Data Reduction pipeline for MOST Guide Stars and Application to two Observing Runs. Veröffentlichungen der Kommission für Astronomie|Communications in Asteroseismology|Communications in Asteroseismology 156 156|
Bibliographic record
Abstract
A Data Reduction pipeline for MOST 1 Guide Star data is presented together with the results obtained for two observing runs in 2004 (i.e.κ 1 Ceti 2004 and HR 1217 2004 runs) containing four and seven Guide Stars respectively.Among these Guide Stars, four are clearly variable with only one known before the MOST observations: the long period variable HD 24338 (M2III).The data reduction relies on the decorrelation technique employed by Reegen et al. (2006) for their data reduction pipeline of MOST Fabry targets.The main difference is that the MOST Guide Star data include no background information.A coarse on-board background subtraction is performed, but leaves considerable residual stray light in the data, which is subject to a more sophisticated reduction 1 Based on data from the MOST (Microvariability & Oscillation of STars) satellite, a Canadian Space Agency mission jointly operated by Dynacon, Inc., the University of Toronto Institute of Aerospace Studies, and the University of British Columbia, with assistance from the University of Vienna, Austria.technique.Since at least four stars are observed simultaneously, common features of the light curves can be recognized and removed.Among different data reduction methods, the decorrelation technique is more versatile and often has better results than differential photometry or data smoothing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.099 |
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".