Effects of freezing on Young’s modulus for twigs of coniferous and deciduous trees and shrubs
Bibliographic record
Abstract
The mechanical behavior of frozen twigs may be important in understanding twig breakage during winter owing to wind or snow loading. While below 0 °C temperatures are known to increase the stiffness of wood, few studies have examined the effects of cold temperatures on the biomechanical properties of twigs of both coniferous and deciduous woody plants. In this study, we compared the effects of –11 to –14 °C temperatures on the Young’s modulus (E) of fine twigs of 14 species of woody plants. Twigs were 13%–304% stiffer when frozen than when thawed at room temperature (21 °C), with conifers showing the greatest percentage increase in stiffness. In general, more flexible twigs (when thawed) showed the greatest percentage increase in E when frozen. Greater stiffening of more flexible twigs may in part be the result of higher water contents but may also reflect differences in the relative importance of supercooling and extracellular freezing, with greater frozen stiffness associated with freezing. Our work suggests a direct link between cell physiology and whole organ biomechanics, and highlights reasons for differences in susceptibility to breakage due to wind or snow loading.
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.
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) | 0.001 | 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".