Optimizing an integrated biovigilance toolbox to study the spatial distribution and dynamic changes of airborne mycobiota, with a focus on cereal rust fungi in western Canada
Bibliographic record
Abstract
In the face of evolving agricultural practices and climate change, tools towards an integrated biovigilance platform to combat crop diseases, spore sampling, DNA diagnostics and predictive trajectory modelling were optimized. These tools revealed microbial dynamics and were validated by monitoring cereal rust fungal pathogens affecting wheat, oats, barley and rye across four growing seasons (2015-2018) in British Columbia and during the 2018 season in southern Alberta. ITS2 metabarcoding revealed disparity in aeromycobiota diversity and compositional structure across the Canadian Rocky Mountains, suggesting a barrier effect on air flow and pathogen dispersal. A novel bioinformatics classifier and curated cereal rust fungal ITS2 database, corroborated by real-time PCR, enhanced the precision of cereal rust fungal species identification. Random Forest modelling identified crop and land-use diversification as well as atmospheric pressure and moisture as key factors in rust distribution. As a valuable addition to explain observed differences and patterns in rust fungus distribution, trajectory HYSPLIT modelling tracked rust fungal urediniospores' northeastward dispersal from the Pacific Northwest towards southern British Columbia and Alberta, indicating multiple potential origins. Our Canadian case study exemplifies the power of an advanced biovigilance toolbox towards developing an early-warning system for farmers to detect and mitigate impending disease outbreaks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".