Why Robust Software Engineering Matters for Atmospheric Composition Retrievals
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
The retrieval of atmospheric composition from remote sensing measurements is a complex process that requires the integration of cross cutting domain knowledge into a coherent software package. The complexity is increased many times over when the software has to handle multiple types of instruments, operating in different spectral regions, each with their own peculiarities. This is further compounded when trying to combine information from multiple instruments for joint retrievals. Yet, there is enough overlap between the radiative transfer and retrieval techniques used by various missions that it is wasteful to continually reinvent the wheel every time. The Reusable Framework for Atmospheric Composition (ReFRACtor) is an extensible multi-instrument atmospheric composition retrieval framework that supports and facilitates data fusion of radiance measurements from different instruments in the ultraviolet, visible, near- and thermal-infrared. This framework is being developed to provide a community available software package that uses robust software engineering practices with well tested, community accepted algorithms and techniques. ReFRACtor is geared not only for the creation of end to end production systems, but also towards independent investigative scientists who need a software package to help answer atmospheric composition questions. We will explain how the use of succint interfaces between components provides advantages for future proofing, flexibility and reusability. Examples will be given for translating the logical separation of mathematical and scientific concepts into software components. We will describe how having a Python interface to fast compiled algorithms is helpful for rapid prototyping of new systems. The experience of early adopter scientists will also be discussed to give a perspective from outside the software development team.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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