High-definition infrared thermography of ice nucleation and propagation in wheat under natural frost conditions and controlled freezing
Bibliographic record
Abstract
MAIN CONCLUSION: An extremely high resolution infrared camera demonstrated various freezing events in wheat under natural conditions. Many of those events shed light on years of misunderstanding regarding freezing in small grains. Infrared thermography has enhanced our knowledge of ice nucleation and propagation in plants through visualization of the freezing process. The majority of infrared analyses have been conducted under controlled conditions and often on individual organs instead of whole plants. In the present study, high-definition (1280 × 720 pixel resolution) infrared thermography was used under natural conditions to visualize the freezing process of wheat plants during freezing events in 2016 and 2017. Plants within plots were found to freeze one at a time throughout the night and in an apparently random manner. Leaves on each plant also froze one at a time in an age-dependent pattern with oldest leaves freezing first. Contrary to a common assumption that freezing begins in the upper parts of leaves; freezing began at the base of the plant and spread upwards. The high resolution camera used was able to verify that a two stage sequence of freezing began within vascular bundles. Neither of the two stages was lethal to leaves, but a third stage was demonstrated at colder temperatures that was lethal and was likely a result of dehydration stress; this stage of freezing was not detectable by infrared. These results underscore the complexity of the freezing process in small grains and indicate that comprehensive observational studies are essential to identifying and selecting freezing tolerance traits in grain crops.
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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) | 0.000 | 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 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".