Bibliographic record
Abstract
Finding an alternative energy source to fossil fuels is becoming increasingly important. This has led many countries to question whether nuclear power, touted as an environmentally friendly source of energy, is the answer. A look at the environmental effects attached to this source of energy—the risk of radiation exposure for communities adjacent to nuclear plants, and nuclear power’s volatile history—coupled with current events provides countries with reason to seriously doubt the safety and sustainability of this energy source. Nuclear plants do immediate damage to the system in which they are built, and that is not the end of their effects. Continuous release of radiological material into the surrounding area that threatens the ecology and nearby communities, the creation of waste, for which there is currently no solution, and a threat of radioactive materials falling into the hands of terrorist activists also weigh heavily against the sustainability of this energy source. The history of the nuclear industry makes it undeniable that more nuclear disasters are inevitable. Every community is vulnerable, whether a nuclear disaster is caused by nature’s wrath, as in Japan, or by human or technological error, as in many previous nuclear accidents, including Chernobyl. The countries of the world have a weighty decision to make about whether nuclear energy is the answer.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.275 | 0.089 |
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".