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Record W2994923295 · doi:10.1139/cjfr-2019-0341

Pedunculate oak decline in southern Belgium: a long-term process highlighting the complex interplay among drought, winter frost, biotic attacks, and masting

2019· article· en· W2994923295 on OpenAlexvenueno aff
Julie Losseau, Mathieu Jonard, Caroline Vincke

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsQuercus roburAbiotic componentQuercus petraeaContext (archaeology)GeographyBiotic componentClimate changeEcologyPhysical geographyForestryBiologyArchaeology

Abstract

fetched live from OpenAlex

Since 2013, pedunculate oak (Quercus robur L.) mortality has been observed in the Ardennes region of Belgium. We aimed to understand the current decline by retrospectively (1945–2015) studying radial growth patterns of trees classified by three health statuses (reference, declining, and dying) and by linking them to abiotic and biotic hazard history, which we recorded and quantified. Our results show that oak mortality in the Ardennes is a long-term process, with 1987 as a tipping point for growth trajectories of declining and dying trees. That year was preceded by two growth crises (1976–1981 and 1984–1987), and it falls within the last major episode of oak decline in Belgium. Among hazards, very cold winters and caterpillar outbreaks have significant impacts on growth-pattern differentiation. Apart from 1976, extreme drought is still rare; however, mild spring droughts, especially in the years n − 1 and n − 2, explain some of the growth loss relative to the reference trees. Finally, masting appears to be an important contributing factor for the death of weakened trees. Given the direct and delayed impacts of the extreme drought of 1976 and subsequent water balance impairment due to winter frosts and mild spring droughts, the health of pedunculate oak is giving cause for concern in the context of climate change.

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)0.002
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 score0.777
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.317
Teacher spread0.272 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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