Does quarter of century of protection differentiate protected from managed mixed fir stands in Polish lowland?
Bibliographic record
Abstract
In Central Europe, there is no forest ecosystem that is exempt from human influence. Nevertheless, in many European regions it is very important to protect late successional forests, as they play an essential role in maintaining biodiversity, ecological function and providing ecosystem services. The objective of this study is to investigate the effects of forest management, or lack of management intervention, on forest structure and natural regeneration at similar stages of stand development. It is also important to know whether the forest structure in protected areas changes towards a higher degree of ‘naturalness’. The study was conducted in the Janów Forests in southeastern Poland in three types of mixed stands. In each stand type, 40 sample plots were established (20 in protected stands and 20 in managed stands). All seedlings and saplings were classified into two categories based on their light requirements. Our study shows that the diverse structure of mixed fir stands can be achieved by passive or low−intensity management that supports natural forest regeneration. DBH structure and species composition of stands did not differ between managed and protected stands. Our studies indicate that in managed mixed fir stands, passive management limited to low intensity salvage cutting promotes the creation of differentiated spatial structure and species composition, similar to protected stands of the same type. To increase the proportion of complex mixed fir stands, the rotation age should be increased. Single tree selection cuttings can help maintain such complex stand structures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".