Pipe3D, a pipeline to analyze integral field spectroscopy DATA: II. Analysis sequence and CALIFA dataproducts
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.033 |
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".