Metagenomic ecosystem monitoring of soft scale insects and mealybug communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it