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Record W4384039255 · doi:10.1163/22941932-bja10131

Anatomical and isotopic traits in grapevine wood rings record environmental variability

2023· article· en· W4384039255 on OpenAlexaff
Nicola Damiano, Giovanna Battipaglia, Paolo Cherubini, Chiara Amitrano, Simona Altieri, Loïc Schneider, Angela Balzano, Chiara Cirillo, Veronica De Micco

Bibliographic record

VenueIAWA Journal - KU Leuven/IAWA Journal · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyVitis viniferaTraitStable isotope ratioAgronomyBotanyEnvironmental science

Abstract

fetched live from OpenAlex

Summary In the Mediterranean region, prolonged droughts affect the growth and reproductive cycles of grapevine. Changes in the physiological processes of grapevine, consequent to variations in environmental factors or cultivation management, are recorded in wood anatomical and isotopic traits in grapevine stems. In this study, we measured the anatomical traits and stable carbon isotope content in the annual rings of Vitis vinifera L. subsp. vinifera ‘Falanghina’ in four vineyards located in southern Italy, characterised by different water availability. The aim was to investigate how wood anatomical traits respond to interannual climatic variations according to local conditions. Wood cores were taken from the stem of the grapevines and subjected to both microscopy and carbon stable isotope analyses to quantify functional wood anatomical traits, such as vessel size and frequency, and the intrinsic water-use efficiency of the grapevine. Wood traits were correlated with data on precipitation and temperature. The results showed that the plants at the four vineyards were characterised by differences in wood structure influencing the grapevine’s physiology under different conditions of water availability. Overall, the analyses showed that the grapevines at the wetter sites developed wood traits, e.g., wide vessels, which favour the efficiency of water flow, while at the drier sites, they developed plant traits, e.g., small vessels, which favour safety against embolism. However, the robustness of such main trends is trait-specific and is influenced by interannual climatic variability.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.229
Teacher spread0.213 · 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.

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

Citations7
Published2023
Admission routes1
Has abstractyes

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