Bibliographic record
Abstract
Abstract Yield stress measurements of gas/liquid/fibre suspensions, were conducted to examine the effect of gas concentration as well as fibre concentration. Obtaining a well mixed stable pulp suspension with gas was difficult. A transparent model system consisting of glass fibres submersed in refractive index matched liquid gave stable suspensions and had the additional advantage of allowing visual observations. The measurements were performed in a baffled shear tester and concentrations were in the range of 0–12.5% vol. gas and 8–14% wt. fibres. Results show that yield stress decreases with increasing gas fraction for constant fibre concentrations. Since the fibre concentration is defined in the liquid phase, the amount of fibres decreases with increasing gas concentration. This fact alone partly explains the decrease in yield stress, but the gas also has a direct impact on the decrease in yield stress. Further, as expected, the yield stress increases with increasing fibre concentrations when the gas fraction is kept constant.
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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".