WildFireSat - unlocking the potential for a global WildFire monitoring service
Bibliographic record
Abstract
To increase its capability to monitor wildland fires, the Government of Canada has initiated the first step of the development of a satellite system dedicated to wildfire monitoring. This system, called WildFireSat, will provide data for the whole of Canada on a daily basis, more specifically in the afternoon when fire activity is at its peak. Data users such as the Canadian Forest Service (CFS) for wildfire management purposes, and Environment and Climate Change Canada (ECCC) for carbon emission reporting and smoke and air quality forecasting purposes, will have access to the data within 30 min of data acquisition. Apart from its direct benefits,WildFireSat is meant to serve as a steppingstone towards the achievement of a longer-term goal: the realization of a future, potentially commercial, satellite constellation that would provide global, continuous, near real-time wildfire monitoring services. WildFireSat could help prepare the user community in Canada and possibly abroad, and thus create the user base that would be needed to make a strong business case for a future global operational wildfire monitoring data service. Other nations are welcomed to join the WildFireSat initiative and to help pave the way towards a global, continuous, near real-time wildfire monitoring service in collaboration with the international community.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".