Impacts of abiotic factors on the fungal communities of ‘Honeycrisp’ apples in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The maintenance of the beneficial plant microbiome to control plant pathogens is an emerging concept of disease management, and necessitates a clear understanding of these microbial communities and the environmental factors that affect their diversity and compositional structure. As such, studies investigating the microbiome of economically significant cultivars within each growing region are necessary to develop adequate disease management strategies. Here, we assessed the relative impacts of growing season, management strategy, and geographical location on the fungal microbiome of 'Honeycrisp' apples from seven different orchard locations in the Atlantic Maritime Ecozone for two consecutive growing years. Though apple fruit tissue was dominated by relatively few fungal genera, significant changes in their fungal communities were observed as a result of environmental factors, including shifts in genera with plant-associated lifestyles (symbionts and pathogens), such as Aureobasidium, Alternaria, Penicillium, Diplodia, and Mycosphaerella. Variation in fungal composition between different tissues of fruit was also observed. We demonstrate that growing season is the most significant factor affecting fungal community structure and diversity of apple fruit, suggesting that future microbiome studies should take place for multiple growing seasons to better represent the host-microbiome of perennial crops under different environmental conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it