Changes in Environmental Conditions Differentially Affect the Bacterial Microbiome Communities in Different Apple Fruit Tissues
Bibliographic record
Abstract
The maintenance and manipulation of the beneficial plant microbiome is a new frontier in ecofriendly disease management, particularly during post-harvest storage. However, the fruit microbiome is highly variable and can be influenced by both biotic and abiotic factors. A comprehensive understanding of how these factors influence microbial communities is necessary in order to unlock the microbiome for sustainable disease management. In this study, we demonstrate the impacts of the growing season and management strategy on the composition and structure of the bacterial microbiome of 'Honeycrisp' apples at harvest from seven different orchards in the Atlantic Maritime Ecozone, over the course of two growing seasons. We show that the bacterial communities associated with core and peel tissues respond differently to changes in external environmental conditions, underscoring the need to include multiple tissue types in future fruit microbiome research. Finally, we characterize the microbial cooperation networks of apple core and peel tissues and identify key microbial taxa influencing these networks.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".