Ecological sustainability of trees of protective forests against air pollution
Bibliographic record
Abstract
The aim is to analyze the resistance of tree and shrub vegetation used to create protective forest belts to air pollution on the basis of experimental studies conducted by other scientists. The research was carried out on the basis of the development of experimental materials on the resistance of tree and shrub vegetation to atmospheric pollution by dust and gases, presented in the works of famous scientists. The obtained results were generalized, the probability of growing gas- and dust-resistant trees in the conditions of climate change in relation to their drought resistance was estimated and the most resistant species of trees and shrubs were recommended. Also identified species of plants that can act as bioindicators of air pollution. Studied by Prysedsky Yu.G. (2014) plant species in terms of resistance to atmospheric pollutants with sulfur, nitrogen and fluorine compounds were divided into four groups: tolerant (resistant), moderately damaged, unstable and with variable resistance. The group of resistant species includes prickly pear, common oak, Tatar honeysuckle and caragana arborescens. These species can be used to create protective forest belts. At the same time, reliable plant indicators of sulfur, nitrogen and fluoride oxides in the air will be unstable plant species — mountain ash and poplar Bolle. Classification of trees and shrubs in terms of their resistance to atmospheric smoke divides plants into three groups: stable, relatively stable, unstable, with the allocation of primary and secondary wood species for forest belts, as well as shrubs. The most resistant to atmospheric smoke are white acacia, elm, white willow, forest pear, poplar, hazel, juniper, forest apple. They can be the main components of protective forest belts in the area of atmospheric smoke. Unstable species — red oak, Scots pine, horse chestnut, viburnum — are bioindicators of atmospheric smoke. There is also a classification of tree species by dust retention M.I. Kalinin (1991). Behind it the most dust of 1 m2 of leaves is retained by white mulberry — 8.1 g, weeping willow — 8.1 g, three-pricked gladiolus — 5.1 g, elm — 4.1 g and field maple — 3.6 g. One tree absorbs the most dust during the growing season in weeping willow — 37.9 kg, Canadian poplar — 34.1 kg, white mulberry — 31.3 kg, ash — 27.1–29.6 kg, maple — 29,2 kg and high island — 24.2 kg. According to Vergeles (2000), poplars have the highest average relative dust resistance — 180 points, common ash — 170, bitter horse chestnut and linden leaf heart — 100 points each.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".