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Record W1520564701

하천 수리특성을 고려한 확률론적 유속공식에 의한 하천유량 산정

2009· article· ko· W1520564701 on OpenAlexaboutno aff
추태호, 이상진, 박상우, 오륜수

Bibliographic record

Venue대한토목학회논문집 B · 2009
Typearticle
Languageko
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsnot available
Fundersnot available
KeywordsRating curveStage (stratigraphy)Entropy (arrow of time)Principle of maximum entropyMathematicsProbabilistic logicGeologyStatisticsThermodynamicsPhysicsGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

To improve stage-discharge curve equation considering water level's function, this study suggested the method that can efficiently compute rivers discharge based on hydraulic characteristics such as river width, area, channel bed slope and entropy concept adopting probabilistic approach. This scheme is proposed to estimate discharge from the velocity formulation based on the entropy function in the equilibrium state derived from the relation between mean and maximum flow velocity. It has been tested using field and laboratory hydraulic data collected from the Alberta university in Canada. As a result it was found that the method proposed in this study was more efficient and accurate comparing with the traditional stage-discharge curve equation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2009
Admission routes1
Has abstractyes

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Same venue대한토목학회논문집 BSame topicHydrological Forecasting Using AIFrench-language works237,207