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Record W1974893848 · doi:10.1081/wct-100104225

CHEMICAL AND SODA PULPING PROPERTIES OF KENAF AS A FUNCTION OF GROWTH

2001· article· en· W1974893848 on OpenAlexaff
Saffet Karakus, D. N. Roy, K. Goel

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2001
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKenafHibiscusLigninChemistryPulp and paper industrySoda pulpingChemical compositionPapermakingYield (engineering)Bast fibreBotanyKraft processKraft paperMaterials scienceFiberComposite materialBiologyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Kenaf (Hibiscus canabinus)is a promising non-wood source of fibre for pulping and papermaking. Because the chemical and physical composition of the plant changes as the plant develops, research is needed to determine its pulping properties at various stages of growth in order to establish the optimum harvesting time. In the present work, the chemical composition and pulping properties of kenaf as a function of growth have been studied. Kenaf plants were harvested at the end of 90, 120, 150, and 200 days (maturity). Extractive-free ground samples of the stem were cooked at three different temperatures, 140, 155, and 170°C, using soda cooking liquor of 32 g/L NaOH, with an active alkali charge of 15% as Na2O, and a liquor-to-wood ratio of 6:1. The differences in the holocellulose and lignin content for 90, 120, and 150-day old kenaf were not significant, while 200-day-old kenaf was significantly different from others. Pulping of kenaf at various stages of growth indicated that soda pulping properties were not significant. In comparing the yields of kenaf pulps, it is observed that; over the whole range of cooking times and temperatures studied, the average yield for 150-day-old kenaf is highest at 60.4%. Kenaf can be harvested at the end of 150-day growth period, based on the results of chemical analyses to achieve higher yield, with lower lignin content.

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 score0.003
Threshold uncertainty score0.231

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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

Citations6
Published2001
Admission routes1
Has abstractyes

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