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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 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.083
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
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