Soil fungal communities are primarily influenced by vegetation rather than fertilizers and fungicides in a lowbush blueberry production system
Bibliographic record
Abstract
Fertilizer and fungicide applications are commonly used in lowbush blueberry cropping systems ( Vaccinium angustifolium Aiton and Vaccinium myrtilloides Michaux) to enhance fruit yield. Such crop inputs can significantly affect soil fungi, a topic that is not well documented, even though these organisms impact the development and survival of lowbush blueberries in natural forest ecosystems. We evaluated soil fungal biomass and community structure in a commercial lowbush blueberry field located in Lac-Saint-Jean, Québec, Canada. Since 2017, mineral fertilizers and fungicide (Proline 480 SC®) have been applied once every 2 years. In-growth sandbags were incubated for 90 days during the 2019 and 2020 growing seasons to collect fungal hyphae biomass in vegetated and unvegetated areas. Soil samples were also collected to analyze the structure of the fungal community using next-generation sequencing. Our results showed that applying fungicide alone increased hyphal biomass in sandbags by 40%, whereas adding both fungicide and fertilizer or fertilizer alone did not change hyphal biomass compared to the control. The structure of the fungal community was only slightly affected by the applications of fungicide and fertilizer, with fungicide decreasing the relative abundances of plant pathogens and fertilizer negatively influencing saprotrophs. Low doses and infrequent applications could explain such weak effects. Among the 33 amplicon sequence variants that were positively associated with the presence of lowbush blueberry plants, eight Penicillium species, four Clavariaceae, two Serendipita species, and one ericoid mycorrhizal fungi ( Oidiodendron maius) were identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".