Anaerobic digestion waste as a nutrient source for closed-loop alkaline cultivation of a cyanobacterial consortium
Bibliographic record
Abstract
The use of anaerobic digestion (AD) wastes as nutrient sources for cyanobacterial cultivation offers a sustainable path to closed-loop bioproduction. This study investigated the potential of liquid and solid digestates from a Candidatus Sodalinema alkaliphilum consortium to support community growth under alkaline conditions. While the AD waste provided sufficient nutrients for growth, the culture crashed by the third cultivation cycle, even after dilution of the digestate to mitigate potential inhibitory factors. Microbial community and metabolite analyses suggested that toxic compounds, including 2,4-di-tert-butylphenol (DTBP), likely played a role in the culture crashes, while Roseinatronobacter and other bacteria or archaea from the digestate were unlikely to be direct causes. This study highlights the potential challenges of using AD waste for the cultivation of cyanobacteria in a closed-loop system and emphasizes the need for further investigation into the mechanisms driving culture crashes to enhance the sustainability of this biotechnological approach. • Anaerobic digestate contained sufficient nutrients, but the bioavailability of nutrients was lacking. • Cyanobacterial cultures crashed by the third growth cycle when digestate was used. • Crashes could not be explained by digestate turbidity and nutrient levels. • Roseinatronobacter and other bacteria or archaea from the digestate are unlikely to have caused culture crashes. • The microbially synthesized toxin 2,4-di-tert-butylphenol may have contributed to cyanobacterial population instability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".