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Record W4401728990 · doi:10.1111/pce.15085

Unique root hydraulic and mechanical properties support the resilience of grapevines adapted to the Atacama Desert

2024· article· en· W4401728990 on OpenAlexaff
Cesar Barrientos‐Sanhueza, Andrés Zurita‐Silva, Thorsten Knipfer, Andrew J. McElrone, Italo F. Cuneo

Bibliographic record

VenuePlant Cell & Environment · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of British Columbia
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsTurgor pressureXylemAridDrought toleranceTranspirationHydraulic conductivityBotanyRoot systemBiologyHorticultureChemistrySoil waterEcologyPhotosynthesis

Abstract

fetched live from OpenAlex

Cortical lacunae caused by drought, especially observed in hybrids originating from Vitis rupestris, disrupt the connection between roots and soil. Yet, the physiological processes behind lacuna formation during drought and its consistency across Vitis species remain unclear. Here, we used a root pressure probe to investigate fine root hydraulic and mechanical properties, in the arid-adapted R-65 and drought-susceptible 101-14Mgt cultivars. We then performed P-V curves, root sap osmolality, and electrolyte leakage (EL) and used fluorescent light microscopy techniques. Only 101-14Mgt showed lacunae formation during drought due to its stiffer cortical tissue, unlike R-65. Lacunae resulted in a notable decline in root hydraulic conductivity during severe drought, with increased EL and root sap osmolality, indicating potential cellular damage. R-65 displayed different and xerophyte-like characteristics featuring a higher turgor loss point and decreased root capacitance, essential for maintaining root structural integrity in arid conditions. Our findings highlight lacuna formation is impacted by root tissue elasticity possibly linked to specific Vitis species favoring deeper rooting. In arid-adapted grapevines, hydraulic regulators such as reduced turgor loss point, and root capacitance could contribute to enhanced drought tolerance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.129

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 head0.018
GPT teacher head0.177
Teacher spread0.159 · 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 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

Citations6
Published2024
Admission routes1
Has abstractyes

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