Nitrogen‐mediated effects of elevated <scp>CO</scp><sub>2</sub> on intra‐aggregate soil pore structure
Bibliographic record
Abstract
Abstract Soil pore structure has a strong influence on water retention, and is itself influenced by plant and microbial dynamics such as root proliferation and microbial exudation. Although increased nitrogen (N) availability and elevated atmospheric CO 2 concentrations (eCO 2 ) often have interacting effects on root and microbial dynamics, it is unclear whether these biotic effects can translate into altered soil pore structure and water retention. This study was based on a long‐term experiment (7 yr at the time of sampling) in which a C 4 pasture grass ( Paspalum notatum ) was grown on a sandy loam soil while provided factorial additions of N and CO 2 . Through an analysis of soil aggregate fractal properties supported by 3D microtomographic imagery, we found that N fertilization induced an increase in intra‐aggregate porosity and a simultaneous shift toward greater accumulation of pore space in larger aggregates. These effects were enhanced by eCO 2 and yielded an increase in water retention at pressure potentials near the wilting point of plants. However, eCO 2 alone induced changes in the opposite direction, with larger aggregates containing less pore space than under control conditions, and water retention decreasing accordingly. Results on biotic factors further suggested that organic matter gains or losses induced the observed structural changes. Based on our results, we postulate that the pore structure of many mineral soils could undergo N‐dependent changes as atmospheric CO 2 concentrations rise, having global‐scale implications for water balance, carbon storage, and related rhizosphere functions.
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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".