Multi-objective optimization for process control of the in-situ bioremediation system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The process control of in-situ bioremediation system is complex and involves multiple objectives. An interactive multi-objective decision-making tool has been developed for process control of the in-situ bioremediation system. The controller consists of three steps. First, the evolutionary multi-objective optimization method is used to identify a set of optimal control strategies and costs corresponding to different efficiency requirements. Secondly, the costs and efficiencies are normalized based on results from a questionnaire that has been developed for acquiring information about the normalization functions. In the third step, the interactive system acquires the user's preferences on tradeoffs of the two objectives of cost and efficiencies. Then the system generates the optimal control strategy using genetic algorithm. The interactive system has been applied to a case study. The results show that the control system can taken into consideration relative importance of each objective and generate a set of optimal strategies based on the particular requirements of the decision maker.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it