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Record W2028405252 · doi:10.1155/2010/876852

Laboratory Characterization and Influence of Mineralogy and Grading on the Performance of Treated and Untreated Granular Materials Used as Surface Pavements in Unpaved Road

2010· article· en· W2028405252 on OpenAlexafffund
Luc Beaulieu, P. Pierre, Jean-Pascal Bilodeau

Bibliographic record

VenueAdvances in Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGneissBrineGeologyCementMineralogyGeotechnical engineeringMaterials scienceGrading (engineering)Compressive strengthComposite materialGeochemistryChemistryEngineeringMetamorphic rockCivil engineering

Abstract

fetched live from OpenAlex

This paper presents the results of a laboratory study on the influence of grading and granular mineralogy sources (granitic gneiss, basalt and limestone) on the performance of treated and untreated granular materials used as surface pavements in unpaved roads. Results of bearing capacity, resilient modulus and compressive strength tests are discussed. Natural brine, calcium chloride, cement and a polymer emulsion treated samples were tested and compared to untreated samples. The combinations of product, mineralogy and grading showing great performance are finally pointed out.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.551

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 communication0.0000.001
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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes2
Has abstractyes

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