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Record W4225144433 · doi:10.5772/intechopen.103093

Extraction, Applications and Characterization of Plant Fibers

2022· book-chapter· en· W4225144433 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

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsNova Scotia Community CollegeAcadia University
Fundersnot available
KeywordsBast fibreCharacterization (materials science)ViscoelasticityMaterials scienceUltimate tensile strengthComposite materialNanotechnology

Abstract

fetched live from OpenAlex

During the second half of the twentieth century, industrial and scientific interests in plant fibers (PFs) have resulted in their resounding comeback as engineering materials. This chapter is concerned with the characterization of PF materials. Good knowledge of the properties of these materials is essential for safe design of the related structures. Bast fibers that are collected from the phloem surrounding the stem of certain dicotyledonous plants, for instance, are among the most used, owing to their higher tensile strength. However, for an optimum utilization of PFs, a relevant assessment of their physico-chemical and mechanical properties is very crucial. As it is now well established, PFs’ properties are largely influenced by their hierarchic composite microstructure and their viscoelastic behavior. This book chapter focuses on the presentation of various experimental approaches used to characterize the elastic and viscoelastic behaviors of plant fibers. Consideration of their blending in sheet form and relevant mechanical properties will also be of interest.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.751
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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