Mid‐infrared spectroscopy is a fast screening method for selecting Arabidopsis genotypes with altered leaf cuticular wax
Bibliographic record
Abstract
Abstract Arabidopsis eceriferum (cer) mutants with unique alterations in their rosette leaf cuticular wax accumulation and composition established by gas chromatography have been investigated using attenuated total reflection (ATR)‐Fourier transform infrared (FTIR) spectroscopy in combination with univariate and multivariate analysis. Objectives of this study were to evaluate the utility of ATR‐FTIR for detection of chemical diversity in leaf cuticles, obtain spectral profiles of cer mutants in comparison with the wild type, and identify changes in leaf cuticles caused by drought stress. FTIR spectra revealed both genotype‐ and treatment‐dependent differences in the chemical make‐up of Arabidopsis leaf cuticles. Drought stress caused specific changes in the integrated area of the CH3 peak, asymmetrical and symmetrical CH2 peaks, ester carbonyl peak and the peak area ratio of ester CO to CH2 asymmetrical vibration. CH3 peak positively correlated with the total wax accumulation. Thus, ATR‐FTIR spectroscopy is a valuable tool that can advance our understanding of the role of cuticle chemistry in plant response to drought and allow selection of superior drought‐tolerant varieties from large genetic resources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".