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Solving problems of garbage and waste disposal as a criterion of public administration efficiency

2022· article· en· W4290805457 on OpenAlexaboutno aff
М. N. Kulapov, P. Sergeev, P. A. Karasev

Bibliographic record

VenueVestnik NSUEM · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Sustainability and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsGarbageReuseContext (archaeology)IncentiveBusinessLegislatureEnvironmental planningPopulationEngineeringEnvironmental resource managementPolitical scienceGeographyWaste managementEconomicsSociology

Abstract

fetched live from OpenAlex

The article analyzed the main problems of the world development related to the increasing pollution of oceans, land and space at the hands of human population and as a result of the activities of legal entities which are producers of the most types of products. Inter-country (by the example of the USA, Canada and some European countries) comparison of the experience in solving the waste management problem in the context of legislative, economical and organizational measures was made. The authors suggested several indicators as the criteria of assessment of efficiency of the system of the measures for prevention of waste formation, recycling, removal and reuse. The problematics in the Russian Federation was also assessed, including the progress of the “Ecology” national project implementation, and the recommendations regarding increase of efficiency of the state and municipal management in this field of social development in dual context of the commitment to the experience of the North American continent and own way of formation of a new model of waste management, with specialized hubs located near large cities and industrial centers acting as central cores, as well as creation of the incentive system for individuals and legal entities regarding the employment of separate waste collection technology.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.880

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)0.0010.000

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 head0.007
GPT teacher head0.213
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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