OpenCCM: An Open-Source Continuous CompartmentalModelling Package
Bibliographic record
Abstract
OpenCCM is a compartmental modelling (Jourdan et al., 2019) software package based on recently developed fully automated flow alignment compartmentalization methods (Vasile et al., 2024).It is primarily intended for large-scale flow-based processes with weak coupling between composition changes, e.g., through (bio)chemical reactions, and convective mass transport in the system.Compartmental modelling is an important approach used to develop reduced-order models (Benner et al., 2020;Chinesta et al., 2017) using a priori knowledge of process hydrodynamics (Jourdan et al., 2019).Compartmental modelling methods, such as those implemented in OpenCCM, enable simulations of these processes with far less computational complexity while still capturing the key aspects of process dynamics.OpenCCM integrates with two multiphysics simulation software packages, OpenCMP (Monte et al., 2022) and OpenFOAM (Greenshields, 2024), allowing for ease of transferring simulation data for compartmentalization. Additionally, it provides users with built-in functionality for computing residence times and exporting for use in other simulation or visualization software, including ParaView (Ayachit, 2015).Post-processing methods are included for mapping simulation results from compartment domains to the original simulation domain, which are useful for visualization purposes and for further simulations in using other software (e.g., multi-scale modelling).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".