Soil properties as key predictors of global grassland production: Have we overlooked micronutrients?
Bibliographic record
Abstract
Fertilisation experiments have demonstrated that nutrient availability is a key determinant of biomass production and carbon sequestration in grasslands. However, the influence of nutrients in explaining spatial variation in grassland biomass production has rarely been assessed. Using a global dataset comprising 72 sites on six continents, we investigated which of 16 soil factors that shape nutrient availability associate most strongly with variation in grassland aboveground biomass. Climate and N deposition were also considered. Based on theory-driven structural equation modelling, we found that soil micronutrients (particularly Zn and Fe) were important predictors of biomass and, together with soil physicochemical properties and C:N, they explained more unique variation (32%) than climate and N deposition (24%). However, the association between micronutrients and biomass was absent in grasslands limited by NP. These results highlight soil properties as key predictors of global grassland biomass production and point to serial co-limitation by NP and micronutrients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".