Assessing soil microbial catabolic diversity in alder and oak plantations at varying developmental stages
Bibliographic record
Abstract
To evaluate the impact of forest plantations on soil microbial catabolic diversity, this study investigated the microbial response to the addition of various carbon-rich substrates in different ages of forest plantations and compared catabolic diversity between a nitrogen-fixing tree species (Alder, Alnus subcordata C. A. M.) and a non-nitrogen-fixing one (Oak, Quercus castaneifolia C. A. M.). The study also analyzed the effects of carbon substrates found in association with roots, such as carbohydrates, carboxylic acids, amino acids, and amine groups, on substrate-induced respiration. The diversity indices (Simpson and Shannon diversity indicies) and Shannon evenness index demonstrated higher values in the oldest plantations than the youngest ones (with an order of 25 > 20 > 15 years). In younger plantations (15 and 20 years old), oak stands exhibited higher microbial diversity compared to alder stands. However, at 25 years old, alder plantation displayed greater diversity than the oak plantation, as indicated by higher values for the Shannon diversity index (2.77), Simpson diversity index (0.93), and Shannon evenness index (0.89). Forest plantations with alder and oak species significantly enhanced catabolic evenness and diversity, thereby increasing the soil capacity to decompose organic matter and the resilience of the soil to disturbance. These findings suggest that both alder and oak plantations can promote soil microbial diversity, but the influence might differ depending on the specific age of the trees. In conclusion, this study highlights the influence of forest plantations on soil microbial catabolic diversity, demonstrating the potential of diverse tree species to promote soil health and resilience through enhanced functional complexity.
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 | 0.001 | 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 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".