Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled dynamic global vegetation models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the “Fire Modeling Intercomparison Project” (FireMIP) that were run using a common protocol and forcing data sets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in previous-season leaf area index and plant productivity in most fire-prone ecosystems, which was largely underestimated by most DGVMs. Hence, our pattern-oriented model evaluation approach allowed us to diagnose that vegetation effects on fire are a main deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it