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Pedestrian spaces of historical center of st. petersburg: problems and future development

2018· article· en· W2905535955 on OpenAlexaboutno aff
A F Krasnopolskii

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicTransportation Systems and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianQuarter (Canadian coin)GeographyRecreationCenter (category theory)St petersburgAttractivenessEnvironmental planningTransport engineeringRegional scienceEngineeringPolitical scienceArchaeologyRussian federationPsychology

Abstract

fetched live from OpenAlex

The emphasis is made on the specificity of pedestrian zones development in the historical center, most of the focus is on their role in the identification of unique architectural and landscape resources and the possibility of their forming in a residential zone on the intra-quarter territories. The factors, influencing the use of the historical and cultural potential of the central part of St. Petersburg in the creation of pedestrian zones, are revealed. It is noted that the optimization of pedestrian traffic, especially the organization of automobile traffic-free zones, can affect a number of indicators of the urban environment, these indicators are considered. The exceptional role of the Neva embankments in the system of urban highways is revealed. The intra-quarter territories of the historical center of St. Petersburg are analyzed from the point of view of the possibility of creating pedestrian zones in them. Sanitary and hygienic qualities, recreational opportunities, commercial attractiveness of intra-quarter pedestrian zones are considered. Suggestions on the development of pedestrian zones for the appropriate purpose are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.197
Teacher spread0.177 · 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 designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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