Changes in litter and nitrogen deposition differentially alter forest soil organic matter biogeochemistry
Bibliographic record
Abstract
Global climate change has altered forest productivity across the globe. Although extreme drought and temperature have lowered forest productivity in some regions, increased productivity has been observed in many forests over the last few decades due to increases in atmospheric carbon dioxide, temperature, and nitrogen (N) deposition. These factors can lead to changes in both the quantity and quality of litterfall and root inputs to soil. Additionally, few studies have investigated multifactorial treatments (e.g., detrital changes and N deposition), on soil Carbon (C) sequestration. To investigate the long-term compositional changes to soil organic matter (SOM) in response to litter and N inputs, soil samples were collected from the University of Michigan Biological Station (UMBS) Detrital Input and Removal Treatment (DIRT) site after 15 years of experimental treatments. The samples were characterized using elemental analysis, targeted SOM compound analyses, nuclear magnetic resonance spectroscopy and microbial biomass and community composition measurements. The exclusions of C inputs (litter and/or roots) resulted in molecular-level biogeochemical changes, however, the soils at UMBS are seemingly more resistant to losses in soil C compared to other DIRT studies. Although the addition treatments (Double Litter, Double Wood, Double Litter + N, and N) led to increased soil C concentrations at UMBS, we found evidence for enhanced SOM decomposition occurring with litter additions. Notably, the observations made from the concurrent treatment of Double Litter + N demonstrated that the responses of individual treatments are not representative of simultaneous applications. Collectively, these results demonstrate that soil C biogeochemistry is sensitive to fluctuations in C and N deposition and overall, these processes are dictated by site-specific ecological properties.
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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 | 0.000 | 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".