Effectiveness of an unprecedented decontamination program on river sediment and radioactive contaminant fluxes
Bibliographic record
Abstract
In the current context of raising concerns related to nuclear accidents and warfare, the lessons learnt from the Fukushima accident in 2011 are of particular interest. Indeed, the Japanese authorities implemented an ambitious decontamination program, which strongly differs from the strategy adopted in Chernobyl where the most contaminated area remains closed to the population nowadays. However, the impact of this strategy on the dispersion of radioactive contaminant fluxes across mountainous landscapes exposed to typhoons remains to be quantified. Based on the unique combination of river monitoring and modelling in a catchment representative of the most impacted area in Japan, we could demonstrate for the first time that decontamination only led to a decrease of 17% of the radionuclide fluxes in the river system. Furthermore, we calculated that 67% of the initial radiocesium remains stored in forests and may contribute to radiocesium dispersion in river systems in response to future erosive events. As the current research was conducted in an area representative of the 1,117 km²-area where remediation was completed early in 2017, it raises questions about the overall sustainability and cost-benefit effectiveness of such a remediation program that generated 9,100,000 m3 of waste for a cost of ~12 billion USD. Only a limited proportion of the initial population returned to their hometown (~30% by 2019), which remains a major challenge for the future of this region, although the primary goal of authorities to decrease the radiation dose rates in the inhabited areas was achieved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".