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Record W1538257767 · doi:10.1002/2014rs005580

Tohoku-Oki earthquake caused major ionospheric disturbances at 450 km altitude over Alaska

2014· article· en· W1538257767 on OpenAlexaff
Yu‐Ming Yang, Xing Meng, A. Komjáthy, O. Verkholyadova, Richard B. Langley, B. T. Tsurutani, A. J. Mannucci

Bibliographic record

VenueRadio Science · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNational Aeronautics and Space Administration
KeywordsIonosphereTECTotal electron contentAltitude (triangle)ThermosphereGeologyAtmospheric sciencesInfrasoundGeodesyMeteorologyEnvironmental scienceGeophysicsPhysics

Abstract

fetched live from OpenAlex

Ionospheric total electron content (TEC) and atmospheric density perturbations were derived from measurements made from instruments on board the Gravity Recovery and Climate Experiment (GRACE) spacecraft. At the time of the Tohoku-Oki earthquake on 11 March 2011, the twin spacecraft were orbiting at an altitude of ~450 km over Alaska. Significant TEC fluctuations (up to 0.6 total electron content unit (TECU; 1 TECU = 1016 el m−2), atmospheric density perturbations (~3.6 · 10−14 kg/m3), and sudden changes in GRACE acceleration (~4 · 10−8 m/s2) were observed ~8 min after the arrival of seismic and infrasound waves on the ground in Alaska, ~20 min after the Tohoku-Oki main shock at 05:46:23 UTC. The results of the three-dimensional ionospheric-thermospheric modeling and infrasound ray-tracing simulations are consistent with the arrival time and physical characteristics of the disturbances at GRACE. This is the first time that ionospheric disturbances associated with an earthquake are clearly attributable to perturbations at such high altitudes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.004
GPT teacher head0.213
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2014
Admission routes1
Has abstractyes

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