Plant Species Richness Increased Belowground Plant Biomass and Substrate Nitrogen Removal in a Constructed Wetland
Bibliographic record
Abstract
Abstract The effects of plant species richness on both above‐ and belowground plant biomass, plant nitrogen (N) pool size, and substrate N concentrations were studied in a full‐scale subsurface vertical‐flow constructed wetland (CW). Results showed that (i) plant species richness increased belowground plant biomass and its N pool size but had no effect on aboveground plant biomass and its N pool size; (ii) plant species richness increased substrate N removal, especially ammonium N removal; and (iii) plant species richness had no effect on plant N use efficiency, suggesting that the N pool size increased with increasing plant species richness. More N accumulation could be removed through harvesting plant biomass. We concluded that the N removal performance of the CW improved by plant species richness through increasing belowground biomass and relevant N pool size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".