Growth responses of <i>Picea abies</i> and <i>Larix decidua</i> to elevation in subalpine areas of Tyrol, Austria
Bibliographic record
Abstract
Microsites related to microenvironmental conditions, including microclimate, seem to be a key factor for the restoration of forests in the subalpine area. Tree growth was studied in Picea abies (L.) Karst. (Norway spruce) and Larix decidua Mill. (European larch) on 30 plots located at different microsites (i.e., different elevations and micro top o graphies combined) within the subalpine zone (16801940 m) of the Schmirn Valley (Tyrol, Austria). The age of the trees studied was 27 years for larch and 28 years for spruce. The mean height and biomass growth decreased significantly with increasing elevation. The effect of elevation and microtopography on growth varied with tree size (age): (1) elevation had little effect on growth of trees less than 0.5 m in height; (2) both elevation and microtopography affected tree growth significantly when the tree height was between 0.5 and 3 m; (3) as trees exceed 3 m in height, tree canopies can fully cover the ground surface and create a forest microclimate causing growth to decline with increasing elevation, irrespective of microtopography. We conclude that the microclimate, associated with microsite, controls growth during the early stages of tree development, but following canopy closure, the local climate (mesoclimate) associated with topography begins to determine tree growth.
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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".