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Ectomycorrhizas increase apoplastic water transport and root hydraulic conductivity in <i>Ulmus americana</i> seedlings

2002· article· en· W1997642869 on OpenAlexaff
Tawfik M. Muhsin, Janusz J. Zwiazek

Bibliographic record

VenueNew Phytologist · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsApoplastWater transportHydraulic conductivityConductanceBotanyStomatal conductanceChemistryWater flowSoil waterHorticulturePhotosynthesisBiologyEnvironmental scienceSoil scienceCell wallPhysics

Abstract

fetched live from OpenAlex

Summary The extent to which water channel transport is responsible for the observed increases in root water flow of ectomycorrhizal plants is reported here. To examine the contribution of water channel transport to root hydraulic conductance, temperatures in the range 4–20°C and mercuric chloride (HgCl 2 ) were used to study the kinetics of water transport in ectomycorrhizal and nonmycorrhizal roots of American elm ( Ulmus americana ) seedlings. Hydraulic conductance declined with decreasing temperatures in both mycorrhizal and nonmycorrhizal seedlings. However, hydraulic conductance and conductivity were higher in the mycorrhizal than the nonmycorrhizal roots at all temperatures studied. Mercuric chloride had a relatively greater impact on root hydraulic conductance in nonmycorrhizal than mycorrhizal roots and activation energy for root hydraulic conductance was significantly higher in mycorrhizal than nonmycorrhizal plants. The results suggest that ectomycorrhizal hyphae increase hydraulic conductance of roots by decreasing water flow resistance of the apoplast rather than by water channel‐mediated transport. The high rates of hydraulic conductance at low root temperatures might be important to plants growing in cold soils and under other challenging environmental conditions that inhibit metabolism and limit water transport.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.980

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)0.0000.000

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 head0.020
GPT teacher head0.198
Teacher spread0.178 · 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

Citations91
Published2002
Admission routes1
Has abstractyes

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