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Record W2990185662 · doi:10.15376/biores.15.1.347-359

Comparison of two bar edge lengths of refining plates on the properties of American old corrugated container pulp during low consistency refining

2019· article· en· W2990185662 on OpenAlexaff
Hui Cai, Zhaoyang Yuan, Guolin Tong, Xin Zhang, Hui Zhang

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersMajor Science and Technology Program for Water Pollution Control and TreatmentPriority Academic Program Development of Jiangsu Higher Education Institutions
KeywordsPulp (tooth)Ultimate tensile strengthMaterials scienceRefining (metallurgy)Composite materialMetallurgy

Abstract

fetched live from OpenAlex

Commercial American old corrugated container pulp (AOCC) boards were used as the starting material for repulping and low consistency (LC) refining to investigate the effects of LC refining plate geometry on the fibre and strength properties of the generated pulp. Specific refining energy that ranged from 0 to 120 kWh/air-dried ton (ADT) of pulp was explored for the two bar edge lengths (BEL) of refining plate patterns, wide 0.99 km/rev BEL and interim 2.01 km/rev BEL. The results showed that fibre length, curl index, kink index, and pulp freeness decreased while fines content increased with increasing refining energy input for both of the two refining plates during LC refining. LC refining with 2.01 BEL plate at low intensity of 0.24 J/m showed less fibre length reduction and more tensile strength development than that obtained with 0.99 BEL plate at high intensity of 0.48 J/m when compared at the same specific energy. Moreover, to achieve the desired tensile strength of the AOCC pulp, lower energy was needed for the interim 2.01 BEL plate than the wide 0.99 BEL plate. Thus, the interim 2.01 km/rev BEL refining plate provided potential for saving refining energy to reach a desired tensile strength of AOCC pulp.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.050
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.301
Teacher spread0.260 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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