Comprehensive multiphase NMR: a promising technology to study plants in their native state
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is arguably one the most powerful tools to study the interactions and molecular structure within plants. Traditionally, however, NMR has developed as two separate fields, one dealing with liquids and the other dealing with solids. Plants in their native state contain components that are soluble, swollen, and true solids. Here, a new form of NMR spectroscopy, developed in 2012, termed comprehensive multiphase (CMP)-NMR is applied for plant analysis. The technology composes all aspects of solution, gel, and solid-state NMR into a single NMR probe such that all components in all phases in native unaltered samples can be studied and differentiated in situ. The technology is evaluated using wild-type Arabidopsis thaliana and the cellulose-deficient mutant ectopic lignification1 (eli1) as examples. Using CMP-NMR to study intact samples eliminated the bias introduced by extraction methods and enabled the acquisition of a more complete structural and metabolic profile; thus, CMP-NMR revealed molecular differences between wild type (WT) and eli1 that could be overlooked by conventional methods. Methanol, fatty acids and/or lipids, glutamine, phenylalanine, starch, and nucleic acids were more abundant in eli1 than in WT. Pentaglycine was present in A. thaliana seedlings and more abundant in eli1 than in WT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".