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Record W3195373155 · doi:10.1021/acsomega.1c02933

Non-Wood Fibers: Relationships of Fiber Properties with Pulp Properties

2021· article· en· W3195373155 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

VenueACS Omega · 2021
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of New Brunswick
FundersBangladesh Council of Scientific and Industrial Research
KeywordsPapermakingPulp (tooth)Kappa numberUltimate tensile strengthLigninComposite materialMaterials scienceTear resistancePulp and paper industryCelluloseKraft processMathematicsChemistryKraft paperDentistryOrganic chemistryEngineeringMedicine

Abstract

fetched live from OpenAlex

In this investigation, the relationship between fiber properties and papermaking properties of 22 non-wood materials at the unrefined and refined states was assessed. The fiber length had positive and the cell wall thickness had negative correlation on the strength properties for the refined pulp. The relationship between papermaking properties with pulp quality, such as fines, curl index, kink index, external fibrillation, and coarseness, was also determined. The correlations of multiple regression equations of fiber quality parameters were 70.4% for the tensile index and 84.9% for the tear index for the refined pulp. The correlations of multiple regression equations of chemical characteristics of the samples were 81.9% for the pulp yield and 42.7% for the kappa number. Holocellulose and α-cellulose had a positive and lignin had a negative effect on the pulp yield.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.056
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.031
GPT teacher head0.189
Teacher spread0.158 · 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