GREENING OF INDUSTRIAL POULTRY TERRITORIES AS ONE OF THE WAYS OF REDUCING THE NEGATIVE IMPACT ON THE ENVIRONMENT
Bibliographic record
Abstract
In the area of operation of large poultry farms, atmospheric air can be polluted by microorganisms, dust, bad organic compounds that are products of decomposition of organic waste, as well as oxides of nitrogen, sulfur, and carbon. The epizootic process in conditions of intensive poultry farming is distinguished by the fact that even weakly virulent and conditionally pathogenic microflora, as a result of recirculation and frequent changes of generations, can increase virulence properties and create a serious epizootic and epidemiological threat. The purpose of the work is to demonstrate greening of industrial poultry farms as one of the ways to reduce the negative impact on the natural environment. The presented material in the work: systematization of achievements in theory and practical application of green plantings to prevent environmental pollution using various options for improving the territories of poultry enterprises. The research methodology is based on general research methods of analysis and synthesis, induction and deduction, observation and abstraction, which systematize the achievements of the theory and practice of modeling systems of various nature in the natural sciences and, in particular, in animal husbandry and plant breeding. As a result of research and experiments, the expediency and effectiveness of using green plantings, which have a great deodorizing ability – retain and absorb gases, have been theoretically and practically substantiated. The positive effect of greenery on physiological indicators (thermoregulation, oxidation processes) and animal productivity has been practically established. The dustiness of the air under the trees is less than in the open area: in May by 20%, in June by 21.8%, in July by 34.1%, in August by 27.7% and in September by 38.7%. During the entire growing season, the average concentration of dust in the open area was 0.9 mg/m3 of air, and under trees – 0.52 mg/m3 of air, i.e. 42.2% less. The most gas-resistant trees and shrubs are: Pennsylvania maple, sycamore, Manchurian hazel, three-spined gorse, gooseberry (all species), common ivy, Cossack juniper, Canadian and Daur moonseed, large-leaved poplar, gray poplar, Canadian poplar, pomegranate, ailant the highest, white acacia, amorphous shrub, pinnate birch, common privet, white mulberry. By alternating plantations with open areas around the places of emission of harmful gases, it is possible to significantly increase the ventilation of the territory in the vertical direction. In a hot climate, green spaces provide protection from dry and dusty winds and at the same time contribute to airing the territory of the enterprise, cleaning its atmosphere from harmful pollutants. Value/originality. The effectiveness of the proposed method of preventing environmental pollution provides new opportunities for poultry enterprises in the preservation of ecosystems and sustainable development of territories. Measures for the protection of atmospheric air should be carried out on the basis of widely distributed research works devoted to the study of the quantitative concentration of pollutants entering the atmosphere and the distance of their spread.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".