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Estimation of debris waste generation

2025· article· en· W4413170793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Safety and Natural Resources · 2025
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDebrisEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.206
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it