<scp>sipgi</scp>: an interactive pipeline for spectroscopic data reduction
Bibliographic record
Abstract
ABSTRACT We present sipgi, a spectroscopic pipeline to reduce optical/near-infrared data from slit-based spectrographs. sipgi is a complete spectroscopic data reduction environment, which retains the high level of flexibility and accuracy typical of the standard ‘by-hand’ reduction methods but is characterized by a significantly higher level of efficiency. This is obtained by exploiting three main concepts: (i) the instrument model: at the core of the data reduction is an analytic description of the main calibration relations (e.g. spectra location and wavelength calibration) that can be easily checked and adjusted on data using a graphical tool; (ii) a built-in data organizer that classifies the data, together with a graphical interface that helps in providing the recipes with the correct input; (iii) the design and flexibility of the reduction recipes: The number of tasks required to perform a complete reduction is minimized, while preserving the possibility of verifying the accuracy of the main stages of data-reduction process with provided tools. The current version of sipgi manages data from the MODS and LUCI spectrographs mounted at the Large Binocular Telescope, and it is our plan to extend sipgi to support other through-slit spectrographs. Meanwhile, to allow using the same approach based on the instrument model with other instruments, we have developed SpectraPy, a spectrograph-independent python library working on through-slit spectra. In its current version, SpectraPy produces two-dimensional wavelength-calibrated spectra corrected by instrument distortions. The current release of sipgi and its documentation can be downloaded fromhttp://pandora.lambrate.inaf.it/sipgi/, while SpectraPy can be found at http://pandora.lambrate.inaf.it/SpectraPy/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.059 |
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".