Detection of meteoroid impacts by the Geostationary Lightning Mapper on the <scp>GOES</scp>‐16 satellite
Bibliographic record
Abstract
Abstract Bolides are detected by the Geostationary Lightning Mapper onboard the GOES ‐16 weather satellite, which takes images of Earth at a rate of 500 Hz in a 1.1 nm wide pass band centered on 777.4 nm wavelength. Ten case studies are discussed. These initial results were obtained using the Level 0 data received during the nonoperational in‐orbit postlaunch test period. GLM positions and timings are sufficiently accurate to assist in trajectory and orbit reconstruction. GLM samples the light curve nearly completely, unaffected by onboard and downlink processes tailored to lightning data. Sufficient data on the instantaneous background scene are provided to reconstruct the baseline drift in the brightest pixels. The agreement to within a factor of 2–3 between measured total radiated energy from GLM and that derived from other space‐borne observations implies that during the bolide's peak brightness the GLM pass band is dominated by continuum emission, rather than O I line emission. The reported flux is corrected for angle‐from‐nadir shifts in the central wavelength of the pass band, which overestimates continuum flux by only up to 20% for most of the GLM field of view, but more so if the bolide is observed far from nadir. Assuming a 6000 K blackbody spectrum, GLM is able to detect bolides with peak visual magnitude (at a normalized 100 km distance) brighter than about −14 in nighttime, and slightly brighter in daytime.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".