Oil Production, Refining and Transportation in Canada
Bibliographic record
Abstract
The article deals with fuel and energy complex of Canada as one of the largest manufacturers of primary energy in the world, which provides up to 6 percent of the world energy supply. Only the Russian Federation, PRC, the United States of America and the Kingdom of Saudi Arabia have larger production volumes. However, oil plays the most significant role in Canada's energy exports. It is estimated that its proven reserves are sufficient to meet the demand for 140 years at current production rate. The relevance of the study, including the analysis of fuel and energy complex of Canada, is due to the fact that such comparison and synthesis of data on the amount of recoverable oil reserves, the volume of its production, imports, exports and transit of oil and oil products, the distribution of oil for transportation (via pipelines, rail, sea, road), strategic oil field, refining and transportation of oil and oil products development projects, as well as implementation of Canada's best practices in the Russian Federation, is being developed for the first time. In addition, the data given in previously published articles on the subject, due to the dynamic development of the industry, are obsolete and do not reflect the real situation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".