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Record W1520564701

하천 수리특성을 고려한 확률론적 유속공식에 의한 하천유량 산정

2009· article· ko· W1520564701 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.021

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