The effects of plant growth-promoting bacteria on seed germination and seedling growth in <i>Crassocephalum rubens</i>
Bibliographic record
Abstract
Crassocephalum rubens, a herb with potential anticancer properties, encounters challenges in production efficiency due to small seed size and elevated temperatures in cultivation areas. Our study investigates the effects of seed pelleting and plant growth-promoting bacteria (PGPB) on germination and seedling growth across temperature variations in Taiwan. Pelleted seeds demonstrate superior germination percentages linked to enhanced seed quality. Elevated temperature, particularly at 30/25 °C, enhanced germination performance, with the highest final germination percentage observed in pelleted seeds treated with water. Unexpectedly, PGPB strains— Klebsiella sp. KsGRB10 and Bacillus sp. BsGFB04—exhibited limited impact on germination rates, showing a marginal increase in seedling growth under 25/20 °C and 30/25 °C, respectively. Air temperature fluctuations influenced seedling vigor, leaf color, and physiological parameters. Remarkably, inoculation with BsGFB04 and KsGRB10 enhanced C. rubens’ tolerance to high-temperature stress conditions. Diurnal measurements in week 4, under 25/20 °C, reveal that PGPB inoculation decreased stomatal conductance and transpiration rate while maintaining the quantum yield of PSII, indicating potentially improved water-use efficiency. This study provides crucial insights into the interplay among PGPB, environmental stress, and the physiology of a wild species, paving the way for further research in the domestication of C. rubens for medicinal herb mass production.
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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 | 0.001 |
| 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".