Bibliographic record
Abstract
Nuclear energy industry is currently undergoing a renaissance due to its competitiveness in the global energy market and its indispensability in the context of the energy transition and the "climate" agenda of global elites. The USA and Canada are among the leading players in the global nuclear technologies market. They possess uranium reserves and technologies for uranium extraction, conversion, enrichment, nuclear fuel production, and nuclear power plants construction. They also rely on their own well-developed nuclear power sectors, which together account for about a third of the world's nuclear power generation. However, in recent years, the United States and Canada have gradually lost ground in many segments of the global nuclear energy market. A reconfiguration of the North American nuclear industry is currently underway. To a large extent, the new institutional structure of the American-Canadian alliance in the global nuclear technology market has already crystallized around Cameco – Westinghouse Electric consortium, formed under the auspices of the investment group Brookfield, and the CANDU ecosystem. For the United States, the main priority in the field of nuclear energy today is increasing uranium enrichment capacity and overcoming dependence on Russia in this area. In addition, Washington and Ottawa aim to strengthen their positions abroad, both in the construction of new reactors and in the supply of nuclear fuel and uranium mining. They are also working on expanding their own nuclear generation capacities and developing small modular reactors. On the international stage, the institutionalization of the American-Canadian alliance in the field of nuclear energy makes it inevitable that the United States and Canada will attempt to regain market share in atomic energy technologies, nuclear fuel, and uranium raw materials. They will also work to undermine the Russian Federation’s position in these markets, reduce its export revenues, and slow down the development of its nuclear energy industry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".