Natural regeneration in a beech-dominated forest managed by close-to-nature principles — a gap cutting based experiment
Bibliographic record
Abstract
European beech ( Fagus sylvatica L.) is increasingly managed by close-to-nature principles, mimicking the gap dynamics of seminatural forests. The prime aim of this study was to analyse natural regeneration reliability under favourable conditions in newly formed gaps. A total of 12 gaps were created by felling three canopy trees for each gap: six gaps in each of the two winters 1996–1997 and 1997–1998. One-half of the gaps were fenced against deer. We recorded advance regeneration density (1997), regeneration density and height (1997–2002), relative light intensity (1997–2002), and volumetric soil moisture content (1997–2002). We also studied the effect of year of establishment, fenced versus unfenced, and position within gap on regeneration. Three or 4 years after gap formation, most gaps had nearly closed. Response of European beech, European ash ( Fraxinus excelsior L. ), and sycamore maple ( Acer pseudoplatanus L.) regeneration to gap formation was limited, and few seedlings were added to the advance regeneration pool during the study period. Other factors, such as relative light intensity, soil moisture, fencing, year of establishment, and position within gaps, all had rather low effects. Thus, the presence of advance regeneration appeared to be a key factor in explaining regeneration patterns in artificially created gaps.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".