OSIRIS: A Decade of Scattered Light
Bibliographic record
Abstract
On 20 February 2001, a converted Russian ICBM delivered Odin, a small Swedish satellite, into low Earth orbit. One of the sensors onboard is a small Canadian spectrometer called OSIRIS. By measuring scattered sunlight from Earth's horizon, or limb, OSIRIS is able to deduce the abundance of trace gases and particles from the upper troposphere into the lower thermosphere. Designed and built on a modest budget, OSIRIS has exceeded not only its 2-yr lifetime but also all expectations. With more than a decade of continuous data, OSIRIS has recorded over 1.8 million limb scans. The complexities associated with unraveling scattered light in order to convert OSIRIS spectra into highquality geophysical profiles have forced the OSIRIS team to develop leading-edge algorithms and computer models. These profiles are being used to help address many science questions, including the coupling of atmospheric regions (e.g., stratosphere–troposphere exchange) and the budgets and trends in ozone, nitrogen, bromine, and other species. One specific example is the distribution and abundance of upper-tropospheric, lightning-produced reactive nitrogen and ozone. Arguably OSIRIS's most important contributions come from its aerosol measurements, including detection and characterization of subvisual cirrus and polar stratospheric and mesospheric clouds. OSIRIS also provides a unique view of the stratospheric aerosol layer, and it is able to identify and track perturbations from volcanic activity and biomass burning.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".