Diagnostic Properties of River Flood Deposit Sediment Collected Annually from 2011 to 2023 at Ten Sites across a Water Reservoir Catchment Contaminated after the Fukushima Daiichi Nuclear Accident Radiocesium Fallout (Mano Dam)
Bibliographic record
Abstract
The current dataset was compiled to investigate sediment and radiocesium transfers in the Mano Dam reservoir catchment from 2011 to 2023, which was contaminated following the Fukushima Daiichi nuclear power plant accident in March 2011. This dataset contains river flood sediments collected annually at ten sites in the Mano Dam reservoir catchment (n = 102) and on potential source soils. Potential source materials include forest soils (n = 24), farmland soils (n = 24) and subsoil (subsoil material and landslide; n = 10). The dataset allows the assessment of spatial heterogeneity in sediment dynamics, erosion processes and contaminant transfer, providing insights into landscape and management influences on sediment sources and deposition patterns. Flood sediment samples were collected in November following major flood events associated with the occurrence of extreme precipitation events such as typhoons or tropical storms. The dataset consists of four CSV files containing data and metadata with variable descriptions. Recommended encoding format: latin1
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".