Next‐Generation Biomass Mapping for Regional Emissions and Carbon Inventories: Incorporating Uncertainty in Wildland Fuel Characterization
Bibliographic record
Abstract
Abstract Biomass mapping is used in variety of applications including carbon assessments, emission inventories, and wildland fire and fuel planning. Single values are often applied to individual pixels to represent biomass of classified vegetation, but each biomass estimate has associated uncertainty that is generally not acknowledged nor quantified. In this study, we developed a geospatial database of wildland fuel biomass values to characterize the inherent variability within and across major vegetation types of the United States and Canada. For vegetation types that had sufficient quantification of biomass by fuel type (e.g., canopy, shrub, herbaceous, fine downed wood, coarse downed wood, and organic soil layers), we developed empirical distribution estimates. Based on available data, fitted distributions will be useful for informing the first‐generation biomass mapping that incorporates variability in loading by vegetation and fuel type and to evaluate potential errors in point estimates given in current map products. Because combustible biomass is a common input in fire and smoke models, variability estimated for fitted distributions can be used to inform data input uncertainty in predictions of wildland fuel consumption and emissions and to provide stochastic inputs of biomass to ensemble simulation models.
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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 | 0.003 | 0.001 |
| 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 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".