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Record W1593939940 · doi:10.1002/9780470027318.a2201

Pulp and Paper Matrices Analysis: Introduction

2000· other· en· W1593939940 on OpenAlex
Bruce Sitholé

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2000
Typeother
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsCatalyst Paper (Canada)
Fundersnot available
KeywordsPapermakingPaperboardPulp (tooth)Pulp and paper industryRaw materialProcess engineeringManufacturing engineeringComputer scienceOperations researchEngineeringBiochemical engineeringChemistryWaste management

Abstract

fetched live from OpenAlex

Abstract The pulp and paper industry is a major player in the economies of many developed countries. As illustrated in Figure 1(a) and (b), the developed world accounts for more than 60% of the world output in pulp, paper and paperboard capacity. In one particular case, namely Canada, the industry contributes over 15% to the trade balance (see Figure 2). In order to ensure continual survival of the industry, it is necessary to continually improve and/or optimize the utilization of raw materials, the pulping processes, the papermaking process, the product quality properties and the protection of the environment. Analytical chemistry has an important role to play in the improvement and/or optimization of these parameters. This article summarizes how and where analytical chemistry is used, or can be used, to achieve the desired goals. Applications of analytical chemistry in the pulp and industry are reviewed in detail in the rest of the articles in this section on pulp and paper.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0060.000

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 head0.002
GPT teacher head0.191
Teacher spread0.188 · 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