Bibliographic record
Abstract
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。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".