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Record W2596347422 · doi:10.5539/jas.v9n4p109

Calculation of Agricultural Drain Spacing Taking into Account Regularity of Water Exchange in the Vadose Zone

2017· article· en· W2596347422 on OpenAlexvenueno aff
Iourii Nikolskii-Gavrilov, V. V. Pchyolkin, Cesáreo Landeros-Sánchez, Saúl Montero-Aguirre

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsVadose zoneGroundwater rechargeWater tableHydraulic conductivityInfiltration (HVAC)Soil scienceWater contentGroundwaterDrainageHomogeneousGeologyAquiferGeotechnical engineeringEnvironmental scienceHydrology (agriculture)MathematicsSoil waterThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Conventional analytical formulae for calculation of subsurface drain spacing for maintaining a desired water table depth in agricultural areas, such as Hooghoudt’s formula, are based on using the ratio between the soil saturated hydraulic conductivity Ks and the groundwater recharge rate q. It is well known that selection of the q value as one of the principle drainage criterion is one of the problems of the drain spacing calculation. In this paper, it is illustrated that for steady state conditions and, in case of homogeneous soil profile, the ratio q/Ks can be substituted by an analytical function that takes into account the regularity of infiltration through the vadose zone. This function can be derived from the soil moisture content in the root zone and other well-known hydrodynamic soil parameters. An example of drain spacing calculation is presented.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations1
Published2017
Admission routes1
Has abstractyes

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