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Record W2317128274 · doi:10.5897/sre.9000180

Analysis of dam deformation measurements with the robust and non-robust methods

2010· article· en· W2317128274 on OpenAlexaboutno aff
Levent Taccedil

Bibliographic record

VenueScientific Research and Essays · 2010
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsDeformation (meteorology)CrestGeodesyDeformation monitoringGeologyGlobal Positioning SystemResearch ObjectGeotechnical engineeringComputer scienceGeographyOceanographyTelecommunications

Abstract

fetched live from OpenAlex

Rapid developments in engineering structures and the growing interest in studying the earth crust movements, the analysis of deformation measurements, measurement methods and precision has revealed new demands. The purpose of this study is to determine the deformations that take place on dam crest due to different water level, water load and dam’s body weight. AltA±nkaya Dam, which is a rock fill dam, was selected as application area and a deformation network consisting of 6 reference and 11 object points was constructed. In this study, deformation measurements were performed between 2000 and 2002. Measurements were made every June and September in that the water level was minimum and maximum, respectively. Hence, measurements were made in 4 periods. All measurements were performed using static GPS measurement method. In this study, Iterative weighted transformation (IWST), Least Absolute Sum (LAS), Congruency test analysis method and Fredericton was used for performing two dimensional deformation analyses.   Key words: GPS, dam, deformation, analysis, congruency test, IWST, LAS, Fredericton.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.101
GPT teacher head0.349
Teacher spread0.248 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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