Ecological analysis of the flora of the Kremenets Mountains National Nature Park (on the example of the mountains Divochi Skeli, Strakhova, Masliatyn, Chercha, Zamkova, Bozha)
Bibliographic record
Abstract
One of the elements that ensure the sustainability of the environment is the vegetation cover. The vegetation cover contributes to the balanced functioning of ecosystems. It is the national parks rich in phytodiversity that attract the attention of scientists. The article presents an ecological analysis of the flora of the Kremenets Mountains National Park. The research was carried out in the areas of the mountains Divochi Skeli, Strakhova, Masliatyn, Chercha, Zamkova, Bozha. 10 florocoenotypes were identified in the study areas. The most numerous is the nemoral forest coenotype. The flora was analyzed for abiotic factors such as light, temperature, humidity, soil. It is these indicators that have an important impact on the formation of vegetation cover and the life processes of plants. It was found that facultative heliophytes, mesothermal plants, mesophytes, and mesotrophs predominate in the study areas. The vegetation cover has clear forest-steppe features. The classification of life forms of plants is given. Relicts and endemics are confined to steppe and calcepetrophilic areas. Flora synanthropization is observed on the study areas.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".