Bibliographic record
Abstract
Abstract The Problem The energy industry is a mainstay in Alberta's advantageous position as a leader in the Canadian economy. In 1999, energy directly contributed 20.7% (approximately $24 billion) of Alberta's total gross domestic product of $115.4 billion. That figure does not include the significant role the energy industry plays in other sectors, such as manufacturing, transportation, construction, and business. In addition, approximately 40% of the investment that drives Alberta's growth is directly attributable to energy(1). While energy plays a key role in Alberta's economy, we must remember that it is a non-renewable resource. In addition, primary production and existing enhanced oil recovery techniques are only capable of recovering a small percentage of our oil reserves. Current estimates by the Alberta Energy and Utilities Board (AEUB) show that there are 44 billion barrels of oil (72% of the original oil in place) and 78 tcf of natural gas (31% of the initial gas in place) in existing reservoirs, which will remain in the ground unless new and innovative technologies are employed. Although innovation is the key to recovering these reserves, it must also be remembered that the window of opportunity to improve recovery through the use of new technology is limited. The average reserve life index for Alberta oil and gas pools is less than ten years. Once these pools reach the end of their economic life and are abandoned, the opportunity to use existing infrastructure and assets, together with new technology, will likely be lost. The Challenge If Alberta is to maintain current levels of oil and gas revenues, timely development of these innovative technologies is a critical priority. While significant research may be carried out by sectors such as government and universities, industrial R &D, particularly by suppliers, is most clearly linked to technology innovation and, hence, to economic growth. However, over the past decade, both the Alberta government and the oil and gas industry have significantly reduced their oil and gas research and development spending to levels well below those seen in other sectors (see Figure 1). Total Canadian oil and gas industry R&D spending is relatively low for a number of reasons. First, the energy industry is cyclical in nature, leading to limited funding available in the troughs, and limited human resources available during peak times. Larger companies, which formerly maintained their own research departments, now tend to look at research as a cost to be reduced, since investors in public companies demand quarterly growth, and neglect long-term research investment. Instead, industry expects technological solutions to be delivered when needed, mostly by suppliers and service companies. In today's economic climate, industry alone cannot bear all of the risks and the costs inherent in research and development. The Opportunity Industry needs to support the development of new technology to solve problems and unlock opportunities that are unique to Alberta. They also need to share the cost, risk, and the revenue reward of technology development with the Alberta government.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.006 |
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".