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Record W2335578656 · doi:10.1139/cjm-2014-0250

Identification and characterization of <i>Acidithiobacillus ferrooxidans</i> YY2 and its application in the biodesulfurization of coal

2014· article· en· W2335578656 on OpenAlexvenueno aff
Xinping Yang, Shimei Wang, Yujiao Liu, Yuanyuan Zhang

Bibliographic record

VenueCanadian Journal of Microbiology · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsPyriteSulfurOxidizing agentFerrousBioleachingAcidithiobacillus ferrooxidansExtracellular polymeric substanceChemistryAcid mine drainageBacteriaCoalNuclear chemistryAcidithiobacillusEnvironmental chemistryMineralogyBiofilmBiologyCopperOrganic chemistry

Abstract

fetched live from OpenAlex

The acidophilic Fe-oxidizing and S-oxidizing bacterium YY2 was isolated from the acid drainage of a coalmine. Based on morphological and physiological characteristics and phylogenetic analysis, it was identified as Acidithiobacillus ferrooxidans. Significant differences were observed in the oxidation efficiency and cell morphology when YY2 was cultured in 9K medium with ferrous ion (Fe(2+)), elemental sulfur (S(0)), and pyrite as the sole energy source. YY2 exhibited marked Fe(2+) oxidation activity; 44.2 g · L(-1) FeSO4 · 7H2O was completely oxidized in 30 h, but the rates of S(0) and pyrite oxidization were slower. After 20 days, the efficiencies of oxidizing 10 g · L(-1) S(0) and 10 g · L(-1) pyrite were approximately 9.6% and 20%, respectively. Cells cultured in pyrite as substrate secreted more extracellular polymeric substances than they did when cultured in Fe(2+) or S(0). Additionally, 75% total sulfur removal and 86% pyritic sulfur removal was achieved in a sequencing batch reactor of biodesulfurization of coal.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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