Impact of the 2019 typhoons on sediment source contributions and radiocesium concentrations in rivers draining the Fukushima radioactive plume, Japan
Why this work is in the frame
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Bibliographic record
Abstract
The Fukushima nuclear accident in March 2011 generated a 3000 km <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> plume of soils heavily contaminated with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>137</mml:mn> </mml:msup> </mml:math> Cs. Decontamination was completed early in 2019. Typhoon Hagibis was the first extreme event that occurred in the region after decontamination. Its impact on sediment sources and sediment <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>137</mml:mn> </mml:msup> </mml:math> Cs contamination was investigated through the application of a sediment fingerprinting procedure using spectrocolorimetry and geochemical properties. Sediment deposits ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>24</mml:mn> </mml:mrow> </mml:math> ) were collected in the Mano and Niida River catchments after the 2019 typhoons, and their signature was compared to that of potential sources (e.g., cropland, forests, and subsurface; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>57</mml:mn> </mml:mrow> </mml:math> ). Results demonstrate the dominance of cropland as the main source of sediment (mean: 54%) followed by forests (41%) with much lower contributions of subsurface material (5%). Overall, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>137</mml:mn> </mml:msup> </mml:math> Cs concentrations in sediment were on average 84%–93% lower than the levels recorded after the accident in 2011, which demonstrates the effectiveness of cropland decontamination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it