"LAWRENCE'S BUTTERFLY" OR REQUIEM FOR THE DREAM OF AN EARTHQUAKE PREDICTION ALGORITHM
Bibliographic record
Abstract
For an earthquake (a discharge of energy), explosion, a snow avalanche, mountain strike and other seismic events to occur, it is necessary to change the thermodynamic indicators of the system. It can be: a sudden change in temperature or pressure in the system (for example, phase transition), a change in the critical mass of the nuclear charge, the appearance of radicals during chemical reactions (chain reactions), a sudden change in volume of the system or its physical-mechanical properties (for example, Rehbinder effect). It is known that the change in ambient temperature is already enough to cause a snow avalanche, and the movement of magma can cause a swarm of earthquakes. Based on this, we can assume that these events, located in different natural environments, have a common thermodynamic mechanism of initiation. To the process of snow avalanche should be added such processes as sliding glaciers, catastrophic collapses, etc. In most cases we cannot prevent these processes and therefore it is important for us to know the forecast of these events to prevent their sudden onset. This article discusses the possibility of predicting earthquakes and snow avalanches in light of modern advances in thermodynamics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.030 |
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".