Identification of ultrastructural and biochemical cuticular markers influencing temperature of ice nucleation in selected genotypes of corn
Bibliographic record
Abstract
Corn is an economically important yet frost-sensitive crop, injured at the moment of ice nucleation. However, the influence of autumn temperatures on subsequent ice nucleation temperature is unknown. A 10-day chilling treatment under phytotron conditions ("mild", 18/6°C) or ("extreme", 10/5°C) generated no-visible damage but induced changes in the cuticle of the four genotypes in this study. The putatively more cold hardy Genotypes 884 and 959 leaves nucleated at colder temperatures compared to the more sensitive Genotypes 675 and 275. After chilling treatment, all four genotypes displayed warmer ice nucleation temperatures, with Genotype 884 expressing the largest shift to warmer nucleation temperatures. Cuticular hydrophobicity reduced while cuticular thickness remained unchanged under the chilling treatment. By contrast, under five-week field conditions, cuticle thickness increased in all genotypes, with Genotype 256 expressing a significantly thinner cuticle. FTIR spectroscopy revealed increases in the spectral regions of cuticular lipids in all genotypes after phytotron chilling treatment, while those spectral regions decreased under field conditions. A total of 142 molecular compounds were detected, with 28 compounds significantly induced under either phytotron or field conditions. Of these, seven compounds were induced under both conditions (Alkanes C31-C33, Ester C44, C46, β-amyrin, and triterpene). While clear differential responses were observed, chilling conditions preceding a frost modified physical and biochemical properties of the leaf cuticle under both phytotron and field conditions indicating this response is dynamic and could be a factor in selecting corn genotypes better adapted to avoiding frost with lower ice nucleation temperature.
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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) | 0.000 | 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 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".