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Use of Microbial Consortia in Bioremediation of Metalloid Polluted Environments

2023· review· en· W4361267983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicroorganisms · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetalloidBioremediationMicroorganismEnvironmental chemistryEnvironmental sciencePollutantContaminationHeavy metalsBiologyEcologyChemistryBacteriaMetal

Abstract

fetched live from OpenAlex

Metalloids are released into the environment due to the erosion of the rocks or anthropogenic activities, causing problems for human health in different world regions. Meanwhile, microorganisms with different mechanisms to tolerate and detoxify metalloid contaminants have an essential role in reducing risks. In this review, we first define metalloids and bioremediation methods and examine the ecology and biodiversity of microorganisms in areas contaminated with these metalloids. Then we studied the genes and proteins involved in the tolerance, transport, uptake, and reduction of these metalloids. Most of these studies focused on a single metalloid and co-contamination of multiple pollutants were poorly discussed in the literature. Furthermore, microbial communication within consortia was rarely explored. Finally, we summarized the microbial relationships between microorganisms in consortia and biofilms to remove one or more contaminants. Therefore, this review article contains valuable information about microbial consortia and their mechanisms in the bioremediation of metalloids.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.093
GPT teacher head0.298
Teacher spread0.206 · 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