CPC’s 50th Anniversary: Celebrating 50 years of open-source software in computational physics
Bibliographic record
Abstract
To celebrate the leading role Computer Physics Communications (CPC) has played in publishing open-source software in computational physics for over 50 years the editors are delighted to announce this Virtual Special Issue. Since 2018, coinciding with the 50th anniversary of the start of the CPC venture, thirty-two invited articles have been published. Each has been peer reviewed and each bears the header ‘CPC 50th anniversary article’. The special issue is in keeping with CPC’s ethos: it is focused on computational physics software and is accompanied by twenty-five software systems. The introduction to the collection also includes a personal reflection on Phil Burke, CPC’s founder, by Alan Hibbert, a lifelong colleague, who joined Queen’s University with Phil in the autumn of 1967. The distinctive feature of CPC is its Program Library which houses and distributes over 3500 open-source programs in computational physics. The introduction concludes with a description of key events in the history of the Program Library, its association with Queen’s University Belfast and its transfer to Elsevier’s Mendeley Data repository.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.088 | 0.049 |
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".