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Record W2984532473 · doi:10.1515/npprj-2019-0076

Insight into fractionation performance of American old corrugated containers pulp in pressure screening

2019· article· en· W2984532473 on OpenAlexaff
Hui Cai, Hui Zhang, Wenyuan Zhu, Guolin Tong, Zhaoyang Yuan

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of British Columbia
FundersMajor Science and Technology Program for Water Pollution Control and Treatment
KeywordsAperture (computer memory)FractionationAnalytical Chemistry (journal)MathematicsMaterials sciencePulp (tooth)GeometryPhysicsOpticsCombinatoricsChemistryChromatographyAcoustics

Abstract

fetched live from OpenAlex

Abstract In this work, the American Old Corrugated Containers (OCC) pulp was screened using pressure screen to investigate the factors affecting the fractionation performance. Three screen cylinders with different geometries, aperture size/contour heights, volumetric reject ratios, and aperture velocities were used. The results showed that the passing ratio of fibres increased with the increase of contour height, volumetric reject ratio, and aperture velocity for all the three screen cylinders. Moreover, among the three cylinders investigated, the 0.81 mm hole cylinder achieved the highest fractionation efficiency with a volumetric reject ratio of 0.1 followed by 0.15 and 0.2 mm slot screens. Freeness change of the accept side of the screen was similar to passage ratio, which shows an increasing trend with the increasing aperture velocity and volumetric reject ratio. However, the reject freeness showed decreasing trends with the increasing aperture velocity V s {V_{s}} and volumetric reject ratio R v {R_{v}} .

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.023
GPT teacher head0.290
Teacher spread0.267 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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