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Record W1556542764 · doi:10.4319/lom.2010.8.202

Revisiting the application of open‐channel estimates of denitrification

2010· article· en· W1556542764 on OpenAlexaff
Helen M. Baulch, Jason J. Venkiteswaran, Peter J. Dillon, Roxane Maranger

Bibliographic record

VenueLimnology and Oceanography Methods · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of WaterlooCégep Marie-VictorinTrent University
Fundersnot available
KeywordsDenitrificationEnvironmental scienceSTREAMSBenthic zoneRiver ecosystemChannel (broadcasting)Hydrology (agriculture)EcosystemComputer scienceNitrogenChemistryEcologyEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Development of an open‐channel method for measurement of denitrification, without the use of expensive isotopic tracers, has generated considerable interest among researchers attempting to quantify N loss from lotic systems. Membrane inlet mass spectrometry allows measurement of small changes in N 2 concentrations, facilitating calculation of whole reach denitrification rates using an N 2 mass balance corrected for gas exchange. The method has been applied successfully within numerous rivers ranging widely in size and denitrification rate. Previous model‐based analyses suggest that the method can be applied in a broader suite of ecosystems, and specifically, that it is well suited to shallow streams where denitrification rates as low as 30–100 µmol N m −2 h −1 may be measurable. This coupled with increasing availability of necessary equipment, relatively low cost of measurements, and the ability to measure denitrification at environmentally relevant spatial scales suggests that broad adoption of the method is likely. In this paper, we revisit this model‐based analysis using alternate models of gas exchange and demonstrate that benthic turbulence‐induced gas exchange will restrict the suite of suitable study streams. Specifically, we note that within shallow streams and fast‐flowing systems denitrification may be measurable only at moderate or high rates. To help facilitate further application of the method, we extend our discussion beyond site selection to discuss assumptions of the open‐channel method, options for estimating the error in denitrification rates, and recommended practices for future studies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.192

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 science0.0000.000
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 head0.017
GPT teacher head0.327
Teacher spread0.310 · 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

Citations21
Published2010
Admission routes1
Has abstractyes

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