Using Infrared Thermography to Study Freezing in Plants
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
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.
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 | 0.001 |
| 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