Exploring the Role of Endophytes in Cannabis sativa L. Polyploidy and Agricultural Trait Improvement
Bibliographic record
Abstract
Here, we examine the effects of ploidy variation in Cannabis sativa L. cell lines on the plant host genotype-associated microbiome. The endophytic microbiome has a protocooperative role in improving plant health and productivity and represents an alternative to synthetic chemical fertilizers and pesticides in sustainable agriculture. This study assessed the effects of seed endophytes on diploid and triploid Haze hemp cultivars. Key phenotypic characteristics were evaluated, revealing significant differences in seed germination in vitro as well as vegetative growth and flowering in phytotron conditions. Endophyte-treated triploid plants exhibited significantly taller heights compared to diploids (p < 0.01). These treated triploid plants also showed longer leaves at nodes 2, 6, and 8, except at node 4, indicating a plant in transition from vegetative growth to the generative developmental stage. Additionally, triploids treated with endophytes displayed the highest number of axillary branches, while endophyte-treated diploids had the fewest (p < 0.05). Both cultivars treated with endophytes exhibited a higher number of inflorescences compared to untreated control plants. This study revealed for the first time a direct correlation between the shifts in diameter of the stem and the biomass in both tested hemp hosts, in association with endophytic microbiomes.
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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) | 0.001 | 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".