In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis
Bibliographic record
Abstract
The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".