Oak contribution to litter nutrient dynamics in an Appalachian forest receiving elevated nitrogen and dolomite
Bibliographic record
Abstract
Ecosystem nitrogen (N), phosphorus (P), and calcium (Ca) fluxes are affected by inputs of atmospheric N. Oak litter may additionally affect these fluxes because of its high-lignin content. We analyzed nutrient dynamics in ambient mixed-species litter in an aggrading hardwood stand at the Fernow Experimental Forest in West Virginia. We separated oak from the mix for analysis (oak) and compared it with total litter (all species) to understand how oak affects nutrient fluxes in the litter layer. The study was conducted under ambient atmospheric deposition, under elevated atmospheric deposition, and under elevated deposition plus mitigation with dolomite. N flux between litterfall and 12 months later indicated a net loss in all-species litter of up to 7.3 kg N·ha–1 and a retention of up to 0.6 kg N·ha–1 in oak. P flux included losses in all species in ambient and in dolomite treatments of up to 0.19 kg P·ha–1 and gains of up to 0.12 kg P·ha–1 in oak in all treatments. Oak mineralized Ca at an average across treatments of 4.6 kg Ca·ha–1 compared with 16 kg Ca·ha–1 in all species, with half of that when trees were dormant. Percent immobilization and release over initial litter were greater in oak than in all species, but nutrient fluxes were lower in oak than in all species because of low oak litter mass. Elevated deposition lowered N and increased P immobilization. Dolomite appeared to affect early N dynamics only. With an increase in litterfall mass when forests mature, these effects are also likely to increase.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".