Seed coat anatomy of <i>Cercis chinensis</i> and its relationship to water uptake
Bibliographic record
Abstract
The hard seed coat of Cercis chinensis Bunge is an important factor of its dormancy. A study of the characteristics of water absorption is vital for understanding seed dormancy and germination. This investigation found that soaking in water at an initial temperature of 80 °C for 5 min was optimal for breaking the hardness of C. chinensis seeds. Scanning electron microscopy (SEM), dye-tracking, and blocking experiments were used to examine the major water entry sites and the relationship between water uptake and seed coat structure during C. chinensis seed imbibition. The SEM images showed that the seed coat consisted of three layers: epidermis, palisade, and sclereid. Special light line, vascular bundle, and counter-palisade layer structures were found in the side of the hilum. The blocking experiments showed that the hilar region was an important water absorption site because, if the region was not blocked, it showed the highest water absorption when imbibed for 3–12 h. However, all parts of the seed coat can absorb water if enough time is allowed after the seed coat hardness has been broken. The dye-tracking test showed that after 3 h water entered the seed only via the hilum fissure. Therefore, the hilum fissure acts as the initial site of water absorption. When more time was allowed, water moved more rapidly on the side with vascular bundles than on the opposite side.
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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 | 0.001 | 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".