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Record W2108285934 · doi:10.1111/gwat.12082

Denitrification in a Deep Basalt Aquifer: Implications for Aquifer Storage and Recovery

2013· article· en· W2108285934 on OpenAlexaff
Dennis Nelson, Jason Melady

Bibliographic record

VenueGround Water · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsBioinformatics Solutions (Canada)
Fundersnot available
KeywordsAquiferNitrateGroundwater rechargeGroundwaterDenitrificationHydrology (agriculture)Environmental scienceWater qualityIrrigationNitrogenEnvironmental chemistryChemistryGeologyEcology

Abstract

fetched live from OpenAlex

Aquifer storage and recovery (ASR) can provide a means of storing water for irrigation in agricultural areas where water availability is limited. A concern, however, is that the injected water may lead to a degradation of groundwater quality. In many agricultural areas, nitrate is a limiting factor. In the Umatilla Basin in north central Oregon, shallow alluvial groundwater with elevated nitrate-nitrogen of <3 mg/L to >9 mg/L is injected into the Columbia River Basalt Group (CRBG), a transmissive confined aquifer(s) with low natural recharge rates. Once recovery of the injected water begins, however, NO3 -N in the recovered water decreases quickly to <3 mg/L (Eaton et al. 2009), suggesting that NO3 -N may not persist within the CRBG during ASR storage. In contrast to NO3 -N, other constituents in the recovered water show little variation, inconsistent with migration or simple mixing as an explanation of the NO3 -N decrease. Nitrogen isotopic ratios (δ(15) N) increase markedly, ranging from +3.5 to > +50, and correlate inversely with NO3 -N concentrations. This variation occurs in <3 weeks and recovery of <10% of the originally injected volume. TOC is low in the basalt aquifer, averaging <1.5 mg/L, but high in the injected source water, averaging >3.0 mg/L. Similar to nitrate concentrations, TOC drops in the recovered water, consistent with this component contributing to the denitrification of nitrate during storage.

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)0.000
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 score0.060
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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