Monte Carlo Aggregating Method for Radon Precursor-Based Earthquake Parameter Prediction in Java, Indonesia
Bibliographic record
Abstract
This study introduces a novel method for predicting earthquake parameters using radon as a precursor to address uncertainties and limitations in the dataset.The dataset comprises radon observation data from Yogyakarta, Indonesia, and earthquake records collected from a radon monitoring site and the USGS earthquake database from December 11, 2022, to August 8, 2023.The proposed method was trained on 80% of the dataset, which was utilized to generate a probability distribution for the Monte Carlo process to handle the constraints of limited precursor data.The results from the Monte Carlo simulations were then used to develop a model for predicting earthquake parameters.Experimental results demonstrate that the proposed method performs well within a monitoring station's radius of 300 and 400 km.At 300 km, the method outperforms in predicting magnitude, distance, and time, with RMSE values of 0.48, 60.60 km, and 57.85 hours, respectively.At 400 km, it achieves excellent performance with RMSE values of 0.61, 76.29 km, and 46.69 hours.This study shows that the proposed method outperforms benchmark methods in predicting earthquake parameters using radon gas as an earthquake precursor.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".