MétaCan
Menu
Back to cohort

Investigation of converging and collimated beam instrument geometry on specular gloss measurements

2018· article· en· W2794426661 on OpenAlexaff
Joanne C. Zwinkels, Éric Côté, John Morgan

Bibliographic record

VenueJournal of Physics Conference Series · 2018
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of WaterlooNational Research Council Canada
Fundersnot available
KeywordsGloss (optics)Specular reflectionCollimated lightOpticsSpecular highlightMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Specular gloss is an important appearance property of a wide variety of manufactured goods. Depending upon the application, e.g. paints, paper, ceramics, etc. different instrument designs and measurement geometries are specified in standard test methods. For a given specular angle, these instrument designs can be broadly classified as converging beam (TAPPI method) and collimated beam (DIN method). In recent comparisons of specular gloss measurements using different glossmeters, very large standard deviations have been reported, well exceeding the manufacturers claims. In this paper, we investigate the effect of instrument beam geometry on gloss measurements. These results indicate that this difference in beam geometry can give the magnitude of gloss differences reported in these comparisons and highlights the importance of educating the user community of best measurement practices and obtaining appropriate traceability for their glossmeters.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.219
Teacher spread0.169 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicMaterial Properties and ProcessingFrench-language works237,207