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Record W4395117820 · doi:10.3389/fpls.2024.1388549

Tissue and cellular localization of condensed tannins in poplar roots and potential association with nitrogen uptake

2024· article· en· W4395117820 on OpenAlexafffund
Rebecca Westley, Dawei Ma, B. J. Hawkins, C. Peter Constabel

Bibliographic record

VenueFrontiers in Plant Science · 2024
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNitrogenProanthocyanidinBotanyBiologyChemistryCondensed tanninBiochemistryPolyphenolOrganic chemistry

Abstract

fetched live from OpenAlex

Condensed tannins are common in vegetative tissues of woody plants, including in roots. In hybrid poplar (Populus tremula x alba; also known as P. x canescens) CT assays indicated they were most concentrated in younger white roots and at the root tip. Furthermore, CT-specific staining of embedded tissue sections demonstrated accumulation in root cap cells and adjacent epidermal cells, as well as a more sporadic presence in cortex cells. In older, brown roots as well as roots with secondary growth (cork zone), CT concentration was significantly lower. The insoluble fraction of CTs was greatest in the cork zone. To determine if CT accumulation correlates with nutrient uptake in poplar roots, a microelectrode ion flux measurement (MIFE™) system was used to measure flux along the root axis. Greatest NH4+ uptake was measured near the root tip, but NO3- and Ca2+ did not vary along the root length. In agreement with earlier work, providing poplars with ample nitrogen led to higher accumulation of CTs across root zones. To test the functional importance of CTs in roots directly, CT-modified transgenic plants could be important tools.

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 score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.177
Teacher spread0.174 · 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
Published2024
Admission routes2
Has abstractyes

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