Comparison of PCR-DGGE and Selective Plating Methods for Monitoring the Dynamics of a Mixed Culture Population in Synthetic Brewery Wastewater
Bibliographic record
Abstract
Enrichment of an activated sludge inoculum in synthetic brewery wastewater, which included glucose, maltose, and ethanol, was conducted in batch experiments to identify the dominant microbes present, to determine methodologies capable of monitoring the mixed culture population dynamics, and to determine the consortium's substrate degradation behavior. These results and methodologies were subsequently used in the determination of the population dynamics of suspended and attached microorganisms in a sequencing batch system in the second part of this research work. The three-membered microbial community comprised two bacterial and one fungal species that were identified as Acinetobacter sp., Enterobacter sp., and Candida sp. PCR-DGGE and plating on selective media were used to track the population dynamics of the consortium during the degradation of different substrates in synthetic wastewater containing glucose, maltose, and ethanol. Enterobacter sp. could degrade glucose and maltose but not ethanol, whereas Acinetobacter and Candida could degrade all three carbon sources. In buffered batch mixed culture experiments, Enterobacter was the predominant bacterium until the sugar concentrations decreased to levels that enabled Acinetobacter and Candida to degrade ethanol. PCR-DGGE was effective for detecting the dominant species, but culture-based methods were more accurate for monitoring the population dynamics of these microorganisms during growth in the wastewater medium.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".