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Record W195278875 · doi:10.17226/22431

Non-Nuclear Methods for Compaction Control of Unbound Materials

2014· book· en· W195278875 on OpenAlexaboutno aff
Munir D. Nazzal

Bibliographic record

VenueTransportation Research Board eBooks · 2014
Typebook
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCompactionNuclear materialControl (management)Aggregate (composite)EngineeringForensic engineeringTransport engineeringCivil engineeringWaste managementGeotechnical engineeringMaterials scienceComputer scienceNanotechnologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Proper compaction of unbound materials, such as soils, aggregate, and recycled materials, is a critical component in the performance of highway pavements and embankments. The most commonly used device to test for proper compaction is the nuclear density gauge. However, due to the costs associated with regulatory compliance and radiation safety training, there is an increased effort to find acceptable non-nuclear devices. This synthesis documents information on national and international experience with non-nuclear devices and methods for measuring compaction of unbound materials. Information used in this study was gathered through a literature review, a survey of state departments of transportation (DOTs) and Canadian provincial transportation agencies, and interviews with selected state DOTs.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.005

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 head0.056
GPT teacher head0.398
Teacher spread0.341 · 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
GenreMethods

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

Citations25
Published2014
Admission routes1
Has abstractyes

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