Gap disturbance regime in an old-growth<i>Fagus</i>–<i>Abies</i>forest in the Dinaric Mountains, Bosnia-Herzegovina
Bibliographic record
Abstract
Due to the scarcity of old-growth forests in much of Europe, there is little quantitative information on disturbance processes that influence forest dynamics. However, this information is crucial for forest management that tries to emulate patterns and processes in natural forests. We quantified the gap disturbance regime in an old-growth forest dominated by European beech ( Fagus sylvatica L.) and silver fir ( Abies alba Miller) in the Dinaric Mountains of Bosnia and Herzegovina. We sampled 87 gaps in four stands using line-intercept sampling. The percentages of forest area in canopy gaps and expanded gaps ranged from 12% to 17.2% and 35.5% to 39.7%, respectively. Although many of the gaps were small (<100 m2) and formed from a single gapmaker, large canopy openings >1000 m2with numerous gapmakers made up a disproportionate amount of the total gap area. More than half the gaps had more than one gapmaker and were often in separate decay classes, indicating gaps had expanded over time during separate disturbance events. Furthermore, 51% of all gapmakers were uprooted or wind-snapped, whereas only 22% died standing. These results suggest that wind disturbance plays an important role in creating intermediate to large canopy openings through both gap formation and gap expansion processes.
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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 | 0.000 |
| 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".