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Record W1986266601 · doi:10.1139/w00-099

A survey of 16S rRNA and<i>amoA</i>genes related to autotrophic ammonia-oxidizing bacteria of the β-subdivision of the class proteobacteria in contaminated groundwater

2000· article· en· W1986266601 on OpenAlexvenueno aff
Iliana A Ivanova, J. Stephen, Yun‐Juan Chang, Julia Brüggemann, Philip E. Long, James P. McKinley, George A. Kowalchuk, David White, Sarah J. Macnaughton

Bibliographic record

VenueCanadian Journal of Microbiology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsProteobacteriaBacteriaAutotrophMicrobiology16S ribosomal RNABiologyOxidizing agentAmmoniaSubdivisionEnvironmental chemistryChemistryEcologyBiochemistryGeneticsGeographyOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we investigated the size and structure of autotrophic ammonia oxidizer (AAO) communities in the groundwater of a contamination plume originating from a mill-tailings disposal site. The site has high levels of dissolved N from anthropogenic sources, and exhibited wide variations in the concentrations of NO3- and NH3 + NH4+. Community structures were examined by PCR-DGGE targeting 16S rDNA with band excision and sequence analysis, and by analysis of amoA fragment clone libraries. AAO population sizes were estimated by competitive PCR targeting the gene amoA, and correlated significantly with nitrate concentration. Most samples revealed novel diversity in AAO 16S rDNA and amoA gene sequences. Both 16S rDNA and amoA analyses suggested that all samples were dominated by Nitrosomonas sp., Nitrosospira sp. being detected in only 3 of 15 samples. This study indicated numerical dominance of Nitrosomonas over Nitrosospira in groundwater, and suggests that groundwater ammonia oxidizers are more similar to those dominating freshwater sediments than bulk 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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations51
Published2000
Admission routes1
Has abstractyes

Explore more

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