Assessment of major causes: nuclear power plant disasters since 1950
Bibliographic record
Abstract
Purpose This paper aims to investigate the major causes of the nuclear power plant (NPP) disasters since 1950, elucidates the commonalities between them and recommends strategies to minimize the risk of NPP disasters. Design/methodology/approach This paper analyzes facts from five case studies: Chernobyl disaster, USSR 1986; Fukushima Daiichi disaster, Japan 2011; Three Mile Island incident, USA 1979; Chalk River Accident, Canada 1952; and SL-1 Accident, USA 1961. A qualitative approach is adopted to compare and contrast the major reasons that led to the accidents, and consequent social and technological impacts of the disasters on environment, society, economy and nuclear industry are analyzed. Findings Although each of the nuclear accidents is unique in terms of its occurrence and impacts, this research study found some common causes behind the accidents. Faulty system design, equipment failure, inadequate safety and warning systems, violation of safety regulations, lack of training of the nuclear operators and ignorance from the operators and regulators side were found to be the major common causes behind the accidents. Originality/value This paper recommends some of the nuclear disaster risk reduction strategies in terms of “lessons learned from the past accidents”. The findings of the research paper would serve as an information tool for the nuclear professionals for informed decision-making and planning for proper preventive measures well in advance so that the mistakes which led to the occurrence of accidents in the past are not repeated in the future.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".