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Record W2084725821 · doi:10.5539/mas.v7n7p42

The Influence of Cover on the Water Quality and Suspended Solids Under the Simulated Rainfall Condition

2013· article· en· W2084725821 on OpenAlexvenueno aff
Syed Muzzamil Hussain Shah, Khamaruzaman Wan Yusof, Zahiraniza Mustaffa, Ahmad Mustafa Hashim

Bibliographic record

VenueModern Applied Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsTurbiditySurface runoffEnvironmental scienceSedimentationLeveeHydrology (agriculture)Suspended solidsErosionWater qualityPlot (graphics)Soil scienceEnvironmental engineeringSedimentMathematicsGeologyGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

Highway embankments could be of equal importance when compared with the agricultural lands for the production of sediments as it carries the detached soil particles in the water which flows over land in the form of surface runoff. The downstream sedimentation from agricultural activities has always been highlighted, whereas the road related impacts are considered trivial. This paper analyzes the results, obtained from a full scale test with rainfall simulation on the fully grass covered surface (Plot-I) and the exposed soil surface (Plot-II) for the inevitable issue of water quality and turbidity affected by the soil erosion. The study was conducted on a filled embankment having slope angle of 30° which observed a marginal difference under the rainfall intensity of 40 mm/hour for both the plots. The results obtained recommend fully grass covered surface to mitigate this problem to an extent. The maximum turbidity and suspended particle values for Plot-I were found to be 75 (NTU) and 11.3 (mg/L) whereas, for Plot-II the values were found to be 1631.5 (NTU) and 52.3 (mg/L) respectively.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

Explore more

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