Detecting changes in arabidopsis cell wall composition using time‐of‐flight secondary ion mass spectrometry
Bibliographic record
Abstract
Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was previously used to characterize lignocellulosic materials, including woody biomass. ToF‐SIMS can acquire both rapid spectral and spatial information about a sample's surface composition. In the present study, ToF‐SIMS was used to characterize the cell walls of stem tissue from the plant model organism, Arabidopsis thaliana . Using principal component analyses, ToF‐SIMS spectra from A . thaliana wild‐type (Col‐0), cellulose mutant ( irx3 ), and lignin mutant ( fah1 ) stem tissues were distinguished using ToF‐SIMS peaks annotated for wood‐derived lignocellulose, where spectra from the irx3 and fah1 were characterized by comparatively low polysaccharide and syringyl lignin content, respectively. Spatial analyses using ToF‐SIMS imaging furthermore differentiated interfascicular fiber and xylem vessels based on differences in the lignin content of corresponding cell walls. These new data support the applicability of ToF‐SIMS peak annotations based on woody biomass for herbaceous plants, including model plant systems like arabidopsis. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.001 | 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 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".