Property‐averaging applied to determination of volume contraction in binary‐solid liquid‐fluidized beds
Bibliographic record
Abstract
Abstract This communication examines experimental information from the literature on the volume contraction that can occur when two monocomponent particle species that have a diameter ratio and a buoyancy‐corrected density ratio on opposite sides of unity are subjected to liquid fluidization as a binary mixture. Attempts are made to predict this volume contraction by applying monocomponent bed expansion equations using averaged properties of the binary solids. It was found that this method works better if the equations are anchored to experimental monocomponent voidages by the fractional bed volume change that they predict than if the equations are used directly. However, greater prediction accuracy can be achieved by correlation of the adjustable parameter G of the Westman, Am Ceramic Soc, 19 , 127–129, (1936) equation, originally applied to binary packed beds.
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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 | 0.000 |
| 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 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".