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URBAN PLANNING MODEL OF WATERFRONT RECREATION ZONES IN THE ALTAI MOUNTAIN REGION

2020· article· en· W3116981684 on OpenAlexaboutno aff
P. V. Skryabin, Natal’ya Sergeeva

Bibliographic record

VenueArchitecture and Engineering · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationNatural (archaeology)GeographyQuarter (Canadian coin)Environmental planningDocumentationUrban planningSettlement (finance)TourismNatural landscapeLand useRegional planningEnvironmental resource managementEnvironmental protectionCivil engineeringArchaeologyBusinessComputer scienceEnvironmental sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Introduction: Over the past quarter of a century, the issue of urban development within regional settlement systems has not been a priority for most policy-makers and professionals. Much more attention has been focused on the issues arising from the expansion of major metropolises: Moscow, St. Petersburg, Yekaterinburg, Kazan, and several others (seven in total). In the meanwhile, the urban development of unique natural landscapes in other regions has been progressing on its own, without major supervision or proper attention from the professional community. For instance, the pristine land along Lake Baikal has undergone urban development without proper planning documentation; vast areas in the Irkutsk Region have been sold off for logging; and the Altai Territory and the Republic of Altai are seeing intensive development of unplanned recreation hubs. Purpose of the study: The study is aimed to create an urban planning model for unique natural landscapes. Methods: We used such methods as multi-factor analysis, photographic footage, opinion poll, and graphical modeling. Results: Out study results in an original model that illustrates the optimal location of new recreation hubs, mindful of preserving the unique environmental qualities of the natural landscape.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.187

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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations4
Published2020
Admission routes1
Has abstractyes

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