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Record W2794340960 · doi:10.1139/cgj-2017-0537

Generic transformation models for some intact rock properties

2018· article· en· W2794340960 on OpenAlexvenueno aff
Jianye Ching, Kuang-Hao Li, Kok‐Kwang Phoon, Meng‐Chia Weng

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsTransformation (genetics)Metamorphic rockIgneous rockGeologySedimentary rockGeotechnical engineeringDatabasePetrologyComputer scienceGeochemistry

Abstract

fetched live from OpenAlex

A global intact rock database of nine parameters, including uniaxial compressive strength and Young’s modulus, is compiled from 184 studies. This database, labeled as “ROCK/9/4069”, consists of 27.5% igneous rock, 59.4% sedimentary rock, and 13.1% metamorphic rock. The vast majority (>95%) of intact rocks in the database are in their natural moisture contents. About 14% of the data points are for weathered rocks and about 4% are foliated metamorphic rock. It is found that most existing transformation models are data-specific or site-specific in the sense that they fit well to their own calibration databases, but do not necessarily fit well to ROCK/9/4069. One can infer that transformation models for intact rocks are more data–site dependent than those for soils. It is evident that ROCK/9/4069 has coverage wider than most existing transformation models. The ROCK/9/4069 database is then adopted to calibrate the bias and variability of existing transformation models. Transformation models with relatively large application ranges and relatively small transformation uncertainties are selected as generic transformation models. These generic models can be valuable for scenarios where site-specific models are not available, e.g., construction projects with insufficient budget or the preliminary design stage of a project.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.206
Teacher spread0.175 · 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.

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

Citations37
Published2018
Admission routes1
Has abstractyes

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