MétaCan
Menu
Back to cohort

Development of novel building composites based on hemp and multi-functional silica matrix

2018· article· en· W2888754085 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

VenueComposites Part B Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
FundersEngineering and Physical Sciences Research CouncilHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsX-ray photoelectron spectroscopyComposite materialMaterials scienceScanning electron microscopeMatrix (chemical analysis)AdhesionChemical engineering

Abstract

fetched live from OpenAlex

This study focuses on the development of novel bio-composites using a silica matrix that provides dual functionality: as a hydrophobic surface treatment and as a binder for hemp-shiv. The hydrophilic nature of hemp shiv, a plant based aggregate, results in composites having poor interfacial adhesion, weak mechanical properties and long drying times. In this work, sol-gel process has been utilised to manufacture durable low density hemp based composites. Morphological characterisation by scanning electron microscopy (SEM) showed that hemp shiv was embedded well in the matrix. Detailed chemical analysis using x-ray photoelectron spectroscopy (XPS) and gas chromatography-mass spectrometry (GC-MS) indicate the presence of water soluble and ethanol soluble extractives leached from the hemp shiv which are incorporated into the silica matrix inducing the binding effect. The composites were water resistant and showed good mechanical performance having the potential to develop novel thermal insulation building materials.

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 categoriesMeta-epidemiology (narrow)
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 score0.292
Threshold uncertainty score1.000

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 head0.022
GPT teacher head0.254
Teacher spread0.232 · 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