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Record W2326258331 · doi:10.1061/41095(365)130

Optimum MASW Survey—Revisit after a Decade of Use

2010· article· en· W2326258331 on OpenAlexaff
Choon B. Park, Mario Carnevale

Bibliographic record

VenueGeoFlorida 2010 · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsOffset (computer science)WavelengthGeologyPhysicsOpticsComputer science

Abstract

fetched live from OpenAlex

As an attempt to study systematically on the optimum source offset— the distance between source and the closest receiver— and total receiver spread length with the MASW method, we present our observations with modeling and field data sets indicating that the most accurate analysis of phase velocities can be accomplished only for wavelengths up to one spread length, and subsequent analysis for the longer wavelengths inevitably involves a certain degee of fluctuating inaccuracy that seems to originate from the Gibbs—phenomenon of Fourier transformation. The inaccuracy, however, seems to be within five percentfor those wavelengths shorter than twice the spread length. Also, results from the field data study suggest that importance of the source offset has been previously underestimated and the maximum wavelength can be extended simply by extending the source offset. In addition, they showedthat phase velocities tend to be underestimated if the source offset is smaller than one spread length. The degree of underestimation, however, appears highly site dependent and sometimes becomes negligible even if the source offsetis as short as only one receiver spacing.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Citations66
Published2010
Admission routes1
Has abstractyes

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Same venueGeoFlorida 2010Same topicSeismic Waves and AnalysisFrench-language works237,207