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Understanding Post Finishing Performance of Xerographic Prints

2012· article· en· W4378447686 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

VenueTechnical programs and proceedings/Technical program and proceedings · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsXerox (Canada)
Fundersnot available
KeywordsAdhesiveAdhesionResidual oilMaterials sciencePressure sensitiveCoatingReagentResidualComposite materialNanotechnologyChemistryComputer scienceLayer (electronics)Organic chemistry

Abstract

fetched live from OpenAlex

Xerographic digital presses have been used for the production of a variety of publications. Some of these applications may employ hot melt adhesives or pressure sensitive adhesives. However, good adhesion can be hard to achieve with xerographic prints due to the presence of residual fuser release agents on the surface of prints. Also this adhesion problem is very complex since the release agent and paper coating chemistries, fusing process, post-finishing materials and processes are all involved. Thus, it is important to understand the chemistry of release reagent and the surface topography and chemistry of substrates as well as the interaction between them. A method to predict the general adhesion properties of the xerographic prints and their behavior towards finishing operations was developed. It was found that the residual oil on the surface of the prints affects the finishing performance. When the surface coverage of oil is above a certain threshold, post finishing problems appear. The surface coverage of oil depends not only on the oil rate per copy but also on the molecular structure of the oil as well as the substrates.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.082
GPT teacher head0.276
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