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Record W2740543916 · doi:10.1111/nph.14715

Root hairs enable high transpiration rates in drying soils

2017· article· en· W2740543916 on OpenAlexfundno aff
Andrea Carminati, J. B. Passioura, Mohsen Zarebanadkouki, Mutez Ali Ahmed, Peter R. Ryan, Michelle Watt, Emmanuel Delhaize

Bibliographic record

VenueNew Phytologist · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
FundersCommonwealth Scientific and Industrial Research OrganisationMcMaster University
KeywordsXylemTranspirationSoil waterHordeum vulgareWater potentialTranspiration streamLaurus nobilisBotanyChemistryBiologyHorticultureAgronomyPoaceaeEcologyPhotosynthesis

Abstract

fetched live from OpenAlex

Do root hairs help roots take up water from the soil? Despite the well-documented role of root hairs in phosphate uptake, their role in water extraction is controversial. We grew barley (Hordeum vulgare cv Pallas) and its root-hairless mutant brb in a root pressure chamber, whereby the transpiration rate could be varied whilst monitoring the suction in the xylem. The method provides accurate measurements of the dynamic relationship between the transpiration rate and xylem suction. The relationship between the transpiration rate and xylem suction was linear in wet soils and did not differ between genotypes. When the soil dried, the xylem suction increased rapidly and non-linearly at high transpiration rates. This response was much greater with the brb mutant, implying a reduced capacity to take up water. We conclude that root hairs facilitate the uptake of water by substantially reducing the drop in matric potential at the interface between root and soil in rapidly transpiring plants. The experiments also reinforce earlier observations that there is a marked hysteresis in the suction in the xylem when the transpiration rate is rising compared with when it is falling, and possible reasons for this behavior are discussed.

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 score0.534
Threshold uncertainty score0.849

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 head0.040
GPT teacher head0.255
Teacher spread0.215 · 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

Citations167
Published2017
Admission routes1
Has abstractyes

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