MONITORING OF PARK AREAS OF THE CITY OF PENZA, IN ORDER TO IDENTIFY NEGATIVE PROCESSES IN THEIR TERRITORIES
Bibliographic record
Abstract
Today, more than half of the world's population lives in cities. Expanding, absorbing woods, meadows, ponds and swamps, covering the earth with pavement, rushing up to the sky and the depths of the city are changing the face of our planet. Given the ability of green spaces to have a positive impact on the environment, they should be as close as possible to the place of life, work, study and rest of people. It is very important that the city was a biocenosis is not absolutely favorable, but at least not harmful to human health. One of the solutions to the city's problems is the organization of parks. Green spaces not only create favorable microclimatic and sanitary conditions, but also increase the artistic expressiveness of architectural ensembles. Parks solve a number of environmental problems in the city. First, reduce air pollution. Best absorb the sounds of trees and shrubs with thick crowns, dense large leaves, with a large number of small branches (maple Holly, Linden, oak petiolate, poplar canadian). The penetration of noise in the Park prevent dirt open space - grass. Reducing noise, the Park meets the task of reducing dust and air pollution. Polluted air in the city, poisoning the blood with carbon monoxide, causes a non-smoker the same harm as Smoking a pack of cigarettes a day. And the organization of the Park with multi-row strips of trees and shrubs with a width of 50 m and a height of 15-20 m reduces the level of air pollution by 70-75 %. Coming to the Park, a person does not leave the boundaries of the city and at the same time experiences psychoemotional relief, removal of irritability. Caring for green spaces, protecting and multiplying them, every resident of the city can make a contribution to improving the ecology of the city. But, despite the development and the desire to make the city more green, in this area there are many problems related to the proper placement, maintenance and use of parks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".