Statistical approach for solid-state NMR spectra of cellulose derived from a series of variable parameters
Bibliographic record
Abstract
Principle component analysis (PCA) was used to extract components from the solid-state nuclear magnetic resonance (NMR) spectra of bacterial cellulose (BC). Polymers such as cellulose have several domain structures, and their structure and dynamics are reflected in the variety of solid-state spectra derived from different parameters. The complexity of the obtained spectra makes the analysis of spectra from relaxation measurements difficult. Multivariate analysis, such as PCA, has been suggested as an option to improve NMR spectral analyses. In this study, we attempted to extract peak components from cross polarization (CP) experiment data from the variable contact time spectra of BC by using PCA. The extracted peaks were annotated according to previous reports, and the existence of crystalline Iα was clearly recognized. The important components of the BC structure (that is, crystalline Iα, amorphous form and mobility) were separated in the PCA-loading plot and CP curve. A multivariate analysis was used to extract components in solid-state nuclear magnetic resonance (NMR) spectra from bacterial cellulose (BC). Polymers such as cellulose have several domain structures, and their structure and dynamics are reflected in the variety of solid-state spectra derived from different parameters. Multivariate analysis, such as principle component analysis (PCA), is suggested as an option to improve analyses of complex NMR spectra from relaxation measurements. In this study we demonstrate the extraction of peak components using PCA from cross polarization experiment data with variable contact time spectra of BC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".