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Record W2912743005 · doi:10.1029/2018jg004741

Residence Time Controls on the Fate of Nitrogen in Flow‐Through Lakebed Sediments

2019· article· en· W2912743005 on OpenAlexaff
Tyler Hampton, Jay P. Zarnetske, Martin A. Briggs, Kamini Singha, Judson W. Harvey, F. D. Day‐Lewis, Farzaneh MahmoodPoor Dehkordy, John W. Lane

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Waterloo
FundersU.S. Geological SurveyCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorNational Science Foundation
KeywordsNitrateEnvironmental chemistryResidence time (fluid dynamics)TRACERBiogeochemical cycleDenitrificationSedimentHydrology (agriculture)Environmental scienceDissolved organic carbonNitrogenChemistryGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract For many glacial lakes with highly permeable sediments, water exchange rates control hydrologic residence times within the sediment‐water interface (SWI) and the removal of reactive compounds such as nitrate, a common pollutant in lakes and groundwater. Here we conducted a series of focused tracer injection experiments in the upper 20 cm of the naturally downwelling SWI in a flow‐through lake on Cape Cod, MA. We systematically varied residence time and reactant controls on nitrate processing, using isotopically labeled 15 N nitrate to monitor the effect of these changes on nitrate removal via denitrification. The addition of acetate, a labile carbon compound, triggered the lake SWI to switch from net production to net removal of nitrate. When acetate was combined with increased residence time created by controlled reductions in water flux, we observed a fivefold increase in nitrate removal, a 26‐fold increase in N 2 production, and a 42‐fold increase in N 2 O production. We demonstrate that water residence time is an important control on the fate of nitrate in these lake SWIs and illustrate that seasonal conditions that alter lake exchange rates and variability in lake carbon may predict dynamic nitrate removal across the SWI. Additionally, observed N 2 O production during the oxic pore water experiments paired with geophysical characterization of the sediment porosity revealed that the lake SWI has less mobile pores occupying upward of 50% of the total porosity volume, which function as reactive microzones for nitrate processing.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.301
Teacher spread0.273 · 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.

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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

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