Contributions of the Brazil’s National Institute for Space Research (INPE) to emergency response in the International Space and Major Disasters Charter
Bibliographic record
Abstract
Abstract Brazil participates, through the National Institute for Space Research (INPE), in the International Charter Space and Major Disasters—Disasters Charter, an international cooperation effort to provide satellite images of major disasters around the world. The Disasters Charter is a joint and voluntary cooperative effort of 17 members. The characteristics of how the Charter works and the support of the satellites CBERS-4, CBERS-4A and AMAZONIA-1, including its cameras, are presented. There were 213 Charter Activations during the studied period (from mid-2018 until the end of 2022) with as many as 368 satellite images being provided (43% from the WFI camera-presented in all three satellites). Floods were the most frequent type of disaster and with the highest number of images provided. Of all the Activations during this period, INPE provided as much as 70% of all the Charter requested remote sensing data. Our internal operational scheme is revealed and the limitations of the satellite imaging are discussed to foster Brazil’s capacity in satellite imaging.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".