Study on Interactions between Endophytes and Hemp for Healthy Plants and Quality Products
Bibliographic record
Abstract
Modern Cannabis sativa L. cell lines encompass various chromosome variations through ploidy, such as diploid, triploid, and tetraploid, while their associated microbial communities are still underexplored. The lack of knowledge on symbiotic mycobiome-hemp interaction can be considered a bottleneck for sustainable plant and cannabinoids production, which is further exacerbated due to the recent legalization of Cannabis in Western countries, resulting in a surge in demand. Consequently, the imperative to investigate hemp's microbial communities has grown, as it plays a pivotal role in enhancing crop health and ensuring product quality, safety, and security. Hence, understanding the mechanism by which microbiome is acting in protocooperation with ploidy in hemp is a vital step towards microbiome-assisted agriculture and future breeding programs.\nThe primary objective of this thesis is to elucidate the correlation between diploid (2n) and triploid (3n) hemp plants and their respective microbiome profiles. Specifically, the diploid and triploid Suver haze cultivar varieties and their associated microbial community composition on seeds, flowers and leaves were examined. The research findings shed light on the intricate interplay between the Cannabis sativa microbiome and host phenotypic characteristics. Notably, we observed that distinct microbial community structures were linked with shifts in plant growth parameters, hormonal activities, and phenotypic traits. Furthermore, our study underscores the dynamic nature of the hemp microbiome across different plant genotypes and growth stages, resulting in distinctive profiles of secondary metabolites. The variation in endophytic community structures between diploid versus triploid plants coincides with the level of plant plasticity to adapt in response to controlled in vitro and phytotron environments. Moreover, we found that differences in microbiome’s composition coincides with specific shifts in phenotypic characteristics of each plant host, offering practical applications for optimizing hemp cultivation.\nIn summary, a combination of microbiology, microscopy, molecular, and phenotypical approaches was applied in addressing the main study’s objectives. Tested plants underscore the significance of considering the microbiome as a pivotal factor in shaping physiological (PSII)\niii\nand phenotypic attributes in Cannabis sativa. A deeper understanding of these complex relationships has the potential to refine cultivation techniques and facilitate the development of hemp varieties with tailored traits, ultimately benefiting both the medical and industrial applications of this versatile plant. Additionally, our research may contribute to identifying biosignature markers of endosymbiosis that enhance the genetic diversity of hemp germplasm during the reproductive seed and flowering stages, potentially improving plant health, agricultural traits, and the quality of products.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".