Analysis of Kaolin Flocculation in Seawater by Optical Backscattering Measurements: Effect of Flocculant Management and Liquor Conditions
Bibliographic record
Abstract
Optical backscattering (OBS) signal values were used to evaluate the flocculation of kaolin slurries and seek the implications of using seawater. Two anionic flocculants were applied to kaolin suspensions at several dosages and in water of varying pH and electrolyte concentration. An OBS height scan method was used to estimate the degree of aggregation, supernatant quality, and solids concentration of the sediments. The residual solids of the supernatant depended of the extent of particle coagulation before flocculant was added, where the pH and salinity displayed a significant impact on flocculation. The OBS results were highly sensitive to the presence of fine particles, which was estimated in parallel from the focused beam reflectance measurements (FBRM.) In seawater, without flocculant added, the samples had increased root-mean-square scattering (Frms) values and larger final sediment volume than samples prepared in water with lower electrolyte concentration. This indicates a higher initial state of aggregation of the particles in seawater. Then, the aggregation degree was best linked to the square-weighted chord length distribution of the FBRM data, which intensifies the sensitivity to coarse aggregates.
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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 | 0.000 |
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".