Continuous Separation of Fines from Fibers in a Wedge-Shaped Vessel
Bibliographic record
Abstract
The continuous removal of fines from a pulp suspension can be achieved by a particle elutriation process using, for example, a wedge-shaped vessel with an inclined tube at one vertical end wall that serves as an inlet for the suspension. Near the other vertical end, the suspension flow is split into a bottom stream containing the recovered pulp and a top stream containing the separated fines. The effect of operating conditions on both the hydrodynamic behavior and the separation efficiency was investigated. There are two critical limits for the operating flow rates of the feed suspension: below a minimum flow rate, fibers settle near the bottom exit and above a maximum flow rate, fibers escape in the top stream. These limiting flow rates do not show a strong dependence on operating conditions, except for a maximum inlet consistency above which the pulp bed expands into the outlet section, thus preventing fractionation. The particle concentration in the top stream was measured with a spectrophotometer, from which the separation efficiency was determined as a function of the inlet and outlet flow rates and the inlet consistency. The separation efficiency increases with increasing split ratio (i.e., the ratio of top to bottom streams) and with decreasing consistency of the feed suspension. Fiber loss in the top stream is about 1% of the fibers entering the vessel.
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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 | 0.000 |
| 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) | 0.000 | 0.001 |
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".