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Record W2329559621 · doi:10.1002/2015wr017821

Comparison of the transport and deposition of <i>Pseudomonas aeruginosa</i> under aerobic and anaerobic conditions

2016· article· en· W2329559621 on OpenAlexafffund
Huixin Zhang, Hongbo Zeng, Ania C. Ulrich, Yang Liu

Bibliographic record

VenueWater Resources Research · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrokinetic phenomenaAnaerobic exerciseIonic strengthDLVO theoryX-ray photoelectron spectroscopySurface chargeDeposition (geology)ChemistryOxygenAnalytical Chemistry (journal)Chemical engineeringMaterials scienceEnvironmental chemistryPhysical chemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Laboratory‐scale columns were employed to study the effect of oxygen and ionic strength on the transport of Pseudomonas aeruginosa PAO1 in porous media. In anaerobic experiments, cells were grown and transport experiments were conducted in a well‐controlled anaerobic chamber. Cell surface electrokinetic potentials were measured and surface elemental composition was analyzed using X‐ray photoelectron spectroscopy (XPS). Transport experimental results showed reduced travel distance of PAO1 with increased ionic strength under aerobic and anaerobic conditions, consistent with calculated Derjaguin‐Landau‐Verwey‐Overbeek (DLVO) theory. The deposition rates of PAO1 were significantly higher in aerobic than in anaerobic condition at higher ionic strength (10 and 100 m M ), although the electrokinetic potentials were similar throughout the tested ionic strength (1, 10, and 100 m M ). No difference in PAO1 deposition rate was observed at 1 m M . XPS analysis showed that variation in cell surface composition due to different growth conditions played a primary role in determining the different deposition behaviors.

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 score0.038
Threshold uncertainty score0.179

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 head0.026
GPT teacher head0.305
Teacher spread0.278 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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