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Urban morphotypes and functional diversity of city environment

2020· article· en· W3016688912 on OpenAlexaboutno aff
V E Stadnikov, I M Yusupova

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyQuarter (Canadian coin)Service (business)RecreationEnvironmental planningDiversity (politics)BusinessSpatial organizationRentingSpace (punctuation)PopulationPublic servicePublic spaceTransport engineeringCivil engineeringArchitectural engineeringEcologyMarketingEngineeringSociologyPolitical sciencePublic administrationArchaeologyComputer science

Abstract

fetched live from OpenAlex

Abstract On the example of the urban space of one of the largest Russian cities, Samara, various environmental morphotypes are analyzed for: mixed use in the ratio of residential and service functions; the nature of the service objects, the spatial organization of the contact of public areas with the building surrounding them. Through the analysis of rental rates of commercial premises in different parts the connection between the demands for different morphotypes of the city is considered. It is found out that regardless of the density of the population in the study area, the development of public activities in the territories depends on the spatial organization of the urban environment: street arrangement, convenience of service function’s access. Commercial demand for premises serves is an indicator of the quality of organization of public spaces of streets. Densely populated areas with chaotic development that does not form a street front are deprived of socially active spaces with developed street retail, while less populated areas with a high-density street network and quarter-perimeter buildings form active spatially organized traffic of people that support the development of street retail.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.359

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 head0.022
GPT teacher head0.156
Teacher spread0.134 · 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 designBench or experimental
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

Citations2
Published2020
Admission routes1
Has abstractyes

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