Characterization of an adapted microbial population to the bioconversion of carbon monoxide into butanol using next-generation sequencing technology
Bibliographic record
Abstract
Biobutanol is increasingly regarded as the second generation biofuel of choice. Microbial production of butanol is however still conventionally based on the utilization of carbohydrates as carbon feedstock. More and more companies are interested in using alternative carbon sources, such as biomass, notably by combining conversion of biomass into syngas via gasification and microbial fermentation of syngas components. To date, only few syngas-fermenting microorganisms that can produce butanol are known. Discovering new microorganisms or microbial consortia capable of fermenting syngas into liquid biofuels, and engineering them to make them commercially attractive is thus primordial in a strategy to develop an economically viable platform for biobutanol production. This presentation will discuss the use of next-generation sequencing technology to perform microbial community analyses of anaerobic undefined mixed cultures, with the objective to identify microbial species particularly adapted to the bioconversion of carbon monoxide (CO), a major component of syngas, into butanol
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".