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Record W2559822717 · doi:10.1093/femsec/fiw246

Biogeography of soil Thaumarchaeota in relation to soil depth and land usage

2016· article· en· W2559822717 on OpenAlexafffund
Xinda Lu, Brent Seuradge, Josh D. Neufeld

Bibliographic record

VenueFEMS Microbiology Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThaumarchaeotaBiologySoil waterEcologySpecies evennessCrenarchaeotaSpecies richnessAbundance (ecology)Archaea

Abstract

fetched live from OpenAlex

Although Thaumarchaeota are important contributors to ammonia oxidation in terrestrial habitats, distributions of ammonia oxidizers along soil depth profiles are poorly understood, especially in relation to distinct land usages. Leveraging the close proximity of forest, field and agricultural plots at the RARE: Charitable Research Reserve, we examined soil thaumarchaeotal biogeography at three different depths (0-15, 15-30 and 30-45 cm) from plots within areas of contrasting land usage. Data from high-throughput sequencing of thaumarchaeotal 16S rRNA gene sequences demonstrated that OTU richness was affected significantly by depth and land-use type. Specifically, thaumarchaeotal diversity was higher in soils from forest sites than from field sites, and lower within 0-15 cm soils than either 15-30 cm or 30-45 cm soils. Soil land-use type influenced the relative abundance of the Soil Crenarchaeota Group (SCG), with a lower relative abundance of SCG in forest sites compared to field sites. At the OTU level, thaumarchaeotal communities changed with increasing soil depth for agricultural soils, in contrast to homogeneous depth profiles generated from forest site samples. Soil pH was the strongest factor impacting thaumarchaeotal community composition and, importantly, the evenness of archaeal taxa. Nitrogen, carbon and soil texture shaped thaumarchaeotal community composition among field site samples.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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.

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

Citations35
Published2016
Admission routes2
Has abstractyes

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