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Record W4362664240 · doi:10.2166/hydro.2023.213

Experimental investigation and modelling of rainfall-induced erosion of semi-arid region soil under various vegetative land covers

2023· article· en· W4362664240 on OpenAlexaff
Odai Al Balasmeh, Tapas Karmaker, Richa Babbar

Bibliographic record

VenueJournal of Hydroinformatics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSurface runoffInfiltration (HVAC)Environmental scienceStrawAridErosionHydrology (agriculture)Groundwater rechargeSoil scienceSoil waterGroundwaterAgronomyGeologyGeotechnical engineeringGeomorphologyEcologyGeography

Abstract

fetched live from OpenAlex

Abstract Laboratory-scale experiments were conducted to investigate the impact of three different vegetative covers (khus, dry leaves, and wheat straw) on soil erosion and runoff under four different surface slopes and two different types of soils. Results were compared with experiments for bare soil under similar conditions for three major parameters: surface runoff, sub-surface flow, and soil loss. It was found that wheat straw reduced the surface runoff from 60 ml/s (for bare soil) to 20 ml/s, while with leaves and khus, it was 40 ml/s. Wheat straw cover increased the infiltration by 60% and reduced soil loss by 85% compared to bare soil. The findings were validated with the HYDRUS-1D simulations for infiltration and surface runoff in bare soil under similar experimental conditions. Experimental findings were found to agree well with the model simulations. The present study can be treated as a nature-based solution to soil erosion, groundwater recharge, and delayed surface runoff in semi-arid regions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.146

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 head0.056
GPT teacher head0.236
Teacher spread0.180 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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