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Sustainable Resilience in Urban Land Use

2022· book-chapter· en· W4226082282 on OpenAlex
José́ G. Vargas-Hernández, Elsa Patricia Orozco-Quijano

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePractice, progress, and proficiency in sustainability · 2022
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSustainabilityEcosystem servicesResilience (materials science)Environmental resource managementEnvironmental planningLand useSustainable developmentPsychological resilienceBusinessUrban planningValue (mathematics)EcosystemBiodiversityGeographyEcologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Humanity is facing a series of important challenges, global warming being one of the most important. Consequently, sustainability and resilience have become key elements in providing a better response to the crisis and in maintaining an equilibrium between ecology, economics, and various social domains. The design and use of urban land should consider the inclusion of a multi-functional green infrastructure to obtain different benefits, from ecosystem services to value creation. Additionally, the urban land-use planning system contributes to economic growth, social development, and environmental sustainability, while biodiversity is able to provide renewal and reorganization capacities for changes in social-ecosystems. All these elements bring forth a different paradigm for the future decisions of communities.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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