A Bayesian experimental design approach for assessing new product performance: An application to disinfectant formulation
Bibliographic record
Abstract
Abstract This paper presents a Bayesian methodology for computer‐aided experimental design for hydrogen peroxide formulations. Hydrogen peroxide is one of the oldest known active antimicrobial chemicals and is used in many cleaning/disinfecting formulations. It is favourable as an active antimicrobial in that it degrades only to water and oxygen, and does not contaminate the environment. However hydrogen peroxide is difficult to stabilise, and disinfecting products based on it soon lose their antimicrobial activity. Moreover, regulatory agencies such as U.S. Environmental Protection Agency (EPA) and Health Canada require that disinfecting products do not lose more than 5–10% of their active concentration throughout their shelf life. Therefore, it is very important while formulating hydrogen peroxide‐based products to test for their stability. An effective way to improve hydrogen peroxide stability in a solution is to use stabilisers. It is desired to use these chemicals in as low concentrations as possible for environmental and economic considerations. On the other hand, due to tight market competition, the new products need to be formulated as quickly as possible, and therefore there is limited time to ensure product stability. In this paper, prior information has been used in the form of a model, based on historical experiments. A Bayesian D‐optimality criterion is used to design a few additional experiments so that the resulting model can have an acceptable prediction power. It is shown that a design which uses the Bayesian D‐optimality criterion taking advantage of prior information can be more efficient than even a resolution IV fractional factorial design in the sense that using fewer trials gives a model with equivalent prediction capability. This can be critical where experiments are expensive to perform.
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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 | 0.001 |
| 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".