A Minute-Level Analysis Between Environmental Gamma Dose Rate and Precipitation
Bibliographic record
Abstract
This study investigates the dynamic relationship between environmental gamma dose rate (GDR) and precipitation using minute-level observational data, which are critical for improving environmental radiation monitoring and early warning systems. Prior research has largely relied on hourly-averaged data, which tend to obscure rapid fluctuations during convective rainfall. To overcome this limitation, this study integrates high-temporal-resolution GDR measurements with spatiotemporal collocated precipitation records collected in Chiayi, Taiwan. An event-based dynamic response framework was applied to quantify the behavior of hundreds of rainfall events. To capture instantaneous responses, the time derivative of dose rate was analyzed, and advanced signal processing methods were combined with explainable artificial intelligence techniques. A LightGBM model was trained, and SHAP (Shapley Additive exPlanations) analysis was used to interpret feature importance across multiple scales. Results show that the instantaneous rate of change in GDR is tightly synchronized with rainfall intensity, while the total increase in GDR is more strongly correlated with cumulative rainfall than with peak intensity. The analysis further demonstrates a scale-dependent shift in dominant drivers: at the minute scale, rainfall and wind gusts are most influential, whereas at the six-hour scale, deep soil temperature and atmospheric pressure prevail. These findings highlight the value of minute-level data and advanced analytical methods for elucidating rainfall-radiation interactions and contribute to a framework for developing more accurate and physically grounded environmental radiation prediction models.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".