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Record W4240547024 · doi:10.32920/ryerson.14646174.v1

CO₂ Measured In The Gas Phase As An Indicator Of Biofilm Metabolism

2021· preprint· en· W4240547024 on OpenAlexfundno aff
Marthinus Kroukamp

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsBiofilmMicroorganismNutrientCarbon dioxideExtrapolationFlux (metallurgy)Environmental scienceBiological systemChemistryEcologyBiologyBacteriaMathematics

Abstract

fetched live from OpenAlex

The behaviour of microorganisms in biofilms is uniquely dependent on their location within the biofilm-matrix and the dynamic interplay between the countless microenvironments that is in constant flux because of physiochemical and inter-cell exchanges. To study microorganisms in the biofilm environment, the above mentioned heterogeneity forces any researcher to either focus on micro-niches (whose data may or may not be suitable for extrapolation to infer information about the whole) or stand back and study a global biofilm parameter (as a sum-total of micro-behaviours but losing information about the diversity). Either way, the positional dependence of behaviour arguably favours in situ studies with the least amount of disruption whether physical or the addition of chemicals. A simple technology was developed to measure in situ biofilm CO₂ production as an indication of overall metabolism, in real-time and non-destructively. The first system developed was a carbon dioxide evolution measurement system (CEMS) with biofilms growing on the inside of a CO₂ and O₂permeable silicone tube with quantification of microbially produced CO₂transferred across the tube wall could. The concept of measuring biofilm CO₂was subsequently expanded to accommodate any biofilm reactor by measuring CO₂in the reactor effluent. By monitoring CO₂the advantage is that aerobic and anaerobic metabolism can be tracked with an combination of microbial community members with varying growth conditions such as temperature and nutrient composition. It furthermore allows the setup of carbon balance over a reactor system to address fundamental biofilm aspects such as percentages of inflowing nutrients used for respiration or quantification of "missing" carbon fractions. The ability to measure metabolism in real-time provides insight into both steady state and transient biofilm responses to changes in their environment while the non-destructive nature of the technique gives the opportunity to determine the possibility of adaptive behaviour in the same biofilm after repeated exposure. Practical applicability of these measurement systems has been demonstrated in a wide range of biofilm related phenomena such as the areas of biofilm architecture, biofilm development and biofilm resilience after physical and antimicrobial attacks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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