Potential and Metabolic Pathway Analysis of Marine Microorganism Fermentation in Bioethanol Production
Bibliographic record
Abstract
The study found that marine yeasts, such as Wickerhamomyces anomalus M15, exhibit high tolerance to salt and inhibitors, making them suitable for seawater fermentation. Additionally, the use of macroalgae and microalgae, such as Ulva fasciata and Chlorella vulgaris , demonstrated significant potential for bioethanol production, with chemical hydrolysis being the most effective pretreatment method. The integration of advanced techniques like artificial neural networks with genetic algorithms (ANN-GA) further optimized the fermentation parameters, enhancing bioethanol yield. Moreover, the study highlighted the importance of specific microbial strains, such as Saccharomyces cerevisiae , in efficiently converting carbohydrates to ethanol. The findings suggest that marine microorganisms and biomass hold substantial promise for sustainable bioethanol production. The high tolerance of marine yeasts to saline conditions and the effective use of macroalgae and microalgae as feedstocks can lead to greener and more efficient bioethanol production processes. The optimization of fermentation parameters through advanced modeling techniques can further enhance ethanol yields, making marine-based bioethanol production a viable alternative to traditional methods.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".