Modelling of dewatering wood pulp in a screw press using statistical and multivariate analysis
Bibliographic record
Abstract
Statistical modeling of a screw press was established by using an experimental design based on the screw rotational speed, the pulp feed consistency, the pulp feed suspension freeness, the inlet pressure, and the counter-pressure at the discharge end. The statistical models showed that the screw press outputs for each pulp could be predicted. When including all data in a global model to predict the outputs of the press for any pulp, a global statistical model was found not to be efficient by using just the five fixed parameters. The solution to this problem was to use a multivariate analysis to include more parameters, mainly about the fiber characteristics (crowding factor, fiber length, fiber width, and fines content). By including these fiber properties, the differences between each pulp were more properly analyzed. The multivariate analysis predicted the press outsets very well in a global model by using eight parameters instead of five. The R2 values of the multivariate prediction model were all higher than 0.70 and had the goodness of prediction (Q2¬¬¬) higher than 0.60.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".