Factors contributing to ice nucleation and sequential freezing of leaves in wheat
Bibliographic record
Abstract
MAIN CONCLUSION: Anatomical, metabolic and microbial factors were identified that contribute to sequential freezing in wheat leaves and likely contribute to supercooling in the youngest leaves and potentially meristematic regions. Infrared thermography (IR) has been used to observe wheat leaves freezing independently and in an age-related sequence with older leaves freezing first. To determine mechanisms that might explain this sequence of freezing several analytical approaches were used: (1) The size of xylem vessels, in proximity to where freezing initiated, was measured to see if capillary freezing point depression explained sequential freezing. The sequence of freezing in the four youngest leaves was correlated, with the largest vessels freezing first. (2) Carbohydrate and amino acids were analyzed to determine if solute concentrations as well as interactions with membranes explained the freezing sequence. Sucrose was highly correlated to the freezing sequence for all leaves suggesting a prominent role for this sugar as compared to other simple sugars and fructans. Among individual free amino acids proline and serine were correlated to the freezing sequence, with younger leaves having the highest concentrations. (3) Microflora within and on leaf surfaces were determined to measure potential freezing initiation. Levels of bacteria and fungi were correlated to the freezing sequence for all leaves, and species or genera associated with high ice nucleation activity were absent in younger leaves. Moisture content and transcript expression of ice binding proteins were also measured. As expected, our results show that no single mechanism explains the freezing sequence observed via infrared analyses. While these multiple mechanisms are operative at different levels according to the leaf age, they seem to converge when it comes to the protection of vital meristematic tissues. This provides potential phenotypic characters that could be used by breeders to develop more winter-hardy genotypes.
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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".