Alien fungal species on asymptomatic live woody plant material imported into Canada
Bibliographic record
Abstract
Undescribed exotic and scientifically unknown fungal species with the potential to be pathogens are often difficult to detect on imported live plant material due to their inconspicuous nature and thus represent an important risk to Canadian forests. We have developed an early warning method based on a random sampling of asymptomatic woody live plant material imported into Canada to detect such potential alien fungal species. We received 150 asymptomatic sample lots collected by Canadian Food Inspection Agency (CFIA) inspectors. Samples were analysed by cloning and sequencing the PCR-amplified fungal nuclear ribosomal internal transcribed spacer (ITS) DNA from plant tissues to reveal fungal diversity. Out of 1845 fungal clones obtained, 267 fungal operational taxonomic units (OTUs) were identified. Using phylogenetic profiling methods, two fungal OTUs were categorized as having potential for a moderate impact on Canadian forests, 37 OTUs had a low impact and 18 OTUs could not be assessed given their low genetic similarity with other ITS sequences in GenBank. In all cases, the potential risk associated with these 57 fungal OTUs is based on (i) the fact that they can be considered unknown species to science; and (ii) they belong to orders, classes, genera and families in which pathogenic species are common. Fungal introductions with potential for a moderate impact on Canadian forests were observed at a very low frequency (0.2%) in the sampling units (clones). Only 1.3% of the CFIA samples had an OTU with a potential moderate impact on Canadian forests and 74% of the samples were free of fungal OTUs that could have any potential impact. This early warning method sheds light on the suite of exotic fungi that may enter Canada via plant material. Additionally, this method provides the tools to assess the potential risk that these fungi may post to Canadian trees and determines the magnitude of asymptomatic material that harbours fungal pathogens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".