Prevention of Catastrophic Volcanic Eruptions, Large Earthquakes underneath Big Cities, and Giant Earthquakes at Subduction Zones
Bibliographic record
Abstract
Catastrophic volcanic eruptions, large earthquakes beneath big cities, or giant earthquakes at subduction zones are apparently the biggest problems facing the sustainability of human society. However, imminent prediction methods for these events have never been established, except that volcanic eruptions can only be predicted by exceptional efforts by dedicated researchers. Even if a prediction method has been established, the method cannot significantly reduce infrastructure damage, although it could slightly reduce the number of fatalities. On the other hand, prevention of eruptions or earthquakes could significantly reduce, not only the number of fatalities, but also infrastructure damage. Therefore, the authors propose (1) gradual energy release by supercritical power generation to prevent catastrophic eruptions; (2) gradual seismic energy release by injecting water into seismic sources to prevent large earthquakes beneath big cities; and (3) exploding existing nuclear warheads underground to prevent giant earthquakes at subduction zones. Necessary technical developments, costs, risks, and problems will also be explained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".