Bibliographic record
Abstract
Introduction Development of Western Canadian heavy oil and bitumen production from the end of price controls in mid 1985 to today has been a roller coaster ride for oil companies and their employees. Activities in response to the current oil price drop suggest this trend is continuing. The results to the oil industry have been fragmented, including inefficient efforts to advance technology and a need for repeated reorganization. The results for many employees have been insecurity, career disruption and general frustration. While some may point out that this period of time has seen a great deal of technical development, I feel that much more could have been accomplished under more stable circumstances. It seems prudent that those of us in the heavy oil industry should periodically stop and ask ourselves why this cycling of activity occurs, and explore options for dampening the severity of the booms and busts. My main purpose in writing this article is to encourage candid dialogue. Data Collection When I was contemplating writing an article on this subject, I talked to a number of people working within the Calgary oil patch. Input was obtained from workers in industry, research, education and the government. Nearly everyone expressed strong opinions, but very few gave me the impression they wanted to be quoted or identified. From the perspective of the 'soft science' of human behaviour, this high interest in expressing opinions but low interest in public ownership of them is likely an important piece of data. Statistical data were not hard to locate, but most of these data were proprietary and unavailable for citation in this article. For example, some investment companies have detailed documents discussing current events in heavy oil and making predictions about its future. It is a little unnerving to read these sterile, economic prognoses of our chosen field of endeavour. One begins to wonder if the investment companies collectively have the ability to influence the economic state of the oil patch more than their workers do. There are also a number of commercial analytical studies on the booms and busts experienced in the Canadian heavy oil industry. I will not try to duplicate or improve upon them. My statistics will be limited to noting the scale of the booms and busts by citing that over 26,000 Canadian workers appear to have been laid off between 1985 and 1994(1). The fraction working in heavy oil was not stated, but it was likely a significant number. It would also be very difficult to determine the number of these people who subsequently found employment back in the heavy oil field. Regardless of these unknown factors, it is apparent that significant disruption to the industry occurs during the boom and bust cycles. Another important conclusion from the historic data is that all booms or busts will end within a few years. They should not be viewed as permanent. As one of my experienced and accomplished friends in heavy oil recently told me, "It is unfortunate that decisions are often made on the assumption that the "current" heavy oil price will go on forever."
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".