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Record W2895368275 · doi:10.26685/urncst.66

Bioremediation of Fracking: Novel Hybrid Biofilm System Using Synthetically Engineered Curli Fibres

2018· article· en· W2895368275 on OpenAlexafffund
Keerthana Pasumarthi, Harshini Ramesh, Maggie Hou, Jennifer Lee

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsBiofilmBacteriaGeology

Abstract

fetched live from OpenAlex

Hydraulic fracturing, a popular mining technique, generates heavy metal contamination in nearby freshwater aquifers. This poses a threat to both the surrounding ecosystems and human health if exposed. Existing methods of heavy metal removal can produce additional hazardous byproducts. This proposal presents the use of a hybrid biofilm filter containing graphene and curli fibres with metal binding sites. Curli fibres are amyloid fibrils found on the extracellular biofilm of Escherichia coli (E. coli). Through the use of plasmid vectors, E. coli will be engineered to produce secreted curli fibres with metal-binding residues. The stability and cohesive properties of the curli fibres augments the adherence to the graphene scaffolding, thus allowing for generation of a hybrid biofilm. With the filtration design and various experimental controls proposed, this model is ready for empirical proof of concept and subsequent quantitative optimization.

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)0.003
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.357
Teacher spread0.309 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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