MétaCan
Menu
Back to cohort
Record W3015742396 · doi:10.1021/acsomega.0c00709

Preparation of a Composite Calcium Silicate Board with Carbide Slag and Coal-Based Solid Waste Activated by Different Alkali Activators

2020· article· en· W3015742396 on OpenAlexaff
Dingyi Wei, Cuifeng Du, Lianfu Zhang, Yanxin Li, Lei Ba

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of China
KeywordsMaterials scienceFlexural strengthCalcium silicate hydrateAlkali metalRaw materialComposite numberGeopolymerCalcium silicateWaste managementCompressive strengthSilicon carbideCementComposite materialMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Overall performance of composite calcium silicate boards (CCSBs) was investigated to further promote their application. The alkali activators were used to fully hydrate the calcium and silicon raw materials, which further improved the comprehensive performance of the CCSBs made of four pure industrial solid wastes. Within the range of dosage in this study, single doping of different proportions of the alkali activator improved the flexural strength of the CCSB. Based on this, the mechanical properties of the CCSB were further improved as the compounded alkali activator was optimized. Flexural strength is improved when the average pore diameter was refined. The freeze-thaw cycle test shows that a compound-doped alkali activator can effectively reduce the mass loss and strength loss, thereby improving the frost resistance of this material. This research discussed an economically affordable approach to prepare the CCSB material made of industrial solid waste.

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 score0.012
Threshold uncertainty score0.521

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)0.0000.000

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 head0.016
GPT teacher head0.266
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

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

Citations10
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueACS OmegaSame topicConcrete and Cement Materials ResearchFrench-language works237,207