Bibliographic record
Abstract
Abstract The convergence of increasing concern about energy supply and increasing public commitment to environmental protection provides an opportunity to mobilize public and private investment in energy innovation. To tap the vast Canadian resource potential, innovative new technologies are required-to unlock the large remaining conventional oil and gas reserves, take advantage of the hundreds of years of remaining production of bitumen, coal, and coal bed methane, and ensure increasing supply from renewable energy options. As Alberta's energy innovation strategy was developed, recognition grew that solutions to the pressing challenges described above emerge when we understand the energy industry as one interconnected system, integrated horizontally along the various energy sources and vertically along the value chain. This led to the creation of the Energy Innovation Network (EnergyINet) as the vehicle to facilitate strategic collaboration and innovation among industry, governments (federal and provincial), and the research community to address the challenges of ensuring an abundant supply of environmentally responsible energy. This paper describes, as an example, the approach taken in the development of oil sands technologies where a government-industry partnership was developed to share resources, build expertise, and lower technology risks. This provided the key tools that have lead to the oil sands becoming a significant resource relative to global energy demand. The paper argues that no one single source of energy will be sufficient to meet world or Canadian demand and consequently for the need for a collaborative initiative to facilitate a long-term (20- to 25-year) effort to implement an integrated energy innovation strategy. This integrated approach is built on the premise that strategic investment in a balanced portfolio of energy innovation "with a focus on common technology platforms and points of leverage across the portfolio" has the greatest potential for returns in economic, environmental, and social terms. Introduction The International Energy Agency (IEA) projects that global primary energy demand will increase by 1.7% per annum from 2000 to 2030, reaching an annual level of 15.3 billion tonnes of oil equivalent. The increase will be equal to two thirds of current demand(1). The world will remain heavily reliant on traditional forms of energy. Though renewables are expected to grow from a low base, they cannot displace fossil fuels as the overriding source of energy in this time scale. Fossil fuels are expected to supply over 90% of global incremental energy demand through 2030. Gas consumption is estimated to double between 2000 and 2030 in view of its cost competitiveness, ample availability, and environmental advantages. Oil will remain the largest fuel source with demand increasing by 1.6% per annum. Canada is the 5th largest energy producer in the world and is a net exporter of energy; these exports account for about 7% of the GDP. Canada is also a world leader in hydroelectric power development (24% of domestic consumption), but there is limited potential for development of new hydroelectric sites. Nuclear energy (5% of domestic consumption) is declining, and there have been no investments in new nuclear facilities for nearly 20 years.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".