Bacterial and fungal communities within and among geographic samples of the hemp pest Psylliodes attenuata from China
Bibliographic record
Abstract
The hemp flea beetle Psylliodes attenuata (Coleoptera: Chrysomelidae: Psylliodes) is a common pest of Cannabis sativa, including cultivars of both medicinal marijuana and industrial hemp. Both the larval and adult stages of this beetle can cause significant damages to C. sativa, resulting in substantial crop losses. At present, little is known about the bacterial and fungal community diversity among populations of this pest insect. In the present study, we obtained P. attenuata samples from nine field sites representing broad industrial hemp productions in China and analyzed their microbial communities using DNA metabarcoding. Bacterial sequences of all the samples were assigned to 3728 OTUs, which belonged to 45 phyla, 1058 genera and 1960 known species. The most common genera were Rickettsia, Wolbachia, and Candidatus_Brownia. Fungal sequences of all the samples were assigned to 910 OTUs, which belonged to 9 phyla, 308 genera and 464 known species. The most common fungal genera were Cladosporium, Cutaneotrichosporon, and Aspergillus. Principal coordinate analysis revealed a significant difference in the bacterial and fungal community structure among the nine P. attenuata populations. Understanding the microbial symbionts may provide clues to help develop potential biocontrol techniques against this pest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".