A Bubble Mixture Experiment Project for Use in an Advanced Design of Experiments Class
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article gives an example of how student-conducted experiments can enhance a course in the design of experiments. We focus on a project whose aim is to find a good mixture of water, soap and glycerin for making soap bubbles. This project is relatively straightforward to implement and understand. At its most basic level the project introduces students to mixture experiments and general issues in experimental design such as choosing and measuring an appropriate response, selecting a design, the effect of using repeats versus replicates, model building, making predictions, etc. To accommodate more advanced students, the project can be easily enhanced to draw on various areas of statistics, such as generalized linear models, robust design, and optimal design. Therefore it is ideal for a graduate level course as it encourages students to look beyond the basics presented in class.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it