Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data
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
The scientific community interested in atmospheric chemistry, gas emissions from vegetation fires, and carbon cycling is currently demanding information on the extent and timing of biomass burning at the global scale. In fact, the area and type of vegetation that is burned on a monthly or annual basis are two of the parameters that provide the greatest uncertainty in the calculation of gas and aerosol emissions and burned biomass. To address this need, an inventory of burned areas at monthly time periods for the year 2000 at a resolution of 1 km 2 has been produced using satellite data and has been made freely available to the scientific community. In this paper, estimates of burned area and number of burn scars for four broad vegetation classes and reported at the country level for the year 2000 are presented using data taken from the inventory. Over 3.5 million km 2 of burned areas were detected in the year 2000, of which approximately 80% occurred in areas described as woodlands and shrublands. Approximately 17% of the burned area occurred in grasslands and croplands, the remaining 3% occurred in forests. Almost 600,000 separate burn scars were detected. Descriptions of vegetation burning activity are given for ten regions. Finally, monthly burned area estimates are presented for the Central African Republic to illustrate the usefulness of these data for understanding, monitoring and managing vegetation burning activities.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it