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Record W1965741647 · doi:10.1520/jai12940

Effects of Proposed Well-Graded Aggregate Gradations on Frost Durability of Concrete

2005· article· en· W1965741647 on OpenAlexaff
Ufuk Dilek, ML Leming

Bibliographic record

VenueJournal of ASTM International · 2005
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsDurabilityFrost (temperature)Aggregate (composite)Materials scienceGeotechnical engineeringComposite materialEnvironmental scienceStructural engineeringForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract More stringent gradation requirements than found in ASTM C 33 are sometimes specified to provide a more well-graded particle size distribution, presumably reducing voids, improving workability, thereby reducing water demand, and requiring less cement. Little information is available regarding the effects of gradation on frost durability. A two-phase study examined the effects of selected aggregate gradations on water demand, selected mechanical properties, and deicer salt scaling resistance. In the first phase, the effects of the “8-to-18” combined aggregate gradation, using crushed stone and natural sand, were examined. In the second phase, the effects of fine aggregate gradation were examined for manufactured sands with different angularities. This study found that water demand and deicer salt scaling were both compromised using the “8-to-18” approach. Little, if any, real difference in water demand and scaling resistance between well-graded and uniformly graded manufactured sands was found, however, angularity did have a significant effect.

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 score0.009
Threshold uncertainty score0.656

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)0.0010.000

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 head0.008
GPT teacher head0.246
Teacher spread0.239 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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