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Record W7083299862 · doi:10.17632/vjtsx5w882.1

EXCAV-CLAY/15/2830 database

2025· dataset· en· W7083299862 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationSheet pileSlabPrecast concretePileSettlement (finance)Table (database)

Abstract

fetched live from OpenAlex

A total of 332 case histories (332 sites) of excavations in clays are collected in the EXCAV-CLAY/15/2830 database, covering 20 countries/regions worldwide, including Austria, Brazil, Canada, China, Egypt, France, Germany, Hong Kong, Indonesia, Italy, Japan, Norway, Portugal, Singapore, South Africa, Switzerland, Taiwan, Thailand, United Kingdom (UK), and United States (US) (see Table S1 in Supplementary Material). Most sites are located in Taipei, Taiwan (71% of the total sites). Each site usually consists of multiple instrumentations (inclinometer and settlement measurement) with multiple excavation stages. The database contains a total of 2830 excavation stages. The types of retaining wall system in the EXCAV-CLAY/15/2830 database consist of concrete diaphragm wall (DW), contiguous pile wall (CPW), secant pile wall (SPW), and sheet piles (SP), whereas the construction methods consist of bottom-up method (BU, temporary steel strut as lateral support) and top-down method (TD, concrete floor slab as lateral support). According to the type of movement-control measure used in each site, the sites in the database are classified into sites with ground improvement (GI sites), sites with buttress walls (BW sites), sites with cross-walls (CW sites), and sites without movement-control measures (NR sites, denoting “no reinforcement”). Some sites are hybrid, e.g., combining both BW and CW. The GI sites collected in this database are with “full improvement” that span across the excavation area, which is usually achieved by a series of overlapping improvement piles, resulting in a “continuous” base slab (e.g., jet grout slab) over a certain range of depth. The basic information for the case histories, including excavation dimensions, field measurements, soil and structural parameters, and system stiffnesses, is included in Supplementary Material (see Table S1).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.253
Teacher spread0.232 · 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
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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