Whole Genome Sequencing Resource of the European Larch Canker Pathogen <i>Lachnellula willkommii</i> for Molecular Diagnostic Marker Development
Bibliographic record
Abstract
The filamentous ascomycete fungus Lachnellula willkommii is the causal agent of European larch canker (ELC), one of the most destructive diseases of larch in Europe and a regulated plant pathogen of quarantine significance in Canada and the United States. L. willkommii was first detected in Massachusetts, North America in 1927 on a larch plantation cultivated with nursery stock imported from Great Britain. Despite the decades of practices aimed at eliminating the pathogen, it has reappeared in coastal areas of Canada and the United States. There is concern ELC could spread throughout the range of eastern larch, a transcontinental species typical of the Boreal forest that spans the North American landscape. There is geographic range overlap between several nonpathogenic indigenous Lachnellula species and the reported distribution of L. willkommii in North America. Morphological and biological methods to distinguish L. willkommii are often inadequate as the fungus does not always produce the phenotypic structures that distinguish it from these other saprophytic Lachnellula species. Whole genome sequencing technologies were used to obtain the draft genome sequences of L. willkommii and six other Lachnellula species. Molecular markers identified from the genomic data may be used to discriminate L. willkommii from its nonpathogenic relatives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".