Bibliographic record
Abstract
Reduced dimension control involves the indirect control of the entire output variable space through the judicious selection of a much smaller number of controlled and manipulated variables. A general framework for the selection of the subspace of manipulated and controlled variables, developed on the basis of minimum variance control theory, is presented. Given the disturbance directions and process gain matrix, expressions for the optimal directions for control are derived. The role of the number of independent disturbances in determining the number of controlled variables and the structure of the resulting reduced dimension controller are clearly shown. The framework is then applied to a simulated dynamic Kamyr digester. Two single-input, single-output reduced dimension controllers (RDCs) are proposed and compared to a 5 × 5 dynamic matrix controller (DMC) that controls all outputs and manipulates all inputs. The RDCs performed very well at the conditions for which they were designed and showed only modest degradation when the process operating point was changed. Despite their much simpler structure, their performance in terms of the full output space was very close to that of the DMC.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".