Data mining classifiers comparison for seismic hazard prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earthquake and seismic hazards are natural disasters which are very difficult to predict. Researchers are working hard to predict these disasters for minimizing loss of life and property. Proposed research used data mining algorithms on seismic bumps dataset which was obtained from coal mines for the seismic hazard prediction. Data mining is a powerful technique used to discover patterns of data. In this research, performance of five data mining classifiers was compared for better prediction of seismic hazard. For preprocessing of this dataset, discretization and resampling techniques were used. For modelling, five data mining classifiers were implemented and compared by using feature selection technique on the basis of confusion matrix measures like success rate, mean absolute error, kappa statistics, precision, recall and f-measure. This analysis showed that Random Forest algorithm achieved highest success rate by using feature selection technique and provided promising results for seismic hazard prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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