Human impact on size, age, and spatial structure in a mixed European larch and Swiss stone pine forest in the Western Italian Alps
Bibliographic record
Abstract
Spatiotemporal development and human impact on dynamic processes were investigated in the mixed European larch (Larix decidua Mill.) and Swiss stone pine (Pinus cembra L.) subalpine forest of Lago Perso (Piedmont, Italy). We mapped and measured all 295 trees (DBH ≥4 cm) and 914 saplings (>10 cm height, <4 cm DBH) in a permanent plot (1 ha). One core per tree was extracted upslope at 50 cm height, and dendrochronological techniques were applied to reconstruct age structure and growth patterns. All of the data collected were stored in a GIS, and tree and stem crown maps were generated and analysed to quantify spatial patterns. Ripley's K(t) univariate and bivariate point pattern analyses were employed to assess the degree of spatial autocorrelation. Documentary research was conducted to reconstruct human land use. The stand is uneven-aged, and there were no obvious age cohorts or other evidence of major disturbances in the past. Stone pine saplings and trees and larch saplings exhibited a clumped structure. The same clumping was not so evident in larch trees. The observed structural changes are mainly related to human land use and grazing regime. Although human influence is still manifest, in the recent decades natural dynamics have become the predominant influence on the forest's structure and processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".