On-orbit characterization and effects of dark offsets from Orbital Sidekick's GHOSt hyperspectral constellation (Conference Presentation)
Bibliographic record
Abstract
Orbital Sidekick's GHOSt (Global Hyperspectral Observation Satellite) constellation had launches in April and June 2023 and in March 2024. These sensors are among the first ever commercial space-based hyperspectral sensors that span the VIS-SWIR range. Post launch, a calibration campaign began to characterize each sensor's optical and radiometric quality. One of the areas of interest that were trended over time are the dark offsets, which are images taken with a closed shutter in front of the entrance slit of the spectrometer. Especially for SWIR-sensitive cryo-cooled detectors, a significant sensitivity in dark offsets is thought to be observed based on season, albedo, and spacecraft orientation. As a result, unlike a CMOS sensor, a "dark" must be collected at least before image collection and subtracted before imaging tasks. This paper discusses the difference in darks between space-based and ground based sensors and impacts on spatial/spectral noise and bad pixel computation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".