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Record W2165931521 · doi:10.1002/2013rs005344

The 2013 Chelyabinsk meteor ionospheric impact studied using GPS measurements

2014· article· en· W2165931521 on OpenAlexaff
Yu‐Ming Yang, A. Komjáthy, Richard B. Langley, Panagiotis Vergados, Mark D. Butala, A. J. Mannucci

Bibliographic record

VenueRadio Science · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNASA HeadquartersJet Propulsion LaboratoryCalifornia Institute of TechnologyOak Ridge Associated Universities
KeywordsMeteor (satellite)IonosphereGlobal Positioning SystemGeodesyRemote sensingEnvironmental scienceMeteorologyGeologyGeographyTelecommunicationsGeophysicsComputer science

Abstract

fetched live from OpenAlex

On 15 February 2013, the Chelyabinsk meteor event (the largest in size since 1908) provided a unique opportunity to observe ionospheric perturbations associated with the ablation and ionospheric impact of the meteor using GPS measurements. The hypersonic bolide generated powerful shock waves while acoustic perturbations in the atmosphere led to the upward propagation of acoustic and gravity waves into the ionosphere. In our research, we applied two different techniques to detect ionospheric disturbances in dual-frequency global positioning system (GPS) measurements during the meteor impact event. The data were collected from near-field GPS networks in Russia, GPS Earth Observation Network (GEONET) in Japan, and Plate Boundary Observatory (PBO) stations in the coterminous U.S. Using a novel wavelet coherence detection technique, we were able to identify three different wave trains in the measurements collected from the nearest GPS station to the meteor impact site, with frequencies of approximately 4.0–7.8 mHz, 1.0 −2.5 mHz, and 2.7–11 mHz at 03:30 UTC. We estimated the speed and direction of arrival of the total electron content (TEC) disturbances by cross-correlating TEC time series for every pair of stations in several areas of the GEONET and PBO networks. The results may be characterized as three different types of traveling ionospheric disturbances (TIDs). First, the higher-frequency (4.0–7.8 mHz) disturbances were observed around the station ARTU in Arti, Russia (56.43°N, 58.56°E), with an estimated mean propagation speed of about 862 ± 65 m/s (with 95% confidence interval). Another type of TID disturbance related to the wave trains was identified in the lower frequency band (1.0–2.5 mHz), propagating with a mean speed of 362 ± 23 m/s. The lower frequency ionospheric perturbations were observed at distances of 300–1500 km away from Chelyabinsk. The third type of TID wave train was identified using the PBO stations in the relative short-period range of 1.5–6 min (2.7–11 mHz) with a mean propagation speed of 733 ± 36 m/s. The observed short-period ionospheric perturbations in the U.S. region is, to the best of our knowledge, the first observational evidence of the coincident the long-range meteor-generated infrasound signals propagating in the ionosphere.

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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations32
Published2014
Admission routes1
Has abstractyes

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