Dynamics of Norway spruce and silver fir natural regeneration in a mixed stand under uneven-aged management
Bibliographic record
Abstract
In a 0.75-ha plot in a Norway spruce (Picea abies Karst.) silver fir (Abies alba Miller) stand in Comelico (Italian eastern Alps), we analysed (i) the distribution and growth of natural regeneration of Norway spruce and silver fir as affected by stand structure and (ii) the age structure of all saplings between 0.2 and 10 m in height in a 30-year-old gap. In both species, most natural regeneration was clumped and located at the margin of the gaps; however, fir saplings were more represented in understorey environments and less represented in gaps as compared with spruce. Age structure of natural regeneration in the selected gap revealed that the majority (75%) of saplings appeared after the formation of the gap; however, for regeneration taller than 2 m (which has a better chance of reaching the uppermost canopy layer), saplings already present at gap formation predominated. We conclude that (i) gap edges represent a preferential regeneration niche in this forest and (ii) saplings established before gap formation can play an important role in gap refilling. These results provide useful information to ensure, through silvicultural practices, favourable conditions for the temporal and spatial continuity of the regeneration process.
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 | 0.001 | 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".