Earthquake forecasting and its verification in northeast India
Bibliographic record
Abstract
The aim of the present study is to analyze the occurrences of future earthquakes using forecasting techniques from past seismicity in northeast India (latitude 20°N–31°N and longitude 87°E–97°E). The present study applies two types of retrospective binary forecasting. The first one is the pattern informatics (PI) method and the other is the Relative Intensity (RI) method. These techniques quantify the spatio-temporal seismicity rate changes in the historic seismicity of the study region. For this purpose, a uniform and complete earthquake catalogue in moment magnitude (Mw > 3) is prepared. The resulting binary forecasts are evaluated with the relative operating characteristics (ROC) diagram. The ROC diagram quantifies the results in terms of a hit rate (fraction of events that are successfully forecasted) versus a false alarm rate (no event occurs in a hotspot box). Evaluation of forecasting results using ROC diagram is more protective than maximum likelihood tests. The result gives a regional seismogenic map where earthquakes are likely to occur during a specified period in the future. The recent India–Nepal border earthquake of 18 September 2011 occurred in one of the forecasted regions. These techniques have been applied for the first time to the Indian subcontinent.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".