Improvement of Instantaneous Point Source Model for Simulating Radionuclide Diffusion in Oceans under Nuclear Power Plant Accidents
Bibliographic record
Abstract
Simulation methods have become an important tool to reveal radionuclide migration during accidental radionuclide releases and predict influences of accidents on the marine environment. The instantaneous point source model is a useful method to simulate the large-scale radionuclide diffusion in marine areas. However, the simulation accuracy of this method requires improvement as it didn’t take radionuclide decay into account. In this study, an improved instantaneous point source model considering radionuclide decay was proposed on the basis of the original model. Furthermore, the instantaneous point source model and the improved version were used to simulate the concentrations of 131I and 137Cs following the Fukushima Dai-ichi nuclear power plant accident. The results showed that the relative error of 131I concentrations decreased from 136.03% to 37.59% when using the improved model; and improvements in relative errors for 137Cs concentrations were not apparent as the simualtion period was much shorter than its half-life period. Therefore, the improved model can accurately simulate the diffusion process for radionuclides following an accident and provides an efficient decision support tool for risk assessment managers and for use in safety guarantees of nuclear power plants during siting and operational phases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".