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Record W2321989837 · doi:10.1139/cjm-2012-0462

Effect of vegetation on the presence and genetic diversity of<i>Bacillus thuringiensis</i>in soil

2012· article· en· W2321989837 on OpenAlexvenueno aff
Ana Paula Scaramal Ricieto, Fernanda Aparecida Pires Fazion, Celso Duarte Carvalho Filho, Laurival A. Vilas-Bôas, Gislayne Trindade Vilas-Bôas

Bibliographic record

VenueCanadian Journal of Microbiology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBacillus thuringiensisVegetation (pathology)BiologyGenetic diversityPolymerase chain reactionBacteriaBacillalesSoil testRiparian zoneBotanyBacillaceaeEcologyGeneSoil waterGeneticsHabitat

Abstract

fetched live from OpenAlex

Bacillus thuringiensis isolates were obtained from soil samples collected at different sites located in the same region but with different vegetation. The sites showed different frequencies of B. thuringiensis, depending on the type of vegetation. Strains of B. thuringiensis were found to be less common in samples of riparian forest soil than in soil of other types of vegetation. The rate of occurrence of B. thuringiensis in the samples also varied according to the vegetation. These results show that whenever this bacterium was found, it showed a high rate of occurrence, indicating that this species could be better adapted to using soil as a reservoir than other Bacillus species. The presence of cry genes was analyzed by polymerase chain reaction, and genes that exhibited activity against Diptera species were the most commonly found. The isolates obtained were characterized by random amplified polymorphic DNA, and 50% were clustered into clonal groups. These results demonstrated the possible occurrence of a high number of genetically similar strains when samples are collected from the same region, even if they are from locations with different vegetation.

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 score0.022
Threshold uncertainty score0.242

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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