MétaCan
Menu
Back to cohort
Record W2138936825 · doi:10.5539/jgg.v6n4p80

Dynamics of Urban Growth in Semarang Metropolitan – Central Java: An Examination Based on Built-Up Area and Population Change

2014· article· en· W2138936825 on OpenAlexvenueno aff
Wiwandari Handayani, Iwan Rudiarto

Bibliographic record

VenueJournal of Geography and Geology · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
FundersUniversitas Diponegoro
KeywordsMetropolitan areaSuburbanizationGeographyJavaBuilt-up areaPopulationPopulation growthRegional scienceEconomic geographyLand useComputer scienceCivil engineeringDemographyEngineering

Abstract

fetched live from OpenAlex

Representation of rapid urban growth followed by high rate of land conversion is clearly observed in the case of Semarang Metropolitan. Located in Java Island, this capital city has been performing as the largest urban area in the Central Java Province. This paper aims to examine urban growth pattern in Semarang Metropolitan by applying two main indicators, i.e., (1) additional built-up area 1972-2009 indicated as land conversion, and (2) population change between 1991-2008. Accordingly, distance is regard as an important parameter to further examine the emerging pattern based on the two indicators. Remote Sensing (RS) and Geographical Information System (GIS) were used to analyze satellite images and built-up area development from the different periods. The analyses result show that suburbanization has been taken place in Semarang Metropolitan. The emerging pattern is very common in Asian cities as it is very much similar with the pattern in view selected cities in the neighboring countries (Jakarta, Bangkok, Metro Manila).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.206
Teacher spread0.190 · 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

Citations34
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geography and GeologySame topicEconomic Growth and Fiscal PoliciesFrench-language works237,207