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Record W4393069698 · doi:10.32782/2224-6282/190-7

THE ROLE AND PLACE OF THE BIOSPHERE ECO-CITY IN THE MODERN MODEL OF HUMAN DEVELOPMENT

2024· article· en· W4393069698 on OpenAlexaff

Bibliographic record

VenueEconomic scope · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsBusiness Development Bank of Canada
Fundersnot available
KeywordsBiosphereSustainable developmentSustainabilityUrbanizationEnvironmental planningEnvironmental resource managementGeographyPolitical scienceEcologyEnvironmental scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

This article explores the significance of biosphere eco-cities in contemporary human development by examining their evolution in response to the challenges faced by traditional urban development models. It explores the key characteristics of biosphere eco-cities, which are guided by the principles of sustainability and have unique features that distinguish them in the modern development landscape. Trends in the development of biosphere eco-cities, such as green technology and regenerative design, are discussed. The importance of biosphere eco-cities for achieving global sustainable development goals, including the UN Sustainable Development Goals, is highlighted. The concept of a biosphere eco-city envisages the creation of urban spaces that prioritize environmental sustainability, striving to achieve a harmonious balance between human activity and the natural environment. These cities integrate the principles of environmental protection, resource efficiency, and social well-being into their design and development. Biospheric eco-cities are key to addressing the challenges posed by rapid urbanization, environmental degradation, and climate change. They seek to create urban environments that are not only sustainable but also contribute to the overall well-being of their inhabitants. This approach is essential to achieving a more balanced and sustainable human-nature relationship.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.957

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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