POSITION PAPER: A Knowledge-Based Approach to Scientific Software Development
Bibliographic record
Abstract
As a relatively mature field, scientific computing has the opportunity to lead other software fields by leveraging its solid, existing knowledge base.Our position is that by following a rational design process, with the right tool support, desirable software qualities such as traceability, verifiability, and reproducibility, can be achieved for scientific software.We have begun development of a framework, Drasil, to put this into practice. Our aims are to ensure complete traceability, to facilitate agility in the face of ever changing scientific computing projects, and ensure that software artifacts can be easily and quickly extracted from Drasil. In particular, we are very interested in certifiable software and in easy re-certification.Using an example-based approach to our prototype implementation, we have already seen many benefits. Drasil keeps all software artifacts (requirements, design, code, tests, build scripts, documentation, etc.) synchronized with each other. This allows for reuse of common concepts across projects, and aids in the verification of software. It is our hope that Drasil will lead to the development of higher quality software at lower cost over the long term.
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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".