Non-Forest Woody Vegetation (Scattered Greenery) Case Study of the Samopse Settlement, Czech Republic
Bibliographic record
Abstract
The development and management of the Czech landscape has been influenced by several key factors in the past. One important factor is the development of society, particularly political changes and ecological development. Others include the level of knowledge and understanding of technologies, scientific knowledge and the non-productive importance of the landscape, as well as the attitude of society and individuals towards the landscape. In the past, non-forest woody vegetation was a standard part of the European agricultural landscape and formed its typical appearance. The onset of collective farming during the second half of the twentieth century resulted in transforming the landscape into open fields without permanent vegetation. The landscape became everyone’s and no-one’s and was subject to orders, tasks and plans. The key goal of this article is to evaluate non-forest woody vegetation from a landscape-ecological aspect and compare the occurrence of non-forest woody vegetation in four landscape types. The submitted study presents various types of non-forest woody vegetation, the species present in elements of scattered greenery and the spatial arrangement depending on the method of management and use of the territory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".