Inorganic and Organic Nitrogen Acquisition by a Fern Dicranopteris dichotoma in a Subtropical Forest in South China
Bibliographic record
Abstract
The fern Dicranopteris dichotoma is an important pioneer species of the understory in Masson pine (Pinus massoniana) forests growing on acidic soils in the subtropical and tropical China. To improve our understanding of the role of D. dichotoma in nitrogen (N) uptake of these forests, a short-term (15)N experiment was conducted at mountain ridge (MR, with low N level) and mountain foot (MF, with high N level). We injected (15)N tracers as (15)NH4, (15)NO3 or (15)N-glycine into the soil surrounding each plant at both MR and MF sites. Three hours after tracer injection, the fern D. dichotoma took up 15NH4+ significantly faster at MF than at MR, but it showed significantly slower uptake of (15)NO3- at MF than at MR. Consequently, (15)NO3- made greater contribution to the total N uptake (50% to the total N uptake) at MR than at MF, but (15)N-glycine only contributed around 11% at both sites. Twenty-four hours after tracer injection, D. dichotoma preferred (15)NH4+ (63%) at MR, whereas it preferred (15)NO3- (47%) at MF. We concluded that the D. dichotoma responds distinctly in its uptake pattern for three available N species over temporal and spatial scales, but mainly relies on inorganic N species in the subtropical forest. This suggests that the fern employs different strategies to acquire available N which depends on N levels and time.
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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 | 0.000 | 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".