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Record W2274905935 · doi:10.1139/cjm-2015-0507

Identification of <i>Bacillus megaterium</i> and <i>Microbacterium liquefaciens</i> genes involved in metal resistance and metal removal

2016· article· en· W2274905935 on OpenAlexvenueno aff
Grisel Fierros‐Romero, Marlenne Gómez‐Ramírez, Ginesa E. Arenas-Isaac, Reynaldo C. Pless, Norma G. Rojas‐Avelizapa

Bibliographic record

VenueCanadian Journal of Microbiology · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsBacillus megateriumMicrobacteriumCobaltNickelMicrobiologyCadmiumBacteriaChemistryMetalBiologyInorganic chemistryGeneticsOrganic chemistry

Abstract

fetched live from OpenAlex

Bacillus megaterium MNSH1-9K-1 and Microbacterium liquefaciens MNSH2-PHGII-2, 2 nickel- and vanadium-resistant bacteria from mine tailings located in Guanajuato, Mexico, are shown to have the ability to remove 33.1% and 17.8% of Ni, respectively, and 50.8% and 14.0% of V, respectively, from spent petrochemical catalysts containing 428 ± 30 mg·kg(-1) Ni and 2165 ± 77 mg·kg(-1) V. In these strains, several Ni resistance determinants were detected by conventional PCR. The nccA (nickel-cobalt-cadmium resistance) was found for the first time in B. megaterium. In M. liquefaciens, the above gene as well as the czcD gene (cobalt-zinc-cadmium resistance) and a high-affinity nickel transporter were detected for the first time. This study characterizes the resistance of M. liquefaciens and B. megaterium to Ni through the expression of genes conferring metal resistance.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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 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

Citations29
Published2016
Admission routes1
Has abstractyes

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