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Record W2334869522 · doi:10.2524/jtappij.62.1251

Introduction of Paprican’s Optical Calibration Program

2008· article· en· W2334869522 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

VenueJAPAN TAPPI JOURNAL · 2008
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsOntario Brain Institute
Fundersnot available
KeywordsCalibrationComputer sciencePhysics

Abstract

fetched live from OpenAlex

光学特性はとりわけ直感的に評価されやすい物性であり,また,直接的に紙やパルプの価格に影響する。光学特性の視覚的な評価や,そのランキング付けは非常に主観性をともない,それは周囲条件にも大きく影響される。それゆえ,幾つかの評価方法が開発されたが,不十分な知識と,測定法間での測定差が市場での不一致と混乱を招いた。そのためにも,基本的な光学特性と,顧客仕様を満たす最良の測定法を知ることは製紙会社にとって必要不可欠である。またキャリブレーションプログラムも信頼のおける測定を維持してゆく上で重要であり,生産的なキャリブレーションプログラムがPaprican(カナダ紙パルプ研究所)によって提供されている。本稿は,5月に催されたL&Wセミナーにおいて,Papricanより発表された光学特性に関するプレゼンテーションをもとに,その重要要点を述べる。基本的な測定法から,光学特性に影響するファクターや,信頼ある測定値を得るために重要なキャリブレーションについて述べる。

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.281

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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