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Record W4400914093 · doi:10.1061/jmcee7.mteng-17448

Thermal Conductivity of Stabilized Loess with Different Types of Lignin

2024· article· en· W4400914093 on OpenAlexaff
Wuyu Zhang, Chaofan Dong, Cheng Lin

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLigninLoessThermal conductivityMaterials scienceComposite materialConductivityGeotechnical engineeringChemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Lignin, an abundant biopolymer derived from plants, is a green binder for stabilizing soil. This study investigates the thermal conductivity of lignin [e.g., sodium ignosulfonate (SL), calcium lignosulfonate (CL), and lignin fiber (LF)] and lignin-stabilized loess. The effects of the source of loess samples, lignin content, curing time, water content, and dry density on the thermal conductivity of the stabilized soils were experimentally evaluated. Furthermore, mineralogy and microstructure of the stabilized loess were investigated using X-ray diffraction, scanning electron microscopy, and mercury intrusion porosimetry tests to provide insights into the mechanisms of lignin-based soil stabilization. The test results showed that lignin had a lower thermal conductivity than water and soil minerals. The addition of lignin reduced the thermal conductivity of loess, with CL and SL causing a slightly greater reduction than LF. Mixing lignin with loess did not generate new crystalline material. The incorporation of CL and SL altered the microstructure of loess, resulting in a densely packed structure with distinct particle bonds and intra-aggregate pores. In contrast, LF provide microscale reinforcement for the soil particles, presenting a loose structure with dominant interaggregate pores.

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 score0.232
Threshold uncertainty score0.363

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 head0.200
Teacher spread0.191 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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