Potential of thin stillage as a low-cost nutrient source for direct cellulose fermentation by <i>Clostridium thermocellum</i>
Bibliographic record
Abstract
Utilization of thin stillage (TS), derived from grain-based ethanol production, was investigated as an alternative source for microbial growth nutrients during direct conversion of cellulose by Clostridium thermocellum DSM 1237. Fermentation end-products synthesized by C. thermocellum grown on media prepared with various concentrations (50-400 g/L) of TS were compared to those synthesized by C. thermocellum grown on reagent grade chemical (reference) medium. Cell-growth in TS media, monitored with the aid of quantitative polymerase chain reactions (qPCR) technique, showed prolonged growth with increasing TS concentration. Final fermentation end-product concentrations from TS media were comparable with those from the reference medium despite lower growth-rates. The volumetric H2 production generated by C. thermocellum grown with medium containing a low concentration (50 g/L) of TS matched the volumetric H2 production by C. thermocellum grown in the reference medium, while higher concentrations (200 g/L) of TS resulted in greater synthesis of ethanol. Supplementation of TS-media with Mg++ enhanced ethanol production, while hydrogen production remained unchanged. These results suggest that TS, an attractive source of low-cost nutrients, is capable of supporting the growth of C. thermocellum and that high concentrations of TS favor synthesis of ethanol over hydrogen from cellulose.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".