Bibliographic record
Abstract
Understanding and managing gas release in yield stress fluids is crucial for both industrial operations and environmental sustainability. Of particular concern are emissions of carbon dioxide and methane from tailings ponds, where uncontrolled releases contribute to greenhouse gas emissions and may also present safety risks. Within these ponds, bubbles may be trapped or ascend, according to their size and the rheology of the pond fluids. As bubbles ascend, the deformed region around the bubble forms a “damaged” zone within which the fluid rheology apparently does not fully recover after passage of the bubble. Equally, future bubbles are attracted toward the pathways of previous bubbles. Potentially, this combination can lead to formation of a root-like network of bubble pathways over time, thereby impacting the overall bubble dynamics. In this paper, we outline laboratory experiments involving rising bubbles in Carbopol, a yield stress fluid. We focus on bubble behavior proximal to a deliberately damaged zone. Specifically, we examine individual bubble trajectories at various distances from a manually sheared vertical layer and for different Carbopol concentrations. We show how bubbles are drawn toward the regions that have been sheared and quantify the critical distance needed in order for bubble rise to be independent of the damaged zone. We quantify bubble trajectory dynamics and show that similar effects can be achieved by a simple toy model that characterizes the “damage” via a deficit in the yield stress.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".