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Record W4389049901 · doi:10.9734/bpi/cras/v5/6649d

Historical Developments of Flax Industry for Improving Fibers Quality and Fabrics Properties

2020· book-chapter· en· W4389049901 on OpenAlexaboutno aff
Hassan Moawad, Wafaa M. Abd El‐Rahim, Gebreil M. M. Gebreil, Mohamed Hashem, Mohamed Zakaria

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsRettingBast fibreLinumLigninFiberCellulosePectinSteam explosionLinseed oilCellulose fiberPulp and paper industryMaterials scienceChemistryPolymer scienceComposite materialFood scienceBotanyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Flax (Linum usitatissimu) is one of the main crops in Canada for the production of linseed oil, which is used in food and chemical industries. The seeds are crushed to make linseed oil, and the remaining cake is used for fodder. Moreover, the boiling of seed oil is used in making paints, varnish and printing ink. The degradation of flax fibers is a crucial aspect in the development of natural fiber.  Cellulose  controls  the major  degradation  behavior  of  flax  fibers. Retting of flax is the separation of fibers and fiber bundles from non-fiber tissues in the stems. Bast fibers are processed by various means that may include retting, breaking, scutching, hackling, and combing. In order to bleach the flax and to keep the fiber tenacity high enough it is necessary to remove the lignin and partially to preserve the pectin. The problem of the classical hydrolyzing treatment with alkalis and oxidizers is due to the effect of these chemicals not only on the pectin and the lignin but also the cellulose itself resulting on the drastically decrease the material strength

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.488
Threshold uncertainty score0.698

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)0.0010.000

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 head0.163
GPT teacher head0.299
Teacher spread0.137 · 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
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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