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Record W2904372252 · doi:10.1190/igc2018-311

Case study on coda duration magnitudes calibration with the use of local seismological networks

2018· article· en· W2904372252 on OpenAlexaff
Sergey Yaskevich, П. А. Дергач, Anton Duchkov, A. V. Myasnikov

Bibliographic record

VenueInternational Geophysical Conference, Beijing, China, 24-27 April 2018 · 2018
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsCodaDuration (music)CalibrationSeismologyGeologyStatisticsMathematicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

SUMMARY In microseismic monitoring applications weak earthquakes are observed. Their magnitude assessments are used for the released energy analysis and for their scaling in terms of this energy. Different approaches are applied now in industry, but the assessed with borehole monitoring system magnitudes are seldom approved with conventional seismological observations. We here show the case study for downhole magnitudes calibration with magnitudes assessed by on-land seismological equipment. Wave coda length based magnitude formula is used and calibrated in the paper. Calibration methodology and approach choice justification are presented in a paper.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.053
GPT teacher head0.264
Teacher spread0.211 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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