Metagenomic ecosystem monitoring of soft scale and mealybug infestations in Australian vineyards
Bibliographic record
Abstract
Abstract Soft scale insects and mealybugs are phloem feeding Hemipterans that are considered majors pests in agricultural and horticultural settings throughout the world. Viticulturally, scale are a major issue due to their ability to secrete honeydew, which facilitates the development of sooty mould and for their propensity as transmission vectors for several viral diseases of grapevine. To facilitate the rapid identification and quantification of vineyard-associated insects a metagenomic-based bioinformatic pipeline was developed for generalised ecosystem monitoring that automated the assembly and classification of insect mitochondrial genomes from shotgun sequencing data using the Barcode of Life Database API. Parthenolecanium corni (European fruit scale), which was thought to be absent from Australian grapevines, was identified as the dominant coccid species infesting all vines sampled, along with secondary infestation by 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 significant member of scale infestations in Australia has significant implications for the development of effective control strategies for this important group of pests.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".