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Record W2051467924 · doi:10.1071/fp10108

Path of water for root growth

2010· article· en· W2051467924 on OpenAlexfundno aff
John S. Boyer, Wendy Kuhn Silk, Michelle Watt

Bibliographic record

VenueFunctional Plant Biology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
FundersCommonwealth Scientific and Industrial Research OrganisationMcMaster University
KeywordsPhloemEcophysiologyBiologyRoot systemAgronomyWater transportSoil waterFibrous root systemBotanyDNS root zonePlant biologyHorticultureSoil scienceEnvironmental scienceEcologyWater flow

Abstract

fetched live from OpenAlex

Do roots obtain water for their growth directly from soil surrounding the growth zone or indirectly, via phloem, from water absorbed elsewhere? Wheat (Triticum aestivum L.) was studied with time-lapse imaging of seminal axile roots, growing in soil and air in a custom-made laboratory rhizotron, before and after excision. The growth data were combined with a theoretical estimate of the amount of water that could be supplied from the phloem. Roots readily extended into air, providing strong evidence that they obtain a portion of their growth-sustaining water internally. The time-lapse experiments indicated that in moist soil, internal sources provided 26–45% of the water for root growth, but the rest came externally from the soil surrounding the growth zone. From the theoretical analysis, the phloem could supply, on average, 64% of the total, accounting for all the internal sources. This indicates that phloem water could be used when root tips cannot access external water, such as in cracks or pores, or regions of dry soil. The distribution of phloem-delivered water for root growth should be considered in whole-plant modelling of root systems. Maximising phloem flux at root tips might confer more efficient use of soil water by crops.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.194
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 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

Citations40
Published2010
Admission routes1
Has abstractyes

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