New Insights into the Fungal Diversity of Cranberry Fruit Rot in Québec Farms Through a Large-Scale Molecular Analysis
Bibliographic record
Abstract
Cranberry fruit rot (CFR) pathogens are widely reported in the literature, but performing large-scale analysis of their presence inside fruit has always been challenging. In this study, a new molecular diagnostic tool, capable of identifying simultaneously 12 potential fungal species causing CFR, was used to better define the impact of CFR across cranberry fields in Québec. For this purpose, 126 fields and 7,825 fruits were sampled in three cranberry farms distributed throughout the province and subjected to comparative analyses of fungal presence and abundance according to cultural practices, sampling times, and cranberry cultivars. All 12 pathogens were detected throughout the study, but as a first major finding, the analyses revealed that four species, Godronia cassandrae, Colletotrichum fructivorum, Allantophomopsis cytisporea, and Coleophoma empetri, were consistently predominant regardless of the parameters studied. Comparison of conventional and organic productions showed a significant reduction in fungal richness and relative abundance. Interestingly, Monilinia oxycocci was found almost exclusively in organic productions, indicating that fungicides had a strong and persistent effect on its population. Surprisingly, there were no significant differences in fungal relative abundance or species richness between fruit sampled at harvest or in storage, suggesting that there may not exist a clear distinction between field and storage rot, as was previously thought. Comparative analysis of fungal species found on eight different cranberry cultivars indicated that they were all infected by the same fungi but could not rule out differences in genetic resistance. This large-scale analysis allows us to draw an exhaustive picture of CFR in Québec and provides new information with respect to its management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".