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DIGITAL PRINTABILITY OF PAPERS MADE FROM INVASIVE PLANTS AND AGRO-INDUSTRIAL RESIDUES

2020· article· en· W3039731115 on OpenAlexaboutno aff
Igor Karlovits, Urška Kavčič, Gregor Lavrič, Andrej Šinkovec, Vladimir Zorić

Bibliographic record

VenueCellulose Chemistry and Technology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
FundersUrban Innovative ActionsEuropean Regional Development FundEuropean Commission
KeywordsChemistryPulp and paper industryPolymer scienceBotanyEngineering

Abstract

fetched live from OpenAlex

The use of an alternative fibre source for papermaking encourages more circular material streams with a focus on local harvesting and a lesser demand for large-scale pulping. The changes in the printing industry with the advent of digital printing technologies, such as electrophotography and ink-jet, demand that the paper should have appropriate printability. Digital printability depends on the printing machine, toner and paper. In this study, we tested six different papers made from invasive plants, namely, Japanese knotweed, black locust, Canadian goldenrod, and agro-industrial residues, such as miscanthus, tomato stems and waste jute bags. The papers were printed by electrophotography printing, and optical print quality parameters, such as optical density, printing unevenness and print gloss, as well as surface roughness and surface resistivity, were measured. The results indicate that average surface roughness and grammage have a high linear correlation with print gloss, and a moderate one with print density. The mottling values of prints have inconsistencies with the surface roughness values because of the specific fiber orientation based structure.

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 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 score0.072
Threshold uncertainty score0.403

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 integrity0.0010.000
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 head0.024
GPT teacher head0.183
Teacher spread0.159 · 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 teacher head, 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

Citations11
Published2020
Admission routes1
Has abstractyes

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