Thinning intensity and frequency enhance <i>Quercus robur</i> L. growth responses to drought in Southern Sweden
Bibliographic record
Abstract
Drought can impact forests directly causing a decrease of growth, but also increase the vulnerability of trees to secondary pests and pathogens, causing additional loss of volume production. To develop new silvicultural strategies, it is crucial to understand if thinning can promote resilience of the remaining trees to drought by enhancing an efficient use of resources. Given projected drier vegetation periods in Southern Sweden, the aim of the study was to determine how tree growth is affected by severe summer droughts under different thinning regimes. We used an experiment established in 1991 in a 40-year-old pure oak (Quercus robur L.) stand with two thinning intensities and an unthinned control. We collected tree cores before and after specific drought events occurring after treatment. We observed that heavy thinning intensity increased drought resistance, and decreased recovery time and growth reduction when the time since the last intervention was 4–5 years. Our results suggest that heavy and frequent thinning interventions would be an appropriate management alternative to alleviate drought stress in pure oak stands close to the northern edge of their distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".