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Record W1996479166 · doi:10.5539/jgg.v4n4p1

Rill Hydraulics - An Experimental Study on Gully Basin in Lateritic Upland of Paschim Medinipur, West Bengal, India

2012· article· en· W1996479166 on OpenAlexvenueno aff
Pravat Kumar Shit, Ramkrishna Maiti

Bibliographic record

VenueJournal of Geography and Geology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsFroude numberRillHydrology (agriculture)Surface runoffStructural basinGeologyWest bengalHydraulicsHydraulic roughnessGeomorphologyFlow (mathematics)Environmental scienceSurface finishGeotechnical engineeringGeometryErosionMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper reports the results of field investigations aimed to establish relationship between morphologic and hydraulic characteristics of rills. A rill network within a small Rangamati Gully Basin, on the bank of Kansai at Paschim Medinipur, in West Bengal, India of 256 m2 was mapped. Experimental basin is exposed to natural rainfall of varying intensity and characterised with an average of 25-35% slope gradient. The depth, length, gradient, width, runoff contributing areas of all the 33 rills within the basin were recorded and thoroughly mapped. Present study incorporates close monitoring of runoff and velocity along the channels during a storm on 31.08.2010 at 15 minutes interval using dye tracer technique. The velocity along each rill was linked with gradient, width-depth ratio and Manning’s roughness coefficient. Analysis shows that flow velocity is not directly controlled by rill gradient (R2=0.15), but is influenced by hydraulic roughness coefficient (R2=0.53). Finally, the relationship between Reynolds number and Froude number reveals that sub-critical turbulent flow is responsible for rilling process. No significant relationship between W/D ratio and channel roughness (n) can be established (R2=0.083) by the present study.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2012
Admission routes1
Has abstractyes

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