Microbial soil biodiversity in beech forests of European mountains
Bibliographic record
Abstract
Fagus sylvatica L. is widely distributed across Europe thanks to its high adaptability in a wide variety of soils and climate. Microbial communities are essential for maintaining forest soil quality and are responsible for forest ecosystem functioning; the ability of soil microorganisms to respond to abiotic stressors (e.g., organic carbon losses, water scarcity, temperature changes) is crucial under ongoing environmental changes and also supports tree health. In this study, soil samples were collected from pure beech plots as part of the COST Action project CLIMO to find differences in microbial community characteristics and evaluate the effects of soil properties on microbial communities across gradients of elevation, latitude, and longitude. Positive relationships were found between organic carbon content and both microbial abundance and dehydrogenase activity. Dehydrogenase and catalase activities were elevation-correlated and microbial activities were longitude-correlated. In the most southern beech plot, the microbial community was abundant and displayed high activities. This shows that microbial communities could help tree populations to better adapt to predicted changes in environmental conditions in the future. We suggest that research into forest health and beech performance should also test soil microbial enzymatic activity, particularly under changing climate conditions, to assist in identifying adaptation strategies.
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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 | 0.000 | 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 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".