Using Infrared Thermography to Study Freezing in Plants
Bibliographic record
Abstract
Factors that determine when and to what extent a plant will freeze are complex. Although thermocouples have served as the main method of monitoring the freezing process in plants, infrared (IR) thermography offers distinct advantages and the use of this latter technology has provided new insights on the processes of ice nucleation and propagation. This technology is based on the fact that freezing is an exothermic event. The temperature and spatial resolution of a high-resolution IR camera has enabled researchers to clearly define initial sites of nucleation as well as monitor the ice front as it spreads into surrounding tissues. Ice nucleation is induced by both extrinsic and intrinsic nucleators. Ice nucleation-active bacteria and moisture are two major extrinsic agents. In herbaceous plants, the influence of extrinsic ice nucleators on ice nucleation can be moderated by thick cuticles or the application of synthetic hydrophobic barriers. The situation in woody plants, however, is different. Woody plants appear to possess native, intrinsic nucleating agents that are as active as many extrinsic agents. However, the identity of the intrinsic nucleating agents in woody plants is not known. Despite the presence of intrinsic nucleating agents, barriers exist in woody plants that inhibit growth of ice from older stems into primary, lateral appendages. This is important because many tissues in woody plants that are frost-sensitive are flowers and primary, elongating shoot tissues that arise from buds attached to older stems. Pictures derived from video segments of the freezing process and data on the ability to block nucleation through the use of hydrophobic kaolin are provided.
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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".