Nitrogen increased aboveground biomass of <i>Leymus chinensis</i> grown in semi‐arid grasslands of inner Mongolia, China
Bibliographic record
Abstract
Abstract There is controversy over whether the addition of nitrogen (N) is the key to the rapid growth of Chinese rye grass Leymus chinensis (Trin.) Tzvel. in natural, semiarid grasslands. We investigated yearly impact of various N additions (0, 91, 183, and 274 kg N ha−1) on the relationships between nutrient traits (plant carbon [C], N, and phosphorous [P]), morphological traits (plant height, leaf number, leaf length, leaf width, stem length and stem diameter) and aboveground biomass in L. chinensis. Results showed that most of the growth characteristics of L. chinensis increased with N rate except for leaf number and stem length. Nitrogen addition increased aboveground biomass, plant height, leaf length and stem length of L. chinensis, which was related to high precipitation during the critical period for the growth of L. chinensis. Nitrogen addition increased the N concentration and N to P ratio of L. chinensis tissue, but decreased the C to N ratio in the leaf and stem of L. chinensis. Compared to the control, N addition increased C and N concentrations and the N to P ratio, but decreased P concentration and the C to N ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".