Diversity of Mycotoxin-Producing Black Aspergilli in Canadian Vineyards
Bibliographic record
Abstract
Several Aspergillus species produce ochratoxin A (OTA) and/or fumonisins on wine and table grapes. The relevant species and their mycotoxins have been investigated in a number of wine-producing regions around the world; however, similar data have not been reported for Canadian vineyards. A multiyear survey of black Aspergilli in Niagara, ON, vineyards was conducted to determine the diversity of species present and to assess the risk of OTA and fumonisin contamination of wine grapes from this region. From 2012 to 2014, 253 black Aspergilli were isolated from soil samples and the fruits of 10 varieties of grapes. The isolates were identified by DNA sequencing: Aspergillus welwitschiae (43%), Aspergillus uvarum (32%), Aspergillus brasiliensis (11%), Aspergillus tubingensis (9%), and Aspergillus niger (4%). Aspergillus carbonarius, the primary OTA producer on grapes in other parts of the world, was isolated only once, and this is the first report for it in Canada. All 10 A. niger strains produced fumonisins, but, in contrast, only 26% of the 109 A. welwitschiae isolates were producers, and no strains of either species produced OTA. Grape samples were analyzed for OTA and fumonisins from sites where strains with mycotoxigenic potential were isolated. Fumonisin B2 (FB2) was detected in 7 of 22 (32%) of these grape samples in the 1-15 ppb range, but no OTA was detected. Additionally, the recently reported nonaminated fumonisins were detected in 3 of 22 grape samples. These results suggest that fumonisin-producing Aspergilli can occur in Ontario vineyards but, at present, the risk of contamination of grapes appears low. The risk of OTA contamination in Niagara wine is also low because of the low prevalence of A. carbonarius.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".