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Record W2785048702

TRANSFORMASI FISIK WILAYAH PERI URBAN DI KELURAHAN MADEGONDO KECAMATAN GROGOL KABUPATEN SUKOHARJO

2018· article· id· W2785048702 on OpenAlexaff
Wildani Miftahul Fauzan, Soedwiwahjono Soedwiwahjono, Nur Miladan

Bibliographic record

VenuePlano Madani : Jurnal Perencanaan Wilayah dan Kota · 2018
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGeographyPeriLand useEnvironmental planningSocioeconomicsCivil engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

The peri-urban area is a strategic area to support the surrounding city needs. The increasing lack of urban areas makes peri-urban areas a potential new destination for city development, because of lower peri-urban land prices, large land reserves, and ease of access to a nearby city. Kelurahan Madegondo (The Administrative Village of Madegondo) is a peri-urban area directly adjacent to the Southern part of Surakarta. Defined as an administrative village, this area has developed and transformed to be a more of an urban area. This research focus on identifying transformations happened in Madegondo in the period of 15 years, specifically from 2002 to 2017. Using Geographic Information System (GIS) and System for Automated Geoscientific Analyses (SAGA) in the process. This study has identified many crucial transformations occurred in Madegondo. Major land-use change has occurred in the period of 15 years, 54 hectares of non-covered land has itself covered with buildings. Building density has shifted in 15 years from mid-density (40,82%) to very high-density (78,39%). Another change identified is circulation transformation, which characteristics have grown to be more complex in 2017.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.000
Research integrity0.0010.002
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 head0.024
GPT teacher head0.209
Teacher spread0.185 · 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 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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