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Linking irradiance‐induced changes in pit membrane ultrastructure with xylem vulnerability to cavitation

2010· article· en· W2136488132 on OpenAlexafffund
Lenka Plavcová, Uwe G. Hacke, John S. Sperry

Bibliographic record

VenuePlant Cell & Environment · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Alberta
FundersCanada Research Chairs
KeywordsXylemMiddle lamellaUltrastructureMembraneScanning electron microscopeBotanyPorosityShadingMaterials scienceCavitationTransmission electron microscopyBiologyChemistryBiophysicsComposite materialCell wallBiochemistryNanotechnology

Abstract

fetched live from OpenAlex

The effect of shading on xylem hydraulic traits and xylem anatomy was studied in hybrid poplar (Populus trichocarpa x deltoides, clone H11-11). Hydraulic measurements conducted on stem segments of 3-month-old saplings grown in shaded (SH) or control light (C) conditions indicated that shading resulted in more vulnerable and less efficient xylem. Air is thought to enter vessels through pores in inter-vessel pit membranes, thereby nucleating cavitation. Therefore, we tested if the ultrastructure and/or chemistry of pit membranes differed in SH and C plants. Transmission electron micrographs revealed that pit membranes were thinner in SH, which was paralleled by lower compound middle lamella thickness. Immunolabelling with JIM5 and JIM7 monoclonal antibodies surprisingly indicated that pectic homogalacturonans were not present in the mature pit membrane regardless of the light treatment. Porosity measurements conducted with scanning electron microscopy were significantly affected by the method used for sample dehydration. Drying through a gradual ethanol series seems to be a better alternative to drying directly from a hydrated state for pit membrane observations in poplar. Scanning electron microscopy based estimates of pit membrane porosity probably overestimated real porosity as suggested by the results from the 'rare pit' model.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.180
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations71
Published2010
Admission routes2
Has abstractyes

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