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Record W7104289656 · doi:10.5281/zenodo.17546355

"LAWRENCE'S BUTTERFLY" OR REQUIEM FOR THE DREAM OF AN EARTHQUAKE PREDICTION ALGORITHM

2025· article· en· W7104289656 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSnowEarthquake predictionFault (geology)Volume (thermodynamics)Thermodynamic processMagmaNatural (archaeology)

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.026
GPT teacher head0.239
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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