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Record W2885079484 · doi:10.3389/fpls.2018.01134

The Sycamore Maple Bacterial Culture Collection From a TNT Polluted Site Shows Novel Plant-Growth Promoting and Explosives Degrading Bacteria

2018· article· en· W2885079484 on OpenAlexaff
Sofie Thijs, Wouter Sillen, Sascha Truyens, Bram Beckers, Jonathan D. Van Hamme, Pieter van Dillewijn, Pieter Samyn, Robert Carleer, Nele Weyens, Jaco Vangronsveld

Bibliographic record

VenueFrontiers in Plant Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsThompson Rivers University
FundersVlaamse regeringFonds Wetenschappelijk Onderzoek
KeywordsRhizospherePhytoremediationBiologyBacteriaMapleMicroorganismBotanyShootInoculationHorticultureSoil waterEcology

Abstract

fetched live from OpenAlex

Military activities have worldwide introduced toxic explosives into the environment with considerable effects on soil and plant-associated microbiota. Fortunately, these microorganisms, and their collective metabolic activities, can be harnessed for site restoration via in situ phytoremediation. We characterised the bacterial communities inhabiting the bulk soil and rhizosphere of sycamore maple (Acer pseudoplatanus) in two chronically 2,4,6-trinitrotoluene (TNT) polluted soils. Three hundred strains were isolated, purified and characterised, a majority of which showed multiple plant growth promoting (PGP) traits. Several isolates showed high nitroreductase enzyme activity and concurrent TNT-transformation. A 12-member bacterial consortium, comprising selected TNT-detoxifying and rhizobacterial strains, significantly enhanced TNT removal from soil compared to non-inoculated plants, increased root and shoot weight, and the plants showed enhanced ability to withstand TNT as estimated by the responses of antioxidative enzymes. The SYCAM culture collection is a significant resource of plant-associated strains with multiple PGP and catalytic properties, available for further genetic and phenotypic discovery and use in field applications.

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 score0.096
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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