Stand structure and dynamics in a mixed and multilayered forest in the Upper Susa Valley, Piedmont, Italy
Bibliographic record
Abstract
Size, age, and spatial structures were studied in a mixed, multilayered forest located in the Upper Susa Valley in Piedmont, Italy, using complete stem mapping, dendrochronology, and spatial analysis on a 1-ha permanent plot. All trees with a diameter >4 cm at 50 cm height (991) and stumps (322) were mapped, measured, and cored. The 639 cross-dated samples were used to reconstruct the disturbance history, and dendroecological results were then compared with information on forest and land use from documentary archives. The stand has undergone substantial shifts in forest structure and species composition over the last 200 years, from an open structure with larch (Larix decidua Mill.), Swiss mountain pine (Pinus uncinata L.), Norway spruce (Picea abies (L.) Karst.), and scattered regeneration to a dense multilayered structure with silver fir (Abies alba Mill.) and Norway spruce with dense regeneration. Shifts in dominance and structure were found to be consistent with land-use changes rather than with disturbance history. These results confirm the importance of multiple sources of independent data to characterize the disturbances that have affected the origin and development of stands heavily impacted by humans. Knowledge of stand history and understanding of potential ecological transformations are essential for the correct application of close-to-nature silvicultural practices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".