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Record W6943916693 · doi:10.17603/ds2-hfdm-bp53

RPI-A and RPI-B - Rensselaer Polytechnic Institute Experiments

2021· dataset· en· W6943916693 on OpenAlexaboutno aff

Bibliographic record

VenueTexas Advanced Computing Center · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeLiquefactionGround motionSection (typography)Range (aeronautics)ScalingTest (biology)

Abstract

fetched live from OpenAlex

Building upon the database of LEAP-UCD-2017, twenty centrifuge model tests of liquefaction and lateral spreading are shared and compared in this archive. Some of the experiments were conducted according to the conventional centrifuge scaling laws; others were conducted according to a generalized scaling law. Please see the general report section of the published project for an overview comparison and background of all of the experiments. The comparisons presented in the general report section will serve as an index to help the users find individual experiments of interest. This data from the model tests is published as ten separate experiments in this archive (one experiment per centrifuge facility). Each "experiment" includes one or more model tests and each model test includes between one and three destructive shaking events. All of the tests modeled a 4 m thick deposit of Ottawa F-65 sand with a 5-degree surface slope in a rigid box. The tests covered a range of ground motion intensities and a range of relative densities to define the median response and the sensitivity of the response to relative density and shaking intensity. The ten centrifuge facilities involved in this test program included Cambridge University (UK), Ehime University (Japan), IFSTTAR (France), NCU (Taiwan), KAIST (Korea), Kyoto University (Japan), RPI (USA), UC Davis (USA), Tokyo Institute of Technology (Japan) and Zhejiang University (China).

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.029
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0290.067

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 head0.022
GPT teacher head0.311
Teacher spread0.290 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueTexas Advanced Computing CenterFrench-language works237,207