Predictive Fuzzy Control of Paper Quality
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
Pulp and paper quality depends on the quality of wood chips which depends on their physical and optical properties. Presently, there is no formally established knowledge concerning the co-influences of the several parameters governing the thermo-mechanical pulp and paper process (TMP). The main goal of this paper is to automatically generate fuzzy knowledge bases (FKBs) to characterize wood chip properties online and apply this information to optimize the TMP process so that pulp quality can be predicted and controlled using wood chip properties (defined by numerical data). The production settings used in this article take into account the hydrosulfite bleaching agent. Learning of the FKBs (using a genetic algorithm) uses measurements obtained from the chip management system (CMSreg). Changes in chip quality are measured by physical information (color analysis and humidity) using CMSreg. The information provided by CMSreg enabled us to predict the ISO brightness of the produced pulp according to a certain charge of hydrosulfite. The developed FKBs are used afterwards to control the optimal hydrosulfite charges using a Monte-Carlo based search algorithm
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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