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Record W2404741757

A Study on the Landscape Elements and Preference of Rural Village - Focused on Daewon-Ri Sanoe-Myun Boeun-Gun, Chungbuk -

2013· article· en· W2404741757 on OpenAlexaboutno aff
Chungshin Park, Seung-Geun Kim

Bibliographic record

VenueJournal of the Korean Institute of Rural Architecture · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSkylineGeographyLandscape assessmentLandscape planningPreferenceRural housingNatural landscapeLandscape designUrban landscapeGovernment (linguistics)Quarter (Canadian coin)Environmental planningEnvironmental resource managementNatural (archaeology)Rural areaArchaeologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This study aims to draw the basis documents for rural landscape management of Daewon-Ri through frequency analysis of landscape elements and preference analysis of rural landscape. The results are as follows. First, according to the frequency analysis of the landscape elements, a distant view is few effect characteristics in rural village landscape planning. It is acted as the landscape elements that degree of integration and skyline of the building to see more nearby than it are the most important. In addition, in the case of the establishment of the landscape management planning, the landscape elements in the close view is the most important. Second, It is thought that the scenery which natural environments and residential quarter match is the most desirable for the par of the landscape preference in the rural village. On the other hand, about the scenery of an old historic building, the residents of a city considers it as an affirmative factor of the rural village landscape, but rural village inhabitants are negative. Finally, it is thought that the excessive public designs by government sponsored enterprise are undesirable for the scene of the village.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.454

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 science0.0010.000
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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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