Heteromorphic seed germination and seedling emergence in the legume <i>Teramnus labialis</i> (L.f.) Spreng (Fabacaeae)
Bibliographic record
Abstract
Seed heteromorphism can influence germination and ultimately seedling establishment, particularly in disturbed habitats. This study compared seed and seedling traits across three distinctly colored seed morphs (viz. light-brown, brown, and dark-brown) of the forage legume, Teramnus labialis (L.f.) Spreng. The best quality seeds (i.e., un-parasitized, filled and un-cracked) were brown: 389.3 quality seeds per 1000 units compared with <270/1000 units for the other two morphs. Length, width, volume, and water content were lowest in the light-brown and highest in the dark-brown seeds. Seed thickness and mass were lower in the light-brown seeds. Dark-brown seeds imbibed fastest from 2 h onwards. Germination was comparable across the morphs after 7 days but was lowest in the light-brown (17% at 21 days) and highest in the dark-brown seeds (36% at 21 days) at 14 and 21 days. At 7 days, seedling emergence in the dark-brown seeds (15.0%) was higher than in the other two morphs (4%–6%); this remained so at 14 and 21 days. Seedling growth (number of leaves, stem height and diameter, and root length) was superior in the dark-brown seeds. Seed heteromorphism in T. labialis may allow its persistence in disturbed habitats, and the dark-brown seeds are best suited for seeding in revegetation projects, given their superior germination capacity and seedling vigor.
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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".