Pipe3D, a pipeline to analyze integral field spectroscopy DATA: II. Analysis sequence and CALIFA dataproducts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present PIPE3D, an analysis pipeline based on the FIT3D fitting tool, developed to explore the properties of the stellar populations and ionized gas of integral field spectroscopy (IFS) data. PIPE3D was created to provide coherent, simple to distribute, and comparable dataproducts, independently of the origin of the data, focused on the data of the most recent IFU surveys (e.g., CALIFA, MaNGA, and SAMI), and the last generation IFS instruments (e.g., MUSE). In this article we describe the different steps involved in the analysis of the data, illustrating them by showing the dataproducts derived for NGC 2916, observed by CALIFA and P-MaNGA. As a practical example of the pipeline we present the complete set of dataproducts derived for the 200 datacubes that comprises the V500 setup of the CALIFA Data Release 2 (DR2), making them freely available through the network. Finally, we explore the hypothesis that the properties of the stellar populations and ionized gas of galaxies at the effective radius are representative of the overall average ones, finding that this is indeed the case.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it