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Record W4283267114 · doi:10.1785/0220220009

Seismic and Infrasound Data Recorded at Regional Seismoacoustic Research Arrays in South Korea from the Six DPRK Underground Nuclear Explosions

2022· article· en· W4283267114 on OpenAlexaff
Brian W. Stump, C. Hayward, Paul Golden, Junghyun Park, Ray Kubacki, Chris Cain, Stephen Arrowsmith, Mihan H. McKenna, SeongJu Jeong, Tina Ivey, M. D. MacPhail, Cathy Chickering Pace, Jeong‐Soo Jeon, Il‐Young Che, Kwangsu Kim, Byung‐Il Kim, Tae Sung Kim, In-Cheol Shin, Myung‐Soon Jun

Bibliographic record

VenueSeismological Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInfrasoundSeismologyGeologySeismometerBroadbandAcousticsTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Five seismoacoustic research arrays and one infrasound research array located across the southern Korean peninsula have been installed, maintained, and are cooperatively operated by Southern Methodist University and Korea Institute of Geoscience and Mineral Resources. The seismoacousitc arrays are each composed of 1–5 broadband seismometers spaced from 0.5 to 1.5 km and 4–16 infrasound sensors spaced from 0.1 to 1.5 km. The arrays—BRDAR, CHNAR, KSGAR, KMPAR, TJIAR, and YPDAR—have recorded regional seismic and infrasound signals from the six underground nuclear explosions conducted by Democratic People’s Republic of Korea. These seismoacoustic data are being made available for researchers interested in studying and quantifying the explosion source functions of these events as well as wave propagation effects in the solid earth and atmosphere as constrained by seismic and infrasound observations at regional distances.

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 score0.011
Threshold uncertainty score0.021

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)0.0010.000

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 head0.217
GPT teacher head0.335
Teacher spread0.118 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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