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Record W3034487719 · doi:10.3997/2214-4609.201902481

Induced Polarisation (IP) Laboratory Measurements on Escherichia Coli (E. Coli)-Sand Mixtures

2019· article· en· W3034487719 on OpenAlexaboutno aff
Tellis A. Martin, Catherine J. Paul

Bibliographic record

Venue25th European Meeting of Environmental and Engineering Geophysics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBacteriaEnvironmental remediationEscherichia coliContaminationMicroorganismIn situChemistryEnvironmental scienceMicrobiologyEnvironmental chemistryMaterials scienceBiologyBiochemistryEcology

Abstract

fetched live from OpenAlex

Summary For the characterization and monitoring of in-situ remediation of chlorinated hydrocarbon contamination, interdisciplinary approaches and geophysical methods are needed to secure water supply of sufficient quality and quantity. Geophysical methods, such as IP (induced polarisation) could be used to investigate bioremediation processes. However, to interpret geophysical field IP data, lab investigations with different kinds of bacteria are necessary to assess the sensitivity of the methods for these specific applications. Therefore, a first experiment was conducted with E. coli bacteria in sterilised Ottawa sand environment. These bacteria-sand-mixtures were harvested at different days and measured with e.g., SIP (spectral IP) under laboratory conditions. A slight increase in phase and a decrease in resistivity were observed after several days of bacterial growth with sand, with a later decrease in phase appearing to coincide with die-off of the bacteria. Scanning electron microscope (SEM) images showed bacteria attached to the sand grains which could modify the grain surface (e.g. increasing the grain surface area and/or form a biofilm) and thus impact IP measurements. In future, the number of bacteria present in the sand will be determined using quantitative polymerase chain reaction (qPCR) to detect bacterial DNA (deoxyribonucleic acid).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.682

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 head0.011
GPT teacher head0.179
Teacher spread0.168 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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