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Dolomitic filler in self-compacting concrete: a review

2020· review· en· W3110196647 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRILEM Technical Letters · 2020
Typereview
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsFinenessFiller (materials)Compatibility (geochemistry)DurabilityMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The utilization of fine powders as fillers in self-compacting concrete (SCC) application is widespread, particularly in Europe. The incorporation of these fillers to attain the self-compatibility properties of SCC seems to be cheaper than the use of chemical admixtures. Among the wide range of potential fillers, dolomitic powders, particularly generated as by-products from quarry’s processing, are locally available and can be used to produce SCC. Few studies have shown that dolomitic powders can be incorporated in the SCC’s mix design, resulting in acceptable fresh and hardened properties of SCC. The particle size distribution and fineness of the dolomitic powder as well as the level of addition are the key factors affecting those properties. The influence of the chemical nature of the dolomitic powder on the properties of SCC, particularly the durability (e.g. alkali-carbonate reaction), is yet to be investigated. Furthermore, more efforts are still required to investigate the use of dolomitic by-products in the production of SCC.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.313
Teacher spread0.280 · 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