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Record W1565059903 · doi:10.5772/18535

Model Predictive Control and Optimization for Papermaking Processes

2011· book-chapter· en· W1565059903 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

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsHoneywell (Canada)
Fundersnot available
KeywordsPapermakingModel predictive controlPaper machineComputer scienceImplementationController (irrigation)Control (management)EngineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Brief description of papermaking processesIn general a paper machine can be divided into four sections: forming section, press section, drying section, and calendering section.In the forming section, the stock flow enters the headbox to be distributed evenly across a continuously running fabric felt called the wire.The newly formed sheet is carried by the wire along the Fourdrinier table, which has a set of drainage elements that promote water removal by various gravity and suction mechanisms.These elements include suction boxes, couch rolls, foils, etc.The solid consistency of the paper web can reach 20% by the time the web leaves the forming section and enters the press section.Figure 1 illustrates the configuration of a Fourdrinier-type paper machine.

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: Methods · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score1.000

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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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