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Record W2004492229 · doi:10.2136/vzj2010.0063

Using Soil Water Content Sensors to Characterize Tillage Effects on Preferential Flow

2011· article· en· W2004492229 on OpenAlexafffund
Priyantha B. Kulasekera, Gary W. Parkin, P. von Bertoldi

Bibliographic record

VenueVadose Zone Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMacroporeInfiltration (HVAC)GroundwaterTillageEnvironmental scienceSoil scienceSoil waterHydrology (agriculture)Soil horizonSurface runoffTransectWater flowWater contentGeologyAgronomyGeographyEcologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Because groundwater is a source of drinking water for many people, there is great concern to protect groundwater resources. Preferential flow through the unsaturated zone is considered one of the main contributory factors in groundwater pollution, which is influenced primarily by soil heterogeneity and management practices. To characterize the degree of preferential flow, the soil water content dynamics during short‐ and long‐term periods in the shallow zone (0–40‐cm depth) of two plots with no‐till (NT) and conventional tillage (CT) soil management practices were investigated for 2 yr using soil water sensors placed horizontally in a rectangular grid along transects across crop rows. Analysis of response times and water content changes during rainfall events confirmed the existence of short‐term preferential flow at the site and that macropores and heterogeneities in soil hydraulic parameters in the shallow zone may have contributed to infiltration into the deeper layers in the NT plot. In the longer term, the pathways of infiltration and redistribution were different between the growing and non‐growing seasons; however, these differences were relatively stable in both plots during the 2‐yr study period.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.703

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 head0.061
GPT teacher head0.215
Teacher spread0.154 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

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