Pedunculate oak decline in southern Belgium: a long-term process highlighting the complex interplay among drought, winter frost, biotic attacks, and masting
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".