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Record W2511155652 · doi:10.3934/microbiol.2016.3.332

Development of bioleaching: proteomics and genomics approach in metals extraction process

2016· article· en· W2511155652 on OpenAlexaff
M. Azizur Rahman

Bibliographic record

VenueAIMS Microbiology · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsCustom Security Industries (Canada)University of Toronto
Fundersnot available
KeywordsBioleachingMetaproteomicsProcess (computing)Biochemical engineeringProteomicsGenomicsBiotechnologyChemistryBiologyComputer scienceEngineeringBiochemistryGene

Abstract

fetched live from OpenAlex

Microbes are key components of the structure and function of bioleaching process. Increasing consciousness of the role of microbes has led to a quick growth of descriptive and investigational studies of their abundance and activities. However, the detail information of complex functional molecules contain in promising microbes which are very important for understanding microbial processes in bioleaching, are lacking. Therefore, molecular functions of microbes in the bioleaching process are very essential to understand about the microbial activities, especially in the process of the extraction of metals in mineral industries. In this review, the current state of proteomics and genomics of bioleaching in metals extraction processes and the major developments of these analytical methods at industrial scales are highlighted.

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 score0.026
Threshold uncertainty score0.343

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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