The role of genomes in advancing biological control research
Bibliographic record
Abstract
This talk was given at the 2018 ESA/ESC/ESBC Joint Meeting in Vancouver, Canada, at the Member Session, “Rapid evolution in biological control systems: implications for safety and effectiveness” on November 11th, 2018 Abstract: While initially confined to the realm of model species (such as Nasonia and Drosophila), genome sequencing, assembly, and annotation is becoming more accessible and feasible for de novo projects. There are several uses for the genomes of biocontrol agents, including for the purpose of improvement or risk assessment. For instance, is it possible to make a zoophytophagous mirid predator like Nesidiocoris tenuis less phytophagous via selection? Or when releasing a parasitoid wasp like Trichogramma brassicae into the field, is it possible to track different species as well as strains of the same species the next season? While very different biocontrol agents, and very different questions, both could be addressed using genomes and the types of data generated by genome projects. Moreover, published genomes become a common good, accessible to all researchers who wish to use it, thus opening several avenues of research previously unavailable. Here, I will highlight the role of genomes in advancing biocontrol research, using examples from our work on the sequencing, assembly, and annotation of the genomes of three biocontrol agents: the mirid bug Nesidiocoris tenuis, the parasitoid wasp Trichogramma brassicae, as well as the predatory mite Amblyseius swirskii.
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.
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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".