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Isolation and characterization of hydrogen‐oxidizing bacteria induced following exposure of soil to hydrogen gas and their impact on plant growth

2006· article· en· W2162007680 on OpenAlexaff
Jiamila Maimaiti, Ye Zhang, Jing Yang, Yan‐Ping Cen, David B. Layzell, Mark B. Peoples, Zhongmin Dong

Bibliographic record

VenueEnvironmental Microbiology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsQueen's UniversitySaint Mary's University
Fundersnot available
KeywordsBiologyBacteria16S ribosomal RNAMicroorganismBurkholderiaFlavobacteriumRoot noduleBioassayBotanySoil microbiologySoil waterNitrogen fixationPseudomonas

Abstract

fetched live from OpenAlex

In many legumes, the nitrogen fixing root nodules produce H2 gas that diffuses into soil. It has been demonstrated that such exposure of soil to H2 can promote plant growth. To assess whether this may be due to H2-oxidizing microorganisms, bacteria were isolated from soil treated with H2 under laboratory conditions and from soils collected adjacent to H2 producing soybean nodules. Nineteen isolates of H2-oxidizing bacteria were obtained and all exhibited a half-saturation coefficient (Ks) for H2 of about 1 ml l(-1). The isolates were identified as Variovorax paradoxus, Flavobacterium johnsoniae and Burkholderia spp. using conventional microbiological tests and 16S rRNA gene sequence analysis. Seventeen of the isolates enhanced (57-254%) root elongation of spring wheat seedlings. Using an Arabidopsis thaliana bioassay, plant biomass was increased by 11-27% when inoculated by one of four isolates of V. paradoxus or one isolate of Burkholderia that were selected for evaluation. The isolates of V. paradoxus found in both H2-treated soil and in soil adjacent to soybean nodules had the greatest impact on plant growth. The results are consistent with the hypothesis that H2-oxidizing bacteria in soils have plant growth promoting properties.

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 score0.256
Threshold uncertainty score0.285

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 communication0.0000.000
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 head0.006
GPT teacher head0.168
Teacher spread0.163 · 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

Citations124
Published2006
Admission routes1
Has abstractyes

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