CO₂ Measured In The Gas Phase As An Indicator Of Biofilm Metabolism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".