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Record W2986610368 · doi:10.1130/abs/2019am-333453

GROUND MOTION ESTIMATION USING SITE RESPONSE ANALYSIS FOR DEEP SEDIMENTARY DEPOSITS

2019· article· en· W2986610368 on OpenAlexafffund
Md. Zillur Rahman, Sumi Siddiqua, ASM Maksud Kamal, Mir Fazlul Karim, Nazrul I. Khandaker

Bibliographic record

VenueAbstracts with programs - Geological Society of America · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Research Institute for Earth Science and Disaster PreventionUniversity of DhakaUniversity of British Columbia
KeywordsGeologyGround motionSedimentary rockResponse analysisRemote sensingMining engineeringSeismologyGeochemistryEngineering

Abstract

fetched live from OpenAlex

▪ The long period seismic waves of the far-field earthquakes will match with the natural periods of the high-rise buildings, and resonance will occur.▪ The damage to high-rise buildings will be increased.▪ The high-rise buildings on the deep and soft sedimentary deposits of the study area are potentially vulnerable to farfield large earthquakes.▪ For deep and soft sedimentary deposits, non-linear site response analysis should be performed. C o n c l u s i o n s

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.243
Teacher spread0.231 · 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 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
Published2019
Admission routes2
Has abstractyes

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