Carbon dynamics in a model grassland with functionally different soil communities
Bibliographic record
Abstract
Summary Impacts of environmental changes on carbon cycling in terrestrial ecosystems are typically correlated with shifts in the composition, biomass and activity of soil faunal communities. Given the role of soil fauna in decomposition, shifts in soil faunal communities may further modify the carbon dynamics of a perturbed system. To test this possibility, we manipulated soil community composition in model grassland ecosystems and used a13CO2‐tracer to follow the assimilation, retention and pathways of recent, photosynthetically fixed carbon. The community treatments formed a nested hierarchy of functional complexity: (1) microbiota only; (2) microbiota and mesofauna; (3) microbiota, mesofauna and macrofauna. These treatments significantly affected the rate of decline in13C‐label respiration rate and the amount of13C‐label retained by the communities: while the rate of decline in13C‐label respiration rate was greatest in the microbiota treatment, the treatment with both micro‐ and mesofauna retained less13C‐label than either the more functionally complex or simple treatments. The presence of macrofauna altered the utilization of13C‐label by Collembola and Enchytraeidae: they decreased the mass of13C‐label utilized by Collembola and increased that utilized by Enchytraeidae. Our results suggest that soil community composition may play a key role in regulating the dynamics of recent, photosynthetically fixed carbon.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".