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Record W4415666811 · doi:10.1007/s44274-025-00424-2

Progress and regression of seismology over the last 300 years

2025· article· en· W4415666811 on OpenAlexaff
Serguei Bychkov

Bibliographic record

VenueDiscover Environment · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEarthquake predictionGeniusEpicenterIntraplate earthquakeRemotely triggered earthquakesUrban seismic riskWork (physics)

Abstract

fetched live from OpenAlex

Since time immemorial, people have attributed the causes of aftershocks, which bring numerous troubles to cities and settlements, to God's punishment for people's sins. The terrible earthquake of November 1, 1755 in Lisbon gave rise to a serious study of earthquake processes, and “thanks” to this catastrophe, geophysics took a huge step forward in the form of numerous scientific papers in the wake of this earthquake and the “cherry on the cake” in the form of the remarkable work of Mr. J. Michell (1760), where the source of the earthquake is not God Punishment, and a seismic wave! The second earthquake that had a significant impact on the development of geophysics was the earthquake in San Francisco on April 18, 1906. Unfortunately, the conclusions of the causes of this disaster, unlike the earthquake in Lisbon, had a negative impact on geophysics in the form of the adoption of Mr. Reid's theory of Elastic recoil, which led science into a dead end, from which geophysicists have not been able to find a way out for more than a hundred years. In this article, made in the style of a historical perspective on the development of geophysics, we will show the progress and regression of the Earth sciences over the past 300 years and reveal the real source of earthquake energy, which was pointed out by the genius W. Stukeley in the distant eighteenth century.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.202
Teacher spread0.197 · 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 designObservational
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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