Poplar leaf rusts: model pathogens for a model treeThis minireview is one of a selection of papers published in the Special Issue on Poplar Research in Canada.
Bibliographic record
Abstract
With the availability of the entire genome of the model tree Populus trichocarpa Torr. & A. Gray and the current genome sequencing project of its rust pathogen Melampsora larici-populina Kleb., rust–poplar interaction research has entered the genomic era. Recent genomics research on poplars has attempted to connect the genetic localizations of loci for qualitative and quantitative disease resistance with putative genes encoding resistance or signalling proteins. The interactions between these putative resistance genes and rust effectors remain unknown. Genomic resources developed for Melampsora spp. promise to contribute to our understanding of the molecular basis of pathogenicity by facilitating the isolation of pathogenicity genes. A multifaceted approach for the identification of such genes that relies largely on trimming and sequence data analysis has been developed. The strategy takes advantage of the resources available and combines EST libraries, bioinformatics data mining for extracellularly expressed secreted proteins, intra- and inter-specific comparative genomics, and testing for the presence of positive selection. It has resulted in the discovery of several putative candidate genes. In silico evidence for candidate genes will be further validated by robust experimental evidence through functional analyses.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".