An apparatus and method of feeding a feed slurry into a separating device
Bibliographic record
Abstract
The present invention provides an apparatus (1) and method for feeding a feed slurry into a device for separating low density particles from the feed slurry. The apparatus (1) comprises a conduit (4, 6, 8) having a slurry inlet (3), a gas feed inlet (5), a plurality of hollow tubes (10) and an outlet (7). The hollow tubes (10) are configured to combine the feed slurry from the slurry inlet (3) and gas from the gas feed inlet (5). The hollow tubes (10) comprise a porous section (16) to generate bubbles of substantially uniform size into the slurry for adhering to the low density particles. Slurry flows in axially aligned hollow tubes as gas is introduced through the porous sections into the slurry. Alternatively, slurry flows around hollow tubes arranged perpendicular to the conduit longitudinal axis as gas is discharged through the porous sections into the slurry. PCT/AU2018/050725, Australian Application No. 2018303328, Brazilian Application No. 11 2020 000928 8, Canadian Application No. 3,069,340, Chilean Application No. 128-2020, Chinese Application No. 201880048005.8, Eurasian Application No. 202090280, European Application No. 18834891.6, Mexican Application No. MX/a/2020/000585, Peruvian Application No. 000083-2020/DIN, US Application No. 16/631787, South African Application No. 2019/08581.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".