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Record W2491400165 · doi:10.3390/w8080326

Assessment of the Impact of Subsurface Agricultural Drainage on Soil Water Storage and Flows of a Small Watershed

2016· article· en· W2491400165 on OpenAlexafffund
Mushombe Muma, Alain N. Rousseau, Silvio José Gumière

Bibliographic record

VenueWater · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
FundersMinistry of Agriculture, Food and Rural AffairsAgriculture and Agri-Food CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsHydrology (agriculture)Environmental scienceWatershedDrainageHydrographSubsurface flowSurface runoffDrainage basinBase flowTime of concentrationGeologyGroundwaterGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

3D hydrological modeling was performed, using CATHY (acronym for CATchment HYdrology model), with the basic objective of checking whether the model could reproduce the effects of subsurface agricultural drainage on stream flows and soil water storage. The model was also used to further our understanding of the impact of soil hydrodynamic properties on watershed hydrology. Flows simulated by CATHY were consistent with traditional subsurface drainage approaches and, for wet years, flows at the outlet of the study watershed corroborated well with observed data. Temporal storage variation analyses illustrated that flows depended not only on the amount of rainfall, but also on its distribution throughout the year. Subsurface agricultural drainage increased base and total flows, and decreased peak flows. Hydrograph separation using simulated results indicated that exfiltration was the most dominant process; peak flows were largely characterized by overland flow; and subsurface drain flow variations were low.

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 score0.529
Threshold uncertainty score0.472

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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