Оцінка обсягів утворення відходів від руйнувань
Bibliographic record
Abstract
As of the beginning of 2022, Ukraine had approximately 8 million residential buildings with a total residential area of 892.1 million square meters. The number of apartments in residential buildings and non-residential structures in Ukraine totaled 15.5 million units. Due to hostilities and regular shelling, the number of damaged and destroyed residential buildings is increasing daily. As of January 2024, over 250,000 buildings have been damaged or destroyed, including 222,000 private homes, over 27,000 multi-apartment buildings, and 526 dormitories. The direct damage from the destruction of these objects is estimated at 58.9 billion USD. The regions with the most destroyed residential buildings include Donetsk, Kyiv, Luhansk, Kharkiv, Chernihiv, and Kherson. The generation of a large amount of debris waste requires planning for the demolition of destroyed buildings, dismantling of rubble, transportation to temporary waste storage sites, and the planning and selection of equipment for processing debris waste. Currently, Ukraine lacks an official methodology for estimating the volume of debris waste. To provide scientifically grounded methodological recommendations for estimating the amount of debris waste, the experience of various countries that have experienced technological disasters or natural disasters was studied, including Japan, Canada, and countries in the Middle East. The experience of managing debris waste in the communities of Chernihiv, Kyiv, and Mykolaiv regions was also examined. As a result of reviewing international experience, it was determined that the most appropriate approach is to use specific waste generation indicators per unit area – the waste generation rate. It is accepted that the area used to determine the volume of debris waste should be within the boundaries of the destruction or demolition (section, floor, or part of the building). A calculation of the waste generation rate was conducted based on the consumption of primary materials for typical series of multi-apartment residential buildings, monolithic-frame multi-apartment residential buildings, and residential buildings in cottage developments, including buildings of preschool and general secondary education institutions and hospitals.The resulting waste generation rates can be used to estimate the calculated amount of waste generated due to the damage or destruction of residential buildings, buildings of general secondary and preschool education institutions, and healthcare facilities as a result of hostilities, terrorist acts, sabotage, or work to eliminate their consequences. The waste generation rates do not apply to engineering structures, transportation infrastructure, or non-residential buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".