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Record W2112315449 · doi:10.1029/2006wr005469

Winter nitrification contributes to excess NO<sub>3</sub><sup>−</sup> in groundwater of an agricultural region: A dual‐isotope study

2007· article· en· W2112315449 on OpenAlexaff
Martine M. Savard, Daniel Paradis, George Somers, Shawna Liao, Éric van Bochove

Bibliographic record

VenueWater Resources Research · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Prince Edward IslandGeological Survey of Canada
Fundersnot available
KeywordsNitrateNitrificationGroundwaterEnvironmental scienceSoil waterHydrology (agriculture)Groundwater rechargeStable isotope ratioDenitrificationSeasonalityNitrogenEnvironmental chemistryEcologyAquiferSoil scienceChemistryGeologyBiology

Abstract

fetched live from OpenAlex

Conventional thinking is that bacterial nitrification leading to labile nitrate in fertilized agricultural soils of northern regions greatly diminishes during winter. We have carried out seasonal water sampling over 2 years to understand the fate of nitrate present in a rapidly responding groundwater/surface water system. Nitrate results show no seasonal δ 15 N trend. Significant δ 18 O downward shifts were observed between the spring‐summer and autumn‐winter periods of 2003–2004 (10.0‰) and 2004–2005 (1.3‰). Using mass‐balance mixing calculations of soil leachate with groundwater and assuming seasonal nitrification, we reproduce the observed water and nitrate oxygen‐isotope trends. These calculations suggest that nitrification takes place throughout all seasons. We also use a hydroclimatic index to establish a relationship between δ 18 O values in nitrate and recharge weighted by temperature. Our findings imply that nitrifying activities occur all year long and that winter nitrate production is high. This conclusion has important implications for modeling the nitrogen cycle of regions where seasonal changes in soil water mark the oxygen isotopes of nitrate.

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)0.003
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 score0.123
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.275
Teacher spread0.244 · 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

Citations51
Published2007
Admission routes1
Has abstractyes

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