MétaCan
Menu
Back to cohort
Record W2101131925 · doi:10.5194/bg-9-4309-2012

Anammox, denitrification and fixed-nitrogen removal in sediments from the Lower St. Lawrence Estuary

2012· article· en· W2101131925 on OpenAlexafffund
Sean A. Crowe, Donald E. Canfield, Alfonso Mucci, Bjørn Sundby, Roxane Maranger

Bibliographic record

VenueBiogeosciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité de MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaDanmarks Grundforskningsfond
KeywordsAnammoxDenitrificationEstuaryEnvironmental scienceNitrogenOceanographyHydrology (agriculture)Environmental chemistryGeologyChemistryDenitrifying bacteriaGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract. Incubations of intact sediment cores and sediment slurries reveal that anammox is an important sink for fixed nitrogen in sediments from the Lower St. Lawrence Estuary (LSLE), where it occurs at a rate of 5.5 ± 1.7 µmol N m−2 h−1. Canonical denitrification occurs at a rate of 11.3 ± 1.1 µmol N m−2 h−1, and anammox is thus responsible for up to 33% of the total N2 production. Both anammox and denitrification are mostly (> 95%) fueled by nitrate and nitrite produced in situ through benthic nitrification. Nitrification accounts for > 15% of the benthic oxygen demand and may, therefore, contribute significantly to the development and maintenance of hypoxic conditions in the LSLE. The rate of dissimilatory nitrate reduction to ammonium is three orders of magnitude lower than denitrification and anammox, and it is insignificant to N-cycling. NH4+ oxidation by sedimentary Fe(III) and Mn(III/IV) in slurry incubations with N isotope labels did not occur at measurable rates; moreover, we found no evidence for NH4+ oxidation by added Mn(III)-pyrophosphate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.311
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.227
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations90
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueBiogeosciencesSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207