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Record W2303879633 · doi:10.1002/ep.12357

Comparison study on copper bioaccumulation by growing <scp><i>P</i></scp><i>ichia kudriavzevii</i> and <scp><i>S</i></scp><i>accharomyces cerevisiae</i>

2016· article· en· W2303879633 on OpenAlexaff
Chunsheng Li, Dandan Zhang, Ning Ma, Dongfeng Wang, Li Laihao, Xianqing Yang, Ying Xu

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsMinistry of Agriculture
FundersNational Natural Science Foundation of China
KeywordsBioaccumulationChemistryEnvironmental chemistryCopperMetal

Abstract

fetched live from OpenAlex

Bioaccumulation via growing microorganisms is a potential technique for treatment of heavy metal pollution. In this study, the Cu tolerance, bioaccumulation properties, and removal ability of growing Pichia kudriavzevii and Saccharomyces cerevisiae in complex environments were investigated comparatively. P. kudriavzevii displayed higher total Cu bioaccumulation capacity and Cu removal rate than S. cerevisiae at various bioaccumulation time and Cu concentrations. The Cu bioaccumulation in both yeasts was obviously improved at low pH and high concentrations of NaCl, which resulted in the increase of Cu removal rate. The maximum Cu removal rate of P. kudriavzevii respectively reached to 55.53% at 20 g/L NaCl and to 77.84% at pH 3, obviously higher than that in normal condition at pH 5 without NaCl addition (13.77%). High concentrations of Zn (0.05 − 0.5 mmol/L) significantly improved the Cu removal ability of P. kudriavzevii while the Cu removal rate was markedly inhibited with the addition of Cd (0.05 − 0.5 mmol/L). Compared with S. cerevisiae, the multi‐stress‐tolerant P. kudriavzevii possessed more powerful Cu tolerance and bioaccumulation ability at low pH, high concentrations of NaCl and with the addition of other heavy metal ions. It suggested that P. kudriavzevii could be used as a potential candidate for Cu removal in complex environments. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1353–1360, 2016

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.231
Teacher spread0.225 · 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.

Study designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

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