Mucilage affects seed water imbibition and germination time of subtropical monsoonal forbs
Bibliographic record
Abstract
The role of seed mucilage in the moist environments of monsoonal subtropics is poorly understood. We studied germination of six forb species from subtropical monsoonal China. Mucilage presence had little to no effect on germination percentage (G%), except for a significant decline for the large-seeded Prunella vulgaris. Dark treatments reduced G% for all species, while the combination of mucilage removal and high temperatures delayed the mean germination time (MGT). Seed fresh mass was negatively correlated with G%, but only for intact seeds incubated at 12/12 hours of 25/35 °C. The MGT of de-mucilaged seeds varied with seed shape index, also under the warmer temperature regime. Temperature and light are fundamental to drive germination processes, and the presence of mucilage influences MGT of subtropical monsoonal species. Presence or absence of mucilage had little to no difference in germination percentage, but can be important to drive germination timing, also promoting water uptake and seed adhesion to soil.
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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".