Influence of weather and day length on intra-seasonal growth of Norway spruce (<i>Picea abies</i>) and European beech (<i>Fagus sylvatica</i>) in a natural montane forest
Bibliographic record
Abstract
Intra-seasonal growth responses of co-occurring European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) Karst.) to weather variability in montane forests can provide useful information on their future growth trends. To improve growth predictions, we aimed to identify (i) the main seasonal windows during which weather variability influences tree-ring growth, (ii) species-specific differences in the response to weather fluctuations, and (iii) teleconnections to remote sites in the Western Carpathians. We monitored intra-seasonal growth dynamics based on proxies extracted growth signals detected by high-resolution dendrometers in the transition zone between the beech and spruce altitudinal belt. Over 12 consecutive seasons in the natural montane forest (1350 m a.s.l.), the main part of spruce (68% to 10 July) and beech (95% to 26 August) annual increment was under the prevailing influence of temperature. After this, precipitation pattern (regarding spruce) and day length became the most influential variables during deceleration and cessation of growth. In addition, synchronous patterns with remote sites in the Western Carpathians were found. The results emphasize the importance of studying the influence of shorter-term weather fluctuations during the season. Our findings suggest that montane spruce tends to be less temperature-demanding and more drought-sensitive than beech, which may favor beech over the spruce under the future climate.
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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 | 0.000 |
| 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) | 0.000 | 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 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".