MétaCan
Menu
Back to cohort

Disclosing the Mechanical Properties of Green Calcium-Silicate-Hydrates by Statistical Nanoindentation Techniques

2011· article· en· W1998319680 on OpenAlex
Luca Sorelli, Daniel Vallée, J.J. Beaudoin, Nicholas X. Randall

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNanoindentationMaterials scienceStoichiometryCalcium silicate hydrateElastic modulusCreepComposite materialIndentation hardnessModulusCementMineralogyMicrostructureChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In order to reduce CO 2 emissions, the cement industry has developed a new class cements. The Calcium-Silicate-Hydrates (CSH) that form are generally characterized by a low stoichiometric ratio for CaO and SiO 2 . This low C/S ratio affects the C-S-H layer structure and has a significant effect on the mechanical properties. This work exploits a novel statistical nanoindentation technique (SNT) to study the effect of the C/S ratio on the mechanical properties of synthetic CSH. Different CSH types were prepared by varying the C/S ratio of the starting materials. After undertaking a grid nanoindentation approach for each sample, the statistical analysis allowed extracting the mechanical properties, such as elastic modulus, hardness and creep. The results of this preliminary work shed new light on the implications of C-S-H stoichiometry on mechanical properties.

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 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 score0.003
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.092
GPT teacher head0.341
Teacher spread0.249 · 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