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Record W4313815653 · doi:10.18280/ijdne.170608

Growth Morphology of Settlement on the Riverside Musi in Palembang

2022· article· en· W4313815653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsnot available
FundersUniversitas SriwijayaUniversitas Brawijaya
KeywordsHuman settlementLand reclamationHarmony (color)Settlement (finance)GeographyUrban morphologyEnvironmental planningUrban planningCivil engineeringArchaeologyBusinessEngineering

Abstract

fetched live from OpenAlex

The growth of settlements on the banks of the Musi River, Palembang as a center of trade and government cannot be separated from the existence of rivers and their flows which then change the morphology of the riverbanks. The morphological development of the city formed as a result of riverbank reclamation is strongly influenced by endogenous and exogenous forces. Physical development by the community that is not in harmony with the river causes environmental problems that affect the quality of the residential environment. The research method uses a qualitative case study approach to explore information on the reality of the field in the research area. Each segment is analyzed for settlement growth which is formulated into four categories and then produces a theme. Overall, the morphology of the settlement growth of the Musi Riverside is in two patterns, namely with urban growth where the riverside settlement area has a cluster and linear pattern that has an impact on the economic growth of the settlement and is not in harmony with the morphology of the riverbank with all its mixing activities. Furthermore, linear growth, where the growth of suburban settlements is only concentrated in areas that provide road access, and riverside land is still a water catchment area.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.334

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 head0.020
GPT teacher head0.226
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