Bibliographic record
Abstract
Electrical power is a resource humans heavily rely on, and it has become a basic human need. Today, the major sources of electricity generation are fossil fuels, renewable energy, and nuclear power. This paper concentrates on electricity generated through nuclear power and compares it to the other electricity generation technologies. The objective behind this paper is to discover the impact that nuclear power has on the total electricity generated in Canada, and in addition on a global scale. The paper presents the current role that nuclear power plays in the global electricity generation, and also the expansions that need to be made in the nuclear power industry to fulfill the future electrical power demands. A number of projections have been made based on the current rate of nuclear reactors being put into operation, which is approximately 4 reactors per year, and current term of reactor operation, which is 45 years. These projections were made for the nuclear power in the world. A major outcome of this analysis projects that between 2030 and 2035, the number of operating nuclear reactors in the world can drop by 50%. If this dangerous trend is not addressed, we can lose a viable, and reliable source of energy. The datasets that were analyzed during the process were taken from multiple open literature sources such as journals, reports, and online databases. The paper presents a comparison between nuclear power and other energy sources, and the positive impact nuclear power can have on the world if needed advancements were made in building new nuclear power plants.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".