Mathematical modelling and its use for design of feeding strategies for L‐Sorbose fermentation
Bibliographic record
Abstract
Abstract Batch sorbitol to sorbose bioconversion by Acetobacter suboxydans using initial sorbitol concentration ( S 0 = 100 g/L) yielded a productivity of 10.11 g/L‐h and 98.6% conversion in 10 h time. The batch kinetics was then used to develop an unstructured mathematical model. Model parameters were identified using a nonlinear regression technique assisted by a computer program which minimized the deviation between the model predictions and actual batch experimental data. F test indicated 99% confidence on the prediction of model using optimized parameters. The batch model was eventually extrapolated to identify nutrient feeding strategies to maintain constant noninhibitory sorbitol supply and eliminate substrate limitation for fed‐batch fermentation in order to improve the sorbose productivity. The adequacy of the fed‐batch model was established by excellent agreement between experimental data and model simulation (except towards the end of fermentation).
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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".