The Effect of Water, Sugars, and Proteins on the Pattern of Ice Nucleation and Propagation in Acclimated and Nonacclimated Canola Leaves
Bibliographic record
Abstract
Infrared video thermography was used to observe ice nucleation temperatures, patterns of ice formation, and freezing rates in nonacclimated and cold acclimated leaves of a spring (cv Quest) and a winter (cv Express) canola (Brassica napus). Distinctly different freezing patterns were observed, and the effect of water content, sugars, and soluble proteins on the freezing process was characterized. When freezing was initiated at a warm subzero temperature, ice growth rapidly spread throughout nonacclimated leaves. In contrast, acclimated leaves initiated freezing in a horseshoe pattern beginning at the uppermost edge followed by a slow progression of ice formation across the leaf. However, when acclimated leaves, either previously killed by a slow freeze (2 degrees C h(-1)) or by direct submersion in liquid nitrogen, were refrozen their freezing pattern was similar to nonacclimated leaves. A novel technique was developed using filter paper strips to determine the effects of both sugars and proteins on the rate of freezing of cell extracts. Cell sap from nonacclimated leaves froze 3-fold faster than extracts from acclimated leaves. The rate of freezing in leaves was strongly dependent upon the osmotic potential of the leaves. Simple sugars had a much greater effect on freezing rate than proteins. Nonacclimated leaves containing high water content did not supercool as much as acclimated leaves. Additionally, wetted leaves did not supercool as much as nonwetted leaves. As expected, cell solutes depressed the nucleation temperature of leaves. The use of infrared thermography has revealed that the freezing process in plants is a complex process, reminding us that many aspects of freezing tolerance occur at a whole plant level involving aspects of plant structure and metabolites rather than just the expression of specific genes alone.
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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 | 0.001 |
| 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".