The Role of Provenance for the Projected Growth of Juvenile European Beech under Climate Change
Bibliographic record
Abstract
European beech is one of the most common tree species in Europe and is generally suggested to play even more of a prominent role in forestry in the future. It seems to have the potential to partially replace Norway spruce, as it is less sensitive to expected warmer and drier conditions. It is, however, not well known in which regions these new plantings would be particularly favourable and if specific provenances may be better adapted to the new conditions than others. Therefore, we estimated the potential early height growth under climate conditions in 2040–2060 for 20 beech provenances across a region covering the Czech Republic and Slovakia. This Central European region is expected to experience considerably drier and warmer conditions in the future. For this exercise, we implemented a new neural network model developed from height growth information obtained from the open-access BeechCOSTe52 database. The simulations are driven by past and future climate data obtained from the WorldClim database of historical climate data and future climate projections. Simulations revealed that provenances originating from drier regions performed on average significantly better than those from regions with good water supply. Moreover, provenances originating from drier regions had a particularly large advantage in the relatively arid regions of Central Czechia and Southern Slovakia. We can also confirm that all provenances showed a high phenotypic plasticity of height growth across the whole investigated region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".