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Record W4251932538 · doi:10.32920/ryerson.14657289

Sustainable Urban Implants

2021· preprint· en· W4251932538 on OpenAlexaboutno aff
Meldan Kutertan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningWork (physics)ArchitectureBusinessUrban planningSustainable developmentSustainable communitySustainable transportSustainable designSustainabilityCivil engineeringEngineeringGeographyPolitical scienceEcology

Abstract

fetched live from OpenAlex

"Sustainable Urban Implants" (SUIs) are sustainable urban cores based on principles of sustainable urban planning and sustainable architectural design practices that are melded together to achieve a most desirable sustainable living environment. As a combined force of urban planning, architecture and community interaction, this type of sustainable development is capable of changing fossil fuel-based city structures. It provides an alternative lifestyle that is enjoyable, economical, diverse and natural and that can enliven communities and strengthen social interaction. Planting SUIs on brownfields of Toronto suburbs, near public transportation nodes, is the strategy that has been identified as being effective and one that has been used in this thesis. As a result of this, private car-based commuting will decrease and public transportation will improve. An improvement of the lifestyle of suburbs will also be observed as current residents of the areas surrounding SUIs will also benefit from the newly provided urban spaces and work places that create employment opportunities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.255
Threshold uncertainty score0.852

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.2550.096

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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