The effect of long-term CO <sub>2</sub> enrichment on carbon and nitrogen content of roots and soil of natural pastureland
Bibliographic record
Abstract
Abstract Increasing levels of atmospheric CO 2 may change C and N dynamics in pasture ecosystems. The present study was conducted to examine the impact of four years of CO 2 enrichment on soil and root composition and soil N transformation in natural pastureland. Plots of open-top growth chambers were continuously injected with ambient CO 2 (350 µL L –1 ) and elevated CO 2 (625 µL L –1 ). Soil cores exposed to ambient and elevated CO 2 treatment were incubated and collected each year. Net N-mineralization rates in soil (NH 4 + -N plus NO 3 ˉ – -N), in addition to total C and N content (%) of soil and root tissues were measured. Results revealed that elevated CO 2 caused a significant reduction in soil NO 3 (P < 0.05), however, no significant CO 2 effect was found on total soil C and N content (%). Roots of plants grown under elevated CO 2 treatment had higher C/N ratios. Changes in root C/N ratios were driven by changes in root N concentrations as total root N content (%) was significantly reduced by 30% (P < 0.05). Overall, findings suggest that the effects of CO 2 enrichment was more noticeable on N content (%) than C content (%) of soil and roots; elevated CO 2 significantly affected soil N-mineralization and total N content (%) in roots, however, no substantial change was found in C inputs in CO 2 -enriched soil.
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 | 0.000 | 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) | 0.000 | 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".