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Record W7001901843

Linseed oil-based concrete surface treatment for building and highway structures in Hong Kong

2010· article· en· W7001901843 on OpenAlexaboutno aff

Bibliographic record

VenueThe HKU Scholars Hub (University of Hong Kong) · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101LiquationDiafiltrationGestational periodFusible alloy
DOInot available

Abstract

fetched live from OpenAlex

This experimental program investigated the effectiveness of concrete surface treatment using four Canadian linseed oil-based sealants on concrete specimens made from G30/20 and G45/20 concretes, which are typically used for building and highway structures in Hong Kong. The tests were conducted at the University of Hong Kong and the University of Manitoba independently, using samples cast from the same mixes in Hong Kong. A total of more than 500 specimens were tested in each university for salt spray resistance, carbonation, bond strength, dripping and ultra-violet weathering. The results show that the four sealants were capable of penetrating through vertical, upward horizontal and downward horizontal concrete surfaces up to depths of 2.5 mm. All the four sealants significantly enhanced the resistance of G30/20 concrete against salt spray attack and carbonation. The resistance of G45/20 concrete to salt spray was also increased. Up to a duration limit of 500 hours of quick ultra-violet weathering, the treated specimens still demonstrated higher resistance against salt spray attack and carbonation than the untreated ones. However, no conclusion can be made regarding the effect on carbonation resistance of G45/20 concrete, since both the treated and untreated specimens showed no signs of carbonation after the test.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 designObservational
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 routes1
Has abstractyes

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