Epidemiology and Ecological Distribution of Tree Tumors in the Territory of Landscape Reserve “Teply stan”
Bibliographic record
Abstract
This study focuses tumors of woody trees in the territory of landscape reserve “Teply stan” in Moscow. Abnormal swellings on the trunk of woody trees are called Tumor. Formation of a swell is an evidence of an infringement of metabolism in a tree's body, is a disease of a tree and is a cause of a tree's premature death. Tumor prevents transportation of water and minerals from roots towards the leaves of a tree and transportation of organic matters from the leaves towards roots. The purpose of this study is to find out some appropriatenesses of spreading of tumors of trees in the landscape reserve “Teply stan”. In this study, landscape reserve divided into 9 study sections according to ecological conditions. Surveillance of all trees and statistical analysis of tumor trees in studied section of landscape reserve have done. The results showed that 57 of the counted 25 thousands trees have tumors. 50 of the tumors trees are belong to birch (Betula pendula).Trees with tumors are distributed non-uniformly, generally in central and east parts of landscape reserve that had ecologic pollution. Mainly birch (Betula pendula), oak (Quercus rubor) and linden (Tilia cordata) are damaged by tumors in the studied territory. The most effected trees with tumors are birch (Betula pendula) tree population. The direct proportion between ecological situation and the number of tumors have found.
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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 | 0.001 | 0.001 |
| 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".