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Sources of Nitrite in Streams of an Intensively Cropped Watershed

2010· article· en· W147795029 on OpenAlexaffabout
Julie Corriveau, Éric van Bochove, Daniel Cluis

Bibliographic record

VenueWater Environment Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSTREAMSWatershedEnvironmental scienceNitriteWater pollutionEnvironmental chemistryEnvironmental engineeringHydrology (agriculture)NitrateEcologyChemistryGeologyBiologyComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

The sources of high in-stream nitrite (NO2(-)) concentrations were investigated in two major streams located in an intensively cropped watershed in Quebec, Canada. Nitrogen retention was determined to evaluate the dynamics in relation to nitrogen transport along both stream branches during summer-low-water and fall-recharge regimes. In the first stream branch, NO2(-) and ammonium (NH4(+)) showed removal patterns during summer-low-water and fall-recharge periods, whereas, in the second branch, NO2(-) and NH4(+) exports occurred during both hydrologic regimes. The study also demonstrated that seepage water is a source of NO2(-) instream, which varies within the watershed stream branches and with the hydrologic regime. The results highlighted a significant reductive microbial activity in seepage water from either denitrification or dissimilatory nitrate reduction to ammonium (DNRA), leading to nitrate (NO3(-)) consumption. Differences in groundwater NO3(-) concentrations feeding each stream branch may have significantly influenced NH4(+) and NO2(-) concentrations found in seepage water, which potentially resulted in quantitatively significant NO2(-) formation.

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 score0.306
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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