The effect of planted tree species on the development of herbaceous vegetation in a reclaimed opencast
Bibliographic record
Abstract
The establishment of plantations is the most common method of opencast reclamation, but few studies have addressed the influence of planted trees on the recovery of biodiversity in new forest sites. The goal of this study was to determine whether the formation of herbaceous vegetation was dependent on the tree species planted on the spoil of a reclaimed oil shale opencast in northeastern Estonia. From 2002 to 2005, the vegetation in eight different site types (distinguished according to the age and composition of tree layer) was surveyed. The results confirmed that the development of herbaceous vegetation was controlled by the dominant species of tree layer. The highest number of herbaceous species was recorded in naturally developed mixed stands. As compared with other site types, the herb layers of the pioneer stage (the vegetation in recently reclaimed sites) and alder stands were the most different. In the terms of the competitive, stress-tolerant, and ruderal strategy types, alder (Alnus spp.) stands tended to enhance the growth of competitors, whereas stress-tolerant species were more abundant in pine stands. Herbaceous vegetation remained sparse in coniferous stands, whereas broad-leaved trees tended to improve the performance of herbaceous species. Our results concur with the suggestions that planting with different tree species is one prerequisite for the development of diverse herb layer.
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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) | 0.001 | 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".