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Record W2774377757 · doi:10.1139/er-2017-0047

Biogeochemical sulfur cycling coupling with dissimilatory nitrate reduction processes in freshwater sediments

2017· article· en· W2774377757 on OpenAlexvenueno aff
Jin Zhu, Yan He, Yishuang Zhu, Minsheng Huang, Yaping Zhang

Bibliographic record

VenueEnvironmental Reviews · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAnammoxBiogeochemical cycleDenitrificationNitrateNitrogen cycleEnvironmental chemistrySulfur cycleCyclingEnvironmental scienceEcologyChemistrySulfurNitrogenDenitrifying bacteriaBiology

Abstract

fetched live from OpenAlex

Dissimilatory nitrate reduction processes including denitrification, anaerobic ammonium oxidation (ANAMMOX), and dissimilatory nitrate reduction to ammonium (DNRA) are crucial nitrogen (N) cycling pathways in freshwater ecosystems. Denitrification has long been considered as the primary pathway of N loss from aquatic environments. Recently, ANAMMOX and DNRA have been gaining more attention in N dynamics at the sediment–water interface. The ubiquitous presence of various sulfur (S) species in sediments makes them an important role on N transport. Interactions between dissimilatory nitrate reduction and the S cycle are mainly embodied by the inhibitory or promoting effects of sulfide on nitrate-reducing pathways, as well as the competition of sulfate with nitrate reduction for substrates. This review summarizes the current progress in the coupling of S cycling with nitrate-reducing pathways in freshwater sediments, the distribution and diversity of related microorganisms, as well as the functional genes encoding related enzymes. Future perspectives of related research are discussed in terms of coupled N cycling with other element cycles and molecular detection of functional bacteria to better understand and manipulate N cycling in freshwater environments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.991

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 communication0.0000.001
Open science0.0000.000
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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 designBench or experimental
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

Citations87
Published2017
Admission routes1
Has abstractyes

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