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Record W4256425259 · doi:10.22215/etd/2016-11350

Parametric Urbanism

2016· dissertation· es· W4256425259 on OpenAlexaboutno aff
Sebastian Wooff

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationMultitudeUrbanismPopulationGeographyEconomic geographyParametric statisticsUrban designEnvironmental planningRegional scienceUrban planningEconomic growthPolitical scienceCivil engineeringEconomicsEngineeringSociologyDemographyMathematicsArchitectureArchaeologyStatistics

Abstract

fetched live from OpenAlex

In 2011, the global population reached 7 billion people -- with more than half residing in urban areas. The world’s population is expected to increase to 9.3 billion by 2050. As the level of urbanization is also expected to rise (from 50 to 70% globally), the bulk of population increase will occur in cities. As a result, even those of us in land-rich countries like Canada will be expected to live at higher densities. Responding to this challenge, this thesis explores the potential of parametric design to facilitate the process of urban design – specifically in assessing the effects of various forces, targets policies and bylaws on the form and density of the city. As such, the goal is to incorporate a multitude of variables into a cohesive system to evaluate the dynamic effects of different parameters on each other and on the form of cities.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score1.000

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)0.0010.003

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designOther design
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
Published2016
Admission routes1
Has abstractyes

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