Interactive effects of warming and increased nitrogen deposition on <sup>15</sup>N tracer retention in a temperate old field: seasonal trends
Bibliographic record
Abstract
Abstract The extent to which increased atmospheric nitrogen (N) deposition will drive changes in plant productivity and species composition over the next century will depend on how other influential global change factors, such as climate warming, affect the N retention of ecosystems. We examined the interactive effects of simulated climate warming and N deposition on the recoveries of 15N‐labeled ammonium and 15N‐labeled nitrate tracers added as a pulse to grass‐dominated, temperate old‐field plots at spring thaw. In addition to the year‐round warming treatment, a winter‐only warming treatment was applied to a set of plots to explore the contribution of this component of climate warming to the overall warming effect. By the end of the plant growing season, there was approximately twice as much 15N enrichment in the plant roots and bulk soil from 15NH4+‐addition plots than from 15NO3−‐addition plots, but there were no effects of warming or N fertilization on 15N recovery. Over winter, approximately half of the excess 15N present in plant shoots was lost, which corresponded with large 15N losses from bulk soil in N fertilized plots and large 15N increases in bulk soil in nonfertilized plots. By the next spring, there was decreased 15N recovery in plants in response to N fertilization, which was largely offset by increases in plant 15N recovery in response to year‐round warming. However, 15N retention in bulk soil, where the major part of the 15N label was recovered, was approximately 40% higher in nonfertilized plots than in N fertilized plots. Overall, our results indicate that climate warming increases plant N sequestration in this system but this effect is overwhelmed by the overall effect of nitrogen deposition on ecosystem N losses.
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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 | 0.001 | 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".