Metagenomic ecosystem monitoring of soft scale insects and mealybug communities
Bibliographic record
Abstract
Soft scale insects and mealybugs are phloem-feeding Hemipterans that are considered major pests in agriculture and horticulture throughout the world. However, correct taxonomic identification in the field can be difficult, making it hard for growers to implement control strategies. In viticulture, soft scale insects are a major issue due to their ability to secrete honeydew, which facilitates the development of sooty mould, and their propensity for being transmission vectors of several viral diseases of grapevine. To facilitate the rapid identification and quantification of vineyard-associated insects a metagenomic-based bioinformatic pipeline (MitoMonitor) was developed for generalised ecosystem monitoring, which automated the assembly and classification of insect mitochondrial genomes from shotgun sequencing data using the Barcode of Life Database API. The proof-of-concept application of MitoMonitor on metagenomic data obtained from eight samples from South Australian vineyards led to the identification of Parthenolecanium corni (European fruit scale)—which was thought to be absent in Australian vineyards—as the dominant coccoid species across the samples, with less frequent, and also lower abundance of Pseudococcus viburni (obscure mealybug) and Pseudo. longispinus (long-tailed mealybug). In addition, parisitoidism by Coccophagus scutellaris (Aphelinidae) wasps was also detected. The discovery of Parth. corni as a member of scale communities in these samples has significant implications for the development of effective control strategies for this important group of pests in affected areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".