A virtual appliance as proxy pipeline for the Solar Orbiter/Metis coronagraph
Bibliographic record
Abstract
Metis is the coronagraph on board Solar Orbiter, the ESA mission devoted to the study of the Sun that will be launched in October 2018. Metis is designed to perform imaging of the solar corona in the UV at 121.6 nm and in the visible range where it will accomplish polarimetry studies thanks to a variable retarder plate. Due to mission constraints, the telemetry downlink on the spacecraft will be limited and data will be downloaded with delays that could reach, in the worst case, several months. In order to have a quick overview on the ongoing operations and to check the safety of the 10 instruments on board, a high-priority downlink channel has been foreseen to download a restricted amount of data. These so-called Low Latency Data will be downloaded daily and, since they could trigger possible actions, they have to be quickly processed on ground as soon as they are delivered. To do so, a proper processing pipeline has to be developed by each instrument. This tool will then be integrated in a single system at the ESA Science Operation Center that will receive the downloaded data by the Mission Operation Center. This paper will provide a brief overview of the on board processing and data produced by Metis and it will describe the proxy-pipeline currently under development to deal with the Metis low-latency data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".