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Record W2330097074 · doi:10.2166/wqrjc.2013.053

Modeling the influence of tile drainage flow and tile spacing on phosphorus losses from two agricultural fields in southern Québec

2013· article· en· W2330097074 on OpenAlexaffabout
Jonathan Morrison, Chandra A. Madramootoo, Mohamed Chikhaoui

Bibliographic record

VenueWater Quality Research Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsTile drainageLoamSurface runoffDrainageEnvironmental scienceTileHydrology (agriculture)Soil waterSubsurface flowPhosphorusSoil scienceGeologyGroundwaterGeotechnical engineeringGeographyEcology

Abstract

fetched live from OpenAlex

Tile drainage is a widely adopted water management practice in the eastern Canadian provinces of Québec and Ontario. It aims to improve the productivity of poorly drained agricultural fields. Nevertheless, studies have also shown that subsurface drainage is a significant pollution pathway to surface water. This study was undertaken to evaluate the effect of tile drain spacing on surface runoff, subsurface drainage flows, and phosphorus (P) loss from two tile-drained agricultural fields located near Bedford, Québec. Field data were used with the DRAINMOD model, and in developed regression models in order to perform the analysis. Both DRAINMOD and the regression models showed good performance. Simulation results indicated that when lateral tile drain spacing is increased, the volume of subsurface drain flow decreases, and the volume of surface runoff increases, at sites with sandy and clay loam soils. For every 5 m increase in drain spacing, total phosphorus (TP) loads in subsurface drainage decreased by 6% at a site with sandy loam soil, and increased by 20% at a site with clay loam soil. TP loads in surface runoff increased as a result of increased drain spacing.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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 designObservational
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

Citations8
Published2013
Admission routes2
Has abstractyes

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