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Record W2017390297 · doi:10.4236/ijg.2014.512122

Inverse Correlations between Daily Average Energy of Global 0.01 to 1 M Earthquakes and Solar Flux Units: Possible Source of Coupling

2014· article· en· W2017390297 on OpenAlexaff
David A. E. Vares, Michael A. Persinger

Bibliographic record

VenueInternational Journal of Geosciences · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCoupling (piping)Flux (metallurgy)Bohr modelMagnitude (astronomy)PhysicsRange (aeronautics)Energy fluxAtmospheric sciencesGeodesyEnvironmental scienceComputational physicsMeteorologyGeologyAstrophysicsChemistryMaterials scienceQuantum mechanics

Abstract

fetched live from OpenAlex

The potential coupling between discrete releases of solar energy within the 2.8 GHz band and increments of seismic energy could suggest alternative mechanisms for solar-terrestrial interactions. Daily measures of Solar Flux Units (SFU) and the average energy of global earthquakes between 0.01 and 1 M for a 5 year period (2009-2013) shared about 25% of the same variance (r = -0.50). Average energies from the other successive 1 M intervals did not display the strength of this effect or were not statistically significant. The slope of the association indicated that for every 10-22 W/m2Hz1 decrease in daily SFUs, the average energy per earthquake in the 0.01 to 1 M range increased over the earth’s surface area on average by ~3 × 10-12 J/m2. The discrepancy of ~1010 in energy density was accommodated by multiplication of the solar unit by the frequency equivalent (1.39 × 1010 Hz/T) of the Bohr magneton and its intrinsic magnetic field strength. Although the results and the convergent quantification suggest a coupling between the mechanisms responsible for fluctuations in 2.8 GHz power from the sun and the energies of the smallest magnitude earthquake activities as predicted, the presence of a third factor related to time or to the sun’s movement through space must still be accommodated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.314

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

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