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Record W2004679852 · doi:10.1080/02773810701700786

Wood Fiber Quality and Kraft Pulping Efficiencies of Trembling Aspen (<i>Populus tremuloides</i> Michx) Clones

2007· article· en· W2004679852 on OpenAlexaffabout
Shawn D. Mansfield, Henrik Weineisen

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPulp (tooth)Kappa numberKraft processLigninPulp and paper industryKraft paperChemistryPaper productionFiberBotanyBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract The natural variation in wood and pulp fiber quality of 15 aspen (Populus tremuloides Michx) clones, represented by 47 trees, was assessed from 4 different sites in British Columbia, Canada. Kraft pulping trials revealed substantial variation in the pulping efficiencies, illustrated by differences of 6% in total pulp yield, ∼30% differences in H‐factor required to attain a target kappa of 21, and differences of up to 2 ISO brightness units in bleachability of kappa 21 pulp. Clearly, enormous variation exists in the natural stands of aspen, and presents some exciting opportunities for selecting clonal aspen for targeted end‐product applications. A comprehensive characterization of wood chemical composition, wood density, and fiber properties indicated that pulp yield is directly related to syringyl lignin monomer composition, and not inherent wood density, regardless of geographic locations, whereas pulp bleachability and viscosity appear to be associated with the inherent cell wall thickness of the starting wood resources (fiber coarseness). These findings suggest that geographic location imparts influences on wood fiber coarseness traits, while substantial genetic variability exists on all sites.

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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 teacher head, 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

Citations37
Published2007
Admission routes2
Has abstractyes

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