Expanding the Library of Secondary Ions That Distinguish Lignin and Polysaccharides in Time-of-Flight Secondary Ion Mass Spectrometry Analysis of Wood
Bibliographic record
Abstract
Extracted pine (Pinus spp.) wood and the holocellulose and cellulose fractions of pine were analyzed by time-of-flight secondary ion mass spectrometry (ToF-SIMS). The main sources of variation among wood constituents were elucidated by principal component analysis (PCA). Peaks characteristic of lignin or polysaccharides were identified through the combination of high mass resolution analyses of pine fractions and high lateral resolution image analyses distinguishing the lignin-rich middle lamella from the secondary cell wall layers in solid wood cross-sections. A collection of peaks was compiled which (1) extends the library of characteristic lignin and polysaccharide secondary ions in wood, (2) can be applied to both high and nominal mass resolution spectra, and (3) is free from peaks that contraindicate between wood components. The removal of additional peaks to avoid mass interferences with common contaminants was also successful. Many of the characteristic peaks were high-intensity fingerprint ions below m/z 100, which provided for rapid analysis of the lignin and polysaccharide biopolymers in woody samples. The analysis also identified important mass interferences with previously reported wood ions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".