,000,000 ($1 billion) is required to set up production of 100,000 B/D – a white poker chip in the world oil business. Winters in the region are sub-arctic with bad mosquitoes in the summers. There is no local infrastructure except for the Tar
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".