Canopy Gap Characteristics and Disturbance Dynamics in Old-Growth<i>Picea abies</i>Stands in Northern Fennoscandia: Is the Forest in Quasi-Equilibrium?
Bibliographic record
Abstract
Emulating natural disturbances in managed forests has been suggested as a potential solution to maintain habitat conditions similar to those observed in old-growth forests. We examined the gap attributes and disturbance history of old-growth Picea abies-dominated stands in the northern boreal vegetation zone of the Pallas-Yllästunturi National Park in northwestern Finland to evaluate the influence of gaps on forest dynamics and the temporal patterns of gap creation. Six stands located at two sites were sampled along 400-m-long linear transects so that all intersected gaps were measured and dated. The average proportion of the forest area in the gaps was 43.1% ± 7.5%. An average gap size was estimated to be 221 m2 ± 198 m2, whereas the median gap size was 170.2 m2. While only 20% of the gaps were smaller than 100 m2, nearly 85% of them were smaller than 300 m2. Gap creation was constant with no distinct peaks from 1965 to 2005. Thus, forest dynamics were driven by continuous small-scale disturbances and were characterized by quasi-equilibrium structure. However, the results of the growth release analysis indicated that more severe disturbance(s) may have occurred almost two centuries ago. Emulating this type of forest dynamics would imply selective or group harvesting of trees as the predominant methods, but larger-scale, more intensive cuttings could also be carried out periodically.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".