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Record W4249168211 · doi:10.29122/alami.v2i1.2796

MODEL PENATAAN KAWASAN SITU UNTUK REDUKSI RISIKO BENCANA BANJIR DI JAKARTA

2018· article· id· W4249168211 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

VenueJurnal Alami Jurnal Teknologi Reduksi Risiko Bencana · 2018
Typearticle
Languageid
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryPhysicsGeography

Abstract

fetched live from OpenAlex

Banjir secara ekologis merupakan peristiwa fisik yang terjadi di dalam lingkungan hidup manusia. Keterbatasan lahan yang tidak dapat menampung pembangunan di wilayah Jakarta, memicu pembangunan di daerah sekitar aliran sungai. Pembangunan di daerah sekitar aliran sungai telah mengubah pola penggunaan lahan. Daerah sekitar sungai merupakan dataran banjir sehingga jika daerah tersebut dibangun menjadi tempat tinggal ataupun permukiman maka permukiman tersebut akan terkena dampak jika air sungai meluap dan menyebabkan banjir. Wilayah Jakarta dilewati oleh 13 sungai yang sering meluap pada musim hujan termasuk Kali Angke dan Pesanggrahan. Penggunaan lahan pada DAS Angke-Pesanggrahan sangat mempengaruhi terjadinya banjir di DAS Angke - Pesanggrahan. Semakin banyak area terbangun pada DAS tersebut maka area penyerapan air juga semakin sedikit sehingga membuat air di Kali Angke dan Pesanggrahan meluap dan menyebabkan banjir. Sehingga perlu adanya klasifikasi tipe zona pemukiman dan juga beberapa metode penyerapan air seperti biopori dan sumur resapan sebagai salah satu cara untuk reduksi risiko banjir.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.005
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0120.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0010.004

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 head0.031
GPT teacher head0.278
Teacher spread0.247 · 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