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Record W3215086644 · doi:10.38028/esi.2021.23.3.002

COMPONENTS OF THE ONTOLOGICAL KNOWLEDGE SPACE FOR ASSESSING THE IMPACT OF ENERGY ON THE QUALITY OF LIFE OF THE POPULATION

2021· article· ru· W3215086644 on OpenAlexfundno aff
Т.Н. Ворожцова, Иванова Ирина Юрьевна, Елена Петровна Майсюк

Bibliographic record

VenueИнформационные и математические технологии в науке и управлении · 2021
Typearticle
Languageru
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersSiberian Branch, Russian Academy of SciencesRussian Foundation for Basic ResearchMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsOntologyComponent (thermodynamics)Space (punctuation)PopulationQuality (philosophy)Natural (archaeology)Quality of life (healthcare)Energy (signal processing)Environmental qualityComputer scienceEnvironmental resource managementEcologyGeographySociologyPsychologyEnvironmental scienceEpistemologyMathematicsBiology

Abstract

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В работе рассматривается онтологический подход к интеграции знаний для поддержки междисциплинарных исследований в области энергетики и экологии с точки зрения оценки качества жизни, предполагающих интеграцию экологической и социальной составляющих. Экологическая составляющая определяется природно-климатическими условиями и состоянием элементов природной среды конкретной территории. Социальная - подразумевает обеспечение потребностей населения в электрической и тепловой энергии, необходимых для комфортного проживания. Для сопоставления положительного и отрицательного влияния функционирования объектов энергетики на население рассматриваются индикаторы качества жизни, как способ оценки этого влияния. Использование онтологического подхода обеспечивает наглядное представление и интеграцию знаний разных предметных областей. Представлены онтологии, детализирующие базовые понятия предметной области исследований антропогенного влияния объектов энергетики, качества жизни и отражающие их интеграцию в едином онтологическом пространстве знаний. This paper examines an ontological approach to integrating knowledge to support interdisciplinary studies in energy and ecology in terms of quality of life assessment. These studies involve the integration of environmental and social components. The environmental component is determined by natural and climatic conditions and the state of elements of the natural environment of a particular territory. The social component implies meeting the demand of the population for electricity and heat, which are necessary for comfortable living. To compare the positive and negative impacts of the operation of energy facilities on the natural environment and the population, quality of life metrics are considered as a way to assess these impacts. We present ontologies detailing the basic concepts of the subject area of research on the anthropogenic impact of energy facilities and quality of life, and reflecting their integration into a single ontological space of knowledge. The use of the ontological approach provides a visual representation and integration of knowledge from different subject areas.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0110.009
Science and technology studies0.0020.003
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.171
GPT teacher head0.450
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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