Interactions among climate, topography, soil structure and rangeland aboveground net primary production
Bibliographic record
Abstract
Aboveground Net Primary Production (ANPP) of rangeland ecosystems is driven by interactions among multiple environmental factors. This study aimed to model the combined effects of precipitation, elevation, and soil conditions on ANPP variation along an elevation gradient. Ground surveys and vegetation sampling were conducted in 2016 through 26 sampling sites along two elevation profiles in the rangelands of Moghan-Sabalan, Ardabil Province, Iran. At each sampling site, the ANPP of each plant functional type (PFT; grasses, forbs, and shrubs) was measured, and soil samples were taken from 0–15 to 15–30 cm depth. Regression analysis and structural equation modeling (SEM) were used to investigate the factors affecting both total and PFT ANPP. Soil variables were the best predictors of grass (R2 = 0.51), forb (R2 = 0.61), shrub (R2 = 0.71), and total (R2 = 0.76) ANPP. The SEM interpretation suggested that precipitation is the most important direct driver of ANPP with R2 values of 0.20 (Total), 0.30 (Shrubs), 0.26 (Grasses), and 0.10 (Forbs). Whereas soil factors were good predictors in the regression models, the SEM models demonstrated that soil factors were generally unimportant compared with climate, likely owing to the close links between soil-forming factors and climate. The results make it possible to estimate annual ANPP combined with climate forecasts and leads to more accurate estimates of future grazing capacity by policy makers and stakeholders.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".