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Record W2150747885 · doi:10.1785/0120100010

Microseismic Noise from Large Ice-Covered Lakes?

2012· article· en· W2150747885 on OpenAlexaffabout
Yu Jeffrey Gu, Luyi Shen

Bibliographic record

VenueBulletin of the Seismological Society of America · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Alberta
FundersNational Science Council
KeywordsMicroseismGeologySeismologyNoise (video)OceanographyComputer science

Abstract

fetched live from OpenAlex

This study examines the background seismic noise in the southern Western Canadian Sedimentary Basin (WCSB) using broadband seismic records from Canadian Rockies and Alberta Network (CRANE) and Canadian National Seismograph Network (CNSN). The cross‐correlations of vertical‐component data reveal highly asymmetric Rayleigh wave signals in the frequency range of 0.02–0.2 Hz. Travel‐time and waveform source migration calculations jointly suggest a persistent noise source near Lesser Slave Lake (LSL), a large ice‐covered lake in Alberta, Canada, during winter months. The source origin remains unclear, though the gravity current and turbulence induced by laterally varying luminosity, ice thickness, lake depth, and lake‐bottom topography could contribute to the observed microseismic signal. Seasonal variations in regional wind energy, ground attenuation, and local industrial and/or recreational activities may also affect the clarity and asymmetry of noise‐correlation functions.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.011
GPT teacher head0.200
Teacher spread0.190 · 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 designNot applicable
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

Citations9
Published2012
Admission routes2
Has abstractyes

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