Preliminary Performance Assessment of Space-based Observations of Hot-spot Events using Microbolometers
Bibliographic record
Abstract
Space-based observations of hot-spot events have numerous direct benefits to life on Earth. Such observations would enhance the health and safety of human beings and would protect quality of the natural environment. Hot-spot data can be used for fire detection and fire monitoring, volcanic monitoring, land cover change monitoring as well as studies related to biomass burning, carbon emissions and climate change. This paper discusses a technology development study undertaken by COM DEV Ltd., under a contract by the Canadian Space Agency (CSA), related to the space-based observation of hot-spot events using an imager employing CSA/INO's microbolometer technology. The main objective of the study was to demonstrate the concept feasibility of hot-spot observations using microbolometers. This paper presents a conceptual instrument design, instrument design optimizations, trade-off studies and sample performance analysis results. This paper discusses a preliminary performance assessment of space-based observations of hot-spot events using the COM DEV/INO's multi-channel imager. The suitability of microbolometer detector technology for forest fire observations is discussed. Some future plans and the usefulness of the instrument concept and the performance model for future hotspot observation missions are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".