Reducing the Presence of Clusters in Bubble Size Measurements for Gas Dispersion Characterizations
Bibliographic record
Abstract
This short communication evaluates a new strategy to sample bubbles in gas dispersion characterizations. Bubble size is measured in a bidimensional flotation cell using the McGill bubble size analyzer under different types of frothers, frother concentrations and superficial gas rates. The original design of this bubble viewer is modified, changing the deflecting system to photograph only a fraction of the bubbles entering the device. As a result, the new design increases the ability to successfully identify bubbles by a maximum of 20% using an automated algorithm. This increase is caused by a reduction in the formation of clusters in the visual field. The improvement, which is a function of the operating conditions, is most significant in the transition from ellipsoidal/ellipsoidal–turbulent regimes (no frother or low frother concentrations) to conditions with an over-agglomeration of bubbles in the visual field (high superficial gas rates and high frother concentrations). A comparison of the bubble size parameters obtained from the original and proposed deflecting systems shows that the new design does not distort the estimated bubble size distributions. To complement the research findings, alternative sampling designs, using new or existing segmentation algorithms, are then proposed to improve gas dispersion characterizations at different scales.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".