Online Application‐Oriented Optimal Scheduling for 2‐keto‐l‐gulonic Acid Production
Bibliographic record
Abstract
An optimal scheduling approach for the 2‐keto‐L‐gulonic acid (2‐KGA) fermentation process is proposed to improve allocation of L‐sorbose resources with the aim of profit maximization in a multi‐bioreactor workshop. The empirical operation in 2‐KGA cultivation under study is to assign the same quantity of L‐sorbose to each batch without taking batch‐to‐batch variations into account, while the optimal scheduling approach presented in this paper will determine L‐sorbose feeding according to the evaluation of the profit‐making ability of the individual batch. Each 2‐KGA batch is classified online according to the prediction of the profit function, so that each batch is assigned into different profit‐making categories. Pseudo‐online scheduling is implemented with the data of industrial 2‐KGA cultivations. A total profit increase of 6–7 % is found to be achievable for the workshop in comparison with the empirical operation.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".