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Record W2115437960 · doi:10.1139/f04-062

Denitrification in the Upper Mississippi River: rates, controls, and contribution to nitrate flux

2004· article· en· W2115437960 on OpenAlexvenueno aff
William B. Richardson, Eric A. Strauss, Lynn A. Bartsch, Emy M. Monroe, Jennifer C. Cavanaugh, Lorrine Vingum, David M. Søballe

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDenitrificationEnvironmental scienceFloodplainHydrology (agriculture)NitrateSedimentAnoxic watersSpring (device)NitrogenEcologyEnvironmental chemistryChemistryGeologyBiology

Abstract

fetched live from OpenAlex

We evaluated patterns of denitrification and factors effecting denitrification in the upper Mississippi River. Measurements were taken over 2 years, during which river discharge ranged from record flooding to base flow conditions. Over the period of study, average denitrification enzyme activity was highest in backwater lakes and lowest in the main channel. Throughout the study reach, highest denitrification enzyme activity occurred during fall and lowest occurred in winter. Rates during spring floods (2001) were only slightly higher than during the preceding winter. Mean unamended denitrification rates ranged from 0.02 (fall 2001 in backwaters) to 0.40 µg N·cm –2 ·h –1 (spring 2001 in backwaters). Laboratory experiments showed that denitrification rates increased significantly with addition of NO 3 – regardless of sediment C content, while rates increased little with addition of labile C (glucose). Denitrification in this reach of the upper Mississippi River appears to be NO 3 – limited throughout the growing season and the delivery of NO 3 – is strongly controlled by river discharge and hydrologic connectivity across the floodplain. We estimate that denitrification removes 6939 t N·year –1 or 6.9% of the total annual NO 3 – input to the reach. Hydrologic connectivity and resultant NO 3 – delivery to high-C sediments is a critical determinant of reach-scale processing of N in this floodplain system.

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 score0.047
Threshold uncertainty score0.999

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations186
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicSoil and Water Nutrient DynamicsFrench-language works237,207