Factors contributing to ice nucleation and sequential freezing of leaves in wheat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it