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Record W2319846545 · doi:10.1515/hf.2010.108

Energy saving potential of high yield pulp (HYP) application by addition of small amounts of bleached wheat straw pulp

2010· article· en· W2319846545 on OpenAlexaff
Hongjie Zhang, Zhirun Yuan, Yonghao Ni

Bibliographic record

VenueHolzforschung · 2010
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New BrunswickFPInnovations
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsPulp (tooth)Pulp and paper industryStrawKraft processKappa numberKraft paperChemistrySpecific energyRefining (metallurgy)HardwoodMaterials scienceEngineeringBotany

Abstract

fetched live from OpenAlex

Abstract High-yield pulp (HYP) has found wide applications in many paper grades. Usually, the strength properties of HYP must be improved and its freeness fine-tuned before sending it for paper machining, by means of refining at a low consistency, which requires energy. In this study, the possibility of avoiding refining of HYP was investigated by adding low percentages of refined and bleached wheat straw pulp (BWSP) to a HYP-containing mixture. The results show that the strength properties of a HYP and a hardwood kraft (HWKP) mixture can be improved with approximately 10% refined BWSP. In this manner, refining energy of 20 kWh t -1 is needed, and the pulp quality is improved to a similar level to that obtained from the same pulp mixture refined with an energy input of 70 kWh t -1 . This approach also works for 100% HYP. The practical implication is that only a small percentage of refined BWSP is needed to improve the strength property of a HYP or HYP/HWKP mixtures, so that less refining energy is required in the low-consistency refining process.

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 score0.017
Threshold uncertainty score0.647

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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