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Record W4396552028 · doi:10.1051/e3sconf/202451903013

Impact of Debris and Non-Debris Flow in Flood Damage at River Confluence, Case Study of Miu-Tuva River Confluence

2024· article· en· W4396552028 on OpenAlexaff
Mayjen Sinema Telaumbanua, Eka Oktariyanto Nugroho, Hadi Kardhana, Muhamad Ismaun, Ricky Pondaag, Rimamunanda Ekamarta, Fatma Nurkhaerani, Azman Syah Barran Roesbianto, Mona Mostafa

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsConfluenceDebris flowDebrisFlood mythHydrology (agriculture)GeologyFloodplainEnvironmental scienceGeographyArchaeologyGeotechnical engineeringCartographyOceanography

Abstract

fetched live from OpenAlex

Hydrodynamic flow is the flow and its properties that move in a natural or artificial condition. While in natural conditions, the flow event that often occurs in the flow of water by carrying material particles with various gradation sourced from the catchment area. The large concentration of material in a body of water will influence the shape of the river’s cross section for a certain period of time until it reaches an equilibrium condition. The purpose of this paper is to determine the hydrodynamic pattern that occurs at the confluence of the Miu River and Tuva River which is caused by fluid flow in non-debris (Newtonian) and debris flows (Non-Newtonian) so that control of the destructive power of water in the river can be optimized. The velocity of non-newtonian flow (debris) is lower than that of newtonian flow (water) because the viscosity of non-newtonian flow (debris) is higher than that of newtonian flow. On the other side, with the incoming discharge flow from the tributary, the velocity at the river cross section tends to increase on the far side of the direction of the incoming tributary flow.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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; a candidate call from one teacher head, not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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