Multivariate Calibration of Semi‐Synthetic Data Sets: Gun Powder Analysis
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
Abstract Quantitative analysis of multi‐component mixtures such as propellant powders is not trivial since it usually requires separation of the mixture constituents. Multivariate calibration combined to the use of semi‐synthetic data sets can eliminate the need for standard solutions preparation, and therefore allow the rapid determination of mixtures provided no intermolecular interactions occur in the systems. Multivariate compositional analyses of FTIR spectra of low‐vulnerability (LOVA), high‐energy low‐vulnerability (HELOVA) and energetic thermoplastic elastomer (ETPE) propellant powder systems were performed using the partial least‐squares (PLS) regression algorithm. All constituents except ethyl centralite (EC) were quantified. Concentrations were predicted within 1% error for the major component (1,3,5‐trinitro‐1,3,5‐triazacyclohexane or RDX), and within 5% error for the minor components (between 12 and 2% nominally by weight). LOVA, HELOVA, and ETPE gun powder samples concentrations were estimated and compared to expected compositions.
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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 | 0.000 | 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 | 0.000 |
| 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