MétaCan
Menu
Back to cohort
Record W3001884711 · doi:10.9734/jerr/2019/v9i417024

Application of Cationic Tapioca to Unmodified Pearl Corn Starch – A Papermaking Handsheet Study

2020· article· en· W3001884711 on OpenAlexaboutno aff
Klaus Dölle, Emily Parsons, Jacob Konecny

Bibliographic record

VenueJournal of Engineering Research and Reports · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsStarchPapermakingCationic polymerizationCorn starchUltimate tensile strengthChemistryFood scienceMaterials sciencePolymer chemistryComposite material

Abstract

fetched live from OpenAlex

An handsheet study was performed to compare the application of unmodified pearl corn starch and cationic tapioca starch on 100% recycled paperboard. To analyze the benefits tensile index, Canadian Standard Freeness, and starch retention was measured. The results found that cationic tapioca starch had the highest tensile index at 61.36 N*m/g for a dosage rate of 16 lbs./ton at a comparable dosage for unmodified pearl corn starch at 48 lbs./ton the tensile index was 56.11 N*m/g. Tests of the Canadian Standard Freeness showed that the unmodified pearl corn starch had the lowest freeness at 34.3 ml. The cationic tapioca starch had a freeness of 53.5 ml. For starch retention, more starch was retained in the sheet with cationic tapioca starch, with only 0.0065 grams ending up in the filtrate, compared to 0.015 grams of filtrate for the sheet containing unmodified pearl corn starch.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.052
GPT teacher head0.332
Teacher spread0.279 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Engineering Research and ReportsSame topicFood composition and propertiesFrench-language works237,207