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Record W2890453722 · doi:10.1111/1758-2229.12690

Recovering cellular biomass from fluids using chemical flocculation

2018· article· en· W2890453722 on OpenAlexafffundabout
Paul A. Kenward, Rachel L. Simister, Connor Morgan‐Lang, Niko Finke, Arne Sturm, Steven Hallam, Sean A. Crowe

Bibliographic record

VenueEnvironmental Microbiology Reports · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsGenome British ColumbiaCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome British Columbia
KeywordsFlocculationBiomass (ecology)Pulp and paper industryEnvironmental scienceBiochemical engineeringChemistryChemical engineeringBiologyEcologyEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Summary We developed an efficient, scalable and inexpensive method for recovering cellular biomass from complex fluid matrices that cannot be processed using conventional filtration methods. The method uses chemical flocculation with iron oxyhydroxides, is capable of recovering greater than 90% of cellular biomass from fluids with more than 10 3 cells ml −1 , and was validated using both mock communities and field samples. High quality DNA can be readily extracted from iron flocs using standard soil extraction kits. We applied chemical flocculation to fracing fluids from British Columbia, Canada and recovered a diversity of microbial taxa including abundant members of the Epsilon‐ and Deltaproteobacteria previously recovered from shale gas operations in the United States. Application of chemical flocculation presents new opportunities for scalable time‐series monitoring and experimentation on complex fluid matrices including microbial community profiling and shotgun metagenomics over gas production well completion cycles.

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 categoriesInsufficient payload (model declined to judge)
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 score0.035
Threshold uncertainty score0.992

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)0.0090.001

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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 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

Citations2
Published2018
Admission routes3
Has abstractyes

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