Bar force measurement in low consistency refining: the effect of plate pattern
Bibliographic record
Abstract
Abstract The effect of plate pattern on forces applied to pulp fibers by refiner bars in low consistency refining is investigated in an AIKAWA 16-inch single-disc refiner. These forces are measured using a custom-built piezoelectric sensor. Trials are conducted using SPF softwood thermomechanical pulp, northern bleached softwood kraft pulp, and aspen hardwood thermomechanical pulp at 3.3 to 3.6 % consistency at rotational speeds of 1200 and 1400 rpm. The pulp is sampled at regular intervals, and the length-weighted fiber length, freeness, tear index, and tensile index are measured for each sample. The results show that the plate with higher bar edge length results in lower mean peak normal and shear forces. The mean peak normal and shear forces at the onset of fiber cutting depend on rotational speed, pulp furnish and plate pattern, and these parameters are lower for a plate pattern with higher bar edge length. In addition, the mean coefficient of friction is a function of plate gap, pulp furnish, and plate pattern. The plate having higher bar edge length results in higher mean coefficient of friction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".