Mapping and development of 30 m landscape fuel data for Ukraine
Bibliographic record
Abstract
This study presents an ∼30 m gridded dataset of landscape fuel parameters for Ukraine, compiled from global satellite-derived vegetation data as well as information from catalogues on existing biotopes and species diversity in Ukraine, and from other literature sources. The data have been structured into 80 fuelbeds according to the Fuel Characteristic Classification System. For direct validation of the developed data, we compared above-ground biomass (AGB) with similar data from European Space Agency (ESA) Climate Change Initiative (CCI), as well as AGB and fuel loading parameters with the 300 m global fuel dataset. Indirect validation involved modelling, the dispersion of pollutants (CO, PM2.5, and PM10) from wildfires in the Kyiv region on 23 March 2022. The comparison showed good agreement between AGB for mature forests and the ESA CCI data, and an overall consistency between the created product for Ukraine and the global fuel map. However, substantial differences in the distribution of fuel characteristics were obtained for the Chornobyl Exclusion Zone, which might be explained by different approaches to fuel parameterization. Pollutant concentrations were predicted with acceptable accuracy taking into account the large uncertainties in fire time localization. This fine-resolution digital product has great potential for various applications, including the assessment of the war-induced environmental impact.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".