MétaCan
Menu
Back to cohort

PRINTING.Paper and printer effects on xerographic print quality

2012· article· en· W2328951745 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoUniversity of Sussex
KeywordsGloss (optics)Digital printingEngineering drawingInkwellSurface finishSurface roughnessMaterials scienceEngineeringComposite materialCoating

Abstract

fetched live from OpenAlex

Abstract This study examines the impact of paper and printer type on the quality of xerographic prints. Ten different uncoated paper substrates were printed using three different commercial xerographic printers. The print quality of the samples (print microgloss, print microgloss nonuniformity, print density, print and gloss mottle, and visual ranking) and the physical and surface characteristics of the papers were measured. It was found that relationship between print mottle and print gloss nonuniformity was dominated by the printer type. While for some printers, these two parameters were positively correlated, in other cases printer appeared to "mask" variations in the paper properties. Multivariate analysis also showed that brightness, opacity, basis weight, 7 5 ° Tappi gloss, and roughness were the top five paper properties that had the most significant effect on the visual ranking and print mottle. Finally, as expected, print roughness was found to be a better predictor of the perceived print quality, however, paper roughness was poorly correlated with the visual ranking of printed samples (R 2 0.5).

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.351
Teacher spread0.293 · 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