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Record W2540868985 · doi:10.1520/acem20150025

Investigation of the Effectiveness of Surfactants with Different Partition Coefficients to Entrain Air in Cement Paste

2016· article· en· W2540868985 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.

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

Bibliographic record

VenueAdvances in Civil Engineering Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsStillwater (Canada)
Fundersnot available
KeywordsPulmonary surfactantCementitiousAir entrainmentMaterials sciencePortland cementCementPartition coefficientChemical engineeringComposite materialChromatographyChemistry

Abstract

fetched live from OpenAlex

Abstract This paper outlines a modified version of ASTM C311/C311M-13 that was applied in the investigation of the effectiveness of different surfactants as air entraining agents (AEAs) in portland cement paste. First, the architecture of surfactant molecules was investigated with respect to their effectiveness as air entraining agents. The work was then generalized to the use of the partition coefficient (log P), a single numerical descriptor of the ratio of hydrophobicity and hydrophilicity of the molecule, to predict the ability to entrain air in concrete. This prediction model showed agreement for all of the surfactants investigated. Ranges of log P are given for satisfactory performance as well as the values that provide the highest volume of air per dosage of surfactant. These findings can help research aimed at admixture development, allow admixture investigation in different cementitious systems, and can provide insights into admixture interactions.

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)0.000
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 score0.116
Threshold uncertainty score0.321

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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