A Data Reduction Pipeline for Gemini-North’s Near-infrared Integral Field Spectrometer
Bibliographic record
Abstract
Abstract We present a python package, called Nifty4Gemini, and its associated Pyraf/Python based pipeline for processing Gemini-North Near-Infrared Integral Field Spectrometer (NIFS) observations. Built on the Gemini IRAF package’s capabilities, Nifty4Gemini's associated NIFS pipeline is a data reduction package which reduces NIFS raw data and produces a flux and wavelength calibrate science cube with the full signal-to-noise ratio, ready for science analysis. It utilizes tasks from the Gemini IRAF package, PyRAF, and packages from the Gemini AstroConda environment. Nifty4Gemini is a configuration-based pipeline framework written in python which is easily extensible to integrate additional pipelines and user-defined scripts. Nifty4Gemini is open source and available for download at https://github.com/mrlb05/Nifty4Gemini with its documentation available at https://nifty4gemini.readthedocs.io/en/latest/ . A permanent version of the software described in this paper is archived at https://zenodo.org/record/1000413 .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.043 |
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".