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Pembuatan Peta 3D Urban Model Untuk Visualisasi Dampak Banjir

2023· article· id· W4393362051 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFaktor Exacta · 2023
Typearticle
Languageid
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

3D modeling is a process to create 3D objects that you want to put in a visual form. A 3D model is a mathematical representation of any three-dimensional object (either inanimate or living). A model is technically graphical until it is visually displayed. Because 3D models are not limited to virtual space. A model can be displayed visually as a two-dimensional image through a process called 3D rendering, or used in non-graphical computer simulations and calculations. In this case, the geographic information system can present a form of modeling of a hydrological phenomenon such as flooding in an area. This study aims to analyze the flood and visualize it in the form of three-dimensional modeling to see the impact of a flood threat due to the Jelateng river’s overflow. This study emphasizes information related to the impact caused by the overflow of the Jelateng river. Making a 3D urban map model will be used as a representation of the appearance of the Jelateng river area and then it will be visualized using DEMNAS data on the arcscene with the animation manager so that the visualization can be seen according to the scenario that will be carried out. The results of the research will be published in a journal so that it can be a reference for some users who want to know related information from the research results

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.009

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 head0.028
GPT teacher head0.272
Teacher spread0.243 · 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