Process operating performance optimality assessment with coexistence of quantitative and qualitative information
Bibliographic record
Abstract
Abstract When the process is working normally, process operating performance optimality assessment makes a further judgment on how well the process is running currently. Sustained optimal process operating performance guarantees the greatest economic benefits. However, the operating performance may deteriorate from the optimal status due to the production condition shifts. It is crucial to accomplish operating performance optimality assessment strategies for a deeper understanding of the process status. A Dynamic Causality Diagram (DCD) explicitly and intuitively describes the causal correlations between variables. However, an actual process has both quantitative and qualitative measurements, which makes the conventional DCD imprecise or even invalid. To handle the above problems, a modified DCD is proposed to assess a complicated process with coexistence of quantitative and qualitative information. An operating performance optimality assessing strategy based on the proposed DCD is then established. The proposed method is successfully applied to a gold hydrometallurgy process, and the operating performance optimality assessment result is satisfied.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".