Neural Optimization of Fed‐batch Streptokinase Fermentation in a Non‐ideal Bioreactor
Bibliographic record
Abstract
Abstract Microbial fermentations involving two or more kinds of competing cells and operating under realistic conditions are difficult to monitor, model and optimize by model‐based methods. They deviate from ideal behavior in two significant aspects: incomplete dispersion in the broth and the influx of disturbances. The approach here has been to optimize the filtered noise and dispersion on‐line through neural networks. This method has been applied to the fed‐batch production of streptokinase (SK). The culture has two kinds of cells — active (or productive) and inactive — and their growth is inhibited by the substrate and the primary metabolite (lactic acid). Using simulated data, the fermentation was optimized by a system of three neural networks, updated continually during successive time intervals. Such sequential optimization with dynamic filtering of inflow noise generated better cell growth and SK activity than static optimization and even an ideal fermentation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".