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Record W4386738896 · doi:10.1111/ppl.14006

The effects of copper deficiency on lignification, xylem vessel structure, and hydraulic traits in hybrid poplar

2023· article· en· W4386738896 on OpenAlexaff
Cameron Hunter, Zimou Sun, Shawn D. Mansfield, Muhammad Shahbaz, Marinus Pilon, Sean M. Gleason

Bibliographic record

VenuePhysiologia Plantarum · 2023
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
FundersNew Phytologist Trust
KeywordsXylemLigninChemistryCell wallApoplastConiferyl alcoholMonomerArabinoxylanPolymerizationBiophysicsBotanyPolymerBiochemistryOrganic chemistryEnzymeBiology

Abstract

fetched live from OpenAlex

Copper (Cu) homeostasis is integral to many plant physiological processes, including lignification of plant cell walls. This link occurs through Cu's role as a cofactor in the apoplastic laccase enzymes that oxidize monolignols that then polymerize to form the hydrophobic lignin polymer, which provides rigidity and strength to the water transport system. In this study, we investigated the effect of Cu deficiency on lignin content and chemistry in poplar stems. We also examined the effect of Cu deficiency on the stiffness of stem wood and the hydraulic properties of leaves. Cu deficiency resulted in a significant reduction in lignin content, an increase in the syringyl to guaiacyl monomer ratio of stem xylem, and no change to stem modulus of elasticity. Accompanying these stem traits, Cu-deficient leaves had higher (less negative) turgor loss points and markedly stiffer mesophyll cell walls. Our results may reflect a novel response in poplar whereby structural stiffness and mechanical stability are maintained in the face of Cu deficiency and reduction in the guaiacyl lignin monomer content.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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