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Succession and community composition of ammonia-oxidizing archaea and bacteria in bulk soil of a Japanese paddy field

2010· article· en· W2011379546 on OpenAlexaff
Chihiro Fujii, Tatsunori Nakagawa, Yuki Onodera, Naoki Matsutani, Katsuhiro Sasada, Reiji Takahashi, Tatsuaki Tokuyama

Bibliographic record

VenueSoil Science & Plant Nutrition · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsNutrasource
FundersNihon University
KeywordsArchaeaAmmonia monooxygenaseAbundance (ecology)Paddy fieldNitrificationEcological successionBiologySoil microbiologySoil waterEcologyAgronomyBotanyGeneChemistryNitrogenBiochemistry

Abstract

fetched live from OpenAlex

The present study describes succession in the abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in the upland and flooded bulk soils of a Japanese rice paddy field over 2 years using a quantitative polymerase chain reaction of both crenarchaeotal and betaproteobacterial ammonia monooxygenase alpha subunit (amoA) genes. A marked increase in the abundance of AOA amoA gene was observed in upland bulk soil after plowing, drainage and rice harvesting. A marked increase was also observed in the abundance of AOB amoA gene after plowing. The abundances of both AOA and AOB amoA genes in flooded bulk soil decreased immediately after flooding. During the middle period of flooding, the abundance of AOA amoA gene increased slightly in the flooded bulk soil. As the flooding subsided, the abundance of AOA amoA gene decreased, whereas that of AOB amoA gene increased. The AOA amoA gene sequences were affiliated with two phylogenetic clusters previously found in marine and soil environments. Both Nitrosospira-like and Nitrosomonas-like clones were detected. Our results revealed that there was a difference in the succession of abundances between AOA and AOB amoA genes in Japanese bulk paddy soil.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
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 head0.010
GPT teacher head0.224
Teacher spread0.215 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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