Projected changes in compound hot-dry events depend on the dry indicator considered
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The intensification of compound hot-dry events due to climate change is a pressing concern, underscoring the need for precise analysis. However, the impact of different dry indicators on projections of these events has not been quantitatively evaluated, nor has its importance been compared with other sources of uncertainty. Here we examine the sensitivity of projected changes in compound hot-dry events to different dry indicators. We use data from 22 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to characterize global dry conditions based on precipitation, runoff, soil moisture, and a multivariate index combining these variables through trivariate copulas. Our findings reveal large differences in projected changes in the likelihood of compound hot-dry events across different dry indicators. While model uncertainty remains the primary source of uncertainty for compound hot-dry event projections, the uncertainty associated with dry indicators is also substantial, surpassing scenario uncertainty in specific regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it