MétaCan
Menu
Back to cohort
Record W2805822592 · doi:10.1515/npprj-2018-3006

Investigation of low consistency reject refining of mechanical pulp for energy savings

2018· article· en· W2805822592 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPulp (tooth)SoftwoodPulp and paper industryConsistency (knowledge bases)Specific energyKappa numberLow energyHardwoodPapermakingProcess engineeringMaterials scienceMathematicsComposite materialKraft processKraft paperEngineeringBotanyDentistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract In this study, the effects of low consistency refining (LCR) energy and intensity on mechanical pulp properties have been studied for three different types of reject pulps (softwood TMP, softwood CTMP and hardwood CTMP), which were refined at varying intensity. Resulting pulp properties have been compared with high consistency refining (HCR) of the same reject pulps. For all furnish types, it was shown that LCR can develop pulp properties matching those developed through HCR with significantly less energy. The resulting pulp properties were found to be affected not only by refining intensity and energy, but also by initial fibre morphology. Pilot LCR trials demonstrated that high freeness reject pulp is initially insensitive to refining intensity as specific energy is applied. This enables the first stage of LCR to be carried out at a higher specific energy and intensity, which can reduce the number of stages of LCR required to reach a target quality. This work shows that low intensity LCR is capable of achieving the same tensile index as HCR pulp at a target freeness of 200 ml CSF.

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.

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)0.008
metaresearch head score (Gemma)0.007
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 score0.028
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.375
Teacher spread0.282 · 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