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Sustainable Urban Planning and Climate Change Adaptation: Disaster Protection Strategies for Global Urban Development

2024· article· en· W4394931569 on OpenAlexaff
Ziying Yin

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMcGill University
Fundersnot available
KeywordsUrbanizationProsperityClimate changeSustainable developmentEnvironmental planningNatural disasterSustainabilityUrban planningNatural resource economicsEnvironmental resource managementBusinessGeographyEconomic growthPolitical scienceEconomicsEngineeringEcologyCivil engineeringMeteorology

Abstract

fetched live from OpenAlex

Urban and rural differentiation emerged since the start of industrial development of human societies. With the need of large amount of human capital to adapt to mass production, rapid urbanization, as a natural course, changed the nature of human lifestyle permanently. Unintended consequences appear along this process of significant economic growth and prosperity, with climate change, which is due to carbon emission, as one of the most prominent problems. Climate change brings various disasters, with typhoons, floods, and wildfires as to name a few. Transition to urban lifestyles, without the realization of sustainability, is one of the primary issues nowadays. This paper discusses the process of urbanization and the usage of crude oils as an automatic method to fuel the cities, and their relations with climate change. It then proposes important criteria to follow for sustainable urban constructions and strategies for disaster prevention.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.051
GPT teacher head0.363
Teacher spread0.313 · 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
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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