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Record W2068312048 · doi:10.2166/wst.2007.458

Nitrogen fertilizer impact on the Wilmot watershed aquifer in Prince Edward Island, Canada

2007· article· en· W2068312048 on OpenAlexaffabout
Éric van Bochove, Martine M. Savard, Georges Thériault, Noura Ziadi, John Macleod

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsGeological Survey of CanadaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLeaching (pedology)NitrateEnvironmental scienceHydrology (agriculture)Groundwater rechargeFertilizerNitrificationGrowing seasonGroundwaterAgronomyWatershedAquiferSoil waterNitrogenSoil scienceEcologyChemistryGeology

Abstract

fetched live from OpenAlex

The objective of this study is to estimate the soil N flux from the vadose zone to the aquifer of the Wilmot watershed (Prince Edward Island, Canada) for a typical three-year cropping rotation (barley-red clover-potato). A conceptual model estimates that 199-221 tons of N were yearly available for leaching at the watershed scale. A significant portion of this N amount was available for leaching at the end of the crop season representing 80-90% of the annual N balance. Drainage water nitrate concentrations were significantly higher after the potato-rotation year than during the crop season. Low nitrate concentrations were measured at spring thaw indicating that most of the nitrate available from the preceding potato crop season was likely leached at the end of fall or during winter. Early spring ionic exchange membrane sampling show a large availability of nitrate in soil possibly throughout winter as well, resulting from soil N mineralization and nitrification over the winter period. These findings are corroborated by the isotope natural abundance analysis of nitrate in groundwater implying that nitrifiers are significantly active during winter, as well as during the crop season, and that leaching of soil nitrates with seasonal signals takes place whenever recharge is occurring.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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