MétaCan
Menu
Back to cohort
Record W2374627301

Study on Modeling Sheet Tensile Strength Based on Papermaking Process Parameters

2010· article· en· W2374627301 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

VenueYingyong jichu yu gongcheng kexue xuebao · 2010
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsUltimate tensile strengthPapermakingMaterials scienceComposite materialSoftwoodFiberCellulose fiberStructural engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

To quantitative analysis the influence of paper making main process parameters on sheet tensile strength and estabish the sheet tensile strength soft sensor model,the experiment was carried out based on the PAGE equation and with the aid of light scattering coefficient technique,and a new method to determining fiber-fiber shear bond strength was also proposed.The effects of beating degree,wet pressure,moisture content and cationic starch on fiber-fiber shear bond strength and sheet relative bonded area(RBA) were investigated with bleached Canada kraft softwood pulp,and the sheet tensile strength soft sensor model related with these process parameters was obtained finally and the model was verified.The results show that the model has good precision and the stand error mean is about 8%.It's helpful to understand sheet tensile strength deeply and further research soft sensor equipments to measure paper web tensile strength on-line for paper mills.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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