D3.6: Quantum Dynamics E-CAM modules V
Bibliographic record
Abstract
In this report for Deliverable 3.6 of E-CAM, 6 software modules in quantum dynamics are presented. All modules stem from the activities initiated during the SAW held at Lyon (France) in June 2019 and the workshop ESDW: Quantum Dynamics, held at Durham University (UK) in July 2019. The modules originate from the input of E-CAM’s academic user base. They have been developed by members of the project (S. Bonella EPF-L), established collaborators (G. Worth University College London, S. Gomez University of Vienna, C. Sanz University of Madrid, D. Lauvergnat Paris-Saclay University, F. Agostini Paris-Saclay University), E-CAM post-doc (A. Chen CEA-Saclay) and new contributors to the E-CAM repository (M. Heindl University of Vienna, K. Parsons Dalhousie University, T. Tran University College London). The presence of new contributors indicates the interest of the community in our efforts. Furthermore, the contributors to modules in WP3 continue to be at different stages of their careers (in particular, K. Parsons, T. Tran, and M. Heindl are PhD students) highlighting the training value of our activities. Following the order of presentation, the 6 modules are named: G-CTMQC, FBTS-MPI, Quantics DD-vMCG MPI/OMP, SHARC-gym, ElVibRot-TID-MPI, and ElVibRot-TD-MPI. In this report, a short description is written for each module, followed by a link to the respective Merge-Request document on the GitLab service of E-CAM. These merge requests contain detailed information about the code development, testing and documentation of the modules. As this deliverable is the last one of its series, a section on the overall impact of the results achieved within the Workpackage Quantum Dynamics was also included, with an overview of the results achieved so far, how these have been disseminated in scientific publications, the industrial impact of the software developed, the training given in the WP which has led to many of the software outputs, and the societal impact of the WP.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.242 | 0.235 |
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".