How does fertilization impact the wild blueberry microbiome?
Bibliographic record
Abstract
Wild blueberries production is regarded as less intensive than other agricultural systems. Fertilization is used to increase soil nutrient availability and improve fruit yield. Wild blueberry plants are also known to depend on their microbiome to overcome the lack of nutrient availability in the soil and their ericoid mycorrhizal (ErM) symbiosis. As fertilization can alter crop microbial communities, our study aimed to measure the impact of this practice in a wild blueberry setting, focusing on the bacterial and fungal communities found in the roots and rhizosphere of Vaccinium angustifolium Ait., both three months and one year after fertilization. Our study indicates that fertilization, whether mineral or organic, has a minimal effect on microbial communities. One year after application, fertilization does not seem to have a negative repercussion on the ErM fungal community as no significant differences were observed in terms of relative abundance of known and putative ErM taxa between the control and the two fertilizer treatments. The fact that fertilization is applied at a low dose and once every other year could explain this absence of effect on the microbial communities. However, longer-term studies are still needed to ensure that repeated fertilization does not cause any detrimental shifts in microbial communities.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".