Book of abstracts of the Scientific Colloquium 'Plant Health at the Age of Metagenomics'
Bibliographic record
Abstract
New techniques allowing processing of large numbers of samples and generating huge volumes of genomic and protein data offer new opportunities to study plant pests. Rather than isolating individual molecules, in order to understand their role in the biology of an organism, it is now possible to investigate the genome as a whole, or the interactions of proteins and other metabolites as a holistic approach. These new techniques allow known pests to be studied in more detail as well as to investigate the genomic diversity within species. The information produced will improve diagnostics and may help in the management of pests, for instance to develop resistant plant species. These new techniques can also detect new species that may have been present for many years unnoticed, as well as emerging pests. They may also help identify the causal agents of diseases which were previously unknown. Genomic data may show similarity between new species found and known pests, which may give indications on the relevance of these newly identified organisms as potential pests. Data needs to be stored in a way that it is findable, accessible, interoperable and reusable (FAIR). Bioinformatics is an important tool to handle and analyse the ever-growing amount of data produced for example by comparing genomic sequences, predicting the activities of proteins or modelling metabolic pathways. What is the biological significance of the data? In a plant health context, one may ask which genes, proteins or other molecules cause organisms to be pathogenic? Which molecules increase virulence? The EPPO/Euphresco Colloquium ‘Plant Health at the age of metagenomics’ will bring together scientists and regulators who are involved in plant health. By sharing experiences and information, together we can better understand the possibilities that metagenomics offers and how to use the vast amounts of information that will become available to improve protection of plant health. A crucial question is what makes a new species that is detected a threat, and warrants phytosanitary action? What in the genome, the proteins and other molecules of a pest makes it potentially invasive in a new region or a risk to a new host? If we shift the focus from pests only to the interaction between plants and pests one can ask what in a plant makes it a host, or more (or less) susceptible to damage by the pest? This colloquium is a very good opportunity to discuss the challenges associated with the study of the genome of pests, populations, and plants, and the opportunities offered to obtain information about which plants are hosts, the extent of damage the pest could cause in a host and the way in which we could mitigate the effect of the pest. In the same way that I see the benefits of the rapid developments in sequencing and bioinformatic analysis capacity, I am looking forward to the fast development of data interpretation, in particular concerning information relevant to the interaction between pests and plants and thereby to the management of pests and diseases.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.251 | 0.134 |
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".