Drivers of woody dominance across global drylands
Bibliographic record
Abstract
Increases in the abundance of woody species have been reported to affect the provisioning of ecosystem services in drylands worldwide. However, it is virtually unknown how multiple biotic and abiotic drivers, such as climate, grazing, and fire, interact to determine woody dominance across global drylands. We conducted a standardized field survey in 304 plots across 25 countries to assess how climatic features, soil properties, grazing, and fire affect woody dominance in dryland rangelands. Precipitation, temperature, and grazing were key determinants of tree and shrub dominance. The effects of grazing were determined not solely by grazing pressure but also by the dominant livestock species. Interactions between soil, climate, and grazing and differences in responses to these factors between trees and shrubs were key to understanding changes in woody dominance. Our findings suggest that projected changes in climate and grazing pressure may increase woody dominance in drylands, altering their structure and functioning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".