Natural regeneration and vegetation changes in wet spruce forests after natural and artificial disturbances
Bibliographic record
Abstract
An extensive area of Norway spruce ( Picea abies (L.) Karst.) forests in the Šumava Mountains, Central Europe, has been affected by a massive bark beetle ( Ips typographus L.) outbreak since the mid-1990s. One part of the area was left without intervention and two types of intervention have been applied in other parts: (1) the classical forest approach, based on the logging of attacked trees and (2) “sanitation”, in which attacked trees were cut down, debarked, and left lying in the stand. The main goal of our research was to test the impact of nonintervention and both types of intervention on the regeneration of the Norway spruce forests. The Norway spruce forests influenced by natural disturbances (bark beetle outbreak and windfalls) regenerated very well if left without intervention. The bark beetle outbreaks and windfalls do not represent a threat to the long-term persistence of the forests. Clearcuts resulted in formation of pioneer stages with a postponed spruce regeneration. In sanitation plots, the reduction of both previous vegetation and tree regeneration was obvious. Generally, both interventions against bark beetle delayed the recovery of Norway spruce forests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".