Evolution of the Population of Bubbles in 2D Fluidized Beds
Bibliographic record
Abstract
Abstract The size of the bubbles in a solid‐gas 2D fluidized bed was estimated by image capture and digitalization using a CCD camera. It was confirmed that the size distribution for the bubbles in a slice of the bed was skewed, varying with the location of the slice. The results were analyzed using the Agarwal (1985) model, which is based on a population balance. For this model, four adjustable parameters are required, as well as the knowledge of bubble size just above the distributor. Experimental data were compared with values predicted by the model. The parameters that the model needs were obtained by three procedures: i) the constants supplied by Agarwal (1985) and Rowe and Everett (1972); ii) the parameters obtained from experimental data for a 1 cm wide slice bed; and iii) the parameters obtained by fitting the bubble diameter to all the bubbles in the bed. The second and third methods provided the best results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".