Statistical approach for solid-state NMR spectra of cellulose derived from a series of variable parameters
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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