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Record W4321596685 · doi:10.9734/jerr/2023/v24i5815

A Study on Laboratory Type Paper Machine Using Nano Fibrillated Cellulose from Recycled Old Corrugated Containerboard as Bio Additive in Board Production

2023· article· en· W4321596685 on OpenAlexaboutno aff
Klaus Dölle, Jacob Darius, Rodrigo Castillo Medina, Owen Henkler, Jonathan F. Day

Bibliographic record

VenueJournal of Engineering Research and Reports · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFiller (materials)Raw materialCalcium carbonateCelluloseMaterials sciencePulp and paper industryWaste managementComposite materialEngineeringChemical engineeringChemistry

Abstract

fetched live from OpenAlex

Raw material, energy, water, and additive cost are challenges for today’s board manufacturing and new sustainable solutions are needed to produce paper products with an favorable environmental footprint. A laboratory Fourdrinier paper machine study manufactured a board product with a targeted basis weight of 80 g/m² without and with the addition of ground calcium carbonate at a targeted filler level of 10%. Nano fibrillated cellulose produced from recycled old corrugated containerboard with a Valley Beater at a Canadian Standard Freeness level of 40 ml was added at 4% based on oven dry basis weight. Results revealed an increased ash and fine retention as well as an increased burst Index, short span compression strength, and tear index for the base paper as well as with and without ground calcium carbonate addition.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.039
GPT teacher head0.338
Teacher spread0.299 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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