Global pattern and drivers of nitrogen saturation threshold of grassland productivity
Bibliographic record
Abstract
Abstract Ecosystem productivity usually exhibits first increase and then saturated response to increasing nitrogen (N) additions, yet the broad‐scale pattern and potential drivers of the N saturation threshold are little investigated. By synthesizing N addition experiments with at least four N‐input levels from the global grasslands, we applied the quadratic‐plus‐plateau model to fit the above‐ground net primary productivity (ANPP)–N rate relationship, and estimated the saturation threshold for N rate (critical N rate, NCR) and ANPP (maximum ANPP, ANPPmax) from the inflection point where ANPP no longer statistically increased with N rate for individual experiments. Based on these estimations, we investigated the spatial pattern and driving factors of NCR and ANPPmax. The mean NCR and ANPPmax were 15.0 and 477.0 g m−2 year−1, respectively, but varied substantially among single‐site experiments. Management strategies (e.g. biomass harvest, different N forms and addition frequencies) minimally influenced both parameters. Structural equation models demonstrated that the spatial differences in NCR and ANPPmax were mainly explained by aridity index, and soil carbon (C)/N ratio also predicted the variation in NCR. Given that grasslands are important not only for the trend and variability of the land C sink but also for the maintenance of pasture yield, the pattern and controls of NCR and ANPPmax, as revealed by the current study, are crucial for constructing robust predictions of C sink capacity and improving N fertilizer management in grasslands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".