MétaCan
Menu
Back to cohort
Record W4390650811 · doi:10.33022/ijcs.v12i6.3486

Pemodelan Simulasi Aliran Udara terhadap Bangunan 3d Berbasis CityGML dan Computational Fluid Dynamics

2023· article· id· W4390650811 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

VenueIndonesian Journal of Computer Science · 2023
Typearticle
Languageid
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Lingkungan di wilayah kota Malang mengalami pertumbuhan permukiman yang semakin padat serta tata kota semakin tak teratur, sehingga menyebabkan suhu menjadi panas dan warga menjadi tidak nyaman. Angin adalah pergerakan udara yang terjadi karena adanya tekanan udara dari tekanan tinggi ke tekanan rendah. Untuk mengetahui pergerakan aliran udara tersebut, pada penelitian ini dibuat suatu bentuk pemodelan simulasi aliran udara CFD dengan menggunakan Ansys dan bentuk pemodelan 3D CityGML pada LoD 2. Pembuatan simulasi aliran udara ini menggunakan data angin yang diperoleh dari BMKG dan dianalisa untuk membandingkan nilai RMSE dengan data observasi lapangan yang didapatkan dengan menggunakan 2 alat hand anemometer digital. Hasil simulasi aliran udara menunjukkan pengaruh model 3D LOD2 terhadap aliran angin berupa perubahan arah dan kecepatan.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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