Age-dependent climate–growth relationships and regeneration of<i>Picea abies</i>in a drought-prone mixed-coniferous forest in the Alps
Bibliographic record
Abstract
Within dry inner Alpine environments, climate warming is expected to affect the development of forest ecosystems by changing species composition and inducing shifts in forest distribution. By applying dendroecological techniques we evaluated the climate sensitivity of radial growth and the establishment of Picea abies (L.) Karst. in a drought-prone mixed-coniferous forest in the Austrian Alps. Time series of annual increments were developed from >220 trees and assigned to four age classes. While radial growth of old P. abies trees (mean ages of 121 and 174 years) had highly significant responses to May–June precipitation, young trees (mean ages 28 and 53 years) were insensitive to precipitation in the current year. Because tree age was closely correlated to height and diameter (r 2 = 0.709 and 0.784, respectively), we relate our findings to the increase in tree size rather than age per se. The synchronicity found among trends in basal area increment and tree establishment suggests that canopy openings increased light and water availability, which favoured growth and establishment of moderately shade-tolerant P. abies. We conclude that, although P. abies is able to regenerate at this drought-prone site, increasing inter-tree competition for water in dense stands gradually lowers competitive strength and restricts scattered occurrence to dry–mesic sites.
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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 | 0.001 |
| 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 | 0.000 | 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".