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Record W2028870108 · doi:10.1021/jf034320o

Significant Increases in Pulping Efficiency in C4H-F5H-Transformed Poplars:  Improved Chemical Savings and Reduced Environmental Toxins

2003· article· en· W2028870108 on OpenAlexaff
Shannon Kelly Huntley, David Ellis, Margarita Gilbert, Clint Chapple, Shawn D. Mansfield

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2003
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLigninPulp (tooth)ChemistryKappa numberMonomerKraft processOrganic chemistryBotanyPulp and paper industryKraft paperBiologyPolymer

Abstract

fetched live from OpenAlex

The gene encoding ferulate 5-hydroxylase (F5H) was overexpressed in poplar (Populus tremula x Populus alba) using the cinnamate-4-hydroxylase (C4H) promoter to drive expression specifically in cells involved in the lignin biosynthetic pathway and was shown to significantly alter the mole percentage of syringyl subunits in the lignin, as determined by thioacidolysis. Analysis of poplar transformed with a C4H-F5H construct demonstrated significant increases in chemical (kraft) pulping efficiency from greenhouse-grown trees. Compared to wild-type wood, decreases of 23 kappa units and increases of >20 ISO brightness units were observed in trees exhibiting high syringyl monomer concentrations. These changes were associated with no significant modification in total lignin content and no observed phenotypic differences. C4H-F5H-transformed trees could increase pulp throughputs at mills by >60% while concurrently decreasing chemicals employed during processing (chemical pulping and bleaching) and, consequently, the amount of deleterious byproducts released into the environment.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.160
Teacher spread0.156 · 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

Citations286
Published2003
Admission routes1
Has abstractyes

Explore more

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