Microbial Communities Performing Hydrogen Solventogenic Metabolism of Volatile Fatty Acids
Bibliographic record
Abstract
1 Abstract This work evaluates four different physicochemical pretreatments (acidic, thermal, acidic-thermal and thermal-acidic) on an anaerobic inoculum used for alcohol production from acetate and butyrate. All experiments were conducted in single batches using acetate and butyrate as substrates at 30 °C and with a pressurized headspace of pure H 2 at 2.15 atm (218.2 MPa). Thermal and acidic-thermal pretreatments lead to higher production of both ethanol and butanol. Mathematical modelling shows that the highest attainable concentrations of ethanol and butanol produced were 122 mg L -1 and 97 mg L -1 for the thermal pretreatment (after 17.5 days) and 87 mg L -1 and 143 mg L -1 for the acidic-thermal pretreatment (after 18.9 days). Acetate was produced in all assays. Thermodynamic data indicated that a high H 2 partial pressure favoured solventogenic metabolic pathways. Finally, sequencing data showed that both thermal and acidic-thermal pretreatments selected mainly the bacterial genera Pseudomonas, Brevundimonas and Clostridium . The acidic-thermal pretreatment selected a bacterial community more adapted to the conversion of acetate and butyrate into ethanol and butanol, respectively. Thermal-acidic pretreatment was unstable, showing significant variability between replicates. Acidic pretreatment showed the lowest alcohol production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".