Bacterial consortia constructed for the decomposition of<i>Agave</i>biomass
Bibliographic record
Abstract
Research has shown that a greater variety of enzymes, as well as variety of microorganisms producing enzymes, can have an overall synergistic effect on the decomposition of lignocellulosic biomass for the production of value-added bio-products. Here, 8 cellulase-degrading bacterial isolates were selected to develop co-, tri-, and tetra-cultures for the decomposition of lignocellulosic biomass. Glucose and xylose equivalents released from imitation biomass media containing 0.5% (w/v) beechwood xylan and 0.5% (w/v) Avicel was measured using di-nitrosalicylic acid for all consortia, along with cell growth and survival. Thereafter, 6 co- and 2 tri-cultures with greatest decomposition were examined for ability to degrade Agave americana fiber. Interestingly, when strains were paired up in co-culture, four pairs: G+5, G+A, C+A1, and G+A1 produced high reducing sugars in 24 h: 6 µM, 8 µM, 8 µM, and finally, 6 µM, respectively. From 4 co-cultures with highest reducing sugar equivalents, tri- and tetra-cultures were produced. The bacterial consortia which had the highest reducing sugars detected were 2 tri-cultures: G + A1 + A4 and G + A1 + 5, displaying levels as high as 9 µM and 5 µM in day 1, respectively. All co- and tri-cultures maintained high cell survival for 14 days with 0.5 g ground Agave. Upon evaluating Agave dry weight after treatment, it was evident that almost half the biomass could be decomposed in 14 days. Scanning electron microscopy of treated Agave supported decomposition when compared with the control. These bacterial consortia have potential for further study of value-added by-product production during metabolism of lignocellulosic biomasses.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".