Links between litter quality and nitrogen oligotrophication in a northern hardwood forest
Bibliographic record
Abstract
Declining nitrogen (N) availability relative to plant demand, known as N oligotrophication, is a widespread phenomenon that has been particularly well documented in northern hardwood forests of the northeast U.S. It is hypothesized that later fall senescence contributes to this trend by increasing tree resorption of N, resulting in higher carbon:nitrogen ratios (C:N) in litterfall and reduced N availability in soil. To examine the effects of litterfall C:N on soil N cycling, we conducted a litter quality manipulation experiment comparing low C:N and high C:N litter with native litter along an elevation and aspect gradient at Hubbard Brook Experimental Forest, NH, USA. We found that potential net ammonification and mineralization rates were positively correlated with litter N and negatively correlated with litter C:N under high C:N litter, but these relationships were not present under native or low C:N litter. Differences in nitrate pools and net mineralization rates between high- and low-quality litter treatments were greater at colder sites, where native litterfall tends to have lower C:N than at low-elevation sites. Together, these results demonstrate that higher C:N litter and a warming climate may contribute to N oligotrophication through effects on microbially driven N cycling rates in organic soils.
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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 | 0.001 | 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".