Utilisation de vecteurs tournants pour l'optimisation de la formulation de mélanges de solvants
Bibliographic record
Abstract
A new strategy is proposed to optimize the design of solvent blends without using complex mathematical models and (or) graphical representations. All calculations are made with standard electronic programs, such as Excel, Lotus, etc. This approach was developed for the cleaning and degreasing industry, which has to find new recipes of solvent blends on a regular basis. The process relies on a visual analysis of the sum of normalized rotating vectors associated with the chemical composition and the physical properties of the individual components. This approach allows for representation of all parameters on a two-dimensional plot, including information about chemical composition, as well as the physical properties to be optimized. The research of a new mixture of halogenated solvents will be used as an example to illustrate the various steps of this technique. This method is not limited to solvent applications; it also applies to all problems that involve comparisons of physical and chemical properties of blends.Key words: solvents, mixtures, components, optimization, cleaning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".