Characterizing the Mobility of Papermaking Fibres During Sedimentation
Bibliographic record
Abstract
The mobility of sedimenting fibre suspensions is characterized here in three different, yet complementary studies. In the first study we present a simple mathematical analysis to define more precisely the term sediment concentration. Through this analysis we correct the sediment concentration for compressibility effects and redefine this parameter as the gel concentration point. In the second study, we visualize the transient settling of radioactively labeled papermaking fibres using a new experimental technique, positron emission tomography (PET). In the third study, we measure the mass distribution of fibres (formation) in the sediment as a function of the initial suspension concentration. The results indicate that the gel concentration point occurs at a crowding number of approximately 16(±4). Two distinct regimes of settling were clearly identified with PET, depending upon the initial crowding number of the suspension (N). With N < 16, hindered settling was observed. With N > 16, fibres began to flocculate, starting with the long fibre fraction. Formation was found to be slightly dependent on N in the region N < 16 and then worsen significantly with N > 16. In summary, these findings indicate that within the suspension conditions found in papermaking 1 < N < 60, that there are two sub-regimes within these limits of differing levels of fibre mobility. These sub-regimes are delineated at N = 16.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".