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Record W2603121752 · doi:10.1139/cjps2013-144

Manipulating lignin deposition

2014· article· en· W2603121752 on OpenAlexaffabout
Mathias Schuetz, Carl J. Douglas, Lacey Samuels, Brian E. Ellis

Bibliographic record

VenueBioOne Complete (BioOne) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsLigninArabidopsis thalianaBiomass (ecology)ArabidopsisCell wallChemistryCarbon fibersBotanyBiologyBiochemistryAgronomyGeneMaterials science

Abstract

fetched live from OpenAlex

Schuetz, M., Douglas, C., Samuels, L. and Ellis, B. 2014. Manipulating lignin deposition. Can. J. Plant Sci. 94: 1043-1049. Since lignin represents one of most durable forms of fixed carbon in plant biomass, we hypothesized that increasing root lignin content for crops whose root systems remained in the soil after harvest would elevate the total amount of carbon retained in the soil in Canadian agroecosystems. The immediate goal of this Greencrop project was, therefore, to gain a better understanding of the molecular mechanisms that control deposition of the lignin polymer in plant cell walls, with a view to eventually manipulating the quantity and location of lignin in crop plant root systems. To this end, we examined two classes of Arabidopsis thaliana proteins - transcription factors, which are believed to play crucial roles in regulating lignin biosynthesis, and ATP binding cassette transporters, which are putative lignin precursor transporters. These studies revealed that a complex network of interacting transcriptional regulators is involved in activating and suppressing the expression of key genes required for secondary cell wall deposition and lignification.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.189
GPT teacher head0.247
Teacher spread0.059 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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