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Record W4412733929 · doi:10.23917/sinektika.vi.4280

Flood Risk Management (Studi Kasus: Sungai Bahodopi, Di Kecamatan Bahodopi, Kabupaten Morowali)

2025· article· id· W4412733929 on OpenAlexaff
Ikram Ikram, Gidion Tefa

Bibliographic record

VenueSinektika Jurnal Arsitektur · 2025
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicGlobal Socioeconomic and Political Dynamics
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsFlood risk managementGeographyFlood mythWater resource managementCartographyEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Tujuan dari studi ini adalah untuk mendapatkan gambaran tentang pengaruh metode Soil Bioengineering dan pendekatan non-struktual terhadap peningkatan Flood Risk Management. Metode penulisan berdasarkan studi literatur dan studi preseden yang menjelaskan berbagai sumber yang berkaitan dengan penerapan Soil Bioengineering sebagai elemen Green Infrastructure dan pendekatan non-struktural untuk menanggulangi bencana banjir akbiat aktivitas industri dan permukiman penduduk di sekitar bantaran sungai. Karakteristik vegetasi akar dalam dan batang kokoh diharapkan dapat menanggulangi terjadinya bencana banjir ke dalam areal permukiman penduduk, dan pendekatan non-struktural juga dapat diterapkan, seperti pengelolaan Daerah Aliran Sungai (DAS) Sungai Bahodopi, yaitu peraturan tata guna lahan dan beberapa aturan mengenai larangan penggunaan tanah disekitar riparian sungai yang berpotensi mengurangi daerah resapan air dan menyebabkan terjadinya bencana banjir. Dengan demikian penerapan Flood Risk Management yang direncanakan dapat mengatasi bencana banjir di areal permukiman padat penduduk.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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