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Record W2137608312 · doi:10.1111/nph.13610

Do changes in spring phenology affect earlywood vessels? Perspective from the xylogenesis monitoring of two sympatric ring‐porous oaks

2015· article· en· W2137608312 on OpenAlexafffund
Gonzalo Pérez‐de‐Lis, Sergio Rossi, Rosa Ana Vázquez Ruiz de Ocenda, Vicente Rozas, Ignacio García‐González

Bibliographic record

VenueNew Phytologist · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologiesU.S. Forest ServiceXunta de GaliciaMinisterio de Ciencia e Innovación
KeywordsPhenologyXylemGrowing degree-dayBiologyTemperate climateMediterranean climateQuercus roburSympatric speciationBotanyFagaceaeEcology

Abstract

fetched live from OpenAlex

This study addresses relationships between leaf phenology, xylogenesis, and functional xylem anatomy in two ring-porous oak species, the temperate Quercus robur and the sub-Mediterranean Q. pyrenaica. Earlywood vessel (EV) formation and leaf phenology were monitored in 2012 and 2013. Ten individuals per species were sampled at each of three sites located in NW Iberian Peninsula. EV areas measured on microcore sections were used to calculate the hydraulic tree diameter (Dh ), in order to model relationships to phenology. Thermal requirements were evaluated using growing degree days (GDD). A species-specific timing of growth resumption was found. The onset of EV formation and budburst were associated to a particular GDD in each species. The onset and duration of EV enlargement affected Dh (and EV size) in Q. robur, but hardly in Q. pyrenaica. The relationship between the timings of EV formation and xylem structure appears to be stronger for the temperate oak, whose larger vessels may result from thermal-induced earlier resumption. In contrast, the sub-Mediterranean oak would maintain a more conservative hydraulic architecture under warming conditions.

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 score0.017
Threshold uncertainty score0.982

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 head0.027
GPT teacher head0.261
Teacher spread0.234 · 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

Citations112
Published2015
Admission routes2
Has abstractyes

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