Relating Mechanical Changes at the Glass Transition with Water Absorption Behavior of Dry Legume Seeds
Bibliographic record
Abstract
This work provides a novel view of explaining water uptake behavior commonly observed in legume seeds. A model proposed by Peleg to describe the mechanical changes in biomaterials transitioning from a glassy state to a rubbery state was used to describe water uptake by legume seeds exhibiting a lag during imbibition and to characterize the mechanical changes of the seed coat as affected by soaking time. The seeds characterized by Pelegs model possessed seed coats with a glass transition temperature higher than ambient soaking conditions. Alternatively, the water uptake behavior of legume seeds that did not exhibit a lag phase was characterized by an exponential equation used in polymer science to model solvent sorption by materials above their glass transition. A modified exponential equation was developed to model the mechanical behavior of these seed coats as affected by soaking time. Seeds characterized by the exponential equation possessed seed coats with a glass transition temperature near ambient soaking conditions. This work demonstrates, through the use of two separate models, water uptake behavior of legume seeds can be explained by the glass transition temperature of the seed coat.
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) | 0.000 | 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".