Response of the natural abundance of<sup>15</sup>N in forest soils and foliage to high nitrate loss following clear-cutting
Bibliographic record
Abstract
Export of microbially produced nitrate from an ecosystem is expected to increase δ15N in the remaining soil organic matter and NH4+. To test the hypothesis that nitrification and nitrate loss induced by clear-cutting cause an increase in soil and foliar δ15N, we measured δ15N in a clear-cut watershed at the Hubbard Brook Experimental Forest, New Hampshire. δ15N ranged from 0.02 in the Oie horizon to 7.7 in the Bs2 horizon prior to clear-cutting and increased significantly by 1.3 in the Oie horizon and 0.9 in the Oa horizon 3 years after clear-cutting. Fifteen years after clear-cutting, δ15N in both O horizons decreased to near-initial values. No significant temporal changes in the Bs2 and C horizons δ15N were observed. Foliar δ15N was highest (1.7) the first 2 years after clear-cutting and was significantly higher than in the reference watershed (mean δ15N = 1.2), decreasing to 0.0 35 years after clear-cutting and to 1.3 911 years after clear-cutting. Increased foliar δ15N coincided with increased stream-water nitrate concentration, suggesting that the increased nitrification responsible for elevated stream-water nitrate may also have caused an enrichment of the plant-available ammonium pool. The response observed in this catchment also suggests that sampling of soil or foliar δ15N may provide a practical alternative to long time series of stream chemistry for evaluating nitrogen saturation of forested ecosystems.
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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 | 0.001 |
| 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".