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Record W2042368461 · doi:10.1139/cjfr-2014-0436

Effects of climate on earlywood vessel formation of <i>Quercus robur</i> and <i>Q.</i> <i>pyrenaica</i> at a site in the northwestern Iberian Peninsula

2015· article· en· W2042368461 on OpenAlexvenueno aff
Borja D. González-González, Rosa Ana Vázquez Ruiz de Ocenda, Ignacio García‐González

Bibliographic record

VenueCanadian Journal of Forest Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsQuercus roburPrecipitationDendrochronologyGrowing seasonEnvironmental scienceBiologyBotanyGeography

Abstract

fetched live from OpenAlex

We analysed climate–growth relationships for two ring-porous oak species (Quercus robur L. and Q. pyrenaica Willd.) at one site in northwestern Spain. Increment cores from 12 living trees per species were taken for ring and earlywood vessel measurements, considering three width variables (for earlywood, latewood, and the whole ring) and six anatomical variables (based on vessel area distribution, number of vessels, and conductivity) in the earlywood. We used standard dendrochronological procedures and compared each mean series with meteorological records of temperature, precipitation, and estimates of soil water content. Earlywood width was negatively related to precipitation in the previous late season for Q. robur, but it was controlled by the previous spring temperature for Q. pyrenaica. These responses were highly dependent on vessel number and probably related to carbohydrate storage dynamics but differed between species. Vessel size showed a stronger relationship to climate and was mainly coupled to water excess during the winter for Q. robur but dependent on quiescence temperature for Q. pyrenaica; also, climatic signal in vessel size was maximized by the largest vessels for Q. pyrenaica but only increased slightly for Q. robur. Consequently, vessel parameters showed a much stronger climate signal than ring width and were successful at highlighting the differences between both oak species.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.031
GPT teacher head0.263
Teacher spread0.232 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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