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Record W1523937704 · doi:10.15376/biores.6.3.3313-3323

Application of NSSC pulping to sugarcane bagasse

2011· article· en· W1523937704 on OpenAlexaboutno aff
Ahmad Samarıha, Alireza Khakifirooz

Bibliographic record

VenueBioResources · 2011
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsBagassePulp (tooth)Pulp and paper industryLigninKappa numberSodium hydroxideMaterials scienceCelluloseAcetoneComposite materialMathematicsChemistryKraft processKraft paperOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The NSSC pulping process was investigated to produce pulp from bagasse for corrugating board manufacture. The chemical contents including cellulose, lignin, ash, and extractives soluble in alcohol-acetone measured 55.75, 20.5, 1.85, and 3.25, respectively. The average fiber length, fiber diameter, lumen width, and cell wall thickness of bagasse were 1.59 mm, 20.96, 9.72, and 5.64 μm, respectively. The optimum conditions, with a yield of 74.95%, were achieved using 20 percent chemicals on the basis of sodium oxide, cooking temperature of 170 °C, and cooking time of 30 minutes. Pulp was refined to freeness 345 and 433 mL CSF according to Canadian standards. 127 g m-2 handsheets from both pulps were made and strength properties measured. Statistical analysis of results indicated that paper derived from freeness 345 and 433 mL CSF had better strength properties in all indices in comparison with NSSC pulp from hardwoods produced at Mazandaran Pulp and Paper factory, Iran.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.181
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2011
Admission routes1
Has abstractyes

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