Analysis of bubble size distributions using the McGill bubble size analyser
Bibliographic record
Abstract
Among the apparently simplest methods to determine bubble size in flotation systems are photographic techniques, ranging from photography through transparent walls to imaging of extracted bubbles. All capture images, which to varying degrees include overlapping, touching or out of focus bubbles. As manual counting limits the total number of bubbles, image analysis software is used to automate the process. Accuracy is thus dependent on image treatment, including counting method and filters. The McGill bubble size analysis method yields single plane, backlit images and utilises software that filters by shape factor. Proven effective for bubble size distributions ranging from approximately 0.5 to 3 mm, regular trends are observed when number (D10) and Sauter (D32) mean diameters are compared. When the method was extended to wide distributions typical of jetting spargers (e.g., 0.2--15 mm), no similar trends were evident. Revision of the analysis process for these two-phase systems included counting by number of holes, which reduced dependence on bubble shape. This allowed for inclusion of small and large bubbles, while excluding bubble clusters. A diameter assignment protocol reflecting individual bubble shape was also developed. Revised output distributions showed increased symmetry, and the D32 vs. D10 trend was recovered. Impact of sample tube diameter on the output bubble size distributions, and types of bias introduced were also investigated. A means of selecting an appropriate sample tube diameter for a given bubble population is presented.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".