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Record W1519598525 · doi:10.15376/biores.5.4.2625-2639

Presence of hardwood chips and its impact on pulp strength properties in the production of bleached softwood kraft pulp

2010· article· en· W1519598525 on OpenAlexaff
Zhibin He, Yonghao Ni, Yajun Zhou

Bibliographic record

VenueBioResources · 2010
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsTembecUniversity of New Brunswick
Fundersnot available
KeywordsSoftwoodHardwoodPulp (tooth)Kraft paperKraft processPulp and paper industryKappa numberUltimate tensile strengthMaterials scienceComposite materialHemicelluloseLigninChemistryBotanyEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of hardwood admixture (15-25% birch or aspen) in kraft cooking on the strength properties of the fully bleached pulp was investigated. Results obtained from both lab- and mill-processed ECF bleached pulps showed that adding 15-25% birch or aspen to the production of fully bleached softwood kraft pulp had a minor effect on the strength properties. No significant effect was observed for the hardwood admixture on the apparent density over a wide range of breaking length. Under the conditions studied, the results showed that pulping of mixed softwood/hardwood chips (chip blending) resulted in overall better strength properties than the pulp blending at a given freeness. It was hypothesized that the softwood fibers would be cooked to a higher kappa number in the cooking of mixed softwood/hardwood chips for the same target kappa number, thus having higher fiber strength due to higher pulp viscosity and preservation of the hemicellulose. This was supported by the results from zero-span tensile strength of the long fiber fraction of the samples from chip blending and pulp blending. The implication is that some softwood kraft pulp mills can add up to 25% of hardwood chips to the kraft cooking of softwood chips without significantly affecting the overall pulp strength properties.

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 score0.295

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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