Lessons Learned from the Occurrences of Major Devastating Mw ≥ 7.5 Earthquakes in the Asian Countries during the last 25 years
Bibliographic record
Abstract
Abstract The loss of human lives, properties and damages due to the occurrences of moderate to large size earthquakes have been a major concern for the economic development of many countries in the world. Earthquakes would continue to occur in a region and would remain among the most devastating natural hazards. Seismically active countries viz., China, India, Japan, USA, Mexico and a few other countries are classified as high earthquake hazard regions while continents/countries with low rate of earthquake occurrence include Africa, Australia, Canada etc. On many occasions in the past, high earthquake hazard countries have experienced major economic setbacks due to the occurrences of major earthquakes. In the present paper, the causative mechanisms of major devastating earthquakes of Mw≥7.5 in the Asian countries (including high hazard countries like China, Japan, India, Taiwan and Nepal) during the past 25 years and major damages rendered by these earthquakes is discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".